Genetic studies of inflammation and cardiovascular disease

PDF

Download

Open document

Flip pages

Copy asset link

Request this asset

Request accessible transcript

Transcript (if available)

Content GENETIC STUDIES OF INFLAMMATION AND CARDIOVASCULAR DISEASE by Jaana Anita Hartiala __________________________________________________________ A Dissertation Presented to the FACULTY OF THE KECK SCHOOL OF MEDICINE UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (MOLECULAR EPIDEMIOLOGY) August 2014 Copyright 2014 Jaana Anita Hartiala DEDICATION For Mikko, without whom none of this would have been possible. i ACKNOWLEDGEMENTS First and foremost I want to thank my dissertation committee chair and mentor, Dr. Hooman Allayee, for the incredible support over the past years. You have created an exceptional working environment, where everybody is treated as your colleague and where learning new things is everyday business. Also, your guidance and encouragement throughout my graduate studies have been exceptional, and I would have not made it this far without it. For that, I thank you from the bottom of my heart. I would also like thank the present and past members of the Allayee lab: Amanda Crow, Amy Zhang, Yesha Patel, Susanna Vikman, Anniina Vikman, Yasamine Currie and Hemani Wijesuriya. It has been a pleasure to work with such a great group of people, who I also consider as my friends. I have enjoyed our get‐ togethers and happy hours and hope many more to come in the future. Also, I would like thank my past mentor Dr. Leena Peltonen, who is indirectly responsible of me being where I am right now. When I started my internship as an undergraduate student in her enormous and well‐known research group in National Public Health Institute in Helsinki, I had no idea I would soon join her research group permanently, and move to Los Angeles within 2 years of my graduation. Leena was always convinced I will continue my studies and continually encouraged me to do so. She has been one the biggest influences in my life, and despite her untimely passing in 2010, I still thrive to make her proud. I would like to thank the outstanding faculty members at USC, with whom I have had the privilege of working with: Dr. Pragna Patel, Dr. David Conti, Dr. Graham Casey, Dr. Fredrick Schumacher and Dr. Frank Gilliland. Working with different projects and with different expertise has given me opportunities to expand my knowledge base and skill sets beyond my own projects. I have learned so much from every one of you. Last, but definitely not least, I would like to thank my family for all their love and support. Especially my husband, Mikko, who have saved my life in more ways than one. With you, I always have a shoulder to lean on. None of this would have been possible without your love and support. Finally, I would like to thank my mom, who has always believed in me and has been incredibly proud of me, whatever I choose to do. Your support and encouragement means a world to me. ii LIST OF TABLES CHAPTER 1 PAGE Table 1. General Characteristics of the GeneBank Participants. 51 Table 2. The Major Genes of LT Biosynthetic Pathway and their Function. 52 Table 3. Association of Previously Reported LT Pathway Variants in Caucasian and African American GeneBank Subjects. 53 Table 4. Association of PLA2G4A and LTA4H SNPs with CAD in the Two‐Stage Analysis. 54 Table 5. Risk of Prospective MACE in Relation to PLA2G4A and LTA4H Variants. 55 Supplemental Table 1. Previously Associated and Haplotype‐tagging Leukotriene Pathway SNPs/Variants Selected for Genotyping 58 Supplemental Table 2. Association Results of Haplotype‐tagging SNPs of LT Pathway Genes in Stages 1 and 2. 67 CHAPTER 2 Table 1. General Characteristics of the Study Population by Case‐Control Status. 96 Table 2. Association of PLA2G4A with Myocardial Infarction. 97 Table 3. Interaction Between PLA2G4A and Dietary Omega‐6 PUFAs on Risk of MI. 98 Table 4. Interaction Between PLA2G4A and Dietary Omega‐3 PUFAs on Risk of MI. 99 Supplementary Table 1. Interaction Between PLA2G4A and Tertiles of Dietary Omega‐6 PUFAs on Risk of MI. 101 Supplementary Table 2. Interaction Between PLA2G4A and Tertiles of Dietary Omega‐3 PUFAs on Risk of MI. 102 iii CHAPTER 3 PAGE Table 1. Description of Cohorts and Datasets Used in this Study. 150 Table 2. Significant and Suggestive Loci Associated with Decreased Circulating MPO Levels in GWAS and Gene‐Centric Analyses. 152 Table 3. Pleiotropic Associations of Chromosome 17q22 SNPs with Gene Expression in Monocytes. 153 Table 4. MPO‐Associated SNPs and Risk of CAD in the CARDIoGRAM Consortium. 154 Supplemental Table 1. Detailed Description of Genotyping Data Prior to Imputation for the Cohorts in this Study. 184 Supplemental Table 2. Characteristics of Loci Demonstrating Association with Serum MPO in GWAS of Subjects with European Ancestry. 185 Supplemental Table 3. Previously Identified SNPs at the CFH Locus that Demonstrate Association with Serum MPO Levels. 186 Supplemental Table 4. Characteristics of Loci Demonstrating Association with Serum MPO in Gene‐Centric Analyses of Subjects with European Ancestry 187 Supplemental Table 5. Characteristics of Loci Demonstrating Association with Serum MPO in Gene‐Centric Analyses of African American Subjects. 188 Supplemental Table 6. Characteristics of Loci Demonstrating Association with Plasma MPO Levels in GWAS of Subjects with European Ancestry. 189 Supplemental Table 7. Results of Conditional Analysis for SNPs Significantly Associated with Plasma MPO Levels. 190 Supplemental Table 8. Comparison of Loci Demonstrating Association with Decreased Circulating MPO Levels in Subjects with European Ancestry. 191 iv LIST OF FIGURES CHAPTER 1 PAGE Figure 1. Relationship Between LT Pathway Variants and Incident Risk of MACE. 56 Figure 2. Effect of LTA4H Variants on Gene Expression and Ex Vivo LTB 4 Production. 57 CHAPTER 2 Figure 1. Endothelial cell gene expression levels as a function of rs12746200 genotype. 100 CHAPTER 3 Figure 1 (A‐B). Results of GWAS for serum MPO levels in subjects of European ancestry. 155 Figure 2 (A‐C). Regional plots for loci demonstrating significant association with serum MPO levels. 157 Figure 3 (A‐C). Results of GWAS for plasma MPO levels in subjects of European ancestry. 160 Figure 4. Serum C3a‐desArg Levels as a Function of CFH rs800292 Genotype. 163 v ABSTRACT Atherosclerosis, the primary cause of cardiovascular disease (CVD), is a complex multi‐factorial process characterized by the accumulation of lipids and fibrous elements in arterial walls. While elevated plasma total cholesterol and triglyceride levels are established CVD risk factors, emerging evidence indicates that inflammatory mechanisms also play a causal role in the pathogenesis of coronary atherosclerosis. My work has focused on using various genetic approaches to investigate the contribution of inflammation to CVD. We comprehensively evaluated the genetic contribution of leukotriene (LT) pathway genes using both haplotype tagging and gene‐dietary interactions approaches. We have also employed unbiased genetic approaches to investigate the genetic determinants of additional inflammatory biomarkers associated with CVD. vi TABLE OF CONTENTS PAGE DEDICATION i ACKNOWLEDGEMENTS ii LIST OF TABLES iii LIST OF FIGURES v ABSTRACT vi INTRODUCTION CARDIOVASCULAR DISEASE (CVD) 1 THE ATHEROGENIC PROCESS 1 CLINICAL AND GENETIC RISK FACTORS 3 GENETIC STUDIES 4 ROLE OF INFLAMMATION IN CVD: MULTIFACETED GENETIC APPROACH 5 REFERENCES 9 CHAPTER 1: GENETIC CONTRIBUTION OF THE LEUKOTRIENE PATHWAY TO CORONARY ARTERY DISEASE SUMMARY 18 TITLE PAGE 20 ABSTRACT 21 INTRODUCTION 23 MATERIALS AND METHODS 25 RESULTS 33 DISCUSSION 39 ACKNOWLEDGEMENTS 43 REFERENCES 44 FIGURE LEGENDS 50 TABLES 51 FIGURES 56 SUPPLEMENTAL MATERIAL 58 CHAPTER 2: ASSOCIATION OF PLA2G4A WITH MYOCARDIAL INFARCTION IS MODULATED BY DIETARY PUFAS SUMMARY 68 TITLE PAGE 70 ABBREVIATIONS 71 ABSTRACT 73 INTRODUCTION 75 MATERIALS AND METHODS 77 RESULTS 82 DISCUSSION 85 ACKNOWLEDGEMENTS 89 REFERENCES 90 FIGURE LEGENDS 95 TABLES 96 FIGURES 100 SUPPLEMENTAL MATERIAL 101 CHAPTER 3: GENOME‐WIDE AND GENE CENTRIC ANALYSES OF CIRCULATING MYELOPEROXIDASE LEVELS IN THE CHARGE AND CARE CONSORTIA SUMMARY 103 TITLE PAGE 105 ABBREVIATIONS 108 ABSTRACT 110 INTRODUCTION 112 MATERIALS AND METHODS 114 RESULTS 118 DISCUSSION 128 ACKNOWLEDGEMENTS 136 REFERENCES 139 FIGURE LEGENDS 148 TABLES 150 FIGURES 155 SUPPLEMENTAL MATERIAL 164 FUTURE CHALLENGES IN CVD GENETICS CONCERNS RELATED TO GWAS 192 RARE VARIANTS 202 INTERACTIONS 203 GUT MICROBIOME 204 REFERENCES 207 REFERENCES AND BIBLIOGRAPHY 216 APPENDICES APPENDIX A: 244 Tang WH, Wu Y, Hartiala J, Fan Y, Stewart AF, Roberts R, McPherson R, Fox PL, Allayee H, Hazen SL (2012) Clinical and genetic association of serum ceruloplasmin with cardiovascular risk. Arterioscler Thromb Vasc Biol. Feb;32(2):516‐22. APPENDIX B: 252 Tang WH, Hartiala J, Fan Y, Wu Y, Stewart AF, Erdmann J, Kathiresan S; CARDIoGRAM Consortium, Roberts R, McPherson R, Allayee H, Hazen SL (2012) Clinical and genetic association of serum paraoxonase and arylesterase activities with cardiovascular risk. Arterioscler Thromb Vasc Biol. Nov;32(11):2803‐12. APPENDIX C: 277 Hartiala J, Bennett B, Tang WH, Wang Z, Stewart A, Roberts R, McPherson R, CARDIoGRAM Consortium, Hazen SL, Allayee H (2014). Comparative Genome‐wide association studies in mice and humans for trimethylamine N‐ oxide, a pro‐atherogenic metabolite of choline and L‐carnitine. Arterioscler Thromb Vasc Biol. Jun;34(6):1307‐13. INTRODUCTION Cardiovascular Disease Cardiovascular disease (CVD) is the leading cause of death in the United States 1 . Atherosclerosis is a disease of the large arteries, and is the primary cause of CVD and stroke 1 . It is a complex multi‐factorial process characterized by the accumulation of lipids and fibrous elements in the arterial walls 1 . The progression of atherosclerosis involves numerous factors and mediators, including plasma lipoproteins, the dynamic exchange of signals between resident endothelial and smooth muscle cells, and infiltrating leukocytes. The first manifestations of this pathogenic condition are often clinically significant endpoints such as myocardial infarction (MI), stroke, and/or sudden death. The Atherogenic Process The atherogenic process is initiated by the accumulation of low‐density lipoprotein (LDL) particles in the sub‐endothelial layer of the artery wall, where they are oxidized by cell‐derived reactive oxygen species (Figure 1, step I). The resulting production of adhesion molecules, chemokines, and growth factors by endothelial cells causes inflammatory cells, comprised predominantly of monocytes, to adhere to the vessel wall and migrate into the sub‐endothelial space. In this microenvironment, the activated monocytes proliferate and differentiate into macrophages, which engulf oxidized LDL particles and subsequently transform into 1 foam cells (Figure 1, step II). Accompanied by a progressive increase in extracellular lipids and intimal smooth muscle cells that have migrated through the media, the resulting ‘fatty streaks’ develop into advanced lesions as the lipid‐laden macrophages undergo apoptosis to form a necrotic core (Figure 1, step III). Pro‐ inflammatory cytokines that are also expressed in the lesion cause the smooth muscle cells to proliferate and secrete collagen and other extracellular matrix proteins, resulting in the formation of a fibrotic cap. Such advanced lesions become increasingly complex with calcification, ulceration at the luminal surface, and hemorrhage from small vessels that grow into the lesion from the media, rendering them unstable and prone to rupture (Figure 1 , step IV). Ultimately, plaque erosion and rupture can lead to clinical events such as MI or stroke 1‐3 . Figure 1. 2 Clinical and Genetic Risk Factors To date, epidemiological and genetic studies have established elevated plasma total cholesterol and triglycerides as causal risk factors for CVD and monitoring of their fasting levels is the standard of care for assessing risk 4,5 . However, lipid lowering therapies are only partially effective in reducing CVD risk and more than 50% of patients with an acute event do not exhibit these traditional risk factors. Thus, there is a critical need for identifying other pathophysiological mechanisms that could be leveraged for development of diagnostic biomarkers and/or lead to novel targets of intervention. Elevated levels of low‐density lipoprotein (LDL), as well as reduced levels of high‐density lipoprotein (HDL), have been associated with atherosclerosis in epidemiological studies, and supported by studies of genetic disorders and animal models 1,6,7 . LDL is synthesized in the liver, and it functions as a cholesterol delivery mechanism to the periphery. Cholesterol can then be used to maintain fluidity of cell membranes, or in the synthesis of steroid hormones. Conversely, HDL is involved in reverse cholesterol transport by removing excess cholesterol from peripheral tissues and transporting it back to the liver. Although extensive epidemiological evidence is clearly supportive of an independent association between plasma levels of HDL cholesterol and cardiovascular disease risk 8 , data from genetic and clinical intervention studies are 3 inconsistent. More recently, cholesterol efflux, the ability of HDL to accept cholesterol from macrophage foam cells, has also been proposed to be predictive of atherosclerotic burden. Thus, it is possible that the functionality of HDL may be as relevant to cardiovascular risk assessment as the absolute plasma levels of HDL‐C 9,10 . Genetic studies In the last several years, genome‐wide association studies (GWAS) have transformed our understanding of the etiology of CVD. For example, only one third of the ~45 loci identified to date for CVD harbor genes known to be involved in lipid metabolism or blood pressure, and none are associated with other risk factors such as type 2 diabetes, obesity, and cardiometabolic traits 10 . Consequently, the underlying biological mechanisms for how these susceptibility genes contribute to CVD, including those with the strongest genetic effects (i.e. chromosome 9p21), have yet to be determined. Notably, a network‐based analysis strongly supported the premise that inflammatory mechanisms play a causal role in the pathogenesis of coronary atherosclerosis 10 . This notion is well documented in the literature 12,13 and current evidence suggests that risk of CVD is associated with blood levels of certain inflammatory biomarkers 14,15 . These include acute phase proteins, such as C‐ reactive protein (CRP), cytokines, such as interleukin (IL‐6), and various adhesion 4 molecules. However, there is debate as to whether these biomarkers are direct mediators of CVD or mere markers of disease. Through Mendelian randomization analyses with genetic data in large datasets, there is evidence for a causal relationship between IL‐6 receptor signaling and CVD 15 but, interestingly, not for CRP 17,18 . Similar studies have yet to be carried out for other inflammatory biomarkers and will be needed in order to determine their causality with CVD. Role of inflammation in CVD: multifaceted genetic approach My work has focused on using various genetic approaches to investigate the contribution of inflammatory pathways to CVD, with a particular emphasis on leukotriene (LT) biosynthesis (Figure 2.). LTs are inflammatory mediators generated from polyunsaturated fatty acids (PUFAs), such as arachidonic acid (AA) or eicosapentaenoic acid (EPA) 19 . Class 4 LTs are pro‐inflammatory byproducts synthesized from AA whereas the EPA‐derived class 5 LTs are much less biologically active 19 . The rate‐limiting step in this pathway is catalyzed by the enzyme arachidonic acid 5‐lipoxgenase (5‐LO), which synthesizes LTA 4 /LTA 5 , followed by subsequent conversion to LTB 4 / LTB 5 and the cysteinyl LTs (LTC 4/5 , LTD 4/5 , and LTE 4/5 ) via enzymatic reactions by LTA4 hydrolase (LTA4H) and LTC4 synthase (LTC4S), respectively 19 . LTs then affect the function of target cells, including monocytes and other pro‐inflammatory leukocytes, through receptor‐mediated signal transduction. For example, LTB 4 is a potent leukocyte chemoattractant 5 whereas the cysteinyl LTs increase vascular permeability and promote vascular smooth muscle cellcontraction 20‐22 . Although involvement of LTs in chronic allergic inflammatory conditions (such as asthma) had been known 3,23,24 , a role for the LT pathway in CVD was previously unrecognized prior to series of biochemical, genetic and pharmacological studies over the past decade. For example, genetic deficiency for 5‐LO in mice protects against aortic lesion formation and leads to other metabolic disturbances 25‐27 . Other mouse studies have reported the involvement of LT pathway genes in atherosclerosis‐related traits as well, including the LT receptors and 5‐LO activating protein (FLAP) 28‐33 . Studies in humans have also provided evidence supporting the notion that LTs participate in atherosclerotic processes. Immunohistochemical studies have Figure 2. 6 shown that 5‐LO, FLAP, and LTA4H are abundantly expressed in arterial walls of CVD patients, with 5‐LO having markedly increased expression in advanced lesions and localizing to macrophages, dendritic cells, and neutrophilic granulocyte 34,35 . In addition, individuals carrying the shorter alleles of a functional 5‐LO promoter polymorphism, consisting of tandem Sp1 binding sites, have significantly increased carotid atherosclerosis and risk of MI, particularly in the context of high dietary AA levels 36‐37 . This is supported by studies that have reported associations between 5‐ LO variants and other pathway genes with CVD‐related phenotypes 38‐43 . Importantly, these genetic studies are bolstered by functional data showing that the associated variants/haplotypes lead to increased gene expression or LT production 36,41,42,44,45 . Despite these reports, evidence for association of LT pathway with CVD traits has not been consistently observed across all studies 46‐49 . Based on these observations, our first study comprehensively evaluated the genetic contribution of LT pathway genes using both haplotype tagging and gene‐dietary interactions approaches. This work, which comprise the first two chapters of my dissertation, have been published. More recently, we have also employed unbiased genetic approaches to investigate the genetic determinants of certain “non‐traditional” inflammatory biomarkers associated with CVD. For these studies, we focused on myeloperoxidase (MPO) which is an inflammatory biomarker that has received a great deal of attention for its potential role in CVD. This lysosomal enzyme is a 7 member of the heme peroxidase superfamily and stored within the azurophilic granules of circulating neutrophils, monocytes, and tissue macrophages 50 . It is released upon leukocyte activation and generates various reactive oxidants and free radicals that play important roles in killing invading parasites and pathogens. These same MPO‐derived oxidants have also been implicated in the formation of atherogenic low‐density lipoprotein (LDL) particles, vascular endothelial injury, and the development of atherosclerotic plaque and its clinical sequelae 51‐58 . MPO has been shown to be markedly enriched in human atherosclerotic lesions 58 where it may promote destabilization and plaque rupture 59,60 . These observations are supported by epidemiological studies demonstrating that high circulating levels of MPO predict major adverse cardiac events (MACE=death, MI, and stroke) in healthy individuals 61,62 and in patients with CVD 62‐68 . Based on the clinical association of MPO with CVD in prior reports, our next study sought to use a large‐scale unbiased genome‐wide analysis to identify loci controlling MPO, and to determine the relationship between associated variants and risk of CVD. This study was also recently published. 8 References 1. Lusis AJ (2000) Atherosclerosis. Nature. 407:233‐41. 2. Allayee H, Roth N, Hodis HN (2009) Polyunsaturated fatty acids and cardiovascular disease: implications for nutrigenetics. J Nutrigenet Nutrigenomics. 2(30): 140‐8. 3. Tymchuck CN, Hartiala J, Patel PI, Mehrabian M, Allayee H (2006). Nonconventional genetic risk factors for cardiovascular disease. Curr Atheroscler Rep. May;8(3): 184‐92. 4. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report (2002). Circulation. 106:3143‐ 421. 5. Graham I, Atar D, Borch‐Johnsen K, Boysen G, Burell G, Cifkova R, Dallongeville J, De Backer G, Ebrahim S, Gjelsvik B, Herrmann‐Lingen C, Hoes A, Humphries S, Knapton M, Perk J, Priori SG, Pyorala K, Reiner Z, Ruilope L, Sans‐Menendez S, Scholte op Reimer W, Weissberg P, Wood D, Yarnell J, Zamorano JL, Walma E, Fitzgerald T, Cooney MT, Dudina A, Vahanian A, Camm J, De Caterina R, Dean V, Dickstein K, Funck‐Brentano C, Filippatos G, Hellemans I, Kristensen SD, McGregor K, Sechtem U, Silber S, Tendera M, Widimsky P, Altiner A, Bonora E, Durrington PN, Fagard R, Giampaoli S, Hemingway H, Hakansson J, Kjeldsen SE, Larsen ML, Mancia G, Manolis AJ, Orth‐Gomer K, Pedersen T, Rayner M, Ryden L, Sammut M, Schneiderman N, Stalenhoef AF, Tokgozoglu L, Wiklund O, Zampelas A (2007) European guidelines on cardiovascular disease prevention in clinical practice: executive summary. Eur Heart J. 28:2375‐414. 6. Assmann, G., Cullen, P., Jossa, F., Lewis, B. & Mancini, M (1999) Coronary heart disease: reducing the risk. Arterioscl Thromb Vasc Biol. 19, 1819–1824. 7. Gordon, D. J. & Rifkind, B. M (1989) High‐density lipoprotein—the clinical implications of recent studies. N Engl J Med. 321, 1311–1316. 8. The Emerging Risk Factors Collaboration (2009) Major lipids, apolipoproteins, and risk of vascular disease. JAMA. 302:1993‐2000. 9 9. Khera AV, Cuchel M, de la Llera‐Moya M, Rodrigues A, Burke MF, Jafri K, French BC, Philips JA, Mucksavage ML, Wilensky RL, Mohler ER, Rothblat GH, Rader DJ (2011) Cholesterol efflux capasity, high‐density lipoprotein function, and atherosclerosis. N Engl J Med. Jan 13;364(2):127‐35. 10. Heinecke J (2011) HDL and cardiovascular disease risk ‐ time for a new approach? N Engl J Med. 364:170‐1. 11. Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, Thompson JR, Ingelsson E, Saleheen D, Erdmann J, Goldstein BA, Stirrups K, Konig IR, Cazier JB, Johansson A, Hall AS, Lee JY, Willer CJ, Chambers JC, Esko T, Folkersen L, Goel A, Grundberg E, Havulinna AS, Ho WK, Hopewell JC, Eriksson N, Kleber ME, Kristiansson K, Lundmark P, Lyytikainen LP, Rafelt S, Shungin D, Strawbridge RJ, Thorleifsson G, Tikkanen E, Van Zuydam N, Voight BF, Waite LL, Zhang W, Ziegler A, Absher D, Altshuler D, Balmforth AJ, Barroso I, Braund PS, Burgdorf C, Claudi‐Boehm S, Cox D, Dimitriou M, Do R, Doney AS, Mokhtari NE, Eriksson P, Fischer K, Fontanillas P, Franco‐Cereceda A, Gigante B, Groop L, Gustafsson S, Hager J, Hallmans G, Han BG, Hunt SE, Kang HM, Illig T, Kessler T, Knowles JW, Kolovou G, Kuusisto J, Langenberg C, Langford C, Leander K, Lokki ML, Lundmark A, McCarthy MI, Meisinger C, Melander O, Mihailov E, Maouche S, Morris AD, Muller‐Nurasyid M, Nikus K, Peden JF, Rayner NW, Rasheed A, Rosinger S, Rubin D, Rumpf MP, Schafer A, Sivananthan M, Song C, Stewart AF, Tan ST, Thorgeirsson G, Schoot CE, Wagner PJ, Wells GA, Wild PS, Yang TP, Amouyel P, Arveiler D, Basart H, Boehnke M, Boerwinkle E, Brambilla P, Cambien F, Cupples AL, de Faire U, Dehghan A, Diemert P, Epstein SE, Evans A, Ferrario MM, Ferrieres J, Gauguier D, Go AS, Goodall AH, Gudnason V, Hazen SL, Holm H, Iribarren C, Jang Y, Kahonen M, Kee F, Kim HS, Klopp N, Koenig W, Kratzer W, Kuulasmaa K, Laakso M, Laaksonen R, Lind L, Ouwehand WH, Parish S, Park JE, Pedersen NL, Peters A, Quertermous T, Rader DJ, Salomaa V, Schadt E, Shah SH, Sinisalo J, Stark K, Stefansson K, Tregouet DA, Virtamo J, Wallentin L, Wareham N, Zimmermann ME, Nieminen MS, Hengstenberg C, Sandhu MS, Pastinen T, Syvanen AC, Hovingh GK, Dedoussis G, Franks PW, Lehtimaki T, Metspalu A, Zalloua PA, Siegbahn A, Schreiber S, Ripatti S, Blankenberg SS, Perola M, Clarke R, Boehm BO, O'Donnell C, Reilly MP, Marz W, Collins R, Kathiresan S, Hamsten A, Kooner JS, Thorsteinsdottir U, Danesh J, Palmer CN, Roberts R, Watkins H, Schunkert H, Samani NJ (2013) Large‐scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 45:25‐33. 12. Hansson GK, Hermansson A (2011) The immune system in atherosclerosis. Nat Immunol. 12:204‐12. 10 13. Libby P, Ridker PM, Hansson GK (2011) Progress and challenges in translating the biology of atherosclerosis. Nature. 473:317‐25. 14. Ridker PM, Hennekens CH, Buring JE, Rifai N (2000) C‐reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med. 342:836‐43. 15. Raman K, Chong M, Akhtar‐Danesh GG, D'Mello M, Hasso R, Ross S, Xu F, Pare G (2013) Genetic markers of inflammation and their role in cardiovascular disease. Can J Cardiol. 29:67‐74. 16. Sarwar N, Butterworth AS, Freitag DF, Gregson J, Willeit P, Gorman DN, Gao P, Saleheen D, Rendon A, Nelson CP, Braund PS, Hall AS, Chasman DI, Tybjaerg‐ Hansen A, Chambers JC, Benjamin EJ, Franks PW, Clarke R, Wilde AA, Trip MD, Steri M, Witteman JC, Qi L, van der Schoot CE, de Faire U, Erdmann J, Stringham HM, Koenig W, Rader DJ, Melzer D, Reich D, Psaty BM, Kleber ME, Panagiotakos DB, Willeit J, Wennberg P, Woodward M, Adamovic S, Rimm EB, Meade TW, Gillum RF, Shaffer JA, Hofman A, Onat A, Sundstrom J, Wassertheil‐Smoller S, Mellstrom D, Gallacher J, Cushman M, Tracy RP, Kauhanen J, Karlsson M, Salonen JT, Wilhelmsen L, Amouyel P, Cantin B, Best LG, Ben‐Shlomo Y, Manson JE, Davey‐Smith G, de Bakker PI, O'Donnell CJ, Wilson JF, Wilson AG, Assimes TL, Jansson JO, Ohlsson C, Tivesten A, Ljunggren O, Reilly MP, Hamsten A, Ingelsson E, Cambien F, Hung J, Thomas GN, Boehnke M, Schunkert H, Asselbergs FW, Kastelein JJ, Gudnason V, Salomaa V, Harris TB, Kooner JS, Allin KH, Nordestgaard BG, Hopewell JC, Goodall AH, Ridker PM, Holm H, Watkins H, Ouwehand WH, Samani NJ, Kaptoge S, Di Angelantonio E, Harari O, Danesh J (2012) Interleukin‐6 receptor pathways in coronary heart disease: a collaborative meta‐analysis of 82 studies. Lancet. 379:1205‐13. 17. Wensley F, Gao P, Burgess S, Kaptoge S, Di Angelantonio E, Shah T, Engert JC, Clarke R, Davey‐Smith G, Nordestgaard BG, Saleheen D, Samani NJ, Sandhu M, Anand S, Pepys MB, Smeeth L, Whittaker J, Casas JP, Thompson SG, Hingorani AD, Danesh J (2011) Association between C reactive protein and coronary heart disease: mendelian randomisation analysis based on individual participant data. BMJ. 342:d548. 18. Nordestgaard BG, Zacho J (2009) Lipids, atherosclerosis and CVD risk: is CRP an innocent bystander? Nutr Metab Cardiovasc Dis. Oct; 19(8):521‐4. 19. Peters‐Golden M, Henderson WR Jr (2007) Leukotrienes. N Engl J Med. 357:1841–1854 11 20. Samuelsson B, Dahlen SE, Lindgren JA, Rouzer CA, Serhan CN (1987) Leukotrienes and lipoxins: structures, biosynthesis, and biological effects. Science. 237:1171‐6. 21. Drazen JM, Israel E, O’Byrne PM (1999) Treatment of asthma with drugs modifying the leukotriene pathway. N Engl J Med. 340:197‐206. [Errata, N Engl J Med 1999; 340:663, 341:1632.] 22. In KH, Asano K, Beier D, Grobholz J, Finn PW, Silverman EK, Silverman ES,Collins T, Fischer AR, Keith TP, Serino K, Kim SW, De Sanctis GT, Yandava C,Pillari A, Rubin P, Kemp J, Israel E, Busse W, Ledford D, Murray JJ, Segal A,Tinkleman D, Drazen JM (1997) Naturally occurring mutations in the human 5‐ lipoxygenase gene promoter that modify transcription factor binding and reporter gene transcription. J Clin Invest. 99:1130‐7. 23. Mehrabian M, Allayee H (2003) 5‐lipoxygenase and atherosclerosis. Curr Opin Lipidol. Oct; 14(15):447‐57. 24. Back M, Hansson GK (2006) Leukotriene receptors in atherosclerosis. Ann Med. 38:493–502. 25. Mehrabian M, Allayee H, Stockton J, Lum PY, Drake TA, Castellani LW, Suh M, Armour C, Edwards S, Lamb J, Lusis AJ, Schadt EE (2005) Integrating genotypic and expression data in a segregating mouse population to identify 5‐ lipoxygenase as a susceptibility gene for obesity and bone traits. Nat Genet. 37:1224–1233. 26. Mehrabian M, Allayee H, Wong J, Shi W, Wang XP, Shaposhnik Z, Funk CD, Lusis AJ (2002) Identification of 5‐lipoxygenase as a major gene contributing to theroslerosis susceptibility in mice. Circ Res 91:120‐6. [Erratum, Circ Res 2002;91(8):e27.] 27. Mehrabian M, Schulthess FT, Nebohacova M, Castellani LW, Zhou Z, Hartiala J, Oberholzer J, Lusis AJ, Maedler K, Allayee H (2008) IdentiWcation of ALOX5 as a gene regulating adiposity and pancreatic function. Diabetologia. 51:978– 988. 28. Ahluwalia N, Lin AY, Tager AM, Pruitt IE, Anderson TJ, Kristo F, Shen D, Cruz AR, Aikawa M, Luster AD, Gerszten RE (2007) Inhibited aortic aneurysm formation in BLT1‐deWcient mice. J Immunol. 179:691–697. 12 29. Aiello RJ, Bourassa PA, Lindsey S, Weng W, Freeman A, Showell HJ (2002) Leukotriene B4 receptor antagonism reduces monocytic foam cells in mice. Arterioscler Thromb Vasc Biol. 22:443–449. 30. Heller EA, Liu E, Tager AM, Sinha S, Roberts JD, Koehn SL, Libby P, Aikawa ER, Chen JQ, Huang P, Freeman MW, Moore KJ, Luster AD, Gerszten RE (2005) Inhibition of atherogenesis in BLT1‐deWcient mice reveals a role for LTB4 and BLT1 in smooth muscle cell recruitment. Circulation. 112:578–586. 31. Jawien J, Gajda M, Rudling M, Mateuszuk L, Olszanecki R, Guzik TJ, Cichocki T, Chlopicki S, Korbut R (2006) Inhibition of Wve lipoxygenase activating protein (FLAP) by MK‐886 decreases atherosclerosis in apoE/LDLR‐double knockout mice. Eur J Clin Invest. 36:141–146. 32. Jawien J, Gajda M, Wolkow P, Zuranska J, Olszanecki R, Korbut R (2008) The eVect of montelukast on atherogenesis in apoE/LDLR‐double knockout mice. J Physiol Pharmacol. 59:633–639. 33. Subbarao K, Jala VR, Mathis S, Suttles J, Zacharias W, Ahamed J, Ali H, Tseng MT, Haribabu B (2004) Role of leukotriene B4 receptors in the development of atherosclerosis: potential mechanisms. Arterioscler Thromb Vasc Biol. 24:369– 375. 34. Qiu H, Gabrielsen A, Agardh HE, Wan M, Wetterholm A, Wong CH, Hedin U, Swedenborg J, Hansson GK, Samuelsson B, Paulsson‐Berne G, Haeggstrom JZ (2006) Expression of 5‐lipoxygenase and leukotriene A4 hydrolase in human atherosclerotic lesions correlates with symptoms of plaque instability. Proc Natl Acad Sci USA. 103:8161–8166. 35. Spanbroek R, Grabner R, Lotzer K, Hildner M, Urbach A, Ruhling K, Moos MP, Kaiser B, Cohnert TU, Wahlers T, Zieske A, Plenz G, Robenek H, Salbach P, Kuhn H, Radmark O, Samuelsson B, Habenicht AJ (2003) Expanding expression of the 5‐lipoxygenase pathway within the arterial wall during human atherogenesis. Proc Natl Acad Sci USA. 100:1238–1243. 36. Allayee H, Baylin A, Hartiala J, Wijesuriya H, Mehrabian M, Lusis AJ, Campos H (2008) Nutrigenetic association of the 5‐lipoxygenase gene with myocardial infarction. Am J Clin Nutr. 88:934–940. 37. Dwyer JH, Allayee H, Dwyer KM, Fan J, Wu H, Mar R, Lusis AJ, Mehrabian M (2004) Arachidonate 5‐lipoxygenase promoter genotype, dietary arachidonic acid, and atherosclerosis. N Engl J Med. 350:29–37. 13 38. Burdon KP, Rudock ME, Lehtinen AB, Langefeld CD, Bowden DW, Register TC, Liu Y, Freedman BI, Carr JJ, Hedrick CC, Rich SS (2010) Human lipoxygenase pathway gene variation and association with markers of subclinical atherosclerosis in the diabetes heart study. Mediators Inflamm. 2010:170153. 39. Carlson CS, Heagerty PJ, Nord AS, Pritchard DK, Ranchalis J, Boguch JM, Duan H, Hatsukami TS, Schwartz SM, Rieder MJ, Nickerson DA, Jarvik GP (2007) TagSNP evaluation for the association of 42 inflammation loci and vascular disease: evidence of IL6, FGB, ALOX5, NFKBIA, and IL4R loci effects. Hum Genet. 121:65–75. 40. Crosslin DR, Shah SH, Nelson SC, Haynes CS, Connelly JJ, Gadson S, Goldschmidt‐Clermont PJ, Vance JM, Rose J, Granger CB, Seo D, Gregory SG, Kraus WE, Hauser ER (2009) Genetic eVects in the leukotriene biosynthesis pathway and association with atherosclerosis. Hum Genet. 125:217–229. 41. Helgadottir A, Manolescu A, Thorleifsson G, Gretarsdottir S, Jonsdottir H, Thorsteinsdottir U, Samani NJ, Gudmundsson G, Grant SF, Thorgeirsson G, Sveinbjornsdottir S, Valdimarsson EM, Matthiasson SE, Johannsson H, Gudmundsdottir O, Gurney ME, Sainz J, Thorhallsdottir M, Andresdottir M, Frigge ML, Topol EJ, Kong A, Gudnason V, Hakonarson H, Gulcher JR, Stefansson K (2004) The gene encoding 5‐lipoxygenase activating protein confers risk of myocardial infarction and stroke. Nat Genet. 36:233–239. 42. Helgadottir A, Manolescu A, Helgason A, Thorleifsson G, Thorsteinsdottir U, Gudbjartsson DF, Gretarsdottir S, Magnusson KP, Gudmundsson G, Hicks A, Jonsson T, Grant SF, Sainz J, O’Brien SJ, Sveinbjornsdottir S, Valdimarsson EM, Matthiasson SE, Levey AI, Abramson JL, Reilly MP, Vaccarino V, Wolfe ML, Gudnason V, Quyyumi AA, Topol EJ, Rader DJ, Thorgeirsson G, Gulcher JR, Hakonarson H, Kong A, Stefansson K (2006) A variant of the gene encoding leukotriene A4 hydrolase confers ethnicityspecific risk of myocardial infarction. Nat Genet. 38:68–74. 43. Iovannisci DM, Lammer EJ, Steiner L, Cheng S, Mahoney LT, Davis PH, Lauer RM, Burns TL (2007) Association between a leukotriene C4 synthase gene promoter polymorphism and coronary artery calcium in young women: the Muscatine Study. Arterioscler Thromb Vasc Biol. 27:394–399. 44. Sanak M, Pierzchalska M, Bazan‐Socha S, Szczeklik A (2000) Enhanced expression of the leukotriene C(4) synthase due to overactive transcription of an allelic variant associated with aspirin‐intolerant asthma. Am J Respir Cell Mol Biol. 23:290–296. 14 45. Vikman S, Brena RM, Armstrong P, Hartiala J, Stephensen CB, Allayee H (2009) Functional analysis of 5‐lipoxygenase promoter repeat variants. Hum Mol Genet. 18(23):4521–4529. 46. Assimes TL, Knowles JW, Priest JR, Basu A, Volcik KA, Southwick A, Tabor HK, Hartiala J, Allayee H, Grove ML, Tabibiazar R, Sidney S, Fortmann SP, Go A, Hlatky M, Iribarren C, Boerwinkle E, Myers R, Risch N, Quertermous T (2008) Common polymorphisms of ALOX5 and ALOX5AP and risk of coronary artery disease. Hum Genet. 123:399–408. 47. Koch W, Hoppmann P, Mueller JC, Schomig A, Kastrati A (2007) No association of polymorphisms in the gene encoding 5‐lipoxygenaseactivating protein and myocardial infarction in a large central European population. Genet Med. 9:123–129. 48. Zee RY, Cheng S, Hegener HH, Erlich HA, Ridker PM (2006) Genetic variants of arachidonate 5‐lipoxygenase‐activating protein, and risk of incident myocardial infarction and ischemic stroke: a nested case–control approach. Stroke. 37:2007–2011. 49. Zintzaras E, Rodopoulou P, Sakellaridis N (2009) Variants of the arachidonate 5‐lipoxygenase‐activating protein (ALOX5AP) gene and risk of stroke: a HuGE gene–disease association review and meta‐analysis. Am J Epidemiol. 169:523– 532. 50. Arnhold J, Flemmig J (2010) Human myeloperoxidase in innate and acquired immunity. Arch Biochem Biophys. 500:92‐106. 51. Nicholls SJ, Hazen SL (2005) Myeloperoxidase and cardiovascular disease. Arterioscler Thromb Vasc Biol. 25:1102‐11. 52. Nicholls SJ, Hazen SL (2009) Myeloperoxidase, modified lipoproteins, and atherogenesis. J Lipid Res. 50 Suppl:S346‐51. 53. Zheng L, Nukuna B, Brennan ML, Sun M, Goormastic M, Settle M, Schmitt D, Fu X, Thomson L, Fox PL, Ischiropoulos H, Smith JD, Kinter M, Hazen SL (2004) Apolipoprotein A‐I is a selective target for myeloperoxidase‐catalyzed oxidation and functional impairment in subjects with cardiovascular disease. J Clin Invest. 114:529‐41. 54. Wu Z, Wagner MA, Zheng L, Parks JS, Shy JM, 3rd, Smith JD, Gogonea V, Hazen SL (2007) The refined structure of nascent HDL reveals a key functional domain for particle maturation and dysfunction. Nat Struct Mol Biol. 14:861‐8. 15 55. Abu‐Soud HM, Hazen SL (2000) Nitric oxide is a physiological substrate for mammalian peroxidases. J Biol Chem. 275:37524‐32. 56. Vita JA, Brennan ML, Gokce N, Mann SA, Goormastic M, Shishehbor MH, Penn MS, Keaney JF, Jr., Hazen SL (2004) Serum myeloperoxidase levels independently predict endothelial dysfunction in humans. Circulation. 110:1134‐9. 57. Eiserich JP, Baldus S, Brennan ML, Ma W, Zhang C, Tousson A, Castro L, Lusis AJ, Nauseef WM, White CR, Freeman BA (2002) Myeloperoxidase, a leukocyte‐ derived vascular NO oxidase. Science. 296:2391‐4. 58. Daugherty A, Dunn JL, Rateri DL, Heinecke JW (1994) Myeloperoxidase, a catalyst for lipoprotein oxidation, is expressed in human atherosclerotic lesions. J Clin Invest. 94:437‐44. 59. Shabani F, McNeil J, Tippett L (1998) The oxidative inactivation of tissue inhibitor of metalloproteinase‐1 (TIMP‐1) by hypochlorous acid (HOCI) is suppressed by anti‐rheumatic drugs. Free Radic Res. 28:115‐23. 60. Fu X, Kassim SY, Parks WC, Heinecke JW (2001) Hypochlorous acid oxygenates the cysteine switch domain of pro‐matrilysin (MMP‐7). A mechanism for matrix metalloproteinase activation and atherosclerotic plaque rupture by myeloperoxidase. J Biol Chem. 276:41279‐87. 61. Meuwese MC, Stroes ES, Hazen SL, van Miert JN, Kuivenhoven JA, Schaub RG, Wareham NJ, Luben R, Kastelein JJ, Khaw KT, Boekholdt SM (2007) Serum myeloperoxidase levels are associated with the future risk of coronary artery disease in apparently healthy individuals: the EPIC‐Norfolk Prospective Population Study. J Am Coll Cardiol. 50:159‐65. 62. Karakas M, Koenig W (2012) Myeloperoxidase production by macrophage and risk of atherosclerosis. Curr Atheroscler Rep. 14:277‐83. 63. Zhang R, Brennan ML, Fu X, Aviles RJ, Pearce GL, Penn MS, Topol EJ, Sprecher DL, Hazen SL (2001) Association between myeloperoxidase levels and risk of coronary artery disease. JAMA. 286:2136‐42. 64. Brennan ML, Penn MS, Van Lente F, Nambi V, Shishehbor MH, Aviles RJ, Goormastic M, Pepoy ML, McErlean ES, Topol EJ, Nissen SE, Hazen SL (2003) Prognostic value of myeloperoxidase in patients with chest pain. N Engl J Med. 349:1595‐604. 16 65. Baldus S, Heeschen C, Meinertz T, Zeiher AM, Eiserich JP, Munzel T, Simoons ML, Hamm CW (2003) Myeloperoxidase serum levels predict risk in patients with acute coronary syndromes. Circulation. 108:1440‐5. 66. Mocatta TJ, Pilbrow AP, Cameron VA, Senthilmohan R, Frampton CM, Richards AM, Winterbourn CC (2007) Plasma concentrations of myeloperoxidase predict mortality after myocardial infarction. J Am Coll Cardiol. 49:1993‐2000. 67. Tang WH, Tong W, Troughton RW, Martin MG, Shrestha K, Borowski A, Jasper S, Hazen SL, Klein AL (2007) Prognostic value and echocardiographic determinants of plasma myeloperoxidase levels in chronic heart failure. J Am Coll Cardiol. 49:2364‐70. 68. Tang WH, Katz R, Brennan ML, Aviles RJ, Tracy RP, Psaty BM, Hazen SL (2009) Usefulness of myeloperoxidase levels in healthy elderly subjects to predict risk of developing heart failure. Am J Cardiol. 103:1269‐74. 17 CHAPTER 1: GENETIC CONTRIBUTION OF THE LEUKOTRIENE PATHWAY TO CORONARY ARTERY DISEASE Summary: In the present study, we carried out a comprehensive genetic analysis of the LT pathway in a relatively large cohort of subjects ascertained through elective cardiac evaluation. Two newly discovered SNPs in LTA4H and PLA2G4A were identified using an unbiased two‐stage study design with dense haplotype‐ tagging SNPs. The rs2540477 variant of LTA4H was associated increased risk of prevalent CVD, whereas the rs12746200 variant of PLA2G4A was cardioprotective, decreasing both the likelihood of having CVD and the risk for experiencing a future MACE over three years of follow‐up. Additionally, we confirmed previous findings with 5‐LO promoter polymorphism and a haplotype (HapK) in LTA4H, and the risk of CVD. Moreover, functional experiments showed increased LTB 4 production in stimulated monocytes from subjects carrying LTA4H variants. However, rs2540477 or the SNPs comprising HapK are located in non‐coding regions of LTA4H and, based on our results, do not affect gene expression. Sequencing of LTA4H coding regions and surrounding areas may help to identify putative amino acid substitutions that are in linkage disequilibrium (LD) with LTA4H variants and/or alternatively spliced isoforms that lead to increased LTB 4 production. My role in this project was the coordination of all aspects of the study, including selection of the GeneBank samples, selection and genotyping of haplotype tagging SNPs, subsequent quality control of the data generated, conducting all statistical analyses, and writing of the 18 manuscript. This study was published in the journal Human Genetics (Hum Genet. 2011 Jun;129(6):617‐27). 19 Genetic Contribution of the Leukotriene Pathway to Coronary Artery Disease Jaana Hartiala 1,2 , Dalin Li 1 , David V. Conti 1 , Susanna Vikman 1,2 , Yesha Patel 1,2 , W.H. Wilson Tang 3 , Marie‐Louise Brennan 3,4,5 , John W. Newman 6 Charles B. Stephensen 6,7 , Patrice Armstrong 6, 7 , Stanley L. Hazen 3,4,5 , and Hooman Allayee 1,2 1 Department of Preventive Medicine and 2 Institute for Genetic Medicine, USC Keck School of Medicine, Los Angeles, CA 90033; Departments of 3 Cardiovascular Medicine and 4 Cell Biology and 5 Center for Cardiovascular Diagnostics and Prevention, Cleveland Clinic, Cleveland, OH 44195; 6 USDA Western Human Nutrition Research Center, University of California Davis, CA 95616; 7 Program in International and Community Nutrition, Department of Nutrition, University of California Davis, CA 95616. Running Title: Leukotriene Genes and Coronary Artery Disease 20 Abstract We evaluated the genetic contribution of the leukotriene (LT) pathway to risk of coronary artery disease (CAD) in 4512 Caucasian and African American subjects ascertained through elective cardiac evaluation. Of three previously associated variants, the shorter “3” and “4” alleles of a promoter repeat polymorphism in ALOX5 increased risk of CAD in African Americans (OR=1.4, 95% CI 1.0‐1.9; p=0.04) whereas a haplotype of LTA4H (HapK) was associated with CAD in Caucasians (OR=1.2, 95% CI 1.01‐1.4; p=0.03). In Caucasians, first‐stage analysis of 254 haplotype‐tagging SNPs in 15 LT pathway genes with follow‐up of 19 variants in stage 2 revealed an LTA4H SNP (rs2540477) that increased risk of CAD (OR=1.2, 95% CI 1.1‐1.5; p=0.003) and a PLA2G4A SNP (rs12746200) that decreased risk of CAD (OR=0.7, 95% CI 0.6‐0.9; p=0.0007). The PLA2G4A rs12746200 variant also decreased risk of experiencing a major adverse cardiac event (MACE=myocardial infarction, stroke, or death) over three years of follow‐up (HR=0.7, 95% CI 0.5‐0.9; p=0.01), consistent with its cardioprotective effect. Functional experiments demonstrated that stimulated monocytes from carriers of LTA4H variants HapK or rs2540477 had 50% (p=0.002) and 33% (p=0.03) higher LTB 4 production, respectively, compared to non‐carriers. These ex vivo results are consistent with LTB 4 being the direct product of the reaction catalyzed by LTA4H and its role in promoting monocyte chemotaxis to sites of inflammation, including the artery wall of atherosclerotic lesions. Taken together, this study provides additional evidence 21 that functional genetic variation of the LT pathway can mediate atherogenic processes and the risk of CAD in humans. Keywords: leukotrienes, genetics, coronary artery disease, polymorphisms 22 Introduction Class four leukotrienes (LTs) are potent pro‐inflammatory mediators synthesized from arachidonic acid, an omega‐6 polyunsaturated fatty acids (PUFAs) 1 . The rate‐limiting step in this pathway is catalyzed by the enzyme arachidonic acid 5‐lipoxgenase (ALOX5). The biologically active LTs are synthesized by subsequent conversion to LTB 4 and the cysteinyl LTs (LTC 4 , LTD 4 , and LTE 4 ) via enzymatic reactions by LTA4 hydrolase (LTA4H) and LTC4 synthase (LTC4S), respectively 1 . LTs then affect the function of target cells, including monocytes and other pro‐inflammatory leukocytes, through receptor‐mediated signal transduction. While LTs have long been known to be involved in chronic allergic inflammatory conditions, such as asthma, the LT pathway has also recently garnered attention for its potential role in coronary artery disease (CAD)‐related traits. This stems from a series of biochemical, genetic, and pharmacological studies over the last few years that have provided evidence for the pro‐atherogenic role of LTs 2‐4 . For example, genetic deficiency for ALOX5 in mice protects against aortic lesion formation and leads to other metabolic disturbances 5‐7 . Other mouse studies have reported the involvement of LT pathway genes in atherosclerosis‐ related traits as well, including the LT receptors and ALOX5 activating protein (ALOX5AP) 8‐13 . 23 Studies in humans have also provided evidence supporting the notion that LTs participate in atherosclerotic processes. Immunohistochemical studies have shown that ALOX5, ALOX5AP, and LTA4H are abundantly expressed in arterial walls of CAD patients, with ALOX5 having markedly increased expression in advanced lesions and localizing to macrophages, dendritic cells, and neutrophilic granulocytes 14‐15 . In addition, individuals carrying the shorter alleles of a functional ALOX5 promoter polymorphism, consisting of tandem Sp1 binding sites, have significantly increased carotid atherosclerosis and risk of myocardial infarction (MI), particularly in the context of high dietary arachidonic acid levels 16‐17 . This is supported by studies that have reported associations between other ALOX5 and ALOX5AP variants with CAD‐related phenotypes 18‐21 . More recently, a 10‐SNP haplotype of LTA4H, designated HapK, has been associated with MI in Caucasians and African Americans, with a more pronounced effect in the latter group 22 , and LTC4S variants have been associated with surrogate measures of CAD, including coronary artery calcification and carotid atherosclerosis 23 . Importantly, these genetic studies are bolstered by functional data showing that the associated variants/haplotypes lead to increased gene expression or LT production 16, 21,22, 24,25 . Despite these reports, evidence for association of LT pathway genes with CAD traits has not been consistently observed across all studies 26‐29 . Thus, the aim of the present study was to comprehensively evaluate the genetic contribution of the LT pathway to CAD in a large cohort of subjects undergoing elective cardiac evaluation. 24 Materials and Methods Study Subjects: GeneBank is a single site (Cleveland Clinic) sample repository generated from patients undergoing elective diagnostic coronary angiography or elective cardiac computed tomographic angiography with extensive clinical and laboratory characterization and longitudinal observation 30,31 . Ethnicity was self‐ reported and information regarding demographics, medical history, and medication use was obtained by patient interviews and confirmed by chart reviews. All clinical outcome data were verified by source documentation. CAD was defined as adjudicated diagnoses of stable or unstable angina, MI (adjudicated definition based on defined electrocardiographic changes or elevated cardiac enzymes), angiographic evidence of ≥ 50% stenosis of one or more major epicardial vessel, and/or a history of known CAD (documented MI, CAD, or history of revascularization). Prospective cardiovascular risk was assessed by the incidence of major adverse cardiac events (MACE) during three years of follow‐up from the time of enrollment, which included nonfatal MI, nonfatal stroke, and all‐cause mortality. Nonfatal events were defined as MI or stroke in patients who survived at least 48 hours following the onset of symptoms. Adjudicated outcomes ascertained over the ensuing 3 years for all subjects following enrollment were confirmed using source documentation. All patients provided written informed consent prior to being enrolled in GeneBank and the study was approved by the Institutional Review Board of the Cleveland Clinic. The present genetics study was approved by the 25 Institutional Review Boards of the Cleveland Clinic and USC Keck School of Medicine. Clinical Laboratory Measurements: Samples were collected from overnight fasted subjects on the day of elective cardiac catheterization. Plasma aliquots were isolated from whole blood collected into EDTA tubes, maintained at 0‐4 C immediately following phlebotomy, processed within 4h of blood draw, and stored at ‐80 C until analysis. Plasma levels of total cholesterol, low‐density lipoproteins (LDL), high‐density lipoproteins (HDL), triglycerides, and high sensitivity C‐reactive protein (CRP) were measured on the Abbott ARCHITECT platform (Abbott Diagnostics, Abbott Park IL). Genotyping: Genomic DNA was extracted from isolated buffy coats using DNeasy isolation kits (Qiagen, Valencia, CA). Genotyping of the 274 haplotype‐tagging SNPs was carried out using the Illumina GoldenGate System, which involves using allele‐ specific primer extension in combination with multiplex PCR with universal primers. Technical details regarding this methodology is available from Illumina, Inc. (www.illumina.com). Haplotype‐tagging SNPs were selected using the HapMap data for Caucasians and the Tagger program 32 . Genotyping of previously associated SNPs/variants and those selected for replication in stage 2 was performed using 26 either fragment analysis, as described elsewhere 17 , or the TaqMan Allelic Discrimination system from Applied Biosystems, Inc. (Foster City, CA) 33,34 . For determination of HapA and HapK, we genotyped the same SNPs as reported by deCode Genetics 21,22 , which were rs17222814, rs10507391, rs4769874, and rs9551963 for HapA and rs61937881 (SG12S16), rs2660880, rs6538697, rs1978331, rs17677715, rs2247570, rs2660898, rs2540482, rs2660845, and rs2540475 for HapK (Supplemental Table 1). Statistical Analyses: Prior to analysis, all variants were tested for Hardy‐Weinberg equilibrium in subjects without CAD using a χ 2 test. SNPs deviating from HWE (p <0.05) were excluded from further analysis. Haplotypes of ALOX5AP (HapA) and LTA4H (HapK) were estimated using an expectation‐maximization (EM) algorithm to generate maximum likelihood estimates of haplotype frequencies, which assigns the probability that each individual possesses a particular haplotype pair. Unconditional multiple logistic regression was used to independently test for association with CAD with adjustment for age and sex under dominant genetic models (as a means to increase sample size and power). The fully adjusted regression models included age, sex, medication use, plasma CRP levels, alcohol consumption, and Framingham ATP‐III risk score (which includes smoking and diabetes status) and the results are reported as odds ratios (OR) with 95% confidence intervals (CI) . Since we were testing specific hypotheses with respect to 27 the HapA and HapK haplotypes of ALOX5AP and LTA4H, respectively, these analyses were performed as tests of a single haplotype compared to all other haplotypes (i.e. carriers of HapA or HapK vs. non‐carriers). For analyses of HapK, we also performed a haplotype score test with all haplotypes having frequencies greater than 1%, as implemented in the Haplo.Stats package. All analyses were performed using SAS version 9.2 (SAS Institute Inc, Cary, NC) or R 2.10.1 (http://www.R‐project.org) and carried out separately in Caucasian and African American subjects. Kaplan‐Meier failure estimates were plotted to illustrate differences in rates for developing a MACE over three years of follow‐up as a function of genotype. Subjects experiencing a MACE within 14 days of enrollment were excluded. Due to sample size limitations, we assumed dominant genetic models and significance was assessed using a log‐rank test. Relative risk for experiencing a MACE was assessed using Cox proportional hazard models with adjustment for age, sex, medication use, plasma CRP levels, alcohol consumption, and Framingham ATP‐III risk score (which includes diabetes status). Other variables tested, but not included in the final model, were BMI and smoking. Adjusted hazard ratio (HR) and 95% CI are reported with 2‐sided p‐values that were considered significant when < 0.05. Time‐to‐event analyses were carried out using Stata 8.2 (StataCorp LP, College Station, TX). 28 Monocyte Samples for Functional Studies: Monocyte samples for the functional analysis of genetic variants came from the baseline visit of a randomized, double blind, placebo‐controlled intervention trial to examine the effect of omega‐3 fatty acid supplementation in subjects with different ALOX5 promoter variants. The registry number for this study is NCT00536185 and details of the study design can be found at the ClinicalTrials.gov website. Healthy adults between 20‐59 years of age who had not been diagnosed with any major disorder and who self‐identified as African American, Black, or a person of African descent, were recruited into the study from three study sites in Davis, Sacramento, and Oakland, California. Potential study participants received a brief interview to characterize general health, previous diagnosis of major diseases, smoking and alcohol usage, medication, nutritional supplements, and other factors. Eligible subjects that had one of the six 5‐LO promoter repeat genotypes of interest (33, 34, 44, 35, 45, 55) were subsequently invited to participate in the study. A fasting blood sample was used for a complete blood count, lipid and chemistry panel analysis to identify any undiagnosed medical problems. Study participants that had a physician‐diagnosed chronic inflammatory disease (arthritis, autoimmune disease, or asthma), CVD, hypertension, diabetes, or a lipid disorder that required regular use of anti‐ inflammatory or lipid‐lowering medication were excluded. Subjects with abnormal results on standard chemistry and lipid panels or a complete blood count that suggested underlying undiagnosed disease were also excluded and referred to their physician for further evaluation. The institutional review boards of The University 29 of California, Davis, Alta Bates Summit Medical Center, the USC Keck School of Medicine reviewed and approved ethical permission for all procedures involving human volunteers and the protocols. Written informed consent was obtained from all study participants. Cell Isolations: At the baseline visit subsequent to enrollment, a fasting blood sample (80ml) from each participant was collected into heparinized tubes and processed within 4h. Plasma was separated after low‐speed centrifugation and the buffy coat containing mononuclear cells (lymphocytes and monocytes) and granulocytes was then removed and diluted with approximately one volume of Hank’s Balanced Salt Solution (HBSS) into a total volume of approximately 32ml. Monocytes were purified using two‐step gradients and washed once with HBSS, followed by positive selection using CD14‐labeled magnetic microbeads with an LS magnetic column (Miltenyi Biotec, Auburn, CA), as described previously 25 . Purity of monocytes was assessed by FACS analysis (BD FACSCalibur) using PE‐labeled anti‐ CD14 antibody (Miltenyi Biotec) and an isotype control reagent (IgG2a; BD Biosciences, San Jose, CA). The median percent purity (25th/75th percentiles) of monocytes was 93% (89%/96%). SNP genotyping was carried out as described above. 30 Real‐time RNA Quantitation: Total RNA was isolated using RNeasy kits from Qiagen, Inc. (Valencia, CA) of which 1g was reverse transcribed using cDNA Archive kits from ABI (Foster City, CA). Real‐time mRNA levels for LTA4H and beta‐ glucuronidase (GUSB) as an endogenous control were determined in triplicate using pre‐developed assays (LTA4H: Hs00168505_m1; GUSB: Hs99999908_m1) from Applied Biosystems Inc. (Foster City, CA). LTA4H mRNA levels were normalized to those of GUSB and the replicates were averaged to determine transcript abundance in each sample relative to a reference sample. The mRNA levels of GUSB, as determined by the average Ct values for the triplicate reactions, did not differ across LTA4H genotypes in these monocyte samples. Ex Vivo LTB 4 Production Analyses: Within 1h after isolation from blood, monocytes (1 x 10 6 /ml) were cultured at 37º C in 5% CO 2 using RPM1 1640 complete media supplemented with 10% heat‐inactivated autologous plasma. The cells were then stimulated with either the calcium ionophore A23187 (Sigma, St. Louis, MO) at a final concentration of 10M or with DMSO as a control culture. After a 60min incubation, the supernatants and cell pellets were collected and the supernatants were extracted using 60mg HLB solid phase extraction cartridges (Waters, Inc; Milford, MA). Oxylipid analytes, including LTB 4 , were chromatographically separated on an ultra‐performance liquid chromatography system equipped with a 2.1 X 150mm Acquity BEH C18 reversed phase column and quantified by negative 31 mode electrospray ionization on a Quattro Micro tandem mass spectrometer (Waters, Inc.). One in twenty samples was analyzed in replicate to assess analytical precision. Differences in mRNA and LTB 4 levels were determined using t‐tests (Statview version. 5.0; SAS Institute Inc., Cary, NC) with p‐values < 0.05 considered as statistically significant. 32 Results Clinical Characteristics of GeneBank Subjects: The general characteristics of the Caucasian and African American GeneBank subjects used in the present study are presented in Table 1. For both ethnicities, subjects with CAD at baseline exhibited the known traditional risk factors for cardiovascular disease, including being older, more likely to be male, and having higher plasma levels of triglycerides and CRP and lower HDL‐cholesterol levels (Table 1). However, plasma total and LDL‐cholesterol levels were lower in subjects with CAD, which is likely due to these patients having a higher prevalence of co‐morbidities and taking lipid‐lowering medications. Evaluation of Previously Reported LT Pathway Variants: Table 2 lists the LT pathway genes selected for the present study and their biological functions. We first evaluated several variants of ALOX5, ALOX5AP, and LTA4H that were previously associated with CAD‐related phenotypes 17,21,22 in 3747 Caucasian and 765 African American subjects from the GeneBank cohort. These included a variable number of tandem Sp1 repeats in the ALOX5 promoter as well as haplotypes of ALOX5AP (HapA) and LTA4H (HapK) (Supplemental Table 1). As in our prior analyses of the ALOX5 repeats 16 , carriers of the functional “short” promoter alleles with “3” and “4” Sp1 repeats (i.e. genotypes of 3/3, 3/4, 4/4, 3/5, 4/5, 3/6, 3/7, 4/6, 4/8) were compared with subjects carrying alleles of > “5” repeats (i.e. genotypes of 5/5, 5/6, 33 5/7, 5/8, 5/9, 6/6, 6/7). In the overall GeneBank cohort, the frequencies of the “3” and “4” repeats were ~1% and 14%, respectively, in Caucasians and 30% and 14%, respectively, in African Americans, which are consistent with previous reports for these ethnicities 17,26,35 . In Caucasians, there was no association of the “3” and “4” alleles with CAD (Table 3) whereas African American carriers of these shorter repeats had increased risk compared to non‐carriers (OR = 1.4; 95% CI, 1.01‐1.9; p = 0.04). Analyses of the ALOX5AP and LTA4H haplotypes, adjusted for age and sex, yielded significant evidence of association with HapK and CAD in Caucasians (OR = 1.2; 95% CI ,1.01‐1.4; p = 0.03) but not with HapA (Table 3). Further adjustment for CRP levels, medication use, alcohol consumption, and Framingham risk score, which includes diabetes and smoking status slightly attenuated these results but still yielded a significant p‐value of 0.05 for HapK. An analysis using a haplotype score test that included all possible LTA4H haplotypes having frequencies greater than 1% (n=13) also yielded consistent evidence for association with only HapK (p = 0.07). By comparison, neither haplotype was associated with CAD in African Americans. Two‐stage Analysis of LT Pathway Genes with Haplotype‐tagging SNPs. In order to comprehensively evaluate the contribution of LT pathway genes to CAD in an unbiased fashion, we next carried out a two‐stage association study in Caucasian subjects. In the first stage, we used HapMap data for individuals of European 34 ancestry (to match GeneBank) and the Tagger program to identify haplotype‐ tagging SNPs in 15 pathway genes that play important roles in LT biosynthesis, signaling, and degradation (Table 2). For this purpose, we chose an r 2 of 0.9 between SNPs and included the genomic region spanning 10kb upstream and 5kb downstream of each gene in order to capture potential regulatory elements. These criteria resulted in the selection of 274 haplotype‐tagging SNPs (listed in Supplemental Table 1), of which 254 were successfully genotyped in stage 1. This dataset included 1000 sequential GeneBank patients, comprised of 804 and 196 subjects with and without CAD, respectively. In order to obtain a more balanced “case‐control” dataset for stage 1, we also included an additional 322 “control” subjects without CAD that were selected from GeneBank. Based on our two‐stage study design, we chose an unadjusted p‐value of <0.05 as the threshold for suggestive evidence of association in the stage 1 analyses. After adjustment for age and sex, 19 SNPs in the ALOX5, ALOX5AP, LTA4H, CYP4F3, MGST1, MGST2, MGST3, and PLA2G4A genes demonstrated nominal evidence of association (Supplemental Table 2). In stage 2, we genotyped these 19 SNPs in 2425 additional Caucasian subjects (395 without CAD/2030 with CAD) and obtained evidence of association with one variant in PLA2G4A (rs12746200; p = 0.03) and another in LTA4H (rs2540477; p = 0.01) (Table 4). The direction of the associations for these two SNPs was consistent in both stages and became even more significant in a combined analysis with all subjects (Table 4). Of 35 note, rs12746200 had a protective effect (OR = 0.7; 95% CI, 0.6‐0.9; p = 0.0007) whereas rs2540477 increased risk of CAD (OR = 1.2; 95% CI, 1.1‐1.5; p = 0.003). After full adjustment for the same covariates described above, the p‐values for the association of PLA2G4A rs12746200 and LTA4H rs2540477 with CAD became 0.0014 and 0.005, respectively. In the GeneBank subjects, rs2540477 was in strong linkage disequilibrium with one of the SNPs that is part of HapK (rs2540482; r 2 = 0.86), raising the possibility that the association we observed with rs2540477 was due to its linkage with HapK. Therefore, we performed a stratified analysis and observed significant association of rs2540477 with CAD even in subjects who did not carry HapK (Table 4). By comparison, a haplotype of HapK that included the rare allele of rs2540477 was not associated with increased risk of CAD, which could have been due to decreased sample size (Table 4). Prospective Analysis with Major Adverse Cardiac Events (MACE). We next determined whether PLA2G4A rs12746200 and LTA4H rs2540477 were also associated with risk of future MACE (MI, stroke, or death) over three years of follow‐up. Consistent with the results of the cross‐sectional analysis, AG/GG carriers of rs12746200 had fewer numbers of MACE compared to the AA genotype group (Figure 1A; log rank p = 0.03), with an adjusted HR of 0.7 (95% CI = 0.5‐0.9; p = 0.01; Table 5). By comparison, rs2540477 did not affect risk of future MACE 36 (Figure 1B; Table 5) and stratifying these analyses by HapK did not alter the results (data not shown). Functional Characterization of Associated Variants. To investigate functional differences between the associated PLA2G4A and LTA4H variants, we used monocytes isolated from 105 healthy African Americans who had participated in a previous clinical intervention study to examine the effects of omega‐3 fatty acid supplementation in subjects with different ALOX5 promoter variants. (See Methods). Only monocytes from the baseline visit were used for this purpose. We first performed real‐time gene expression experiments, in reference to beta‐ glucuronidase (GUSB) as an endogenous control, which did not reveal significant differences in LTA4H mRNA levels between carriers of either LTA4H rs2540477 or HapK (Figure 2A). However, ex vivo LTB 4 production in response to stimulation with the calcium ionophore A23187 was significantly higher by approximately 50% in monocytes from subjects carrying HapK compared to non‐carriers (9.2 ± 3.8nmol/L vs. 5.9 ± 3.3nmol/L; p = 0.002) (Figure 2B). Similarly, TC/CC carriers of rs2540477 had 33% increased LTB 4 production compared to TT subjects (7.5 ± 3.6nmol/L vs. 5.9 ± 3.3nmol/L; p = 0.03) (Figure 2B). By comparison, LTB 4 production in control cultures of monocytes incubated with DMSO was very low and not significantly different between carriers and non‐carriers of either HapK (0.42 ± 0.40nmol/L vs. 0.76 ± 1.3nmol/L, respectively; p = 0.39) or rs2540477 (0.58 ± 0.82nmol/L vs. 0.76 ± 37 1.4nmol/L, respectively; p = 0.55). In addition, since these subjects were recruited based on carrying different ALOX5 promoter alleles, we also stratified the analyses of LTA4H mRNA expression and LTB 4 production by ALOX5 genotype. However, this did not alter the results with rs2540477 and HapK (data not shown). Since only two subjects carried the rare allele of rs12746200, we were not able to determine whether this PLA2G4A variant affected mRNA and/or LT production in these monocyte samples. 38 Discussion In the present study, we carried out a comprehensive genetic analysis of the LT pathway in a relatively large cohort of subjects ascertained through elective cardiac evaluation. Of the 15 genes studied, our results provide evidence for the contributions of ALOX5, LTA4H, and PLA2G4A to risk of CAD. Two newly discovered SNPs in LTA4H and PLA2G4A were identified using an unbiased two‐stage study design with dense haplotype‐tagging SNPs. While the association of LTA4H rs2540477 with prevalent CAD may have been due to linkage disequilibrium between this variant and HapK, we also observed an independent effect in subjects who did not carry HapK. These results suggest that multiple susceptibility alleles of LTA4H may exist in the population that increase risk of CAD. By comparison, the rs12746200 variant of PLA2G4A was cardioprotective, decreasing both the likelihood of having CAD and the risk for experiencing a future MACE over three years of follow‐up. Thus, the results from these cross‐sectional and longitudinal analyses provide consistent evidence for a protective effect of this PLA2G4A variant. These latter findings are also novel since, to our knowledge, PLA2G4A was not evaluated in previous genetic studies of the LT pathway 18,20,36 . Our results also corroborate previously reported associations of CAD‐related phenotypes with an ALOX5 promoter polymorphism 16,17 and HapK in LTA4H 20,22 . In this regard, the shorter ALOX5 “3” and “4” promoter repeats, which lead to increased ALOX5 expression 16,25 , were only associated with CAD in African 39 Americans. One possibility for this observation may be due to the 10‐fold higher frequency of the “3” allele in African Americans compared to Caucasians (~30% vs. 1‐2%). However it should also be noted that the “3” and “4” repeats may not contribute appreciably to CAD and/or MI in Caucasians, as has been reported by other groups 37,38 . Conversely, we observed association of HapK in Caucasians but not African Americans, which may have been due to the relatively fewer African Americans subjects in our study population. Interestingly, HapK is rare in subjects from the African continent and its presence in African Americans has been suggested to be a consequence of admixture with individuals of European descent 22 . Since HapK was previously associated more strongly with MI in African Americans than in Caucasians, this raises the question of whether HapK interacts with other genetic variants that are specific to or more frequent in subjects of African ancestry. As discussed above, one such variant could be the “3” repeat allele of the ALOX5 promoter and could be addressed in studies of larger African American cohorts. In this regard, it should be noted that we did not adjust for population admixture in this African American sample, which could also potentially confound the results of association studies. Lastly, we did not observe association of ALOX5AP HapA with CAD risk in either African Americans or Caucasians. The genetic contribution of HapA to cardiovascular phenotypes is not entirely clear since attempts to replicate its association have yielded both positive 39,40 and negative 27‐29,41‐43 results. 40 Another important aspect of our study are the ex vivo functional experiments demonstrating increased LTB 4 production in stimulated monocytes from subjects carrying HapK or rs2540477. These results provide a biologically plausible mechanism for the increased risk of CAD conferred by these LTA4H variants since LTB 4 is a direct product of the reaction catalyzed by this enzyme and is a potent chemotactic molecule that mediates the recruitment of neutrophils, monocytes, and other leukocytes to sites of inflammation, including the arterial wall of atherosclerotic lesions. Moreover, LTB 4 ‐mediated activation of leukocytes can induce myeloperoxide release and further contribute to the progression of atherosclerosis 44,45 . Presumably, rs2540477 or the SNPs comprising HapK are not the underlying causal variant(s) since they are located in non‐coding regions of LTA4H and, based on our results, do not affect gene expression. In depth sequencing of LTA4H coding regions in subjects carrying HapK or rs2540477 may help to identify putative amino acid substitutions that are in linkage disequilibrium with these variants and/or alternatively spliced isoforms and lead to increased LTB 4 production. Additional studies will be required in order to distinguish between these possibilities as well as to functionally characterize the PLA2G4A rs12746200 variant. One limitation of our study was not correcting for multiple comparisons, which raises the possibility that the associations we have detected are false positives. Although such an adjustment would be more appropriate for the two‐ 41 stage analyses rather than the specific hypotheses we tested with previously associated variants/haplotypes, this potential problem is mitigated by the consistency of the results for rs12746200 and rs2540477 in both stages and in the longitudinal analyses with rs12746200. However, since GeneBank subjects were ascertained through cardiac evaluation at a tertiary care center, it is still important to confirm these associations in independent populations recruited through other study designs, such as traditional case‐control datasets. In this regard, it is possible that the contribution of LT pathway genes to cardiovascular phenotypes are more readily detectable in the context of dietary PUFAs that serve as the substrates for LT biosynthesis. Such a concept would be analogous to our previous studies with ALOX5 and MI 16 and it will be interesting to determine whether the LTA4H and PLA2G4A variants we identify herein exhibit nutrigenetic interactions with dietary PUFAs and CAD as well. Since dietary information was not collected as part of subject recruitment in GeneBank, we were not able to carry out such gene‐dietary analyses in this study. Nonetheless, our results provide additional evidence that functional genetic variation in the LT pathway can modulate atherosclerotic processes and the risk of CAD in humans. 42 Acknowledgements This work was supported by National Institutes of Health grants RO1HL079353, R21AT003411, P60MD0222, the General Clinical Research Center of the Cleveland Clinic/Case Western Reserve University CTSA (UL1RR024989), and U.S. Department of Agriculture grant CRIS Project # 5306‐51530‐006‐00D. The Cleveland Clinic GeneBank study is supported by National Institutes of Health grants P01HL076491, P01HL098055, P01HL087018, and R01HL103866. P.A. was supported through a fellowship award from the Gustavus & Louise Pfeiffer Research Foundation. Supplies and funding for measuring fasting plasma lipid and CRP levels were provided for by Abbott Diagnostics, Inc. A portion of this work was conducted in a facility constructed with support from the National Institutes of Health Research Facilities Improvement Program (RR10600, CA62528, and RR14514) from the National Center for Research Resources. Conflict of Interest Statement The authors declare no conflicts of interest. 43 References 1. Peters‐Golden M, Henderson WR, Jr. (2007) Leukotrienes. N Engl J Med 357: 1841‐54. 2. Back M, Hansson GK (2006) Leukotriene receptors in atherosclerosis. Ann Med. 38: 493‐502. 3. Mehrabian M, Allayee H (2003) 5‐Lipoxygenase and atherosclerosis. Curr Opin Lipidol. 14: 447‐57. 4. Tymchuk CN, Hartiala J, Patel PI, Mehrabian M, Allayee H (2006) Nonconventional genetic risk factors for cardiovascular disease. Curr Atheroscler Rep. 8: 184‐92 5. Mehrabian M, Allayee H, Stockton J, Lum PY, Drake TA, Castellani LW, Suh M, Armour C, Edwards S, Lamb J, Lusis AJ, Schadt EE (2005) Integrating genotypic and expression data in a segregating mouse population to identify 5‐lipoxygenase as a susceptibility gene for obesity and bone traits. Nat Genet. 37: 1224‐33. 6. Mehrabian M, Allayee H, Wong J, Shi W, Wang XP, Shaposhnik Z, Funk CD, Lusis AJ (2002) Identification of 5‐lipoxygenase as a major gene contributing to atherosclerosis susceptibility in mice. Circ Res. 91: 120‐6. 7. Mehrabian M, Schulthess FT, Nebohacova M, Castellani LW, Zhou Z, Hartiala J, Oberholzer J, Lusis AJ, Maedler K, Allayee H (2008) Identification of ALOX5 as a gene regulating adiposity and pancreatic function. Diabetologia. 51: 978‐88. 8. Ahluwalia N, Lin AY, Tager AM, Pruitt IE, Anderson TJ, Kristo F, Shen D, Cruz AR, Aikawa M, Luster AD, Gerszten RE (2007) Inhibited aortic aneurysm formation in BLT1‐deficient mice. J Immunol. 179: 691‐7. 9. Aiello RJ, Bourassa PA, Lindsey S, Weng W, Freeman A, Showell HJ (2002) Leukotriene B4 receptor antagonism reduces monocytic foam cells in mice. Arterioscler Thromb Vasc Biol. 22: 443‐9. 10. Heller EA, Liu E, Tager AM, Sinha S, Roberts JD, Koehn SL, Libby P, Aikawa ER, Chen JQ, Huang P, Freeman MW, Moore KJ, Luster AD, Gerszten RE (2005) Inhibition of atherogenesis in BLT1‐deficient mice reveals a role for LTB4 and BLT1 in smooth muscle cell recruitment. Circulation. 112: 578‐86. 44 11. Jawien J, Gajda M, Rudling M, Mateuszuk L, Olszanecki R, Guzik TJ, Cichocki T, Chlopicki S, Korbut R (2006) Inhibition of five lipoxygenase activating protein (FLAP) by MK‐886 decreases atherosclerosis in apoE/LDLR‐double knockout mice. Eur J Clin Invest. 36: 141‐6. 12. Jawien J, Gajda M, Wolkow P, Zuranska J, Olszanecki R, Korbut R (2008) The effect of montelukast on atherogenesis in apoE/LDLR‐double knockout mice. J Physiol Pharmacol. 59: 633‐9. 13. Subbarao K, Jala VR, Mathis S, Suttles J, Zacharias W, Ahamed J, Ali H, Tseng MT, Haribabu B (2004) Role of leukotriene B4 receptors in the development of atherosclerosis: potential mechanisms. Arterioscler Thromb Vasc Biol. 24: 369‐75. 14. Qiu H, Gabrielsen A, Agardh HE, Wan M, Wetterholm A, Wong CH, Hedin U, Swedenborg J, Hansson GK, Samuelsson B, Paulsson‐Berne G, Haeggstrom JZ (2006) Expression of 5‐lipoxygenase and leukotriene A4 hydrolase in human atherosclerotic lesions correlates with symptoms of plaque instability. Proc Natl Acad Sci USA. 103: 8161‐6. 15. Spanbroek R, Grabner R, Lotzer K, Hildner M, Urbach A, Ruhling K, Moos MP, Kaiser B, Cohnert TU, Wahlers T, Zieske A, Plenz G, Robenek H, Salbach P, Kuhn H, Radmark O, Samuelsson B, Habenicht AJ (2003) Expanding expression of the 5‐lipoxygenase pathway within the arterial wall during human atherogenesis. Proc Natl Acad Sci USA. 100: 1238‐43. 16. Allayee H, Baylin A, Hartiala J, Wijesuriya H, Mehrabian M, Lusis AJ, Campos H (2008) Nutrigenetic association of the 5‐lipoxygenase gene with myocardial infarction. Am J Clin Nutr. 88: 934‐40. 17. Dwyer JH, Allayee H, Dwyer KM, Fan J, Wu H, Mar R, Lusis AJ, Mehrabian M (2004) Arachidonate 5‐lipoxygenase promoter genotype, dietary arachidonic acid, and atherosclerosis. N Engl J Med. 350: 29‐37. 18. Burdon KP, Rudock ME, Lehtinen AB, Langefeld CD, Bowden DW, Register TC, Liu Y, Freedman BI, Carr JJ, Hedrick CC, Rich SS (2010) Human lipoxygenase pathway gene variation and association with markers of subclinical atherosclerosis in the diabetes heart study. Mediators Inflamm: 170153. 45 19. Carlson CS, Heagerty PJ, Nord AS, Pritchard DK, Ranchalis J, Boguch JM, Duan H, Hatsukami TS, Schwartz SM, Rieder MJ, Nickerson DA, Jarvik GP (2007) TagSNP evaluation for the association of 42 inflammation loci and vascular disease: evidence of IL6, FGB, ALOX5, NFKBIA, and IL4R loci effects. Hum Genet. 121: 65‐75. 20. Crosslin DR, Shah SH, Nelson SC, Haynes CS, Connelly JJ, Gadson S, Goldschmidt‐Clermont PJ, Vance JM, Rose J, Granger CB, Seo D, Gregory SG, Kraus WE, Hauser ER (2009) Genetic effects in the leukotriene biosynthesis pathway and association with atherosclerosis. Hum Genet. 125: 217‐29. 21. Helgadottir A, Manolescu A, Thorleifsson G, Gretarsdottir S, Jonsdottir H, Thorsteinsdottir U, Samani NJ, Gudmundsson G, Grant SF, Thorgeirsson G, Sveinbjornsdottir S, Valdimarsson EM, Matthiasson SE, Johannsson H, Gudmundsdottir O, Gurney ME, Sainz J, Thorhallsdottir M, Andresdottir M, Frigge ML, Topol EJ, Kong A, Gudnason V, Hakonarson H, Gulcher JR, Stefansson K (2004) The gene encoding 5‐lipoxygenase activating protein confers risk of myocardial infarction and stroke. Nat Genet. 36: 233‐9. 22. Helgadottir A, Manolescu A, Helgason A, Thorleifsson G, Thorsteinsdottir U, Gudbjartsson DF, Gretarsdottir S, Magnusson KP, Gudmundsson G, Hicks A, Jonsson T, Grant SF, Sainz J, O'Brien S J, Sveinbjornsdottir S, Valdimarsson EM, Matthiasson SE, Levey AI, Abramson JL, Reilly MP, Vaccarino V, Wolfe ML, Gudnason V, Quyyumi AA, Topol EJ, Rader DJ, Thorgeirsson G, Gulcher JR, Hakonarson H, Kong A, Stefansson K (2006) A variant of the gene encoding leukotriene A4 hydrolase confers ethnicity‐specific risk of myocardial infarction. Nat Genet. 38: 68‐74. 23. Iovannisci DM, Lammer EJ, Steiner L, Cheng S, Mahoney LT, Davis PH, Lauer RM, Burns TL (2007) Association between a leukotriene C4 synthase gene promoter polymorphism and coronary artery calcium in young women: the Muscatine Study. Arterioscler Thromb Vasc Biol. 27: 394‐9. 24. Sanak M, Pierzchalska M, Bazan‐Socha S, Szczeklik A (2000) Enhanced expression of the leukotriene C(4) synthase due to overactive transcription of an allelic variant associated with aspirin‐intolerant asthma. Am J Respir Cell Mol Biol. 23: 290‐6. 25. Vikman S, Brena RM, Armstrong P, Hartiala J, Stephensen CB, Allayee H (2009) Functional Analysis of 5‐Lipoxygenase Promoter Repeat Variants. Hum Mol Genet. 18(23): 4521‐4529. 46 26. Assimes TL, Knowles JW, Priest JR, Basu A, Volcik KA, Southwick A, Tabor HK, Hartiala J, Allayee H, Grove ML, Tabibiazar R, Sidney S, Fortmann SP, Go A, Hlatky M, Iribarren C, Boerwinkle E, Myers R, Risch N, Quertermous T (2008) Common polymorphisms of ALOX5 and ALOX5AP and risk of coronary artery disease. Hum Genet. 123: 399‐408. 27. Koch W, Hoppmann P, Mueller JC, Schomig A, Kastrati A (2007) No association of polymorphisms in the gene encoding 5‐lipoxygenase‐ activating protein and myocardial infarction in a large central European population. Genet Med. 9: 123‐9. 28. Zee RY, Cheng S, Hegener HH, Erlich HA, Ridker PM (2006) Genetic variants of arachidonate 5‐lipoxygenase‐activating protein, and risk of incident myocardial infarction and ischemic stroke: a nested case‐control approach. Stroke. 37: 2007‐11. 29. Zintzaras E, Rodopoulou P, Sakellaridis N (2009) Variants of the arachidonate 5‐lipoxygenase‐activating protein (ALOX5AP) gene and risk of stroke: a HuGE gene‐disease association review and meta‐analysis. Am J Epidemiol. 169: 523‐32. 30. Bhattacharyya T, Nicholls SJ, Topol EJ, Zhang R, Yang X, Schmitt D, Fu X, Shao M, Brennan DM, Ellis SG, Brennan ML, Allayee H, Lusis AJ, Hazen SL (2008) Relationship of paraoxonase 1 (PON1) gene polymorphisms and functional activity with systemic oxidative stress and cardiovascular risk. JAMA. 299: 1265‐76. 31. Nicholls SJ, Tang WH, Scoffone H, Brennan DM, Hartiala J, Allayee H, Hazen SL (2010) Lipoprotein (a) levels and long‐term cardiovascular risk in the contemporary Era of statin therapy. J Lipid Res. Oct;51(10):3055‐61. 32. de Bakker PI, Yelensky R, Pe'er I, Gabriel SB, Daly MJ, Altshuler D (2005) Efficiency and power in genetic association studies. Nat Genet. 37: 1217‐23. 33. Livak KJ (1999) Allelic discrimination using fluorogenic probes and the 5' nuclease assay. Genet Anal. 14: 143‐9. 34. Livak KJ (2003) SNP genotyping by the 5'‐nuclease reaction. Methods Mol Biol. 212: 129‐47. 47 35. Lima JJ, Zhang S, Grant A, Shao L, Tantisira KG, Allayee H, Wang J, Sylvester J, Holbrook J, Wise R, Weiss ST, Barnes K (2006) Influence of leukotriene pathway polymorphisms on response to montelukast in asthma. Am J Respir Crit Care Med. 173: 379‐85. 36. Bevan S, Dichgans M, Wiechmann HE, Gschwendtner A, Meitinger T, Markus HS (2008) Genetic variation in members of the leukotriene biosynthesis pathway confer an increased risk of ischemic stroke: a replication study in two independent populations. Stroke. 39: 1109‐14. 37. Gonzalez P, Reguero JR, Lozano I, Moris C, Coto E (2007) A functional Sp1/Egr1‐tandem repeat polymorphism in the 5‐lipoxygenase gene is not associated with myocardial infarction. Int J Immunogenet. 34: 127‐30. 38. Maznyczka A, Braund P, Mangino M, Samani NJ (2008) Arachidonate 5‐ lipoxygenase (5‐LO) promoter genotype and risk of myocardial infarction: a case‐control study. Atherosclerosis. 199: 328‐32. 39. Helgadottir A, Gretarsdottir S, St Clair D, Manolescu A, Cheung J, Thorleifsson G, Pasdar A, Grant SF, Whalley LJ, Hakonarson H, Thorsteinsdottir U, Kong A, Gulcher J, Stefansson K, MacLeod MJ (2005) Association between the gene encoding 5‐lipoxygenase‐activating protein and stroke replicated in a Scottish population. Am J Hum Genet. 76: 505‐9. 40. Shah SH, Hauser ER, Crosslin D, Wang L, Haynes C, Connelly J, Nelson S, Johnson J, Gadson S, Nelson CL, Seo D, Gregory S, Kraus WE, Granger CB, Goldschmidt‐Clermont P, Newby LK (2008) ALOX5AP variants are associated with in‐stent restenosis after percutaneous coronary intervention. Atherosclerosis. 201: 148‐54. 41. Lemaitre RN, Rice K, Marciante K, Bis JC, Lumley TS, Wiggins KL, Smith NL, Heckbert SR, Psaty BM (2009) Variation in eicosanoid genes, non‐fatal myocardial infarction and ischemic stroke. Atherosclerosis. 204: e58‐63. 42. Linsel‐Nitschke P, Gotz A, Medack A, Konig IR, Bruse P, Lieb W, Mayer B, Stark K, Hengstenberg C, Fischer M, Baessler A, Ziegler A, Schunkert H, Erdmann J (2008) Genetic variation in the arachidonate 5‐lipoxygenase‐ activating protein (ALOX5AP) is associated with myocardial infarction in the German population. Clin Sci (Lond). 115: 309‐15. 48 43. Tsai AK, Li N, Hanson NQ, Tsai MY, Tang W (2009) Associations of genetic polymorphisms of arachidonate 5‐lipoxygenase‐activating protein with risk of coronary artery disease in a European‐American population. Atherosclerosis. 207: 487‐91. 44. Nicholls SJ, Hazen SL (2005) Myeloperoxidase and cardiovascular disease. Arterioscler Thromb Vasc Biol. 25: 1102‐11. 45. Samuelsson B (1983) Leukotrienes: mediators of immediate hypersensitivity reactions and inflammation. Science. 220: 568‐75. 49 Figure Legends Figure 1. Relationship Between LT Pathway Variants and Incident Risk of MACE. (A) Kaplan‐Meier survival analyses demonstrating that carriers (AG/GG) of PLA2G4A variant rs12746200 have significantly lower a rate of MACE over three years of follow‐up (p = 0.03). (B) There is no difference in the rate of MACE between carriers and non‐carriers of the LTA4H variant rs2540477. MACE were defined as the occurrence of MI, stroke, or all‐cause mortality over three years of follow‐up from the time of enrollment. Figure 2. Effect of LTA4H Variants on Gene Expression and Ex Vivo LTB 4 Production. (A) LTA4H mRNA levels in monocytes are not significantly different between carriers and non‐carriers of the HapK or rs2540477 variants. Real‐time PCR was carried out in triplicate and expression levels were normalized to GUSB as an endogenous control. (B) Monocytes isolated from carriers of HapK or rs2540477 produce significantly higher levels of LTB 4 than non‐carriers. Monocytes were isolated from healthy subjects and stimulated with the calcium ionophore A23187 for 60min. LTB 4 was measured in the supernatant by negative mode electrospray ionization tandem mass spectrometry. Data are shown as mean ± SE and the number of samples analyzed for each genotype is given in parentheses. 50 Table 1. General Characteristics of the GeneBank Participants. Caucasian African American Without CAD (n = 913) With CAD (n = 2834) p‐value Without CAD (n = 263) With CAD (n = 502) p‐value Age (years) 59 ± 12 66 ± 11 <0.0001 57 ± 11 61 ± 11 <0.0001 Male/Female (n) 478/435 2140/694 <0.0001 99/164 264/238 <0.0001 BMI (kg/m 2 ) 29.7 ± 6.8 29.4 ± 5.6 0.92 33.6 ± 8.1 31.2 ± 7.1 <0.0001 Total cholesterol (mg/dl) 177 ± 37 168 ± 40 <0.0001 182 ± 38 176 ± 50 0.01 HDL‐cholesterol (mg/dl) 42 ± 13 37 ± 11 <0.0001 51 ± 15 46 ± 15 <0.0001 LDL‐cholesterol (mg/dl) 108 ± 31 100 ± 33 <0.0001 111 ± 72 106 ± 43 0.13 Triglycerides (mg/dl) 129 ± 81 155 ± 105 <0.0001 123 ± 81 128 ± 83 0.32 CRP (mg/l) 2.2 (3.4) 3.1 (5.7) <0.0001 2.2 (3.7) 3.0 (5.6) 0.008 DBP (mmHg) 75 ± 12 74 ± 12 0.004 81 ± 13 77 ± 13 0.0002 SBP (mmHg) 133 ± 20 135 ± 21 0.003 139 ± 22 140 ± 23 0.53 Data are shown as mean ± SD with the exception of CRP levels which are shown as median (IQR). Two‐sided p‐values are reported between subjects with and without CAD at baseline. 51 Table 2. The Major Genes of LT Biosynthetic Pathway and their Function. Gene (symbol) Function Cytosolic phospholipase A2 group 4A (PLA2G4A) Releases arachidonic acid from cell membranes. Cytosolic phospholipase A2 group 5 (PLA2G5) Releases arachidonic acid from cell membranes. Arachidonic acid 5‐Lipoxygenase (ALOX5) Incorporates oxygen into arachidonic acid and forms LTA 4 . ALOX5 Activating Protein (ALOX5AP) Presents arachidonic acid to and activates ALOX5. LTA 4 Epoxide Hydrolase (LTA4H) Converts LTA 4 to LTB 4 . LTC 4 Synthase (LTC4S) Converts LTA 4 to the first cysteinyl LT, LTC 4 . LTB 4 Receptor 1 (LTB4R) Receptor for LTB 4 . LTB 4 Receptor 2 (LTB4R2) A second receptor for LTB 4 . Cysteinyl Leukotriene Receptor 1 (CysLTR1) Receptor for cysteinyl LTs (LTC 4 , LTD 4 , and LTE 4 ). Cysteinyl Leukotriene Receptor 2 (CysLTR2) A second receptor for the cysteinyl LTs. Microsomal glutathione S‐transferase 1 (MGST1) Cysteinyl LT metabolism. Microsomal glutathione S‐transferase 2 (MGST2) Cysteinyl LT metabolism. Microsomal glutathione S‐transferase 3 (MGST3) Cysteinyl LT metabolism. Prostaglandin reductase 1 isoform 1 (PTGR1) Catalyzes degradation of LTB 4 . Cytochrome P450 4F3 (CYP4F3) Catalyzes degradation of LTB 4 . 52 Table 3. Association of Previously Reported LT Pathway Variants in Caucasian and African American GeneBank Subjects. SNP/Variant (Gene) Risk Allele/Haplotype Frequency OR (95% CI) Caucasian Without CAD (n) With CAD (n) Non‐carriers Carriers * p‐value a Promoter repeats (ALOX5) 0.15 (886) 0.17 (2736) 1.0 1.1 (1.0‐1.4) 0.2 b HapA (ALOX5AP) 0.15 (913) 0.15 (2834) 1.0 1.1 (0.9‐1.3) 0.3 c HapK (LTA4H) 0.14 (912) 0.16 (2830) 1.0 1.2 (1.01‐1.4) 0.03 African American Without CAD (n) With CAD (n) Non‐carriers Carriers * p‐value a Promoter repeats (ALOX5) 0.43 (261) 0.48 (495) 1.0 1.4 (1.01‐1.9) 0.04 b HapA (ALOX5AP) 0.06 (263) 0.06 (502) 1.0 1.1 (0.7‐1.6) 0.8 c HapK (LTA4H) 0.05 (263) 0.06 (502) 1.0 0.9 (0.5‐1.4) 0.5 OR (95% CI), odds ratio (95% confidence intervals). * p‐values were obtained from a logistic regression with adjustment for age and gender. a Combined frequencies of the “3” and “4” repeat alleles are given; Non‐carriers = genotypes of 5/5, 5/6, 5/7, 5/8, 5/9, 6/6, 6/7; carriers = genotypes of 3/3, 3/4, 4/4, 3/5, 4/5, 3/6, 3/7, 4/6, 4/8. b HapA = GTGA derived from SNPs rs17222814 (G), rs10507391 (T), rs4769874 (G), rs9551963 (A). c HapK = CGTATTTCGG derived from SNPs rs61937881 (SG12S16) (C), rs2660880 (G), rs6538697 (T), rs1978331 (A), rs17677715 (T), rs2247570 (T), rs2660898 (T), rs2540482 (C), rs2660845 (G), rs2540475 (G). Sample sizes are shown based on complete genotype and phenotype data for each variant. 53 Table 4. Association of PLA2G4A and LTA4H SNPs with CAD in the Two‐Stage Analysis. SNP (Gene) MAF OR (95% CI) rs12746200 (PLA2G4A) Without CAD (n) With CAD (n) AA AG/GG * p‐value Stage 1 0.11 (516) 0.10 (802) 1.0 0.8 (0.6 ‐ 1.0) 0.05 Stage 2 0.11 (390) 0.08 (1982) 1.0 0.6 (0.5 ‐ 0.9) 0.03 Combined 0.11 (906) 0.09 (2784) 1.0 0.7 (0.6 ‐ 0.9) 0.0007 rs2540477 (LTA4H) Without CAD (n) With CAD (n) TT TC/CC * p‐value Stage 1 0.22 (515) 0.26 (803) 1.0 1.3 (1.0 ‐ 1.7) 0.02 Stage 2 0.20 (382) 0.25 (1980) 1.0 1.4 (1.1 ‐ 1.7) 0.02 Combined 0.21 (897) 0.25 (2783) 1.0 1.3 (1.1 ‐ 1.5) 0.003 HapK non‐carriers 0.08 (663) 0.11 (1973) 1.0 1.3 (1.1 ‐ 1.8) 0.01 HapK carriers 0.57 (234) 0.60 (810) 1.0 1.7 (0.6 – 5.0) 0.34 MAF, minor allele frequency. OR (95% CI), odds ratio (95% confidence intervals). * p‐values were obtained from a logistic regression with adjustment for age and gender. Sample sizes are shown based on complete genotype and phenotype data for each variant. 54 Table 5. Risk of Prospective MACE in Relation to PLA2G4A and LTA4H Variants. rs12746200 (PLA2G4A) AA (n = 2943) AG/GG (n = 589) * p‐value Number of events (%) 404 (14) 61 (10) 0.03 * Adjusted HR (95% CI) 1.0 0.7 (0.5 ‐ 0.9) 0.01 rs2540477 (LTA4H) TT (n = 2095) TC/CC (n = 1435) * p‐value Number of events (%) 269 (14) 193 (13) 0.57 * Adjusted HR (95% CI) 1.0 1.1 (0.8 ‐ 1.3) 0.59 * Hazard Ratios (HRs) were calculated only in Caucasians with adjustment for age, sex, medication use (statins and/or aspirin), plasma CRP levels, alcohol consumption, and Framingham ATP‐III risk score. Sample sizes are shown based on complete genotype and phenotype data for each variant. 55 Figure 1. 56 Figure 2. 57 Supplemental Table 1. Previously Associated and Haplotype‐tagging Leukotriene Pathway SNPs/Variants Selected for Genotyping. BP Position Follow Up Reference Gene Chromosome (NCBI Build 36) SNP/Variant Category in Stage 2 ALOX5 chr10 45189558 Sp1 promoter repeat previously associated Dwyer et al; Allayee et al 2008 ALOX5 chr10 45189754 rs4987105 previously associated In et al 1997; Allayee et al 2008 ALOX5 chr10 45192870 rs3824612 ALOX5 chr10 45196679 rs10900212 ALOX5 chr10 45198056 rs2228064 previously associated In et al 1997; Allayee et al 2008 ALOX5 chr10 45198806 rs11239501 ALOX5 chr10 45198914 rs745986 X ALOX5 chr10 45200062 rs4948671 X ALOX5 chr10 45211490 rs2029253 ALOX5 chr10 45217161 rs7099684 ALOX5 chr10 45221095 rs2115819 ALOX5 chr10 45224720 rs10900213 ALOX5 chr10 45232777 rs11239524 ALOX5 chr10 45234533 rs4948672 ALOX5 chr10 45234750 rs12264801 ALOX5 chr10 45236840 rs6593485 ALOX5 chr10 45237123 rs7080713 ALOX5 chr10 45240512 rs2228065 ALOX5 chr10 45241988 rs3780906 ALOX5 chr10 45243534 rs2279435 ALOX5 chr10 45244029 rs1565096 ALOX5 chr10 45244159 rs28395872 ALOX5 chr10 45244974 rs3780908 ALOX5 chr10 45245221 rs3780909 ALOX5 chr10 45248917 rs7099874 ALOX5 chr10 45250811 rs12765320 ALOX5 chr10 45254378 rs702366 ALOX5 chr10 45256230 rs2291427 ALOX5 chr10 45257998 rs10751383 ALOX5 chr10 45258558 rs28395869 ALOX5 chr10 45258752 rs1051713 ALOX5 chr10 45259623 rs2229136 58 ALOX5 chr10 45261054 rs1803918 ALOX5AP chr13 30194938 rs4617690 ALOX5AP chr13 30197476 rs9508829 ALOX5AP chr13 30197553 rs17222814 previously associated ‐ HapA Helgadottir et al. 2004 ALOX5AP chr13 30204629 rs11616333 ALOX5AP chr13 30207830 rs4769055 ALOX5AP chr13 30208579 rs9579646 ALOX5AP chr13 30208919 rs4075131 ALOX5AP chr13 30209344 rs4597169 ALOX5AP chr13 30209557 rs9578196 ALOX5AP chr13 30210096 rs10507391 previously associated ‐ HapA Helgadottir et al. 2004 ALOX5AP chr13 30210178 rs12429692 ALOX5AP chr13 30210689 rs4769873 X ALOX5AP chr13 30212174 rs4503649 ALOX5AP chr13 30212455 rs3885907 ALOX5AP chr13 30214738 rs10162089 ALOX5AP chr13 30215452 rs12431114 ALOX5AP chr13 30215878 rs4254165 ALOX5AP chr13 30216068 rs17238738 ALOX5AP chr13 30217133 rs17245120 ALOX5AP chr13 30222253 rs9671124 ALOX5AP chr13 30223643 rs11147439 ALOX5AP chr13 30224441 rs4769874 previously associated ‐ HapA Helgadottir et al. 2004 ALOX5AP chr13 30225032 rs9579648 ALOX5AP chr13 30225840 rs9315048 ALOX5AP chr13 30230547 rs9551963 previously associated ‐ HapA Helgadottir et al. 2004 ALOX5AP chr13 30232698 rs9508835 ALOX5AP chr13 30234177 rs9315051 ALOX5AP chr13 30235558 rs3935645 ALOX5AP chr13 30235640 rs3935644 ALOX5AP chr13 30235877 rs4769060 ALOX5AP chr13 30238117 rs17222842 ALOX5AP chr13 30239435 rs4445746 CYP4F3 chr19 15609762 rs4239619 X CYP4F3 chr19 15611803 rs1159776 X CYP4F3 chr19 15611891 rs3794987 CYP4F3 chr19 15612566 rs1290616 59 CYP4F3 chr19 15613650 rs1290619 CYP4F3 chr19 15614423 rs1290622 CYP4F3 chr19 15616442 rs759998 CYP4F3 chr19 15620764 rs2283609 CYP4F3 chr19 15623678 rs2683035 CYP4F3 chr19 15625071 rs2683043 CYP4F3 chr19 15630478 rs4646517 CYP4F3 chr19 15630775 rs4646520 CYP4F3 chr19 15633233 rs1063803 CYP4F3 chr19 15633414 rs7258240 CYP4F3 chr19 15636612 rs11668475 CYP4F3 chr19 15637343 rs2733761 CYP4F3 chr19 15637511 rs7246556 CYP4F3 chr19 15637785 rs1543284 CYP4F3 chr19 15638633 rs4808350 CYSLTR1 chrX 77418759 rs320992 CYSLTR1 chrX 77419281 rs320991 CYSLTR1 chrX 77456265 rs321006 CYSLTR1 chrX 77474256 rs321028 CYSLTR1 chrX 77481373 rs321023 CYSLTR2 chr13 48166248 rs9568082 CYSLTR2 chr13 48181488 rs912278 CYSLTR2 chr13 48181796 rs2407249 CYSLTR2 chr13 48182126 rs4620866 CYSLTR2 chr13 48185325 rs9568087 CYSLTR2 chr13 48187410 rs1359112 LTA4H chr12 94917392 rs12302993 LTA4H chr12 94917718 rs61937881 (SG12S16) previously associated ‐ HapK Helgadottir et al. 2006 LTA4H chr12 94922959 rs2540500 LTA4H chr12 94925383 rs2660880 previously associated ‐ HapK Helgadottir et al. 2006 LTA4H chr12 94927330 rs2072510 LTA4H chr12 94927741 rs6538697 previously associated ‐ HapK Helgadottir et al. 2006 LTA4H chr12 94929208 rs2660900 LTA4H chr12 94933332 rs1978331 previously associated ‐ HapK Helgadottir et al. 2006 LTA4H chr12 94938582 rs17677715 previously associated ‐ HapK Helgadottir et al. 2006 LTA4H chr12 94946508 rs2247570 previously associated ‐ HapK Helgadottir et al. 2006 60 LTA4H chr12 94949834 rs7956370 LTA4H chr12 94950128 rs2660898 previously associated ‐ HapK Helgadottir et al. 2006 LTA4H chr12 94950633 rs2540489 LTA4H chr12 94954551 rs2660899 LTA4H chr12 94955210 rs11108385 LTA4H chr12 94957553 rs17025122 LTA4H chr12 94958877 rs7971150 LTA4H chr12 94959011 rs2540482 previously associated ‐ HapK Helgadottir et al. 2006 LTA4H chr12 94959370 rs2540479 LTA4H chr12 94960784 rs7314867 LTA4H chr12 94961685 rs2540477 X LTA4H chr12 94962684 rs2660845 previously associated ‐ HapK Helgadottir et al. 2006 LTA4H chr12 94962772 rs7966098 X LTA4H chr12 94963121 rs10735340 LTA4H chr12 94965424 rs2540475 previously associated ‐ HapK Helgadottir et al. 2006 LTB4R chr14 23853254 rs2224122 LTB4R chr14 23855900 rs1046587 LTB4R chr14 23860976 rs4981504 LTB4R2 chr14 23836982 rs12887789 LTB4R2 chr14 23848483 rs2516564 LTB4R2 chr14 23849434 rs1950505 LTC4S chr5 179153244 rs730012 LTC4S chr5 179157930 rs2291418 LTC4S chr5 179159892 rs166624 MGST1 chr12 16383782 rs6488840 MGST1 chr12 16390096 rs7314840 MGST1 chr12 16391118 rs7970208 MGST1 chr12 16391338 rs4149187 MGST1 chr12 16391532 rs2239675 X MGST1 chr12 16391831 rs9332876 MGST1 chr12 16393811 rs7294985 MGST1 chr12 16396505 rs1913262 MGST1 chr12 16399223 rs7966563 MGST1 chr12 16401346 rs2075237 MGST1 chr12 16404428 rs4149197 MGST1 chr12 16404631 rs2287150 MGST1 chr12 16405457 rs2287152 X 61 MGST1 chr12 16405785 rs3844373 MGST1 chr12 16406188 rs4149203 X MGST1 chr12 16407212 rs7312090 X MGST1 chr12 16407546 rs3852576 MGST1 chr12 16408261 rs11875 MGST1 chr12 16409226 rs9332949 MGST1 chr12 16410652 rs1024839 MGST1 chr12 16411893 rs7303782 MGST2 chr4 140797333 rs795605 X MGST2 chr4 140797463 rs3755995 MGST2 chr4 140797556 rs3755994 MGST2 chr4 140797950 rs17050847 MGST2 chr4 140798905 rs924199 MGST2 chr4 140801382 rs795602 MGST2 chr4 140804481 rs8191997 MGST2 chr4 140805200 rs8192002 MGST2 chr4 140806151 rs8192004 MGST2 chr4 140806575 rs2132845 MGST2 chr4 140806892 rs4147586 MGST2 chr4 140807435 rs1000222 MGST2 chr4 140810397 rs8192018 MGST2 chr4 140812714 rs3822044 MGST2 chr4 140816149 rs8192047 MGST2 chr4 140816502 rs8192049 MGST2 chr4 140818503 rs8192060 MGST2 chr4 140819575 rs795589 MGST2 chr4 140819757 rs706349 MGST2 chr4 140819847 rs1027473 MGST2 chr4 140820486 rs4241932 MGST2 chr4 140821241 rs1587265 MGST2 chr4 140821384 rs2262377 MGST2 chr4 140825134 rs8192075 MGST2 chr4 140825897 rs7664313 MGST2 chr4 140827452 rs795593 MGST2 chr4 140828033 rs13124794 MGST2 chr4 140836324 rs2646076 62 MGST2 chr4 140836433 rs8192100 MGST2 chr4 140841268 rs2646035 MGST3 chr1 163857300 rs1913836 MGST3 chr1 163858433 rs6672933 MGST3 chr1 163859346 rs11809495 MGST3 chr1 163862507 rs1415502 MGST3 chr1 163864886 rs2348733 MGST3 chr1 163865270 rs7549530 MGST3 chr1 163866428 rs6657788 MGST3 chr1 163866770 rs4147592 MGST3 chr1 163868090 rs9333378 X MGST3 chr1 163868190 rs9333379 X MGST3 chr1 163869111 rs6692264 MGST3 chr1 163871934 rs10800120 MGST3 chr1 163875065 rs16848502 MGST3 chr1 163877088 rs7554034 MGST3 chr1 163878079 rs9333433 MGST3 chr1 163882781 rs957644 MGST3 chr1 163883944 rs1878076 MGST3 chr1 163884596 rs10918226 MGST3 chr1 163886336 rs9333471 X MGST3 chr1 163887226 rs2271657 X MGST3 chr1 163887416 rs11799886 X MGST3 chr1 163888227 rs7549511 MGST3 chr1 163888431 rs10918228 MGST3 chr1 163888831 rs2297765 MGST3 chr1 163889286 rs2297763 X MGST3 chr1 163893579 rs1878075 MGST3 chr1 163898130 rs11506 MGST3 chr1 163899968 rs7518869 MGST3 chr1 163901049 rs3820362 MGST3 chr1 163904165 rs2348728 MGST3 chr1 163905667 rs12080699 PLA2G4A chr1 185052842 rs2223304 PLA2G4A chr1 185069849 rs12022299 PLA2G4A chr1 185074966 rs3820185 63 PLA2G4A chr1 185089907 rs2076075 PLA2G4A chr1 185094740 rs6696406 PLA2G4A chr1 185096715 rs12404877 PLA2G4A chr1 185097043 rs12720497 PLA2G4A chr1 185098351 rs6685652 PLA2G4A chr1 185111549 rs2223307 PLA2G4A chr1 185113221 rs17591814 PLA2G4A chr1 185114197 rs17591849 PLA2G4A chr1 185115809 rs12746200 X PLA2G4A chr1 185117002 rs10798065 PLA2G4A chr1 185117583 rs1980444 PLA2G4A chr1 185117780 rs17526478 PLA2G4A chr1 185119077 rs10752982 PLA2G4A chr1 185121829 rs10911946 PLA2G4A chr1 185122871 rs12749354 PLA2G4A chr1 185123859 rs7540602 PLA2G4A chr1 185139077 rs726706 PLA2G4A chr1 185139853 rs6656909 PLA2G4A chr1 185146514 rs6662687 PLA2G4A chr1 185164688 rs12726519 PLA2G4A chr1 185180450 rs6683416 PLA2G4A chr1 185181800 rs11587539 PLA2G4A chr1 185184143 rs7555140 PLA2G4A chr1 185186737 rs10489409 PLA2G4A chr1 185193992 rs6683363 PLA2G4A chr1 185200178 rs932476 PLA2G4A chr1 185200715 rs12720662 PLA2G4A chr1 185218052 rs7545121 PLA2G4A chr1 185219032 rs4402086 PLA2G4A chr1 185219254 rs7526089 PLA2G4A chr1 185220818 rs10489410 PLA2G4A chr1 185222053 rs10752989 PLA2G4A chr1 185222405 rs12720702 PLA2G4A chr1 185228010 rs761517 PLA2G5 chr1 20261563 rs586458 64 PLA2G5 chr1 20262461 rs12028351 PLA2G5 chr1 20262909 rs17354650 PLA2G5 chr1 20265160 rs12080954 PLA2G5 chr1 20265528 rs1891318 PLA2G5 chr1 20267300 rs606980 PLA2G5 chr1 20267988 rs11573185 PLA2G5 chr1 20268084 rs10916703 PLA2G5 chr1 20268886 rs11573190 PLA2G5 chr1 20269002 rs11573191 PLA2G5 chr1 20269261 rs668630 PLA2G5 chr1 20269827 rs656110 PLA2G5 chr1 20271639 rs719543 PLA2G5 chr1 20275354 rs11573219 PLA2G5 chr1 20277752 rs521179 PLA2G5 chr1 20279173 rs11573238 PLA2G5 chr1 20280768 rs11573253 PLA2G5 chr1 20281961 rs525380 PLA2G5 chr1 20283382 rs660414 PLA2G5 chr1 20283919 rs2020887 PLA2G5 chr1 20287686 rs11573279 PLA2G5 chr1 20290794 rs11573298 PLA2G5 chr1 20291927 rs622450 PLA2G5 chr1 20296754 rs656755 PTGR1 chr9 113361328 rs7037323 PTGR1 chr9 113368836 rs10980948 PTGR1 chr9 113371319 rs7026971 PTGR1 chr9 113375366 rs1556027 PTGR1 chr9 113375685 rs10122818 PTGR1 chr9 113375944 rs1322258 PTGR1 chr9 113380814 rs13290557 PTGR1 chr9 113381197 rs17320055 PTGR1 chr9 113381967 rs10491726 PTGR1 chr9 113383197 rs12555931 PTGR1 chr9 113385873 rs10980953 PTGR1 chr9 113387222 rs10120479 65 PTGR1 chr9 113388334 rs2273786 PTGR1 chr9 113392296 rs7868786 PTGR1 chr9 113392413 rs1053968 PTGR1 chr9 113400022 rs10817193 PTGR1 chr9 113404867 rs1475106 PTGR1 chr9 113405915 rs966627 PTGR1 chr9 113411445 rs12342716 Note: Haplotype‐tagging SNPs are those not denoted with a category. 66 Supplemental Table 2. Association Results of Haplotype‐tagging SNPs of LT Pathway Genes in Stages 1 and 2. Stage 1 Stage 2 Combined Gene SNP OR (95% CI) *p‐value OR (95% CI) *p‐value OR (95% CI) *p‐value ALOX5 rs745986 2.10 (1.14 ‐ 3.89) 0.01 1.09 (0.66 ‐ 1.81) 0.73 0.96 (0.81‐1.14) 0.62 ALOX5 rs4948671 0.74 (0.57 ‐ 0.97) 0.05 0.97 (0.69 ‐ 1.36) 0.29 0.97 (0.82‐1.14) 0.67 ALOX5AP rs4769873 6.56 (0.81 ‐ 53.36) 0.03 2.42 (0.52 ‐ 11.16) 0.21 1.09 (0.84‐1.41) 0.54 CYP4F3 rs4239619 0.72 (0.55 ‐ 0.95) 0.02 0.96 (0.74 ‐ 1.24) 0.77 0.83 (0.69‐1.00) 0.05 CYP4F3 rs1159776 1.32 (1.01 ‐ 1.74) 0.04 0.92 (0.71 ‐ 1.20) 0.54 1.08 (0.90‐1.30) 0.39 LTA4H rs2540477 1.33 (1.04 ‐ 1.71) 0.02 1.35 (1.06 ‐ 1.73 0.02 1.29 (1.09‐1.52) 0.003 LTA4H rs7966098 1.33 (1.02 ‐ 1.73) 0.04 1.06 (0.82 ‐ 1.36) 0.66 1.17 (0.98‐1.39) 0.08 MGST1 rs2239675 1.76 (0.75 ‐ 4.11) 0.03 1.14 (0.87 ‐ 1.50) 0.61 0.96 (0.81‐1.15) 0.65 MGST1 rs2287152 1.28 (1.00 ‐ 1.63) 0.05 0.97 (0.77 ‐ 1.23) 0.81 1.12 (0.95‐1.32) 0.18 MGST1 rs4149203 1.28 (1.01 ‐ 1.64) 0.04 1.02 (0.81 ‐ 1.29) 0.87 1.15 (0.98‐1.36) 0.09 MGST1 rs7312090 1.32 (1.03 ‐ 1.68) 0.03 1.01 (0.80 ‐ 1.28) 0.91 1.16 (0.98‐1.37) 0.08 MGST2 rs795605 0.49 ( 0.30 ‐ 0.80) 0.005 0.79 (0.47 ‐ 1.32) 0.38 0.99 (0.84‐1.17) 0.88 MGST3 rs9333378 1.44 (0.99 ‐ 2.10) 0.05 1.14 (0.81 ‐ 1.60) 0.46 1.06 (0.90‐1.25) 0.51 MGST3 rs9333379 4.54 (0.81 ‐ 25.25) 0.05 0.56 (0.19 ‐ 1.60) 0.3 0.96 (0.78‐1.19) 0.73 MGST3 rs9333471 1.59 (1.01 ‐ 2.49) 0.04 1.07 (0.70 ‐ 1.62) 0.76 0.93 (0.79‐1.10) 0.37 MGST3 rs2271657 0.69 (0.54 ‐ 0.89) 0.004 1.00 (0.79 ‐ 1.27) 0.99 0.83 (0.71‐0.99) 0.03 MGST3 rs11799886 2.80 (1.14 ‐ 6.90) 0.02 0.70 (0.39 ‐ 1.28) 0.26 0.99 (0.83‐1.19) 0.94 MGST3 rs2297763 0.59 (0.36 ‐ 0.94) 0.03 0.84 (0.55 ‐ 1.26) 0.4 0.89 (0.76‐1.06) 0.17 PLA2G4A rs12746200 0.80 (0.59 ‐ 1.0) 0.05 0.71 (0.52 ‐ 0.96) 0.03 0.70 (0.57‐0.86) 0.0007 *Adjusted for age & sex SNPs showing consistent evidence of association in stages 1 and 2 are shown in bold. 67 CHAPTER 2: ASSOCIATION OF PLA2G4A WITH MYOCARDIAL INFARCTION IS MODULATED BY DIETARY POLYUNSATURATED FATTY ACIDS Summary: In the present study, we provide evidence that risk of MI was decreased in Costa‐Rican carriers of a PLA2G4A variant (rs12746200). These results are consistent with our recent observations that rs12746200 was associated with decreased risk of CVD in a Caucasian patient population of Northern European descent. The present study thus replicates our previous observations with a more clinically significant CVD outcome, and in an independent case‐control dataset of different ethnicity. Additionally, nutrigenetic analyses demonstrated that the cardioprotective association of rs12746200 occurs primarily among subjects with high dietary omega‐6 PUFA intake. These results suggest that rs12746200 can mitigate, in part, the pro‐atherogenic effects of omega‐6 PUFAs in this Costa Rican population. Taken together with previous studies, these results suggest that CVD phenotypes in humans can be influenced through interactions between genetic variation in the LT pathway and dietary PUFAs that serve as substrates for the biosynthetic enzymes. It would be of interest to determine whether other LT pathway genes that have been associated with CVD exhibit nutrigenetic interactions as well. In this study, my roles were overall project design, genotyping of the PLA2G4A variant in the Costa Rican samples, performing quality control of the data, conducting all statistical analyses, and writing the manuscript. This study was 68 published in the American Journal of Clinical Nutrition (Am J Clin Nutr. 2012 Apr;95(4):959‐65). 69 Association of PLA2G4A with Myocardial Infarction is Modulated by Dietary Polyunsaturated Fatty Acids Jaana Hartiala 1,2 , Elizabeth Gilliam 1,2 , Susanna Vikman 1,2 , Hannia Campos 3 , and Hooman Allayee 1,2 1 Department of Preventive Medicine, 2 Institute for Genetic Medicine, USC Keck School of Medicine, Los Angeles, CA 90033; 3 Department of Nutrition, Harvard University School of Public Health, Boston, MA 02115. Running Title: Polyunsaturated fatty acids, PLA2G4A, and myocardial infarction 70 Abbreviations: CI, confidence interval CVD, cardiovascular disease DHA, docosahexaenoic acid ECG, electrocardiogram EPA, eicosapentaenoic acid FFQ, food frequency questionnaire HAEC, human aortic endothelial cells LT, leukotriene MI, myocardial infarction n‐3, omega‐3 n‐6, omega‐6 OR, odds ratio ox‐PAPC, oxidized 1‐palmitoyl‐2‐arachidonoyl‐sn‐glycero‐3‐phosphatidylcholine PC, principal component 71 PUFA, polyunsaturated fatty acids SNP, single nucleotide polymorphism WHO, World Health Organization 72 Abstract Background: Leukotrienes (LTs) are pro‐inflammatory molecules derived from dietary polyunsaturated fatty acids (PUFAs) and have been associated with cardiovascular disease (CVD). We previously reported that an A>G variant (rs12746200) of the cytosolic phospholipase A2 group IVA gene (PLA2G4A), which encodes the enzyme that liberates PUFAs from cellular membranes for LT synthesis, decreases risk of CVD. Objective: We sought to replicate these initial observations with a more clinically relevant phenotype such as myocardial infarction (MI) and determine whether dietary PUFAs mediate this association. Design: Rs12746200 was genotyped in a Costa Rican case‐control dataset (n=3971) and tested for association with MI. Functional experiments were carried out to determine whether rs12746200 led to differences in mRNA expression. Results: Risk of MI was significantly lower in AG/GG subjects compared to AA homozygotes (OR = 0.86, 95% CI 0.75, 0.99; p=0.040). The reduced risk of MI was observed primarily in AG/GG subjects who were above the median for intake of dietary omega‐6 (n‐6) PUFAs (OR = 0.71, 95% CI 0.59, 0.87; p‐interaction=0.005). A similar analysis with dietary omega‐3 (n‐3) PUFAs did not reveal a statistically significant nutrigenetic association (p‐interaction=0.23). Functional analysis in 73 human aortic endothelial cells showed that the carriers of the G allele had significantly lower PLA2G4A gene expression (p=0.014), consistent with the athero‐ protective association of this variant. Conclusions: These results replicate the association of rs12746200 with CVD phenotypes and provide evidence that the protective association of this functional PLA2G4A variant is mediated by dietary PUFAs. Keywords: leukotrienes, genetics, dietary polyunsaturated fatty acids, myocardial infarction 74 Introduction Leukotrienes (LTs) are mediators of inflammation synthesized from dietary polyunsaturated fatty acids (PUFAs) in various leukocytes, such as neutrophils and monocytes 1 . Class 4 LTs have potent pro‐inflammatory properties and are derived from omega‐6 (n‐6) PUFAs, such as arachidonic acid. By comparison, class 5 LTs derived from omega‐3 (n‐3) PUFAs, such as eicosapentaenoic acid/docosahexaenoic acid (EPA/DHA), are much less biologically active. Upon activation of the cell by calcium, arachidonic acid or EPA is liberated from cellular membranes by the enzyme cytosolic phospholipase A2 group 4A (PLA2G4A) and oxidized by arachidonic acid 5‐lipoxgenase (ALOX5) to form either LTA 4 or LTA 5 , respectively These intermediaries are subsequently converted to LTB 4 /LTB 5 and the cysteinyl LTs (LTC 4 /LTC 5 , LTD 4 /LTD 5 , and LTE 4 /LTE 5 ) via enzymatic reactions by LTA4 hydrolase (LTA4H) and LTC4 synthase (LTC4S), respectively 1 . LTs then affect the function of target cells, including monocytes and other pro‐inflammatory leukocytes, through receptor‐mediated signal transduction. While LTs have long been known to be involved in chronic allergic inflammatory conditions, such as asthma, the LT pathway has also recently garnered attention for its potential role in cardiovascular disease (CVD) 2‐5 . This stems from a series of recent biochemical, genetic, and pharmacological studies in mice and humans that have provided evidence for the pro‐atherogenic role of LTs. For example, genetic deficiency of ALOX5 protects against aortic lesion formation 75 and leads to other metabolic disturbances 6‐9 . Other mouse studies have implicated ALOX5 activating protein (ALOX5AP) and the LT receptors in the involvement of atherosclerosis‐related traits as well 10‐15 . In humans, genetic variation in ALOX5, ALOX5AP, LTA4H, and LTC4S has also been associated with various CVD phenotypes 16‐24 . Interestingly, promoter variants of ALOX5 have been shown to interact with dietary PUFAs to influence carotid atherosclerosis and risk of myocardial infarction (MI) as well 16, 25 . More recently, we carried out a comprehensive genetic evaluation of the LT pathway, which replicated some of these previously reported associations, and provided evidence that a variant of PLA2G4A (rs12746200) was associated with decreased risk of CVD 26 . In the present study, we sought to replicate the association of PLA2G4A with a more clinically significant CVD phenotype, namely MI, and determine whether the association could be mediated by dietary PUFAs. 76 Materials and Methods Subjects and Methods Study Subjects: Cases for this study were adult patients who were survivors of a first acute MI as diagnosed by a cardiologist at any of the recruiting hospitals in the Central Valley of Costa Rica. A study cardiologist confirmed all cases according to the World Health Organization (WHO) criteria for MI, which requires typical symptoms plus either elevation in cardiac enzymes concentration or diagnostic changes in the electrocardiogram (ECG). Enrollment was carried out in the step‐ down unit of the recruiting hospitals and cases were ineligible for participation if they (a) died during hospitalization, (b) were over 75 years of age on the day of their first MI, (c) were physically or mentally unable to answer the questionnaire, or (d) had a previous hospital admission related to CVD. For each case, one population‐based control subject, matched for age (  5 years), sex, and county of residence, was recruited. The controls were randomly selected using data from the National Census and Statistics Bureau of Costa Rica. Control subjects were ineligible if they had ever suffered an MI or if they were physically or mentally unable to answer the questionnaires. The catchment area consisted of 34 counties in the Central Valley of Costa Rica and participants were recruited between 1994 and 2004. Participation was 98% for cases and 88% for controls. All subjects gave informed consent on documents approved by the Human Subjects Committee of 77 the Harvard School of Public Health and University of Costa Rica. Approval for the present study was also obtained from the Institutional Review Board of the USC Keck School of Medicine. Trained personnel visited all study participants at their homes for data collection. Fieldworkers collected anthropometrical measurements with study subjects wearing light clothing and without shoes. All measurements were performed in duplicate and the average was used for analyses. Socio‐demographic characteristics, medical history, and lifestyle habits were collected using a general questionnaire. Dietary intake was collected using a food frequency questionnaire (FFQ) that has been developed and validated specifically to assess fatty acid intake among the Costa Rican population 27 . The validity coefficient for the assessment of arachidonic acid using the FFQ was high (0.53), a finding consistent with the performance of the FFQ for other fatty acids 27 , and the correlation coefficient between the FFQ and seven 24‐hour recalls was 0.62. Biological samples were always collected in the morning after an overnight fast. Blood samples (20 ml) were drawn in 0.1% EDTA‐containing tubes after a 12‐14 hour fast and immediately stored at 4 °C. Within 36 hours, the samples were centrifuged at 2,500 rpm for 20 minutes at 4 °C to isolate and aliquot plasma. The samples were then sealed and stored under N 2 at ‐80 °C until analysis. For the present analyses, genotype information, complete data on all the descriptive variables, and potential confounders were available from 1936 cases and 2035 controls. 78 Genotyping: Genotyping of the PLA2G4A single nucleotide polymorphism (SNP) rs12746200 was carried out with the Applied Biosystems, Inc. (ABI) TaqMan system 28 using a custom assay from ABI’s “Assays by Design” service. Gene Expression Analyses: Human aortic endothelial cells (HAECs) were isolated from aortic explants of 132 heart transplant donors, as described previously 29 . Briefly, the extracted cells were grown to 90% confluence and treated in duplicate for 4h with either control media or with media containing oxidized 1‐palmitoyl‐2‐ arachidonoyl‐sn‐glycero‐3‐phosphatidylcholine (ox‐PAPC). RNeasy and DNeasy kits (Qiagen, Inc, Valencia, CA) were used to isolate cytosolic RNA and genomic DNA. PLA2G4A mRNA levels were determined using Affymetrix HT‐HU133A microarrays, as described elsewhere 29 . Intensity values were normalized with the RMA normalization method in R 2.5.0 utilizing justRMA function in the Bioconductor affy package. Expression values were averaged between duplicate arrays per treatment and study subject 29 . Genotyping was performed by the ABI TaqMan system as described above. 79 Statistical Analyses: The significance of differences in health characteristics and potential confounders were assessed by Wilcoxon rank sum test for continuous variables, and by χ 2 test for binary variables. Prior to all analyses, rs12746200 was tested for Hardy‐Weinberg equilibrium using a χ 2 test. Odds ratios (OR) and 95% confidence intervals (CI) were estimated from multiple unconditional logistic regression assuming dominant genetic model. Median intake of dietary omega‐6 (6.93 g/d) and omega‐3 (1.02 g/d) PUFAs in the control group were used to stratify subjects into low and high groups. The fully adjusted model included age, sex, county of residence, dietary fat percentage from total energy intake, smoking, household income, history of diabetes, hypertension, or hypercholesterolemia, obesity, and family history of MI. Due to the high correlation in the intake of dietary PUFAs, the gene‐dietary interaction analyses were additionally adjusted for the reciprocal PUFA where dietary omega‐6 and omega‐3 levels were treated as continuous variables. Other variables tested, but not included in the final model, were total calorie intake, saturated fat intake, monounsaturated fat intake, polyunsaturated fat intake, cholesterol intake, fiber intake, and ethnicity. Difference in the mean gene expression levels in human endothelial cells were evaluated using a linear regression. Expression levels were normally distributed both for untreated and ox‐PAPC treated HAECs. The donors of the HAECs were anonymous, and thus individual information, such as ethnicity, was unknown. As described by Romanoski et al 29 , modest population stratification was seen using principal component (PC) analysis for this data, although removing samples outside 80 the two first PCs did not affect the results of their study. All data were analyzed with the Statistical Analysis Systems software version 9.2 (SAS Institute Inc., Cary, NC) or with the Stata version 8.2 (StataCorp LP, College Station, TX). 81 Results Clinical Characteristics of the Study Population: Table 1 lists the clinical characteristics of the Costa Rican cases and controls used for this study. As expected, the cases exhibited several risk factors associated with MI, including more likely to have diabetes, hypertension, family history of MI, lower household income, and to consume more calories per day, with higher intake of dietary cholesterol and various fatty acids (Table 1). Association of PLA2G4A with MI: To replicate our previously reported association of PLA2G4A, we genotyped the rs12746200 A>G polymorphism in 3971 individuals from the Costa Rican dataset. The frequency of the G allele was ~14% in Costa Ricans and comparable to that observed in subjects of Northern European descent 26 . To increase the power for detecting an association, we assumed a dominant genetic model in our analyses. As shown in Table 2, risk of MI was significantly lower in AG/GG subjects compared to individuals homozygous for the A allele (OR = 0.86, 95% CI 0.75, 0.99; p=0.04). Adjustment for covariates, such as age, sex, area of residence, total fat intake, smoking, and household income, did not attenuate this association (Table 2). Inclusion of history of diabetes, hypertension, or hypercholesterolemia, obesity, and family history of MI as additional covariates did not change the effect estimate for association of rs12746200 with MI but 82 decreased the statistical significance, which may be due, in part, to reduced sample size in the fully adjusted model (Table 2). Using a panel of ancestry informative markers that were available in this dataset 30 , we also included the proportion of European, West African, and Amerindian admixture as covariates in these analyses. However, this adjustment did not alter the results either (data not shown). Nutrigenetic Interaction Between PLA2G4A and Dietary PUFAs on Risk of MI: We next determined whether the association of rs12746200 with MI was modulated by dietary levels of omega‐6 or omega‐3 PUFAs. Given that this variant decreases risk of MI, we used AA homozygotes above the median intake of dietary omega‐6 PUFAs (≥ 6.93 g/day) as the reference group since these subjects would be considered at most risk for MI (Table 3). Although no association was observed in the low dietary omega‐6 PUFA group, these analyses revealed a significant gene‐dietary interaction where decreased risk of MI was observed in AG/GG subjects who were above the median for dietary omega‐6 PUFAs (OR = 0.71, 95% CI 0.59, 0.87; p‐interaction= 0.005). These results remained significant even after the addition of various covariates in the model (Table 3). Gene‐dietary interaction analyses using tertiles of omega‐6 PUFA intake also yielded consistent results (Supplementary Table 1). We next carried out the reciprocal analysis with dietary omega‐3 PUFAs with AA homozygotes who were below the median for dietary omega‐3 intake (< 1.02g/day) as the reference group (Table 4). This analysis did not reveal a statistically 83 significant interaction between genotype, dietary omega‐3 PUFAs, and risk of MI (Table 4). An analysis using tertiles of omega‐3 PUFA intake also did not reveal a significant gene‐dietary interaction (Supplementary Table 2). Functional Characterization of Rs12746200: In order to determine whether rs12746200 is a functional variant, we evaluated PLA2G4A gene expression levels in HAECs as a function of genotype. HAECs from 132 donors were treated for 4h with either media alone or oxidized 1‐palmitoyl‐2‐arachidonoyl‐sn‐glycero‐3‐ phosphatidylcholine (ox‐PAPC). The expression of PLA2G4A was normally distributed under both treatment conditions and did not change in response to treatment with ox‐PAPC (data not shown). As shown in Figure 1, gene expression levels under control conditions were marginally, but significantly, lower in AG/GG subjects compared to AA homozygotes (8.6 ± 0.13 vs. 8.9 ± 0.06; p=0.014). PLA2G4A mRNA levels were similarly lower in AG/GG subjects compared to AA homozygotes (8.7 ± 0.14 vs. 9.1 ± 0.06; p=0.012) when the cells were treated with ox‐PAPC (Figure 1). 84 Discussion In the present study, we provide evidence that risk of MI was modestly decreased by ~15% in AG/GG carriers of the PLA2G4A rs12746200 variant relative to AA subjects. This association remained significant in a fully adjusted model that included admixture proportions as covariates and was supported by the relatively weak, but significant, effect of rs12746200 on PLA2G4A expression levels (discussed further below). These results are consistent with our recent observations that rs12746200 was associated with decreased risk of CVD in a Caucasian patient population of Northern European descent 26 . However, rs12746200 had a stronger association in Caucasians where it reduced both risk of prevalent CVD and future major adverse cardiac events by ~30% despite being slightly less frequent (~10%). Such differences are not entirely surprising in genetic association studies and can be attributed to various factors, including those related to the study populations, sample size, functional effects, and/or the CVD phenotypes being investigated. Nonetheless, these results replicate our previous observations with a more clinically significant CVD outcome and in an independent case‐control dataset of different ethnicity. Another important aspect of our study is the nutrigenetic analyses, which demonstrate that the cardioprotective association of rs12746200 occurs primarily in AG/GG subjects who had high dietary omega‐6 PUFA intake. These results suggest that rs12746200 can mitigate, in part, the pro‐atherogenic effects of 85 omega‐6 PUFAs in this Costa Rican population 25 . However, the G allele does not appear to provide any further risk reduction in subjects who have low omega‐6 PUFA intake. In addition, omega‐6 PUFAs are direct substrates for PLA2G4A, supporting the notion that observed gene‐dietary interactions reflect true biological associations. Of note, these analyses were adjusted for levels of the reciprocal group of dietary omega‐3 PUFAs, which, by comparison, did not demonstrate significant interactions with genotype on risk of MI. This may be due to the relatively low consumption of fish by the population in the Central Valley region of Costa Rica 31 , from which this sample was collected. Taken together with previous studies 16, 25 , these results suggest that CVD phenotypes in humans can be influenced through interactions between genetic variation in the LT pathway and dietary PUFAs that serve as substrates for the biosynthetic enzymes. It would be of interest to determine whether other LT pathway genes that have been associated with CVD 17, 18, 20‐22 exhibit nutrigenetic interactions as well. PLA2G4A encodes a calcium‐dependent cytosolic phospholipase that is expressed in a variety of tissues, including endothelial cells 32 . In addition to liberating PUFAs from cellular membranes for synthesis of lipid mediators, PLA2G4A also upregulates endothelial cell expression of intracellular adhesion molecule (ICAM‐1) 33 , which is a known mediator of monocyte recruitment to the artery wall. In this regard, our functional experiments are consistent with rs12746200 being associated with decreased risk of MI since PLA2G4A mRNA levels were lower in 86 HAECs from AG/GG subjects than AA homozygotes. This would presumably lead to lower levels of pro‐inflammatory class 4 LTs (and possibly ICAM‐1) and thus provide a biologically plausible mechanism for why rs12746200 decreases risk of MI primarily in the context of high dietary omega‐6 PUFAs. However, we are not able to definitely prove this hypothesis since LT production was not determined in the HAEC samples used herein. Moreover, other classes of arachidonic acid and EPA‐ derived lipid mediators, such as lipoxins, resolvins, and maresins, have been recognized as having important roles in the resolution of inflammatory processes 34 . Thus, determining whether these PUFA derivatives are different as a function of PLA2G4A genotype may also provide further insight into the molecular mechanism by which rs12746200 leads to decreased risk of MI. Lastly, rs12746200 is located in intron 3 of PLA2G4A and it is not known whether this region contains a regulatory element that controls gene expression. Based on HapMap data for Hispanics, rs12746200 is not in linkage disequilibrium with other variants of PLA2G4A, including a functional K651R polymorphism (rs2307198) that increases in vitro activity by nearly two‐fold 35 . Thus, additional studies will be required to determine whether rs12746200 is the causal variant underlying the association we observe with MI or is linked to another heretofore unidentified SNP that affects expression levels and/or enzyme activity. There are several limitations that should be considered in our study. First, MI was defined according to WHO criteria based on clinical symptoms, ECG 87 abnormalities, and elevated cardiac enzymes. By comparison, the universal definition of MI uses more sensitive and specific serological biomarkers and more specific imaging techniques 36 . Thus, there is the potential for disease misclassification in our study, although this is unlikely to be different with regard to PLA2G4A genotype and would only bias the effect estimates towards the null. Additionally, since this study sample is comprised of Costa Ricans, the nutrigenetic associations with PLA2G4A may not be applicable to the general population. Lastly, chronic kidney disease has previously been associated with all‐cause mortality and incident MI in longitudinal studies and has been suggested to have a similar pathogenesis as CVD 37 . However, since kidney function measures were not collected this sample, we were not able test whether it confounded the association of rs12746200 with MI. In conclusion, we demonstrate that a functional variant of PLA2G4A decreases risk of MI in Costa Rican subjects and particularly in those with high intake of omega‐6 PUFAs. These results implicate another member of the LT biosynthetic pathway in the development of CVD through a nutrigenetic interaction and suggest that personalized dietary recommendations may be more effective based on an individual’s genetic makeup. More comprehensive genetic and functional analyses will be required to clarify the role of PLA2G4A in atherosclerotic processes and to determine the underlying causal variant(s). 88 Acknowledgements We thank the study participants and the staff of Proyecto Salud Coronaria, San Jose, Costa Rica. This work was supported by NIH grants HL079353 (H.A.) and HL060692 (H.C.). A portion of this work was conducted in a facility constructed with support from Research Facilities Improvement Program Grant Number C06 (RR10600‐01, CA62528‐01, RR14514‐01) from the National Center for Research Resources. Gene expression levels and genomic DNA for the functional studies with HAECs were kindly provided by Dr. Aldons J. Lusis at UCLA. HA and HC designed the study and obtained funding. JH, EG, and SV generated and analyzed data. JH and HA wrote the manuscript. The authors declare no conflicts of interest associated. 89 References 1. Peters‐Golden M, Henderson WR, Jr (2007) Leukotrienes. N Engl J Med. 357:1841‐54. 2. Mehrabian M, Allayee H (2003) 5‐Lipoxygenase and atherosclerosis. Curr Opin Lipidol. 14:447‐57. 3. Tymchuk CN, Hartiala J, Patel PI, Mehrabian M, Allayee H (2006) Nonconventional genetic risk factors for cardiovascular disease. Curr Atheroscler Rep. 8:184‐92. 4. Back M, Hansson GK (2006) Leukotriene receptors in atherosclerosis. Ann Med. 38:493‐502. 5. Riccioni G, Back M, Capra V (2010) Leukotrienes and atherosclerosis. Curr Drug Targets. 11:882‐7. 6. Mehrabian M, Allayee H, Wong J, Shi W, Wang XP, Shaposhnik Z, Funk CD, Lusis AJ (2002) Identification of 5‐lipoxygenase as a major gene contributing to atherosclerosis susceptibility in mice. Circ Res. 91:120‐6. 7. Mehrabian M, Allayee H, Stockton J, Lum PY, Drake TA, Castellani LW, Suh M, Armour C, Edwards S, Lamb J, Lusis AJ, Schadt EE (2005) Integrating genotypic and expression data in a segregating mouse population to identify 5‐lipoxygenase as a susceptibility gene for obesity and bone traits. Nat Genet. 37: 1224‐33. 8. Mehrabian M, Schulthess FT, Nebohacova M, Castellani LW, Zhou Z, Hartiala J, Oberholzer J, Lusis AJ, Maedler K, Allayee H (2008) Identification of ALOX5 as a gene regulating adiposity and pancreatic function. Diabetologia. 51:978‐ 88. 9. Poeckel D, Funk CD (2010) The 5‐lipoxygenase/leukotriene pathway in preclinical models of cardiovascular disease. Cardiovasc Res. 86:243‐53. 10. Aiello RJ, Bourassa PA, Lindsey S, Weng W, Freeman A, Showell HJ (2002) Leukotriene B4 receptor antagonism reduces monocytic foam cells in mice. Arterioscler Thromb Vasc Biol. 22:443‐9. 90 11. Subbarao K, Jala VR, Mathis S, Suttles J, Zacharias W, Ahamed J, Ali H, Tseng MT, Haribabu B (2004) Role of leukotriene B4 receptors in the development of atherosclerosis: potential mechanisms. Arterioscler Thromb Vasc Biol. 24:369‐75. 12. Heller EA, Liu E, Tager AM, Sinha S, Roberts JD, Koehn SL, Libby P, Aikawa ER, Chen JQ, Huang P, Freeman MW, Moore KJ, Luster AD, Gerszten RE (2005) Inhibition of atherogenesis in BLT1‐deWcient mice reveals a role for LTB4 and BLT1 in smooth muscle cell recruitment. Circulation. 112:578–586. 13. Jawien J, Gajda M, Rudling M, Mateuszuk L, Olszanecki R, Guzik TJ, Cichocki T, Chlopicki S, Korbut R (2006) Inhibition of five lipoxygenase activating protein (FLAP) by MK‐886 decreases atherosclerosis in apoE/LDLR‐double knockout mice. Eur J Clin Invest. 36:141‐6. 14. Ahluwalia N, Lin AY, Tager AM, Pruitt IE, Anderson TJ, Kristo F, Shen D, Cruz AR, Aikawa M, Luster AD, Gerszten RE (2007) Inhibited aortic aneurysm formation in BLT1‐deficient mice. J Immunol. 179: 691‐7. 15. Jawien J, Gajda M, Wolkow P, Zuranska J, Olszanecki R, Korbut R (2008) The effect of montelukast on atherogenesis in apoE/LDLR‐double knockout mice. J Physiol Pharmacol. 59:633‐9. 16. Dwyer JH, Allayee H, Dwyer KM, Fan J, Wu H, Mar R, Lusis AJ, Mehrabian M (2004) Arachidonate 5‐lipoxygenase promoter genotype, dietary arachidonic acid, and atherosclerosis. N Engl J Med. 350:29‐37. 17. Helgadottir A, Manolescu A, Thorleifsson G, Gretarsdottir S, Jonsdottir H, Thorsteinsdottir U, Samani NJ, Gudmundsson G, Grant SF, Thorgeirsson G, Sveinbjornsdottir S, Valdimarsson EM, Matthiasson SE, Johannsson H, Gudmundsdottir O, Gurney ME, Sainz J, Thorhallsdottir M, Andresdottir M, Frigge ML, Topol EJ, Kong A, Gudnason V, Hakonarson H, Gulcher JR, Stefansson K (2004) The gene encoding 5‐lipoxygenase activating protein confers risk of myocardial infarction and stroke. Nat Genet. 36: 233‐9. 18. Helgadottir A, Manolescu A, Helgason A, Thorleifsson G, Thorsteinsdottir U, Gudbjartsson DF, Gretarsdottir S, Magnusson KP, Gudmundsson G, Hicks A, Jonsson T, Grant SF, Sainz J, O'Brien S J, Sveinbjornsdottir S, Valdimarsson EM, Matthiasson SE, Levey AI, Abramson JL, Reilly MP, Vaccarino V, Wolfe ML, Gudnason V, Quyyumi AA, Topol EJ, Rader DJ, Thorgeirsson G, Gulcher JR, Hakonarson H, Kong A, Stefansson K (2006) A variant of the gene encoding leukotriene A4 hydrolase confers ethnicity‐specific risk of myocardial infarction. Nat Genet. 38: 68‐74. 91 19. Carlson CS, Heagerty PJ, Nord AS, Pritchard DK, Ranchalis J, Boguch JM, Duan H, Hatsukami TS, Schwartz SM, Rieder MJ, Nickerson DA, Jarvik GP (2007) TagSNP evaluation for the association of 42 inflammation loci and vascular disease: evidence of IL6, FGB, ALOX5, NFKBIA, and IL4R loci effects. Hum Genet. 121: 65‐75. 20. Iovannisci DM, Lammer EJ, Steiner L, Cheng S, Mahoney LT, Davis PH, Lauer RM, Burns TL (2007) Association between a leukotriene C4 synthase gene promoter polymorphism and coronary artery calcium in young women: the Muscatine Study. Arterioscler Thromb Vasc Biol. 27:394‐9. 21. Freiberg JJ, Tybjaerg‐Hansen A, Sillesen H, Jensen GB, Nordestgaard BG (2008) Promotor polymorphisms in leukotriene C4 synthase and risk of ischemic cerebrovascular disease. Arterioscler Thromb Vasc Biol. 28:990‐6. 22. Freiberg JJ, Dahl M, Tybjaerg‐Hansen A, Grande P, Nordestgaard BG (2009) Leukotriene C4 synthase and ischemic cardiovascular disease and obstructive pulmonary disease in 13,000 individuals. J Mol Cell Cardiol. 46:579‐86. 23. Crosslin DR, Shah SH, Nelson SC, Haynes CS, Connelly JJ, Gadson S, Goldschmidt‐Clermont PJ, Vance JM, Rose J, Granger CB, Seo D, Gregory SG, Kraus WE, Hauser ER (2009) Genetic eVects in the leukotriene biosynthesis pathway and association with atherosclerosis. Hum Genet. 125:217–229. 24. Burdon KP, Rudock ME, Lehtinen AB, Langefeld CD, Bowden DW, Register TC, Liu Y, Freedman BI, Carr JJ, Hedrick CC, Rich SS (2010) Human lipoxygenase pathway gene variation and association with markers of subclinical atherosclerosis in the diabetes heart study. Mediators Inflamm. 170153. 25. Allayee H, Baylin A, Hartiala J, Wijesuriya H, Mehrabian M, Lusis AJ, Campos H (2008) Nutrigenetic association of the 5‐lipoxygenase gene with myocardial infarction. Am J Clin Nutr. 88:934‐40. 26. Hartiala J, Li D, Conti DV, Vikman S, Patel Y, Tang WH, Brennan ML, Newman JW, Stephensen CB, Armstrong P, Hazen SL, Allayee H (2011) Genetic contribution of the leukotriene pathway to coronary artery disease. Hum Genet. 129:617‐27. 92 27. Kabagambe EK, Baylin A, Allan DA, Siles X, Spiegelman D, Campos H (2001) Application of the method of triads to evaluate the performance of food frequency questionnaires and biomarkers as indicators of long‐term dietary intake. Am J Epidemiol. 154:1126‐35. 28. Livak KJ (1999) Allelic discrimination using fluorogenic probes and the 5' nuclease assay. Genet Anal. 14:143‐9. 29. Romanoski CE, Lee S, Kim MJ, Ingram‐Drake L, Plaisier CL, Yordanova R, Tilford C, Guan B, He A, Gargalovic PS, Kirchgessner TG, Berliner JA, Lusis AJ (2010) Systems genetics analysis of gene‐by‐environment interactions in human cells. Am J Hum Genet. 86:399‐410. 30. Ruiz‐Narvaez EA, Bare L, Arellano A, Catanese J, Campos H (2010) West African and Amerindian ancestry and risk of myocardial infarction and metabolic syndrome in the Central Valley population of Costa Rica. Hum Genet. 127:629‐38. 31. Baylin A, Kabagambe EK, Ascherio A, Spiegelman D, Campos H (2003) Adipose tissue alpha‐linolenic acid and nonfatal acute myocardial infarction in Costa Rica. Circulation. 107:1586‐91. 32. Alberghina M (2010). Phospholipase A(2): new lessons from endothelial cells. Microvasc Res. 80:280‐5. 33. Hadad N, Tuval L, Elgazar‐Carmom V, Levy R (2011) Endothelial ICAM‐1 protein induction is regulated by cytosolic phospholipase A2alpha via both NF‐kappaB and CREB transcription factors. J Immunol. 186:1816‐27. 34. Spite M, Serhan CN (2010) Novel lipid mediators promote resolution of acute inflammation: impact of aspirin and statins. Circ Res. 107:1170‐84. 35. Reed KA, Tucker DE, Aloulou A, Adler D, Ghomashchi F, Gelb MH, Leslie CC, Oates JA, Boutaud O (2011) Functional characterization of mutations in inherited human cPLA deficiency. Biochemistry. 50:1731‐8. 36. Thygesen K, Alpert JS, White HD, Jaffe AS, Apple FS, Galvani M, Katus HA, Newby LK, Ravkilde J, Chaitman B, et al. (2007) Universal definition of myocardial infarction. Circulation. 116:2634‐53. 37. Schrier RW (2006) Role of diminished renal function in cardiovascular mortality: marker or pathogenetic factor? J Am Coll Cardiol. 47:1‐8. 93 38. Willett W (1998) Nutritional Epidemiology. 2nd ed. New York: Oxford University Press. 94 Figure Legends Figure 1. Endothelial cell gene expression levels as a function of rs12746200 genotype. Under control conditions (untreated) or after incubation with ox‐PAPC, PLA2G4A mRNA levels are significantly lower in human aortic endothelial cells from AG/GG subjects compared to AA homozygotes. PLA2G4A mRNA levels were obtained from Affymetrix HT‐HU133A microarrays and a t‐test was used to assess the difference in means between two genotype groups. Data are shown as mean ± SEM. *p=0.01. 95 Table 1. General Characteristics of the Study Population by Case‐Control Status. Trait Controls (n = 2035) Cases (n = 1936) 1 p‐value Male/Female (%) 74/26 74/26 0.87 Age (Years) 58 ± 11 58 ± 11 0.76 History of hypercholesterolemia (%) 26.9 30.3 0.02 History of diabetes (%) 14.2 24.2 <0.0001 History of hypertension (%) 29.5 38.1 <0.0001 Family history of MI (%) 7.8 12.3 <0.0001 BMI > 30 (%) 17.2 13.7 0.003 Current smokers (%) 21.9 40.3 <0.0001 Ever smokers (%) 60.7 69.5 <0.0001 Monthly household income (US$) 569 ± 423 500 ± 390 <0.0001 European admixture (%) 57.8 ± 7.9 57.5 ± 8.1 0.20 Amerindian admixture (%) 38.4 ± 7.4 38.5 ± 7.4 0.60 West African admixture (%) 3.9 ± 3.5 4.1 ± 4.1 0.20 Total calories (Kcal/day) 2453 ± 773 2709 ± 951 <0.0001 Dietary cholesterol (mg/1000 Kcal) 117.9 ± 52.4 127.0 ± 58.8 <0.0001 Dietary fiber (g/day) 8.2 ± 3.3 8.5 ± 3.3 0.0005 Total fat (% of total energy) 31.8 ± 5.8 32.4 ± 5.9 0.002 Saturated fat (% of total energy) 10.4 ± 2.7 11.1 ± 2.9 <0.0001 Monounsaturated fat (% of total energy) 11.8 ± 3.8 11.9 ± 3.5 0.007 2 Omega‐6 PUFAs (g/day) 9.4 ± 7.1 10.2 ± 8.5 <0.0001 2 Omega‐3 PUFAs (g/day) 1.13 ± 0.57 1.20 ± 0.69 <0.0001 All data are shown as mean ± SD or %. 1 2‐sided p‐values are reported between cases and controls using a Wilcoxon rank sum test for continuous variables, or a χ 2 test for dichotomous variables. 2 Adjusted for total energy intake using the residuals method 38 . 96 Table 2. Association of PLA2G4A with Myocardial Infarction. Minor (G) Allele Frequency Odds Ratio (95% CI) Model Controls (n) Cases (n) AA N AG/GG N p‐value 1 0.150 (2035) 0.137 (1936) 1.00 2905 0.86 (0.75, 0.99) 1066 0.040 2 0.150 (2031) 0.137 (1930) 1.00 2900 0.87 (0.75, 0.99) 1061 0.045 3 0.152 (1873) 0.137 (1788) 1.00 2680 0.84 (0.73, 0.98) 981 0.038 4 0.152 (1786) 0.137 (1648) 1.00 2510 0.88 (0.75, 1.03) 924 0.11 Data are reported as either frequencies or odds ratios (OR) with 95% confidence intervals (CI), as estimated using unconditional logistic regression analysis. P‐values are derived from Wald’s χ 2 test. Model 1: Adjusted for age, sex, and area of residence. Model 2: Model 1 plus adjustment for fat percentage of total energy intake. Model 3: Model 2 plus adjustment for smoking and household income. Model 4: Model 3 plus adjustment for history of diabetes, hypertension, or hypercholesterolemia, obesity, and family history of MI. 97 Table 3. Interaction Between PLA2G4A and Dietary Omega‐6 PUFAs on Risk of MI. High Dietary Omega‐6 PUFA (≥ 6.93 g/day) Low Dietary Omega‐6 PUFA (< 6.93 g/day) Model Genotype Group N Odds Ratio (95% CI) N Odds Ratio (95% CI) p‐interaction 1 AA 1466 1.00 1434 0.97 (0.81, 1.15) 0.005 AG/GG 559 0.71 (0.59, 0.87) 502 1.03 (0.83, 1.29) 2 AA 1466 1.00 1434 1.00 (0.84, 1.19) 0.006 AG/GG 559 0.72 (0.59, 0.87) 502 1.07 (0.85, 1.34) 3 AA 1373 1.00 1307 0.98 (0.81, 1.18) 0.016 AG/GG 521 0.71 (0.58, 0.88) 460 1.02 (0.80, 1.30) 4 AA 1311 1.00 1201 0.95 (0.78, 1.16) 0.043 AG/GG 499 0.75 (0.60, 0.94) 425 1.00 (0.78, 1.29) Data are reported as odds ratios (OR) with 95% confidence intervals (CI), as estimated using unconditional logistic regression analysis. P‐values are derived from Wald’s χ 2 test. Model 1: Adjusted for age, sex, area of residence and omega‐3 PUFAs. Model 2: Model 1 plus adjustment for fat intake as a percentage of total energy. Model 3: Model 2 plus adjustment for smoking and household income. Model 4: Model 3 plus adjustment for history of diabetes, hypertension, or hypercholesterolemia, obesity, and family history of MI. Reference genotype group for each model is “AA” in the high dietary omega‐6 PUFA strata. 98 Table 4. Interaction Between PLA2G4A and Dietary Omega‐3 PUFAs on Risk of MI. Low Dietary Omega‐3 PUFAs (< 1.02 g/day) High Dietary Omega‐3 PUFAs (≥ 1.02 g/day) Model Genotype Group N Odds Ratio (95% CI) N Odds Ratio (95% CI) p‐interaction 1 AA 1400 1.00 1500 1.04 (0.88, 1.23) 0.23 AG/GG 518 0.95 (0.77, 1.16) 543 0.82 (0.67, 1.02) 2 AA 1400 1.00 1500 1.01 (0.85, 1.16) 0.23 AG/GG 518 0.95 (0.77, 1.16) 543 0.80 (0.65, 1.00) 3 AA 1277 1.00 1403 1.01 (0.84, 1.21) 0.29 AG/GG 473 0.93 (0.75, 1.16) 508 0.80 (0.63, 1.00) 4 AA 1183 1.00 1329 0.05 (0.87, 1.27) 0.30 AG/GG 436 0.97 (0.77, 1.22) 488 0.86 (0.67, 1.09) Data are reported as odds ratios (OR) with 95% confidence intervals (CI), as estimated using unconditional logistic regression analysis. P‐values are derived from Wald’s χ 2 test. Model 1: Adjusted for age, sex, area of residence and omega‐6 PUFAs. Model 2: Model 1 plus adjustment for fat intake as a percentage of total energy. Model 3: Model 2 plus adjustment for smoking and household income. Model 4: Model 3 plus adjustment for history of diabetes, hypertension, or hypercholesterolemia, obesity, and family history of MI. Reference genotype group for each model is “AA” in the low dietary omega‐3 PUFA strata 99 Genotype Group mRNA levels (log 2 ) untreated ox-PAPC 7.5 8.5 9.5 9.0 8.0 7.0 AA (n=104) AG/GG (n=28) * AA (n=104) AG/GG (n=28) * Figure 1. 100 Supplementary Table 1. Interaction Between PLA2G4A and Tertiles of Dietary Omega‐6 PUFAs on Risk of MI. Tertile 3 (≥ 9.72g/day) Tertile 2 (5.26‐9.72g/day) Tertile 1 (< 5.26g/day) Model Genotype Group N Odds Ratio (95% CI) N Odds Ratio (95% CI) N Odds Ratio (95% CI) p‐interaction 1 AA 969 1.00 1000 1.14 (0.95, 1.36) 931 1.03 (0.82, 1.29) 0.024 AG/GG 363 0.71 (0.57, 0.89) 374 0.99 (0.79, 1.24) 324 0.90 (0.69, 1.17) 2 AA 969 1.00 1000 1.15 (0.96, 1.37) 931 1.08 (0.86, 1.36) 0.026 AG/GG 363 0.71 (0.57, 0.89) 374 1.00 (0.80, 1.26) 324 0.94 (0.72, 1.23) 3 AA 911 1.00 929 1.11 (0.92, 1.34) 840 1.02 (0.80, 1.30) 0.046 AG/GG 338 0.71 (0.56, 0.90) 352 0.95 (0.75, 1.21) 291 0.87 (0.66, 1.16) 4 AA 876 1.00 865 1.09 (0.89, 1.33) 771 0.98 (0.75, 1.26) 0.076 AG/GG 325 0.74 (0.58, 0.95) 333 0.97 (0.75, 1.24) 266 0.87 (0.64, 1.17) Data are reported as odds ratios (OR) with 95% confidence intervals (CI), as estimated using unconditional logistic regression analysis. P‐values are derived from Wald’s χ 2 test. Model 1: Adjusted for age, sex, area of residence and omega‐3 PUFAs. Model 2: Model 1 plus adjustment for fat intake as a percentage of total energy. Model 3: Model 2 plus adjustment for smoking and household income. Model 4: Model 3 plus adjustment for history of diabetes, hypertension, or hypercholesterolemia, obesity, and family history of MI. Reference genotype group for each model is “AA” in the high dietary omega‐6 PUFA strata. 101 Supplementary Table 2. Interaction Between PLA2G4A and Tertiles of Dietary Omega‐3 PUFAs on Risk of MI. Tertile 1 (< 0.83g/day) Tertile 2 (0.83‐1.25g/day) Tertile 3 (≥ 1.25g/day) Model Genotype Group N Odds Ratio (95% CI) N Odds Ratio (95% CI) N Odds Ratio (95% CI) p‐interaction 1 AA 943 1.00 963 1.10 (0.93, 1.30) 994 1.09 (0.93, 1.30) 0.114 AG/GG 318 1.00 (0.80, 1.26) 396 0.95 (0.77, 1.18) 347 0.95 (0.73, 1.23) 2 AA 943 1.00 963 1.07 (0.91, 1.27) 994 1.04 (0.84, 1.30) 0.110 AG/GG 318 1.00 (0.80, 1.26) 396 0.93 (0.75, 1.16) 347 0.90 (0.70, 1.17) 3 AA 846 1.00 905 1.13 (0.95, 1.36) 929 1.08 (0.85, 1.36) 0.191 AG/GG 290 0.97 (0.76, 1.24) 362 0.97 (0.77, 1.23) 329 0.92 (0.70, 1.22) 4 AA 780 1.00 851 1.16 (0.96, 1.41) 881 1.09 (0.85, 1.40) 0.253 AG/GG 266 1.00 (0.77, 1.29) 344 1.03 (0.81, 1.32) 314 0.96 (0.71, 1.30) Data are reported as odds ratios (OR) with 95% confidence intervals (CI), as estimated using unconditional logistic regression analysis. P‐values are derived from Wald’s χ 2 test. Model 1: Adjusted for age, sex, area of residence and omega‐6 PUFAs. Model 2: Model 1 plus adjustment for fat intake as a percentage of total energy. Model 3: Model 2 plus adjustment for smoking and household income. Model 4: Model 3 plus adjustment for history of diabetes, hypertension, or hypercholesterolemia, obesity, and family history of MI. Reference genotype group for each model is “AA” in the low dietary omega‐3 PUFA strata. 102 CHAPTER 3: GENOME‐WIDE AND GENE‐CENTRIC ANALYSES OF CIRCULATING MYELOPEROXIDASE LEVELS IN THE CHARGE AND CARE CONSORTIA Summary: In the present study, we used meta‐analyses of both GWAS and gene‐ centric data, to identify distinct loci associated with serum and plasma MPO levels. The most significant locus for serum MPO levels in Caucasians was observed with SNP rs800292 in the CFH gene on chromosome 1, which was replicated in African‐ American subjects. Functional analysis showed that the A allele of rs800292, which is associated with decreased MPO levels, was associated with lower serum levels of C3a‐desArg, a cleavage product generated by complement activation. These observations may mechanistically link stress‐related oxidative tissue injury to localized regulation of complement activity in a variety of chronic inflammatory disorders. Despite the clinical association of both plasma and serum MPO levels with both prevalent cardiovascular phenotypes and incident risk for major adverse cardiac events, genetic variants associated with circulating MPO levels do not show association with history of CVD in ~80,000 subjects from the CARDIoGRAM consortium. Additional studies will be needed to gain a better understanding of the functional basis for the association between circulating MPO levels and the identified variants in CFH and MPO, as well as to determine the clinical implications for inflammatory diseases that could be mediated in part through MPO‐related activity. In this study, my roles were to coordinate the overall statistical analysis plan among the other research groups in the MPO Consortium, perform the primary 103 GWAS analysis in the GeneBank study; collect and harmonize summary level data from the participating cohorts in the MPO Consortium; conduct all meta‐, conditional, and eQTL analyses, and write the manuscript. This study was recently published in Human Molecular Genetics (Hum Mol Genet. 2013 Aug 15;22(16):3381‐ 93). 104 Genome‐wide and Gene‐Centric Analyses of Circulating Myeloperoxidase Levels in the CHARGE and CARe Consortia Alexander P. Reiner, 1,36 Jaana Hartiala, 2,36 Tanja Zeller, 3,36 Joshua C. Bis, 4,36 Josée Dupuis, 5,6,36 Myriam Fornage, 7,36 Jens Baumert, 8,36 Marcus E. Kleber, 9,10,36 Philipp S. Wild 11 , Stephan Baldus 3 , Suzette J. Bielinski, 12 João D. Fontes, 5,13 Thomas Illig, 14,15 Brendan J. Keating, 16,17 Leslie A. Lange, 18 Francisco Ojeda, 3 Martina Müller‐ Nurasyid, 19,20,21 Thomas F. Munzel 11 , Bruce M. Psaty, 1,4,22,23 Kenneth Rice, 24 Jerome I. Rotter, 25 Renate B. Schnabel, 3 W.H. Wilson Tang, 26 Barbara Thorand, 8 Jeanette Erdmann 27 , CARDIoGRAM Consortium, 28 David R. Jacobs Jr, 29 James G. Wilson, 30 Wolfgang Koenig, 31,36 Russell P. Tracy, 32,36 Stefan Blankenberg, 3,36 Winfried März, 10,33,36 Myron D. Gross, 34,36 Emelia J. Benjamin, 5,13,35,36 Stanley L. Hazen, 26,36 and Hooman Allayee, 2,36* 1 Department of Epidemiology, University of Washington, Seattle, WA; 2 Department of Preventive Medicine and Institute for Genetic Medicine, USC Keck School of Medicine, Los Angeles CA; 3 Department of General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany; 4 Cardiovascular Health Research Unit and Department of Medicine, University of Washington, Seattle, WA; 5 National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, and 6 Department of Biostatistics, Boston University 105 School of Public Health, Boston, MA; 7 Brown Foundation Institute of Molecular Medicine and Human Genetics Center, Division of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX; 8 Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; 9 LURIC Study non profit LLC, Freiburg, Germany; 10 Department of Public Health, Social and Preventive Medicine, Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany; 11 Departments of Medicine II, University Medical Center and Center for Thrombosis and Haemostasis, Johannes Gutenberg University Mainz, Mainz, Germany; 12 Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN; 13 Department of Medicine, Boston University School of Medicine, Boston, MA; 14 Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; 15 Hannover Unified Biobank, Hannover Medical School, Hannover, Germany; 16 Center for Applied Genomics and Division of Human Genetics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, PA; 17 Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, PA; 18 Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC; 19 Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; 20 Department of Medicine I, University Hospital Grosshadern; 21 Chair of Epidemiology and Chair of Genetic 106 Epidemiology, Ludwig‐Maximilians‐Universität, Munich, Germany; 22 Department of Health Services, University of Washington, Seattle, WA; 23 Group Health Research Institute, Group Health Cooperative, Seattle, WA; 24 Department of Biostatistics, University of Washington, Seattle; 25 Medical Genetics Institute, Cedars‐Sinai Medical Center, Los Angeles, CA; 26 Department of Cardiovascular Medicine and Center for Cardiovascular Diagnostics and Prevention, Cleveland Clinic, Cleveland, OH; 27 Medizinische Klinik II and Deutsches Zentrum für Herz‐Kreislaufforschung e.V. (DZHK), Universität zu Lübeck, Lübeck, Germany; 28 A full list of authors and affiliations for the CARDIoGRAM Consortium is provided in the supplemental data; 29 Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN; 30 Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS; 31 Department of Internal Medicine II–Cardiology, University of Ulm Medical Center, Ulm, Germany; 32 Departments of Pathology and Biochemistry, University of Vermont College of Medicine, Burlington, VT; 33 Synlab Center of Laboratory Diagnostics, Heidelberg, Germany; 34 Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN; 35 Department of Epidemiology, Boston University School of Public Health, Boston, MA. 28 A full list of authors and affiliations for CARDIoGRAM is provided in the supplemental data. 36 These authors contributed equally to this work. 107 Abbreviations age‐related macular degeneration (AMD) African Americans (AA) atypical hemolytic uremic syndrome (HUS) Cardiovascular Health Study (CHS) Coronary Artery Risk Development in Young Adults (CARDIA) complement component 2 (C2) complement factor H (CFH) C‐reactive protein (CRP) coronary artery disease (CAD) Framingham Heart Study (FHS) genome‐wide association study (GWAS) high density lipoprotein (HDL) leucine zipper putative tumor suppressor 1 (LZTS1) linkage disequilibrium (LD) 108 minor allele frequency (MAF) myeloperoxidase (MPO) N‐actetylgalactosaminyltransferase 2 (GALNT2) proton ATPase subunit B (ATP6V1B) quantile‐quantile (Q‐Q) signal‐regulatory protein beta 2 (SIRPB2) systemic lupus erythematosis (SLE) 109 Abstract Increased systemic levels of myeloperoxidase (MPO) are associated with risk of coronary artery disease (CAD). To identify the genetic factors that are associated with circulating MPO levels, we carried out a genome‐wide association study (GWAS) and a gene‐centric analysis in subjects of European ancestry and African Americans (AA). A locus on chromosome 1q31.1 containing the complement factor H (CFH) gene was strongly associated with serum MPO levels in 9305 subjects of European ancestry (lead SNP rs800292; p=4.89x10 ‐41 ) and in 1690 AA subjects (rs505102; p =1.05 x 10 ‐8 ). Gene‐centric analyses in 8335 subjects of European ancestry additionally identified two rare MPO coding sequence variants that were associated with serum MPO levels (rs28730837, p=5.21x10 ‐12 ; rs35897051, p=3.32x10 ‐8 ). A GWAS for plasma MPO levels in 9260 European ancestry subjects identified a chromosome 17q22 region near MPO that was significantly associated (lead SNP rs6503905; p=2.94x10 ‐12 ) but the CFH locus did not exhibit evidence of association with plasma MPO levels. Functional analyses revealed that rs800292 was associated with levels of complement proteins in serum. Variants at chromosome 17q22 also had pleiotropic cis effects on gene expression. In a case‐ control analysis of ~80,000 subjects from CARDIoGRAM, none of the identified SNPs were associated with CAD. These results suggest that distinct genetic factors regulate serum and plasma MPO levels, which may have relevance for various acute and chronic inflammatory disorders. The clinical implications for CAD and a better 110 understanding of the functional basis for the association of CFH and MPO variants with circulating MPO levels requires further study. 111 Introduction Myeloperoxidase (MPO) is a lysosomal enzyme stored within the azurophilic granules of circulating neutrophils, monocytes, and tissue macrophages 1 . It is released upon leukocyte (both neutrophils and monocytes) activation and generates various reactive oxidants and free radicals that play important roles in killing invading parasites and pathogens. These same MPO‐derived oxidants have also been implicated in the formation of atherogenic low‐density lipoprotein particles, the development of dysfunctional high density lipoprotein (HDL) particles, catalytic consumption of nitric oxide (NO), vascular endothelial injury, and development of atherosclerotic plaque and its clinical sequelae 2‐4 . Furthermore, high circulating levels of MPO, as measured in serum, plasma, or leukocytes, predict major adverse cardiac events in healthy individuals, and in patients with coronary artery disease (CAD) or heart failure 5‐8 . In addition, MPO has been shown to be positively correlated with traditional and inflammatory CAD risk factors such as age, sex, blood pressure, body mass index, cigarette smoking, glucose, white blood cell count, and C‐reactive protein (CRP) levels 9‐11 . Heritability estimates for serum MPO range from 25‐30% 9, 12 , suggesting that variation in serum MPO levels has a significant genetic component. Loss‐of‐ function mutations in the MPO gene that result in total or partial MPO deficiency are rare in the population (~1 in 2,000) 13, 14 and have been associated with increased susceptibility to infection as well as protection from CAD 15 . Candidate 112 gene studies have also identified common variants of MPO that have been associated with CAD as well as circulating MPO levels 16‐19 . However, a more complete understanding of the genetic factors controlling circulating MPO levels is still lacking. Therefore, the aim of the present study was to use large‐scale unbiased genome‐wide and targeted gene‐centric analyses to identify loci controlling serum and plasma MPO levels and to determine whether MPO‐ associated variants influence risk of CAD. To our knowledge, these analyses would represent the first genome‐wide association study (GWAS) for circulating MPO levels. 113 Materials and Methods Study Populations: The populations with serum and/or plasma MPO levels used in the present study were the GeneBank, CARDIA, CHS, FHS, MONICA/KORA, GHS, and LURIC cohorts. Details of these study populations are provided in the online Supplemental Materials section. The CARe Consortium is a National Heart Lung and Blood Institute supported resource for analyses of the association of genotypes with heart, lung, and blood phenotypes 46 . All participants in each study provided written informed consent prior to being enrolled and the studies were approved by the Institutional Review Boards of the participating institutions. Circulating Measurements: Serum MPO levels were measured in the GeneBank, CARDIA, CHS, FHS, and MONICA/KORA cohorts whereas plasma MPO was measured in the GeneBank, GHS I and GHS II, and LURIC studies. The various assays used for these measurements in each cohort are shown in Table 1. The CardioMPO assay is FDA and EU cleared, and appropriate as an in vitro diagnostic test for use in patient care. Serum levels of C3a‐desArg, a downstream cleave product of complement activation, were measured using an OptEIA ELISA kit from BD Biosciences (San Diego, CA). 114 Genome‐wide Genotyping and Imputation: The cohorts included in this meta‐ analysis of GWAS data used different genotyping platforms: the Affymetrix Human SNP Array 6.0 in the GeneBank, CARDIA, GHS I and II, and LURIC2 cohorts; the Affymetrix 500K Array Set for the FHS and LURIC1 cohorts; and the Illumina HumanCNV370‐Duo for the CHS cohort. As a standard approach for facilitating meta‐analyses, all studies used their genotype data to impute unmeasured, autosomal SNPs using either the CEU data from HapMap (release 22 or 24, build 36) or the 1000 Genomes project. Imputation was performed using either the MACH 1.0.16, Beagle 3.2, or BIMBAM 0.99 programs. Imputation results were filtered at an r 2 threshold of 0.5 and a MAF threshold of 0.01. For imputed genotypes, we used dosage information (i.e. a value between 0.0 ‐ 2.0 calculated using the probability of each of the three possible genotypes) in the regression model implemented in PLINK. Details of the genotyping platforms and imputation methods for the GWAS are provided in Table 1 and Supplemental Table 1. For gene‐centric analyses, subjects with serum MPO measurements from the CHS, FHS, CARDIA, and MONICA/KORA cohorts were genotyped using the custom IBCv2 genotyping array that contains high SNP marker density and LD coverage for approximately 2,100 genes related to cardiovascular, inflammatory, hemostasis/coagulation, and metabolic phenotypes (47). A total of 49,320 SNPs are present on the IBC array, including approximately 15,000 SNPs in candidate genes that were not present in HapMap. Additional details regarding SNP selection and 115 the tagging approach that was used have been described previously 47 . IBC genotyping was not available in participants from the three cohorts with plasma MPO measurements. Expression Quantitative Trait Locus (eQTL) Analysis: The functional effects of the identified SNPs on gene expression in cis were determined using microarray data from monocytes of 1467 individuals in the GHS I and II cohorts, as described previously 48 . Briefly, total RNA was isolated from purified monocytes using RNeasy Mini kits (Qiagen, Hilden, Germany) and hybridized to Illumina HT‐12 v3 BeadChips (www.illumina.com). Quality control filtering resulted in 22,305 genes that were considered to be significantly expressed in monocytes and tested for association using the available genotype data. When the numbers of homozygotes for the minor allele of a SNP was lower than 30, they were grouped with heterozygotes. Association of gene expression levels with SNPs was tested using analysis of variance models 48 . Statistical Methods: SNPs were evaluated for association with natural log transformed circulating MPO levels using linear regression analyses, with adjustment for age and sex. Due to the difficulty of harmonizing MPO assays across cohorts, SNP association results for each ethnic group were combined using an 116 effective sample‐weighted Z‐score meta‐analysis method, as implemented in the software METAL 49 , including a test for heterogeneity. Effect estimates within and across all cohorts are expressed as beta coefficients with standard errors. GWAS and gene‐centric meta‐analyses for serum and plasma MPO levels (from the relevant cohorts) were carried out separately. Differences in serum C3a‐desArg levels as a function of genotype was carried out with linear regression using natural log‐transformed values. Associations with Coronary Artery Disease (CAD). The Coronary Artery Disease Genome‐wide Replication And Meta‐Analysis (CARDIoGRAM) Consortium represents a GWAS meta‐analysis of CAD comprising a discovery set of ~22,000 cases and ~65,000 controls 50 . For each cohort in CARDIoGRAM, logistic regression was first used to test for association with CAD assuming an additive genetic model with adjustment for age and sex and taking into account the uncertainty of possibly imputed genotypes. Subsequently, a meta‐analysis was performed separately for every SNP from each study that passed the quality control criteria using a fixed effects model with inverse variance weighting 50 . The results of this default meta‐ analysis were used to determine whether SNPs affecting circulating MPO levels were also associated with CAD. 117 Results GWAS for Serum MPO Levels: We first carried out a meta‐analysis of GWAS data for serum MPO levels in 9305 subjects of European ancestry. The characteristics of the Cleveland Clinic GeneBank (GeneBank), the Coronary Artery Risk Development in Young Adults (CARDIA) Study, Cardiovascular Health Study (CHS), and Framingham Heart Study (FHS) cohorts and datasets used for these analyses are summarized in Table 1. The observed variability in MPO levels, which could have also been influenced by either sample storage related effects or acute minor infections at the time of blood collection, made it difficult to harmonize MPO assays across cohorts. Therefore, SNP association results were combined using an effective sample‐weighted Z‐score meta‐analysis method. The quantile‐quantile (Q‐ Q) and Manhattan plots for serum MPO levels are presented in Figure 1A and 1B. The observed genomic control factors in the four discovery cohorts were near unity (GeneBank=0.999; CHS=1.015; FHS=1.004; CARDIA=1.004), suggesting that the GWAS results were not strongly confounded by underlying population stratification. SNPs at three loci on chromosomes 1q31.3, 6p21.32, and 20p13 exceeded the pre‐ specified threshold for genome‐wide significance (5.0x10 ‐8 ). The characteristics of the lead SNPs at these three loci and their association with serum MPO levels are summarized in Table 2 and Supplementary Table 2. The lead SNP at 1q31.3, rs800292 (p=4.89x10 ‐41 ), is a non‐synonymous Val62Ile (GTA>ATA) substitution in the complement factor H gene (CFH) with a minor allele frequency (MAF) of 0.23 in 118 Europeans. The CFH‐CFHR3‐CFHR1 genomic region on chromosome 1q31.3 contains several other variants that were also significantly associated with serum MPO levels, which show varying levels of linkage disequilibrium (LD) with rs800292 (Figure 2A). Of note, rs800292 is in low LD with other CFH variants, such as rs1061170 (Tyr402His), previously identified as susceptibility alleles for other disease phenotypes (Supplementary Table 3). To determine whether the other SNPs in this region represent independent association signals, we also ran analyses taking into the account the effect of the lead SNP rs800292. These conditional analyses revealed that the strength of the association for the other SNPs in this region was attenuated and did not exceed the genome‐wide threshold for significance (Supplementary Table 3). Using the GeneBank cohort, we also analyzed the loci associated with serum MPO levels with further adjustment for CAD, history of hypertension, lipid levels, or white blood cell count. However, these additional adjustments did not alter the effect estimates or p‐values compared to those adjusted for only age and sex (data not shown). The locus on chromosome 6p21.32 contains several immune system related genes, including complement component 2 (C2) and HLA (Figure 2B). The lead SNP (rs3134931; p=1.49x10 ‐8 ) is located within intron 2 of NOTCH4 and is not in strong LD (r 2 <0.4) with any of the other 130 variants in this region that demonstrate suggestive (p<5.0x10 ‐6 ) association with serum MPO levels (Figure 2B). One SNP at chromosome 20p13, which is not in LD (r 2 <0.2) with other variants in this region, 119 was also significantly associated with serum MPO levels (rs6042507; p=4.30x10 ‐8 ) (Figure 2C). This SNP encodes a non‐synonymous Ala215Glu substitution (GCG>GAG) in exon 3 of the signal‐regulatory protein beta 2 gene (SIRPB2), which belongs to family of genes expressed predominantly in neutrophils and monocytes. Suggestive evidence of association with serum MPO levels was also observed on chromosome 1q42.13 (rs2144300; p=2.52x10 ‐6 ) containing N‐ actetylgalactosaminyltransferase 2 (GALNT2) and a region on 8p21.3 (rs1390943; p=9.38x10 ‐7 ) containing the vacuolar proton ATPase subunit B (ATP6V1B) and leucine zipper putative tumor suppressor 1 (LZTS1) genes (Figure 1B; Table 2; Supplemental Table 2). Gene‐Centric Analyses for Serum MPO Levels: Using CARe data from the CARDIA (n=1262), CHS (n=3085), and FHS (n=2660) cohorts, as well 1328 additional subjects of European ancestry from the Monitoring of Trends and Determinants in Cardiovascular Disease Cooperative Health Research in the Region of Augsburg (MONICA/KORA) cohort, we carried out gene‐centric analyses using the IBC 50K custom SNP array. This chip has dense SNP coverage for ~2100 CAD candidate genes, including CFH, C2, and GALNT2 (but not NOTCH4 or SIRBP2) and contains specific rare amino acid substitutions that would otherwise not be captured by imputed GWAS datasets. As a result, the gene‐centric analyses allowed us to 120 validate and fine map a subset of the loci identified in the GWAS meta‐analysis, as well as potentially identify other variants associated with serum MPO levels. As shown in Table 2 and Supplemental Table 4, strong association of serum MPO levels was observed with variants in CFH where the lead SNPs yielded p‐values of 6.65x10 ‐43 (rs6680396) and 4.74x10 ‐42 (rs505102). The rs6680396 and rs505102 variants are in high LD (r 2 =0.87) with each other in subjects of European descent (based on the 1000 Genome Project CEU data), and also with the lead SNP identified in the GWAS for serum MPO levels (rs800292). A combined analysis for the CFH locus using all available unique individuals in the GWAS and IBC datasets, including the GeneBank cohort (combined n=10,524), further strengthened the association of rs6680396 with serum MPO levels (p=9.34x10 ‐45 ). The gene‐centric analyses also identified a SNP on chromosome 6p21.32 (rs9332739) in C2 that was strongly associated with serum MPO levels (p=4.83x10 ‐10 ) (Table 2; Supplemental Table 4). Rs9332739 (MAF=0.044) encodes a non‐synonymous Glu318Asp (GAG>GAC) substitution in C2 and is located ~287kb proximal to the lead SNP (rs3134931) in NOTCH4 that was identified in the GWAS for serum MPO levels (Figure 2B). However, in an analysis that included GeneBank (combined n=10,524) the association of rs9332739 with serum MPO levels was less significant than in the IBC analyses (p=1.76x10 ‐7 ), as this variant was not associated with serum MPO levels in GeneBank (p=0.39). The gene‐centric analyses also identified two rare variants (MAF~1%) of MPO (the structural gene for the enzyme) on chromosome 121 17q22 that were significantly associated with serum MPO levels (Table 2; Supplemental Table 4). One SNP encodes an Ala332Val substitution (rs28730837; GCG>GTG; p=5.21x10 ‐12 ) and the other interrupts the first position (AG>CG) of the 3' splice junction of intron 11 (rs35897051; p=3.32x10 ‐8 ). Since these rare variants were specifically included on the IBC array and not present in any of the imputed GWAS datasets, we were not able to carry out a combined analysis with all subjects. In addition to these rare variants, another SNP (rs8081967, MAF=0.36) located ~724kb telomeric to MPO in intron 23 of TRIM37 exhibited suggestive evidence of association (p=2.13x10 ‐6 ) with serum MPO levels (Table 2; Supplemental Table 4). Of note, rs8081967 also demonstrated suggestive evidence in the GWAS analyses for serum MPO levels (p=6.59x10 ‐5 ), which reached significance (p=1.44x10 ‐8 ) in an analysis using all unique subjects in the GWAS and IBC datasets (combined n=10,524). In addition, we also observed suggestive evidence of association with rs2144300 in GALNT2 (p=2.52x10 ‐7 ) in the IBC analyses (Table 2; Supplemental Table 4) but this association did not achieve genome‐wide significance (p=2.68x10 ‐7 ) in a combined analysis with all unique subjects in the GWAS and IBC datasets (n=10,524). Replication of Loci for Serum MPO Levels in African Americans (AA): To replicate the association findings observed with serum MPO levels and determine whether the loci have similar effects in other ethnicities, we used IBC SNP array data 122 available in 1690 AA from the CARDIA (n=1047) and CHS (n=643) cohorts. Consistent with the results in subjects of European ancestry, rs505102 in CFH was significantly associated (p=1.05x10 ‐8 ) with serum MPO levels in AA (Table 2; Supplemental Table 5), thus providing independent validation of this locus in a different ethnic group. The IBC analyses in AA also yielded suggestive evidence for association of serum MPO levels with rs2814778 (p=6.48x10 ‐8 ) (Table 2; Supplemental Table 5), which is located in the 5’ UTR of the Duffy blood group antigen gene (DARC) on chromosome 1q23.3. Because rs2814778 is an important determinant of circulating neutrophil count in AA, we further adjusted this analysis for white blood count. The association was strongly attenuated (p=0.02), suggesting that the association of rs2814778 with serum MPO in AA is largely mediated through the number of circulating neutrophils. In addition to validating the association of CFH with serum MPO levels observed in subjects of European ancestry, there was supportive evidence in AA for association of serum MPO levels with rs9332739 in C2 (p=0.0045) and the rare Ala332Val (rs28730837) substitution in MPO (p=0.061) (Supplemental Table 5) but not with the other variant in MPO (rs35897051; p=0.93) or with rs2144300 in GALNT2 (p=0.77). GWAS for Plasma MPO Levels: We next carried out a GWAS meta‐analysis for plasma MPO levels in subjects of European ancestry from the GeneBank, Gutenberg Health Study (GHS), and Ludwigshafen Risk and Cardiovascular Health (LURIC) 123 cohorts (total n=9260). The Q‐Q and Manhattan plots for the plasma MPO analyses are shown in Figure 3A and 3B and the genomic control factors were 0.999, 1.008, 0.980, 0.970, and , 0.991 for the GeneBank (n=2191), GHS I (n=2997), GHS II (n=1178), LURIC1 (n=794), and LURIC2 cohorts (n=2100) cohorts, respectively. The most significant locus for plasma MPO levels mapped to chromosome 17q22 near MPO with the lead SNP (rs6503905) yielding a p‐value of 2.94x10 ‐12 (Figure 3B; Table 2; Supplemental Table 6). The variant, rs6503905 (MAF =0.37), encodes a putative synonymous substitution (GCG>GCA; Ala14Ala) in the predicted gene C17orf71, which is located ~930kb telomeric of MPO. As shown in Figure 3C and Supplemental Table 6, several other SNPs spanning a ~1Mb interval in this region and within different LD blocks were also significantly associated with plasma MPO levels, including rs9911753 (p=1.51x10 ‐9 ), rs2680701 (p=4.98x10 ‐10 ), and rs12940923 (p=3.85x10 ‐9 ). However, rs6503905 is only in moderate LD with rs9911753 (r 2 =0.28) and completely unlinked to rs2680701 and rs12940923 (r 2 <0.1 for each SNP). In addition, rs9911753 is in strong LD (r 2 =0.87) with rs8081967, which was identified in our combined GWAS/IBC analyses for serum MPO levels (Table 2). We next re‐performed the analyses conditioned on rs6503905, rs9911753, rs2680701, or rs12940923. The association of these SNPs with plasma MPO was attenuated when conditioned on each other, with only rs6503905 exceeding the genome‐wide threshold for significance (p=9.07x10 ‐9 ) when the analyses were conditioned on rs12940923 (Supplemental Table 7). 124 In addition to the chromosome 17q22 locus, we also observed suggestive evidence for association of plasma MPO levels with the region surrounding GALNT2 (Figure 3B; Table 2; Supplemental Table 6), although the lead SNP (rs12049351; p=1.08x10 ‐6 ) differs from that identified for serum MPO levels in the GWAS/IBC analyses (rs2144300). Rs12049351 is not in LD with rs2144300 and is located ~485kb proximal to GALNT2 in between ABCB10 and TAF5L. We also evaluated the reciprocal association of all identified SNPs that were available across all genotyping platforms/datasets with both plasma and serum MPO levels. SNPs identified in our GWAS for plasma MPO levels also demonstrated varying degrees of association with serum MPO levels whereas reciprocal associations were not observed with plasma MPO levels (Supplemental Table 8). Since the SNPs on chromosome 17q22 and rs12049351 at the ABCB10‐TAF5L‐GALNT2 locus demonstrated association with both serum and plasma MPO levels (Supplemental Table 8), we also carried out a meta‐analysis across all independent subjects (combined n=16,376). These analyses yielded p‐values of 1.5x10 ‐11 and 5.4x10 ‐9 for association of rs6503905 and rs12049351, respectively, with circulating MPO levels. Functional Effects of MPO‐Associated Variants: To determine whether any of the identified SNPs were functional, we carried out biochemical and expression quantitative trait locus (eQTL) analyses. For the rs800292 variant in CFH, we measured serum levels of C3a‐desArg , a downstream cleavage product of 125 complement activation, in a subset of 171 subjects from the GeneBank cohort. These GeneBank subjects were selected to represent equal numbers of the GG, AG, and AA genotypes for rs800292 (n=57 each) and matched for age, sex, and CAD status. As shown in Figure 4, serum C3a‐desArg levels were significantly lower in carriers of the A allele compared to GG homozygotes in a dose‐dependent manner (p=0.04). To further evaluate the functional effects of SNPs associated with circulating MPO levels, we used previously generated microarray data in monocytes from 1467 subjects in the GHS I and II cohorts 48 . Significant and directionally consistent cis eQTLs were observed with SNPs located on chromosome 17q22 and mRNA levels of MPO as well as several other genes in this region, including RAD51C, SEPT4, and TRIM37 (Table 3). For MPO and RAD51C, these associations remained significant even after conditioning on the lead eQTL SNPs, which suggest that this region may have pleiotropic effects on the expression of multiple genes. No significant eQTL associations were detected with SNPs at other loci that were associated with serum or plasma MPO levels, including the CFH locus on chromosome 1. Effects of MPO‐associated variants on risk of CAD: To further investigate the significance of the loci for circulating MPO levels, we evaluated the association of the identified SNPs with prevalent CAD using the CARDIoGRAM consortium. This consortium represents a GWAS meta‐analysis of subjects with history of CAD and is 126 comprised of ~22,000 cases and ~65,000 controls. As shown in Table 4, none of the SNPs for which CARDIoGRAM data were available showed significant evidence of association with prevalent CAD. 127 Discussion Using meta‐analyses of both GWAS and gene‐centric data, we identified distinct loci that were associated with serum and plasma MPO levels. The most significant locus for serum MPO levels in subjects of European ancestry was observed with CFH on chromosome 1, which was replicated in AA subjects. Of the three highly associated and linked CFH SNPs in subjects of European ancestry (rs505102, rs800292, and rs6680396), only rs505102 and rs800292 are in strong LD in AA. As a result of this different LD pattern, candidate causal SNPs responsible for the association with lower serum MPO levels could include rs505102, rs800292, or another SNP in LD with these variants. Of these, rs800292 encodes a Val62Ile substitution in CFH and has been previously associated with decreased risk of age‐ related macular degeneration (AMD) in a Japanese population 20 . Other CFH variants have also been associated with AMD 21‐24 , as well as meningococcal susceptibility 25 , IgA‐induced nephropathy 26 , atypical hemolytic uremic syndrome (HUS) 27 , membranoproliferative glomerulonephritis type II 28 , and systemic lupus erythematosis (SLE) 29 . However, it is not known whether MPO plays a biological role in these inflammatory diseases. In addition, the association of the other disease‐associated CFH variants with serum MPO levels was attenuated when the analyses were conditioned on rs800292, even though these SNPs are in weak LD (r 2 <0.30) with rs800292 in subjects of European ancestry. Thus, while several independent alleles at the CFH locus influence multiple inflammatory‐related 128 disease phenotypes, the strong association signal at this locus with serum MPO levels is primarily due to rs800292 or other tightly linked variants. Complement fixation is well known as a potent trigger of leukocyte activation and degranulation, and serum generation is associated with activation of protease cascades, including complement proteins, such as C3a and C3b 30‐32 . Thus, an association between a genetic variant for a complement protein and serum MPO levels, but not plasma MPO levels, can be mechanistically rationalized and could be related to leukocyte activation and partial degranulation during blood coagulation. A role for the complement system in influencing serum MPO levels is also supported by the results of our gene‐centric analyses, which identified an amino acid substitution in C2 (rs9332739) for serum MPO levels that has also been previously associated with AMD 33 . The minor alleles of rs800292 in CFH and rs9332739 in C2 protect against the development of AMD and are associated with decreased serum MPO levels. The disease protective Ile62 CFH variant (rs800292), which is located within the SCR2 domain, has been shown to increase binding of CFH to C3b and lead to greater inactivation of fluid‐phase and surface‐bound C3b 34 . Presumably, this would reduce complement activation and leukocyte activation during serum separation, thereby leading to decreased MPO release from neutrophils and/or monocytes. This notion is consistent with our functional data showing that the A allele of rs800292, which is associated with decreased MPO levels, is associated with lower serum levels of C3a‐desArg, a cleavage product 129 generated by complement activation. In addition, clotting factors and proteases that activate complement proteins and trigger degranulation of MPO‐rich phagocytes, such as neutrophils, are present in serum but are mostly depleted in plasma. Therefore, CFH and C2 variants that decrease complement activation, either through increased CFH or reduced C2 activity, could be one potential mechanism through which they lead to decreased MPO levels in serum. By analogy, a common Asp42Gly variant of DARC has been associated with serum, but not plasma, concentrations of several pro‐inflammatory chemokines 35 . The mechanism appears to be due to the release of these chemokines during blood coagulation as a result of differential binding to DARC 35 . Whether the association of complement pathway variants with AMD (and other inflammatory diseases) may be causally related to enhanced MPO‐related inflammatory processes remains to be determined. MPO variants, including those identified herein, have not been reported to be associated with AMD, meningococcal susceptibility, IgA‐induced nephropathy, HUS, membranoproliferative glomerulonephritis type II, or SLE, suggesting that circulating MPO levels may not play a causal role in the pathogenesis of such inflammatory diseases. On the other hand, MPO‐induced oxidative damage to lipoproteins and the vascular wall have been implicated in the development of atherosclerosis and its clinical sequelae, and systemic levels of MPO predict cardiovascular risk. Oxidative stress and lipid peroxidation are also involved in 130 other chronic inflammatory diseases, including AMD. In addition to regulating the complement system, CFH is a major binding protein of malodialdehyde, a common lipid peroxidation product 36 . Taken together, these observations may mechanistically link stress‐related oxidative tissue injury to localized regulation of complement activity in a variety of chronic inflammatory disorders. In addition to loci containing genes of the complement system, the region containing SIRPB2 also demonstrated genome‐wide significant association with serum MPO levels in subjects of European ancestry. SIRPB2 is a member of the signal‐regulatory protein (SIRPs) family of transmembrane glycoproteins that are expressed predominantly on myeloid cells and involved in regulation of innate immunity and complement receptor‐mediated phagocytosis 37 . The role of SIRPB2 in antigen‐specific proliferation and activation of T‐cells 38 may provide an additional biological link for its association with serum MPO levels. By comparison, the gene‐ centric analyses in AA subjects identified a promoter variant of DARC (rs2814778) that achieved near genome‐wide significance for association with serum MPO levels. Interestingly, the G allele of this variant is highly prevalent in subjects of African descent and results in the loss of the Duffy antigen on red blood cells, which has been associated with resistance to malaria infection 39 and low neutrophil count 40 . Thus, this is consistent with our observations that the A allele of rs2814778 is associated with increased serum MPO levels since MPO is most abundantly present in neutrophils. 131 As a comparative analysis, we also carried out a GWAS for plasma MPO levels in subjects of European descent. These results identified multiple SNPs in a large ~1Mb interval encompassing MPO on chromosome 17q22 that demonstrated association with both plasma and serum MPO levels. These results are further supported by our gene‐centric analyses, which identified rare MPO SNPs that were also associated with serum MPO levels, including the previously reported rs28730837 Ala332Val substitution 9 . By contrast, the CFH locus only demonstrated association with MPO levels in serum. Based on the LD structure in the chromosome 17q22 region, the four SNPs that we identified are not tightly linked to each other. However, with the exception of rs6503905, which remained strongly associated with plasma MPO levels after taking into account the effect of rs12940923, the association signals with the remaining SNPs were attenuated in the conditional analyses. Of note, rs12940923 is in relatively strong LD (r 2 =0.67) with a ‐463 G>A promoter polymorphism (rs2333227) in MPO that has previously been associated with plasma MPO levels 41 . The association of rs12940923 and rs6503905 with circulating MPO levels is also supported by the directionally consistent and strong cis eQTLs these variants exhibit with MPO expression, even after conditioning on the lead eQTL SNP. Similar cis genetic effects were observed on the expression of other genes at this locus (i.e. RAD51C) as well. Taken together, these data indicate that several SNPs at chromosome 17q22 contribute independently to circulating MPO levels and suggest that this locus may contain a regulatory region(s) with pleiotropic effects on gene expression. 132 Another interesting observation from our analyses of plasma MPO levels is the suggestive association between circulating MPO levels and variants in the vicinity of GALNT2. For example, an intronic variant (rs2144300) of GALNT2, which we identified in both GWAS and gene‐centric analyses for serum MPO levels, has previously been associated with lower HDL levels 42 . GALNT2 is involved in protein O‐linked glycosylation and, while its role in regulating MPO levels is not immediately evident, it is interesting to note that MPO binds to HDL within atherosclerotic lesions via apolipoprotein A‐1 and catalyzes HDL oxidation 43‐45 , thereby impairing its cardio‐protective properties. By comparison, the GWAS for plasma MPO levels identified another SNP (rs12049351) located ~485kb proximal to GALNT2 in between ABCB10 and TAF5L that was suggestively associated with plasma MPO levels. Rs12049351 was also associated with serum MPO levels whereas rs2144300 was not associated with plasma MPO levels. These results suggest that specific alleles in this region may control both serum and plasma MPO levels whereas other independent variants are associated with only levels in serum, possibly through a mechanism related to HDL metabolism. While the results of our analyses have revealed potentially interesting loci that control circulating MPO levels, several limitations of our study should be noted. First, circulating MPO levels could be affected by environmental and/or transient factors, such as physical activity or acute infections, which our analyses did not take into account. Given the apparent effect of serum generation on complement and 133 leukocyte activation, differences in handling and/or processing of blood samples in the various cohorts may have also increased experimental variability and led to additional confounders. Furthermore, GeneBank recruited consecutive patients undergoing elective diagnostic coronary angiography, which would enrich for subjects with CAD, and lead to MPO levels that are on average higher compared to the other population‐based studies. This could explain, in part, the variation seen in circulating MPO levels across cohorts even when using the same assay, and the observed heterogeneity in the association results for some of the identified loci. However, these limitations are somewhat mitigated by our use of a sample‐ weighted Z‐score meta‐analysis method and the high level of significance obtained from the analyses. In summary, our comprehensive genetic studies in two ethnicities identified several unique loci that are associated with either serum or plasma MPO levels. These results indicate a potentially prominent role for the complement system in influencing serum MPO levels, presumably via leukocyte activation. By comparison, independent variants at the MPO locus were strongly associated with plasma MPO levels but modestly associated with serum levels. Despite the clinical association of both plasma and serum MPO levels with both prevalent cardiovascular phenotypes and incident risk for major adverse cardiac events, genetic variants associated with circulating MPO levels do not show association with history of CAD in ~80,000 subjects from the CARDIoGRAM consortium. Additional studies will be needed to 134 gain a better understanding of the functional basis for the association between circulating MPO levels and the identified variants in CFH and MPO, as well as to determine the clinical implications for inflammatory diseases that could be mediated in part through MPO‐related activity. 135 Acknowledgements See the Supplemental data. Disclosures Dr. Hazen (SLH) is named as co‐inventor on pending and issued patents held by the Cleveland Clinic relating to cardiovascular diagnostics. Dr. Hazen reports he has been paid as a consultant or speaker for the following companies: Cleveland Heart Lab, Inc., Esperion, Liposciences Inc., Merck & Co., Inc., and Pfizer Inc. Dr. Hazen reports he has received research funds from Abbott, Cleveland Heart Lab, Esperion and Liposciences, Inc. Dr. Hazen has the right to receive royalty payments for inventions or discoveries related to cardiovascular diagnostics from Abbott Laboratories, Cleveland Heart Lab, Inc., Frantz Biomarkers, LLC, and Siemens. Funding The Cleveland Clinic GeneBank study is supported by NHLBI grants P01HL098055, P01HL076491, R01HL103866, P20HL113452, and R01HL103931. The Cardiovascular Health Study (CHS) was supported by NHLBI contracts N01‐HC‐ 136 85239, N01‐HC‐85079 through N01‐HC‐85086; N01‐HC‐35129, N01 HC‐15103, N01 HC‐55222, N01‐HC‐75150, N01‐HC‐45133, HHSN268201200036C and NHLBI grants HL080295, HL087652, HL105756 with additional contribution from NINDS. Additional support was provided through AG‐023629, AG‐15928, AG‐20098, and AG‐027058 from the NIA. See also http://www.chs‐nhlbi.org/pi.htm. Additional support was provided by R01HL71862. DNA handling and genotyping was supported in part by National Center of Advancing Translational Technologies CTSI grant UL1TR000124 and NIDDK grant DK063491 to the Southern California Diabetes Endocrinology Research Center. [CARe: NHLBI contract HHSN268200960009C]. The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by NHLBI contracts N01‐HC‐55015, N01‐HC‐55016, N01‐HC‐55018, N01‐ HC‐55019, N01‐HC‐55020, N01‐HC‐55021, N01‐HC‐55022 and grant HL087641, NHGRI contract U01HG004402, and NIH contract HHSN268200625226C. The Framingham Heart Study is supported by NHLBI contract N01‐HC‐25195) and Affymetrix, Inc contract N02‐HL‐6‐4278 (for genotyping services), and by grants from NHLBI (HL64753; HL076784), NINDS (NS17950) and NIA (AG08122, AG16495, AG028321. The CARDIA study is supported by NHLBI contracts NO1‐HC‐48047, NO1‐HC‐48048, NO1‐HC‐48049, NO1‐HC‐48050, and NO1‐HC‐95095. Genotyping and statistical analyses of the CARDIA white participants was supported by grants U01‐HG‐004729, U01‐HG‐004446, and U01‐HG‐004424 from the National Human Genome Research Institute. H.A. was supported by R01ES021801 from NIEHS. The MONICA/KORA Augsburg studies were financed by the Helmholtz Zentrum 137 München, German Research Center for Environmental Health, Neuherberg, Germany and supported by grants from the German Federal Ministry of Education and Research (BMBF). Part of this work was financed by the German National Genome Research Network (NGFNPlus, project number 01GS0834) and through additional funds from the University of Ulm. Furthermore, the research was supported within the Munich Center of Health Sciences (MC Health) as part of the Ludwig Maximilians University (LMU) innovative. The Gutenberg Health study (GHS) is funded through the government of Rheinland‐Pflaz (No. AZ 961‐ 386261/733), the research program “Wissen schafft Zuknunft” and the “Schwerpunkt Vaskuläre Prävention” of the Johannes Gutenberg University of Mainz and its contract with Boehringer Ingelheim and PHILIPS medical systems, including an unrestricted grant for the GHS. This project has also been supported by the National Genome Network “NGFNplus” by the Federal Ministry of Education and Research, Germany (No. 01GS0833 and 01GS0831, projects A3/D1), and by joint funding from the Agence Nationale de la Recherche, France (contract ANR 09 GENO 106 01) and from the Federal Ministry of Education and Research, Germany. LURIC has received funding from the European Community's Seventh Framework Programme (FP7/2007‐2013) under grant agreement no. 201668; AtheroRemo. 138 References 1. Arnhold, J. and Flemmig, J. (2010) Human myeloperoxidase in innate and acquired immunity. Arch. Biochem. Biophys., 500, 92‐106. 2. Nicholls, S.J. and Hazen, S.L. (2005) Myeloperoxidase and cardiovascular disease. Arterioscler. Thromb. Vasc. Biol., 25, 1102‐1111. 3. Nicholls, S.J., Zheng, L. and Hazen, S.L. (2005) Formation of dysfunctional high‐density lipoprotein by myeloperoxidase. Trends Cardiovasc. Med., 15, 212‐219. 4. Nicholls, S.J. and Hazen, S.L. (2009) Myeloperoxidase, modified lipoproteins, and atherogenesis. J. Lipid Res., 50 Suppl, S346‐351. 5. Brennan ML, Penn MS, Van Lente F, Nambi V, Shishehbor MH, Aviles RJ, Goormastic M, Pepoy ML, McErlean ES, Topol EJ, Nissen SE, Hazen SL (2003) Prognostic value of myeloperoxidase in patients with chest pain. N Engl J Med. 349:1595‐1604. 6. Baldus, S., Heeschen, C., Meinertz, T., Zeiher, A.M., Eiserich, J.P., Munzel, T., Simoons, M.L. and Hamm, C.W. (2003) Myeloperoxidase serum levels predict risk in patients with acute coronary syndromes. Circulation, 108, 1440‐1445. 7. Tang, W.H., Tong, W., Troughton, R.W., Martin, M.G., Shrestha, K., Borowski, A., Jasper, S., Hazen, S.L. and Klein, A.L. (2007) Prognostic value and echocardiographic determinants of plasma myeloperoxidase levels in chronic heart failure. J. Am. Coll. Cardiol., 49, 2364‐2370. 8. Karakas, M. and Koenig, W. (2012) Myeloperoxidase production by macrophage and risk of atherosclerosis. Curr Atheroscler Rep, 14, 277‐283. 9. Schnabel RB, Lunetta KL, Larson MG, Dupuis J, Lipinska I, Rong J, Chen MH,Zhao Z, Yamamoto JF, Meigs JB, Nicaud V, Perret C, Zeller T, Blankenberg S, Tiret L, Keaney JF Jr, Vasan RS, Benjamin EJ. (2009) The relation of genetic and environmental factors to systemic inflammatory biomarker concentrations. Circ Cardiovasc Genet. 2, 229‐237. 139 10. Walker, A.E., Seibert, S.M., Donato, A.J., Pierce, G.L. and Seals, D.R. (2010) Vascular endothelial function is related to white blood cell count and myeloperoxidase among healthy middle‐aged and older adults. Hypertension, 55, 363‐369. 11. Tang, W.H., Wu, Y., Nicholls, S.J. and Hazen, S.L. (2011) Plasma myeloperoxidase predicts incident cardiovascular risks in stable patients undergoing medical management for coronary artery disease. Clin. Chem., 57, 33‐39. 12. Hoy, A., Tregouet, D., Leininger‐Muller, B., Poirier, O., Maurice, M., Sass, C., Siest, G., Tiret, L. and Visvikis, S. (2001) Serum myeloperoxidase concentration in a healthy population: biological variations, familial resemblance and new genetic polymorphisms. Eur. J. Hum. Genet., 9, 780‐ 786. 13. Romano, M., Dri, P., Dadalt, L., Patriarca, P. and Baralle, F.E. (1997) Biochemical and molecular characterization of hereditary myeloperoxidase deficiency. Blood, 90, 4126‐4134. 14. Marchetti, C., Patriarca, P., Solero, G.P., Baralle, F.E. and Romano, M. (2004) Genetic characterization of myeloperoxidase deficiency in Italy. Hum. Mutat., 23, 496‐505. 15. Kutter, D., Devaquet, P., Vanderstocken, G., Paulus, J.M., Marchal, V. and Gothot, A. (2000) Consequences of total and subtotal myeloperoxidase deficiency: risk or benefit ? Acta Haematol., 104, 10‐15. 16. Nikpoor, B., Turecki, G., Fournier, C., Theroux, P. and Rouleau, G.A. (2001) A functional myeloperoxidase polymorphic variant is associated with coronary artery disease in French‐Canadians. Am. Heart J., 142, 336‐339. 17. Wainstein, R.V., Wainstein, M.V., Ribeiro, J.P., Dornelles, L.V., Tozzati, P., Ashton‐Prolla, P., Ewald, I.P., Vietta, G. and Polanczyk, C.A. (2010) Association between myeloperoxidase polymorphisms and its plasma levels with severity of coronary artery disease. Clin. Biochem., 43, 57‐62. 18. Ergen, A., Isbir, S., Timirci, O., Tekeli, A. and Isbir, T. (2011) Effects of myeloperoxidase ‐463 G/A gene polymorphism and plasma levels on coronary artery disease. Mol. Biol. Rep., 38, 887‐891. 140 19. Asselbergs, F.W., Reynolds, W.F., Cohen‐Tervaert, J.W., Jessurun, G.A. and Tio, R.A. (2004) Myeloperoxidase polymorphism related to cardiovascular events in coronary artery disease. Am. J. Med., 116, 429‐430. 20. Arakawa S, Takahashi A, Ashikawa K, Hosono N, Aoi T, Yasuda M, Oshima Y, Yoshida S, Enaida H, Tsuchihashi T, Mori K, Honda S, Negi A, Arakawa A, Kadonosono K, Kiyohara Y, Kamatani N, Nakamura Y, Ishibashi T, Kubo M (2011) Genome‐wide association study identifies two susceptibility loci for exudative age‐related macular degeneration in the Japanese population. Nat. Genet., 43, 1001‐1004. 21. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, Bracken MB, Ferris FL, Ott J, Barnstable C,Hoh J ( 2005) Complement factor H polymorphism in age‐ related macular degeneration. Science. 308, 385‐389. 22. Neale BM, Fagerness J, Reynolds R, Sobrin L, Parker M, Raychaudhuri S, Tan PL, Oh EC, Merriam JE, Souied E, Bernstein PS, Li B, Frederick JM, Zhang K, Brantley MA Jr, Lee AY, Zack DJ, Campochiaro B, Campochiaro P, Ripke S, Smith RT, Barile GR, Katsanis N, Allikmets R, Daly MJ, Seddon JM (2010) Genome‐wide association study of advanced age‐related macular degeneration identifies a role of the hepatic lipase gene (LIPC). Proc Natl Acad Sci USA. 107, 7395‐7400. 23. Chen W, Stambolian D, Edwards AO, Branham KE, Othman M, Jakobsdottir J, Tosakulwong N, Pericak‐Vance MA, Campochiaro PA, Klein ML, Tan PL, Conley YP, Kanda A, Kopplin L, Li Y, Augustaitis KJ, Karoukis AJ, Scott WK, Agarwal A, Kovach JL, Schwartz SG, Postel EA, Brooks M, Baratz KH, Brown WL; Complications of Age‐Related Macular Degeneration Prevention Trial Research Group, Brucker AJ, Orlin A, Brown G, Ho A, Regillo C, Donoso L, Tian L, Kaderli B, Hadley D, Hagstrom SA, Peachey NS, Klein R, Klein BE, Gotoh N, Yamashiro K, Ferris Iii F, Fagerness JA, Reynolds R, Farrer LA, Kim IK, Miller JW, Cortón M, Carracedo A, Sanchez‐Salorio M, Pugh EW, Doheny KF, Brion M, Deangelis MM, Weeks DE, Zack DJ, Chew EY, Heckenlively JR, Yoshimura N, Iyengar SK, Francis PJ, Katsanis N, Seddon JM, Haines JL, Gorin MB, Abecasis GR, Swaroop A (2010) Genetic variants near TIMP3 and high‐ density lipoprotein‐associated loci influence susceptibility to age‐related macular degeneration. Proc Natl Acad Sci USA. 107, 7401‐7406. 141 24. Kopplin LJ, Igo RP Jr, Wang Y, Sivakumaran TA, Hagstrom SA, Peachey NS, Francis PJ, Klein ML, SanGiovanni JP, Chew EY, Pauer GJ, Sturgill GM, Joshi T, Tian L, Xi Q, Henning AK, Lee KE, Klein R, Klein BE, Iyengar SK (2010) Genome‐wide association identifies SKIV2L and MYRIP as protective factors for age‐related macular degeneration. Genes Immun. 11, 609‐621. 25. Davila S, Wright VJ, Khor CC, Sim KS, Binder A, Breunis WB, Inwald D, Nadel S, Betts H, Carrol ED, de Groot R, Hermans PW, Hazelzet J, Emonts M, Lim CC, Kuijpers TW, Martinon‐Torres F, Salas A, Zenz W, Levin M, Hibberd ML, International Meningococcal Genetics Consortium (2010) Genome‐wide association study identifies variants in the CFH region associated with host susceptibility to meningococcal disease. Nat. Genet., 42, 772‐776. 26. Gharavi AG, Kiryluk K, Choi M, Li Y, Hou P, Xie J, Sanna‐Cherchi S, Men CJ, Julian BA, Wyatt RJ, Novak J, He JC, Wang H, Lv J, Zhu L, Wang W, Wang Z, Yasuno K, Gunel M, Mane S, Umlauf S, Tikhonova I, Beerman I, Savoldi S, Magistroni R, Ghiggeri GM, Bodria M, Lugani F, Ravani P, Ponticelli C, Allegri L, Boscutti G, Frasca G, Amore A, Peruzzi L, Coppo R, Izzi C, Viola BF, Prati E, Salvadori M, Mignani R, Gesualdo L, Bertinetto F, Mesiano P, Amoroso A, Scolari F, Chen N, Zhang H, Lifton RP (2011) Genome‐wide association study identifies susceptibility loci for IgA nephropathy. Nat. Genet. 43, 321‐327. 27. Caprioli J, Noris M, Brioschi S, Pianetti G, Castelletti F, Bettinaglio P, Mele C, Bresin E, Cassis L, Gamba S, Porrati F, Bucchioni S, Monteferrante G, Fang CJ, Liszewski MK, Kavanagh D, Atkinson JP, Remuzzi G, International Registry of Recurrent and Familial HUS/TTP (2006) Genetics of HUS: the impact of MCP, CFH, and IF mutations on clinical presentation, response to treatment, and outcome. Blood. 108, 1267‐1279. 28. Abrera‐Abeleda MA, Nishimura C, Smith JL, Sethi S, McRae JL, Murphy BF, Silvestri G, Skerka C, Józsi M, Zipfel PF, Hageman GS, Smith RJ (2006) Variations in the complement regulatory genes factor H (CFH) and factor H related 5 (CFHR5) are associated with membranoproliferative glomerulonephritis type II (dense deposit disease). J Med Genet. 43, 582‐ 589. 142 29. Zhao J, Wu H, Khosravi M, Cui H, Qian X, Kelly JA, Kaufman KM, Langefeld CD, Williams AH, Comeau ME, Ziegler JT, Marion MC, Adler A, Glenn SB, Alarcón‐Riquelme ME; BIOLUPUS Network; GENLES Network, Pons‐Estel BA, Harley JB, Bae SC, Bang SY, Cho SK, Jacob CO, Vyse TJ, Niewold TB, Gaffney PM, Moser KL, Kimberly RP, Edberg JC, Brown EE, Alarcon GS, Petri MA, Ramsey‐Goldman R, Vilá LM, Reveille JD, James JA, Gilkeson GS, Kamen DL, Freedman BI, Anaya JM, Merrill JT, Criswell LA, Scofield RH, Stevens AM, Guthridge JM, Chang DM, Song YW, Park JA, Lee EY, Boackle SA, Grossman JM, Hahn BH, Goodship TH, Cantor RM, Yu CY, Shen N, Tsao BP (2011) Association of genetic variants in complement factor H and factor H‐related genes with systemic lupus erythematosus susceptibility. PLoS Genet. 7, e1002079. 30. Goldstein, I.M., Roos, D., Kaplan, H.B. and Weissmann, G. (1975) Complement and immunoglobulins stimulate superoxide production by human leukocytes independently of phagocytosis. J. Clin. Invest., 56, 1155‐ 1163. 31. Baehner, R.L. (1975) Microbe ingestion and killing by neutrophils: normal mechanisms and abnormalities. Clin. Haematol., 4, 609‐633. 32. Boxer, L.A. and Smolen, J.E. (1988) Neutrophil granule constituents and their release in health and disease. Hematol. Oncol. Clin. North Am., 2, 101‐134. 33. Yu Y, Bhangale TR, Fagerness J, Ripke S, Thorleifsson G, Tan PL, Souied EH, Richardson AJ, Merriam JE, Buitendijk GH, Reynolds R, Raychaudhuri S, Chin KA, Sobrin L, Evangelou E, Lee PH, Lee AY, Leveziel N, Zack DJ, Campochiaro B, Campochiaro P, Smith RT, Barile GR, Guymer RH, Hogg R, Chakravarthy U, Robman LD, Gustafsson O, Sigurdsson H, Ortmann W, Behrens TW, Stefansson K, Uitterlinden AG, van Duijn CM, Vingerling JR, Klaver CC, Allikmets R, Brantley MA Jr, Baird PN, Katsanis N, Thorsteinsdottir U, Ioannidis JP, Daly MJ, Graham RR, Seddon JM (2011) Common variants near FRK/COL10A1 and VEGFA are associated with advanced age‐related macular degeneration. Hum Mol Genet. 20, 3699‐3709. 34. Tortajada, A., Montes, T., Martinez‐Barricarte, R., Morgan, B.P., Harris, C.L. and de Cordoba, S.R. (2009) The disease‐protective complement factor H allotypic variant Ile62 shows increased binding affinity for C3b and enhanced cofactor activity. Hum. Mol. Genet., 18, 3452‐3461. 143 35. Schnabel RB, Baumert J, Barbalic M, Dupuis J, Ellinor PT, Durda P, Dehghan A, Bis JC, Illig T, Morrison AC, Jenny NS, Keaney JF Jr, Gieger C, Tilley C,Yamamoto JF, Khuseyinova N, Heiss G, Doyle M, Blankenberg S, Herder C, Walston JD, Zhu Y, Vasan RS, Klopp N, Boerwinkle E, Larson MG, Psaty BM, Peters A, Ballantyne CM, Witteman JC, Hoogeveen RC, Benjamin EJ, Koenig W, Tracy RP (2010) Duffy antigen receptor for chemokines (Darc) polymorphism regulates circulating concentrations of monocyte chemoattractant protein‐1 and other inflammatory mediators. Blood. 115, 5289‐5299. 36. Weismann D, Hartvigsen K, Lauer N, Bennett KL, Scholl HP, Charbel Issa P, Cano M, Brandstätter H, Tsimikas S, Skerka C, Superti‐Furga G, Handa JT, Zipfel PF, Witztum JL, Binder CJ (2011) Complement factor H binds malondialdehyde epitopes and protects from oxidative stress. Nature. 478, 76‐81. 37. Oldenborg, P.A., Gresham, H.D. and Lindberg, F.P. (2001) CD47‐signal regulatory protein alpha (SIRPalpha) regulates Fcgamma and complement receptor‐mediated phagocytosis. J. Exp. Med., 193, 855‐862. 38. Piccio, L., Vermi, W., Boles, K.S., Fuchs, A., Strader, C.A., Facchetti, F., Cella, M. and Colonna, M. (2005) Adhesion of human T cells to antigen‐presenting cells through SIRPbeta2‐CD47 interaction costimulates T‐cell proliferation. Blood, 105, 2421‐2427. 39. Hadley, T.J. and Peiper, S.C. (1997) From malaria to chemokine receptor: the emerging physiologic role of the Duffy blood group antigen. Blood, 89, 3077‐ 3091. 40. Reich D, Nalls MA, Kao WH, Akylbekova EL, Tandon A, Patterson N, Mullikin J, Hsueh WC, Cheng CY, Coresh J, Boerwinkle E, Li M, Waliszewska A, Neubauer J, Li R, Leak TS, Ekunwe L, Files JC, Hardy CL, Zmuda JM, Taylor HA, Ziv E, Harris TB, Wilson JG (2009) Reduced neutrophil count in people of African descent is due to a regulatory variant in the Duffy antigen receptor for chemokines gene. PLoS Genet. 5, e1000360. 41. Reynolds, W.F., Chang, E., Douer, D., Ball, E.D. and Kanda, V. (1997) An allelic association implicates myeloperoxidase in the etiology of acute promyelocytic leukemia. Blood, 90, 2730‐2737. 144 42. Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, Clarke R, Heath SC, Timpson NJ, Najjar SS, Stringham HM, Strait J, Duren WL, Maschio A, Busonero F, Mulas A, Albai G, Swift AJ, Morken MA, Narisu N, Bennett D, Parish S, Shen H, Galan P, Meneton P, Hercberg S, Zelenika D, Chen WM, Li Y, Scott LJ, Scheet PA, Sundvall J, Watanabe RM, Nagaraja R, Ebrahim S, Lawlor DA, Ben‐Shlomo Y, Davey‐Smith G, Shuldiner AR, Collins R, Bergman RN, Uda M, Tuomilehto J, Cao A, Collins FS, Lakatta E, Lathrop GM, Boehnke M, Schlessinger D, Mohlke KL, Abecasis GR (2008) Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet. 40, 161‐169. 43. Zheng L, Nukuna B, Brennan ML, Sun M, Goormastic M, Settle M, Schmitt D, Fu X, Thomson L, Fox PL, Ischiropoulos H, Smith JD, Kinter M, Hazen SL (2004) Apolipoprotein A‐I is a selective target for myeloperoxidase‐catalyzed oxidation and functional impairment in subjects with cardiovascular disease. J Clin Invest. 114, 529‐541. 44. Wu, Z., Wagner, M.A., Zheng, L., Parks, J.S., Shy, J.M., 3rd, Smith, J.D., Gogonea, V. and Hazen, S.L. (2007) The refined structure of nascent HDL reveals a key functional domain for particle maturation and dysfunction. Nat Struct Mol Biol, 14, 861‐868. 45. Undurti, A., Huang, Y., Lupica, J.A., Smith, J.D., DiDonato, J.A. and Hazen, S.L. (2009) Modification of high density lipoprotein by myeloperoxidase generates a pro‐inflammatory particle. J. Biol. Chem., 284, 30825‐30835. 46. Musunuru K, Lettre G, Young T, Farlow DN, Pirruccello JP, Ejebe KG, Keating BJ, Yang Q, Chen MH, Lapchyk N, Crenshaw A, Ziaugra L, Rachupka A, Benjamin EJ, Cupples LA, Fornage M, Fox ER, Heckbert SR, Hirschhorn JN, Newton‐Cheh C, Nizzari MM, Paltoo DN, Papanicolaou GJ, Patel SR, Psaty BM, Rader DJ, Redline S, Rich SS, Rotter JI, Taylor HA Jr, Tracy RP, Vasan RS, Wilson JG, Kathiresan S, Fabsitz RR, Boerwinkle E, Gabriel SB, NHLBI Candidate Gene Association Resource (2010) Candidate gene association resource (CARe): design, methods, and proof of concept. Circ Cardiovasc Genet. 3, 267‐275. 145 47. Keating BJ, Tischfield S, Murray SS, Bhangale T, Price TS, Glessner JT, GalverL, Barrett JC, Grant SF, Farlow DN, Chandrupatla HR, Hansen M, Ajmal S, Papanicolaou GJ, Guo Y, Li M, Derohannessian S, de Bakker PI, Bailey SD, Montpetit A, Edmondson AC, Taylor K, Gai X, Wang SS, Fornage M, Shaikh T, GroopL, Boehnke M, Hall AS, Hattersley AT, Frackelton E, Patterson N, Chiang CW, Kim CE, Fabsitz RR, Ouwehand W, Price AL, Munroe P, Caulfield M, Drake T, Boerwinkle E, Reich D, Whitehead AS, Cappola TP, Samani NJ, Lusis AJ, Schadt E, Wilson JG, Koenig W, McCarthy MI, Kathiresan S, Gabriel SB, Hakonarson H, Anand SS, Reilly M, Engert JC, Nickerson DA, Rader DJ, Hirschhorn JN, Fitzgerald GA (2008) Concept, design and implementation of a cardiovascular gene‐centric 50 k SNP array for large‐scale genomic association studies. PLoS One. 3, e3583. 48. Zeller T, Wild P, Szymczak S, Rotival M, Schillert A, Castagne R, Maouche S, Germain M, Lackner K, Rossmann H, Eleftheriadis M, Sinning CR, Schnabel RB, Lubos E, Mennerich D, Rust W, Perret C, Proust C, Nicaud V, Loscalzo J, Hübner N, Tregouet D, Münzel T, Ziegler A, Tiret L, Blankenberg S, Cambien F (2010) Genetics and beyond‐‐the transcriptome of human monocytes and disease susceptibility. PLoS One. 5, e10693. 49. Willer, C.J., Li, Y. and Abecasis, G.R. (2010) METAL: fast and efficient meta‐ analysis of genomewide association scans. Bioinformatics, 26, 2190‐2191. 146 50. Schunkert H, König IR, Kathiresan S, Reilly MP, Assimes TL, Holm H, Preuss M, Stewart AF, Barbalic M, Gieger C, Absher D, Aherrahrou Z, Allayee H, Altshuler D, Anand SS, Andersen K, Anderson JL, Ardissino D, Ball SG, Balmforth AJ, Barnes TA, Becker DM, Becker LC, Berger K, Bis JC, Boekholdt SM, Boerwinkle E, Braund PS, Brown MJ, Burnett MS, Buysschaert I; Cardiogenics, Carlquist JF, Chen L, Cichon S, Codd V, Davies RW, Dedoussis G, Dehghan A, Demissie S, Devaney JM, Diemert P, Do R, Doering A, Eifert S, Mokhtari NE, Ellis SG, Elosua R, Engert JC, Epstein SE, de Faire U, Fischer M, Folsom AR, Freyer J, Gigante B, Girelli D, Gretarsdottir S, Gudnason V, Gulcher JR, Halperin E, Hammond N, Hazen SL, Hofman A, Horne BD, Illig T, Iribarren C, Jones GT, Jukema JW, Kaiser MA, Kaplan LM, Kastelein JJ, Khaw KT, Knowles JW, Kolovou G, Kong A, Laaksonen R, Lambrechts D, Leander K, Lettre G, Li M, Lieb W, Loley C, Lotery AJ, Mannucci PM, Maouche S, Martinelli N, McKeown PP, Meisinger C, Meitinger T, Melander O, Merlini PA, Mooser V, Morgan T, Mühleisen TW, Muhlestein JB, Münzel T, Musunuru K, Nahrstaedt J, Nelson CP, Nöthen MM, Olivieri O, Patel RS, Patterson CC, Peters A, Peyvandi F, Qu L, Quyyumi AA, Rader DJ, Rallidis LS, Rice C, Rosendaal FR, Rubin D, Salomaa V, Sampietro ML, Sandhu MS, Schadt E, Schäfer A, Schillert A, Schreiber S, Schrezenmeir J, Schwartz SM, Siscovick DS, Sivananthan M, Sivapalaratnam S, Smith A, Smith TB, Snoep JD, Soranzo N, Spertus JA, Stark K, Stirrups K, Stoll M, Tang WH, Tennstedt S, Thorgeirsson G, Thorleifsson G, Tomaszewski M, Uitterlinden AG, van Rij AM, Voight BF, Wareham NJ, Wells GA, Wichmann HE, Wild PS, Willenborg C, Witteman JC, Wright BJ, Ye S, Zeller T, Ziegler A, Cambien F, Goodall AH, Cupples LA, Quertermous T, März W, Hengstenberg C, Blankenberg S, Ouwehand WH, Hall AS, Deloukas P, Thompson JR, Stefansson K, Roberts R, Thorsteinsdottir U, O'Donnell CJ, McPherson R, Erdmann J; CARDIoGRAM Consortium, Samani NJ (2011) Large‐scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet. Mar 6;43(4):333‐8. 147 Figure Legends Figure 1. Results of GWAS for serum MPO levels in subjects of European ancestry. The Q‐Q (A) and Manhattan (B) plots are shown for the meta‐analysis of 9305 subjects from the GeneBank, CHS, FHS, and CARDIA cohorts. Figure 2. Regional plots for loci demonstrating significant association with serum MPO levels on chromosomes 1q31.3 (A), 6p21.32 (B), and 20p13 (C). For each locus, a 1Mb region is shown, centered on the lead SNP (purple diamond). Genes in the selected intervals are indicated in the bottom panel. Figure 3. Results of GWAS for plasma MPO levels in subjects of European ancestry. The Q‐Q (A) and Manhattan (B) plots are shown for the meta‐analysis of 9260 subjects from the GeneBank, GHS I, GHS II, LURIC1, and LURIC2 cohorts. (C) Regional plot of a 1Mb interval on chromosome 17q22 demonstrates several independent SNPs that exceed the genome‐wide threshold for significance. The bottom panel shows the LD pattern across this region using CEU data from HAPMAP. 148 Figure 4. Serum C3a‐desArg Levels as a Function of CFH rs800292 Genotype. Serum levels of C3a‐desArg are significantly lower in carriers of the A allele compared to GG homozygotes in a dose‐dependent manner. Data were measured in a subset of age‐, sex‐, and CAD status‐matched GeneBank subjects and shown as untransformed mean ± SE. P‐value was obtained using linear regression analyses with natural‐log transformation. 149 Table 1. Description of Cohorts and Datasets Used in this Study. Cohort Ethnicity N Age (y) M/F MPO Levels (pmol/L) Source (Assay) Genotyping Platform Number of SNPs GeneBank European European 2189 2191 62.5 ± 11.2 62.5 ± 11.2 1554/635 1555/636 760.1 ± 719.5 225.5 ± 293.9 Serum (CardioMPO) Plasma (CardioMPO) Affymetrix 6.0 (Imputed) Affymetrix 6.0 (Imputed) 2,421,770 2,421,770 CHS European European AA 2667 3085 643 71.9 ± 5.0 72.2 ± 5.2 72.8 ± 5.6 1028/1639 1327/1758 238/405 345.6 ± 280.1 353.1 ± 318.6 382.1 ± 330.7 Serum (CardioMPO) Serum (CardioMPO) Serum (CardioMPO) Illumina CNV370 (Imputed) IBCv2 SNP array IBCv2 SNP array 2,397,181 46,423 47,046 FHS European European 2940 2660 61.0 ± 9.5 61.0 ± 9.5 1359/1581 1218/1442 334.3 ± 218.8 335.4 ± 220.1 Serum (OXIS) Serum (OXIS) Affymetrix 500K (Imputed) IBCv2 SNP array 2,486,777 46,930 CARDIA European European AA 1509 1262 1047 25.6 ± 3.3 40.7 ± 3.3 39.5 ± 3.8 701/808 580/682 440/607 137.0 ± 97.0 139.1 ± 98.2 108.4 ± 85.6 Serum (CardioMPO) Serum (CardioMPO) Serum (CardioMPO) Affymetrix 6.0 (Imputed) IBCv2 SNP array IBCv2 SNP array 2,409,479 46,506 46,346 MONICA/ KORA European 1328 52.7 ± 10.6 700/628 134.2 ± 67.5 Serum (Mercodia) IBCv2 SNP array 44,142 150 GHS I GHS II European European 2996 1178 55.8 ± 10.9 55.1 ± 10.9 1453/1543 589/589 327.7 ± 189.6 290.9 ± 132.7 Plasma (CardioMPO) Plasma (CardioMPO) Affymetrix 6.0 (Imputed) Affymetrix 6.0 (Imputed) 2,614,503 2,612,433 LURIC1 LURIC2 European European 794 2100 59.9 ± 12.0 63.5 ± 10.0 523/271 1488/610 241.2 ± 71.0 174.0 ± 61.6 Plasma (Immundiagnostik) Plasma (Immundiagnostik) Affymetrix 500K (Imputed) Affymetrix 6.0 (Imputed) 5,979,070 6,420,716 151 Table 2. Significant and Suggestive Loci Associated with Decreased Circulating MPO Levels in GWAS and Gene‐Centric Analyses. Locus (Nearest Gene(s)) Lead SNP * Position (bp) † Effect/ Other Allele EAF Beta (SE) p‐value MPO Levels Ethnicity Analysis I 2 ‐ statistic 1q31.3 (CFH) rs800292 194,908,856 A/G 0.23 ‐0.15 (0.01) 4.89x10 ‐41 Serum European GWAS 89.7 6p21.32 (NOTCH4‐C2‐HLA) rs3134931 32,298,598 T/C 0.73 ‐0.05 (0.01) 1.49x10 ‐08 Serum European GWAS 0 20p13 (SIRPB2) rs6042507 1,407,060 A/C 0.11 ‐0.09 (0.02) 4.30x10 ‐08 Serum European GWAS 38.5 1q42.13 (GALNT2) rs2144300 228,361,539 C/T 0.39 ‐0.05 (0.01) 2.52x10 ‐6 Serum European GWAS 34.5 8p21.3 (ATP6V1B2‐LZTS1) rs1390943 20,126,170 G/T 0.33 ‐0.05 (0.01) 9.38x10 ‐7 Serum European GWAS 30.9 1q31.3 (CFH) rs6680396 194,899,093 G/A 0.22 ‐0.13 (0.01) 6.65x10 ‐43 Serum European IBC 88.5 6p21.32 (C2) rs9332739 32,011,783 C/G 0.044 ‐0.12 (0.02) 4.83x10 ‐10 Serum European IBC 0 17q22 (MPO) rs28730837 53,710,396 T/C 0.014 ‐0.27 (0.04) 5.21x10 ‐12 Serum European IBC 21.6 17q22 (MPO) rs35897051 53,703,225 C/A 0.007 ‐0.31 (0.06) 3.32x10 ‐8 Serum European IBC 63.9 17q22 (TRIM37) rs8081967 54,427,483 T/C 0.36 ‐0.05 (0.01) 2.13x10 ‐6 Serum European IBC 37.5 1q42.13 (GALNT2) rs2144300 228,361,539 C/T 0.39 ‐0.04 (0.01) 2.52x10 ‐7 Serum European IBC 0 1q31.3 (CFH) rs505102 194,886,125 G/A 0.71 ‐0.15 (0.03) 1.05x10 ‐8 Serum AA IBC 0 1q23.3 (DARC) rs2814778 157,441,307 G/A 0.78 ‐0.21 (0.04) 6.48x10 ‐8 Serum AA IBC 44.3 17q22 (C17orf71) rs6503905 54,642,236 A/G 0.37 ‐0.06 (0.01) 2.94x10 ‐12 Plasma European GWAS 39.7 17q22 (RNF43) rs2680701 53,793,300 G/A 0.81 ‐0.06 (0.01) 4.98x10 ‐10 Plasma European GWAS 42.4 17q22 (PPM1E) rs9911753 54,337,956 G/A 0.39 ‐0.05 (0.01) 1.51x10 ‐9 Plasma European GWAS 44.7 17q22 (MPO) rs12940923 53,724,848 A/T 0.84 ‐0.07 (0.01) 3.85x10 ‐9 Plasma European GWAS 44.5 1q42.13 (ABCB10‐TAF5L‐ URB2‐GALNT2) rs12049351 227,784,624 C/G 0.79 ‐0.05 (0.01) 1.08x10 ‐6 Plasma European GWAS 0 * SNP base pair (bp) positions are given according to NCBI build 36.1 of the reference human genome sequence. † The allele that lowers MPO levels is referred to as the effect allele. Units for betas are natural log transformed circulating MPO levels in pmol/L. 152 Table 3. Pleiotropic Associations of Chromosome 17q22 SNPs with Gene Expression in Monocytes. Gene SNP Effect/Other Allele Beta (SE) p‐value *Conditioned Beta (SE) *Conditioned p‐value MPO Levels MPO rs12940923 A/T ‐0.25 (0.02) 8.49x10 ‐35 ‐‐ ‐‐ Plasma rs2680701 G/A ‐0.19 (0.02) 7.42x10 ‐25 ‐0.04 (0.03) 0.191 Plasma rs6503905 A/G ‐0.08 (0.02) 4.32 x10 ‐7 ‐0.04 (0.02) 0.007 Plasma rs9911753 G/A ‐0.07 (0.01) 3.36x10 ‐7 ‐0.02 (0.01) 0.089 Plasma rs8081967 T/C ‐0.07 (0.01) 3.57x10 ‐6 ‐0.02 (0.01) 0.115 Serum SEPT4 rs9911753 G/A ‐0.09 (0.02) 7.43x10 ‐7 ‐‐ ‐‐ Plasma rs8081967 T/C ‐0.08 (0.02) 8.64x10 ‐6 ‐0.01 (0.05) 0.769 Serum RAD51C rs9911753 G/A ‐0.09 (0.01) 1.92x10 ‐39 ‐‐ ‐‐ Plasma rs8081967 T/C ‐0.09 (0.01) 2.16x10 ‐35 ‐0.02 (0.02) 0.369 Serum rs6503905 A/G ‐0.09 (0.01) 5.18x10 ‐30 ‐0.03 (0.01) 0.003 Plasma rs2680701 G/A ‐0.05 (0.01) 3.65x10 ‐7 ‐0.10 (0.01) 4.84x10 ‐28 Plasma rs12940923 A/T ‐0.05 (0.01) 3.29x10 ‐6 ‐0.10 (0.01) 1.69x10 ‐21 Plasma TRIM37 rs8081967 T/C ‐0.05 (0.01) 1.67x10 ‐14 ‐‐ ‐‐ Serum rs9911753 G/A ‐0.04 (0.01) 2.12x10 ‐11 ‐0.02 (0.02) 0.341 Plasma rs6503905 A/G ‐0.04 (0.01) 7.36x10 ‐8 ‐0.001 (0.01) 0.909 Plasma *Conditioned on lead eQTL SNP for each gene. Units for betas are log 2 transformed signal intensities obtained from Illumina HT‐12 v3 microarrays. eQTL results are only shown for SNPs identified in GWAS for plasma MPO levels and for which data were available in the monocyte dataset. 153 Table 4. MPO‐Associated SNPs and Risk of CAD in the CARDIoGRAM Consortium. MPO Levels (Analysis) Locus (Nearest Gene(s)) SNP Allele Frequency OR (95% CI) p‐value n Serum (GWAS) 1q31.3 (CFH) rs800292 G 0.74 0.99 (0.96 ‐ 1.03) 0.71 78,841 Serum (GWAS) 1q42.13 (GALNT2) rs2144300 C 0.40 1.03 (1.0 ‐ 1.06) 0.06 83,756 Serum (GWAS) 8p21.3 (ATP6V1B2‐LZTS1) rs1390943 G 0.33 0.99 (0.96 ‐ 1.02) 0.57 78,176 Serum (IBC) 1q31.3 (CFH) rs6680396 G 0.23 1.01 (0.98 ‐ 1.05) 0.43 81,028 Serum (IBC) 6p21.32 (C2) rs9332739 C 0.08 1.00 (0.94 ‐ 1.07) 0.98 79,862 Serum (IBC) 17q22 (TRIM37) rs8081967 C 0.65 1.01 (0.98 ‐ 1.03) 0.75 80,337 Plasma (GWAS) 17q22 (C17orf71) rs7502947 * G 0.34 0.99 (0.96 ‐ 1.02) 0.37 71,939 Plasma (GWAS) 17q22 (RNF43) rs2680701 G 0.80 1.00 (0.96 ‐ 1.03) 0.91 76,793 Plasma (GWAS) 17q22 (PPM1E) rs9911753 G 0.39 1.00 (0.97 ‐ 1.03) 0.91 82,162 Plasma (GWAS) 17q22 (MPO) rs12940923 T 0.15 1.03 (0.99 ‐ 1.07) 0.16 82,301 Plasma (GWAS) 1q42.13 (ABCB10‐TAF5L‐ URB2‐GALNT2) rs12049351 C 0.80 0.97 (0.94 ‐ 1.01) 0.12 83,470 Results are only shown for index SNPs or proxy variants that were available in the CARDIoGRAM Consortium. * Used as proxy for rs6503905 (r 2 = 0.70). 154 -log 10 (observed p-value) -log 10 (expected p-value) A Figure 1. 155 CFH C2-NOTCH4-HLA SIRPB2 GALNT2 ATP6V1B2-LZTS1 B Figure 1. 156 A Figure 2. 157 B Figure 2. 158 C Figure 2. 159 -log 10 (observed p-value) -log 10 (expected p-value) A Figure 3. 160 B MPO-BZRAP1-RNF43-PPM1E-C17orf71 ABCB10-TAF5L-URB2-GALNT2 Figure 3. 161 C Figure 3. 162 0 1000 2000 3000 4000 5000 6000 7000 rs800292 Genotype GA (n=57) p=0.04 AA (n=57) GG (n=57) C3a desArg Levels (ng/ml) Figure 4. 163 Supplemental Material Genome‐wide and Gene‐Centric Analyses of Circulating Myeloperoxidase Levels in the CHARGE and CARe Consortia Authors: Alexander P. Reiner et al. 164 Supplemental Methods Detailed Description of Cohorts Used GeneBank Study: The Cleveland Clinic GeneBank study is a single site sample repository generated from consecutive patients undergoing elective diagnostic coronary angiography or elective cardiac computed tomographic angiography with extensive clinical and laboratory characterization and longitudinal observation. Subject recruitment occurred between 2001 and 2006. Ethnicity was self‐reported and information regarding demographics, medical history, and medication use was obtained by patient interviews and confirmed by chart reviews. All clinical outcome data were verified by source documentation. Coronary artery disease (CAD) was defined as adjudicated diagnoses of stable or unstable angina, myocardial infarction (MI) (adjudicated definition based on defined electrocardiographic changes or elevated cardiac enzymes), angiographic evidence of ≥ 50% stenosis of one or more major epicardial vessel, and/or a history of known CAD (documented MI, CAD, or history of revascularization). The GeneBank Study has been used previously for discovery and replication of novel genes and risk factors for atherosclerotic disease 1‐4 . Serum and plasma MPO levels were measured in samples obtained upon entry into GeneBank. All patients provided written informed consent prior to being enrolled in GeneBank and the study was approved by the Institutional Review Board of the Cleveland Clinic. 165 Coronary Artery Risk Development in Young Adults (CARDIA): CARDIA is a longitudinal study of the evolution of coronary heart disease risk, started in 1985‐86 in 5,115 African American and European American men and women, then aged 18‐ 30 years 5 . The CARDIA sample was recruited at random during 1985‐86 primarily from geographically based populations in Birmingham AL, Chicago IL, and Minneapolis MN and, in Oakland, CA, from the membership of the Kaiser‐ Permanente Health Plan. Examinations after baseline were year 2 (1987‐88, n=4624, 90% retention), year 5 (1990‐91, n=4352, 85% retention), year 7 (1992‐93, n=4086, 80% retention), year 10 (1995‐96, n=3950, 79% retention), year 15 (2000‐ 2001, n=3672, 74% retention) and year 20 (2005‐06, n=3549, 72% retention). Serum MPO activity levels were measured at the year 15 visit. Written informed consent was obtained from participants at each examination and all study protocols were approved by the institutional review boards of the participating institutions. For the present analysis, genotype data and serum MPO levels were available in 1509 subjects. Cardiovascular Health Study (CHS): The CHS is a population‐based, observational study of risk factors for clinical and subclinical cardiovascular diseases 6 . The study recruited participants 65 years and older from 4 US communities (Forsyth County, North Carolina; Sacramento County, California; Washington County, Maryland; and Pittsburgh, Pennsylvania) in 2 phases: 5201 participants from 1989 to 1990, and 687 (primarily African American participants) from 1992 to 1993. CHS participants 166 completed standardized clinical examinations and questionnaires at study baseline and at 9 annual follow‐up visits. Follow‐up for clinical events occurs every 6 months. Serum MPO activity was measured at cohort entry on 2667 European Americans. Framingham Heart Study (FHS): The FHS started in 1948 with 5209 randomly ascertained participants from Framingham, MA, who underwent biannual examinations to investigate cardiovascular disease and its risk factors 7 . In 1971, the Offspring cohort was recruited, comprised of 5124 children of the original cohort and the children’s spouses 8 , followed by the Third Generation cohort consisting of 4095 children of the Offspring cohort in 2002. Serum MPO was measured in 2940 FHS participants of European ancestry at the seventh exam (1998‐2001) of the Offspring cohort. The MONItoring of trends and determinants in CArdiovascular disease/Cooperative Health Research in the Region of Augsburg (MONICA/KORA). The MONICA/KORA study consists of a series of independent population‐based epidemiological surveys of participants living in the region of Augsburg, Southern Germany 9 and was initially conducted to estimate the prevalence and distribution of cardiovascular risk factors among individuals aged 25 to 74 years as part of the World Health Organization MONICA project. All survey participants were residents of German nationality identified through the registration office and were examined in 1984/85 (S1), 1989/90 (S2) and 1994/95 (S3). All participants underwent 167 standardized examinations including blood withdrawals for serum/plasma and DNA isolation. The Gutenberg Health Study (GHS): The GHS was initiated in 2007 as a community‐ based, prospective cohort study with participants between the age 35 and 74 years 10 . All participants were randomly drawn from the local registry offices in the city of Mainz and the district of Mainz‐Bingen. The sample was stratified according to sex (50% women) and in equal number for decades of age. A large variety of non‐invasive cardiovascular phenotypes have been assessed and blood samples were drawn for biomarker measurements and genetic analyses. Genetic analysis was conducted in 3463 individuals in 2008 (GHS I) and 1439 individuals in 2009 (GHS II). The present analysis was based on 2,996 (GHS I) and 1,178 (GHS II) subjects (total n=4174) on whom plasma MPO measurements and GWAS data were available. All participants gave written consent. The LURIC Study: The Ludwigshafen Risk and Cardiovascular Health (LURIC) study includes consecutive white patients of Caucasian origin (17 to 92 years of age) hospitalized for coronary angiography between June 1997 and May 2001 11 . Clinical indications for angiography were chest pain or non‐invasive tests consistent with myocardial ischemia. To limit clinical heterogeneity, individuals suffering from acute illness other than acute coronary syndromes, chronic non cardiac diseases and a history of malignancy within the five past years were excluded. The present study includes data of 2894 participants for whom genotyping data and plasma 168 MPO levels were available. The study was approved by the ethics review committee at the “Landesärztekammer Rheinland‐Pfalz” and written informed consent was obtained from each of the participants. The CARDIoGRAM Consortium Executive Committee: Sekar Kathiresan 1,2,3 , Muredach P. Reilly 4 , Nilesh J. Samani 5,6 , Heribert Schunkert 7,79 Executive Secretary: Jeanette Erdmann 7,79 Steering Committee: Themistocles L. Assimes 8 , Eric Boerwinkle 9 , Jeanette Erdmann 7,79 Alistair Hall 10 , Christian Hengstenberg 11 , Sekar Kathiresan 1,2,3 , Inke R. König 12 , Reijo Laaksonen 13 , Ruth McPherson 14 , Muredach P. Reilly 4 , Nilesh J. Samani 5,6 , Heribert Schunkert 7,79 , John R. Thompson 15 , Unnur Thorsteinsdottir 16,17 , Andreas Ziegler 12 Statisticians: Inke R. König 12 (chair), John R. Thompson 15 (chair), Devin Absher 18 , Li Chen 19 , L. Adrienne Cupples 20,21 , Eran Halperin 22 , Mingyao Li 23 , Kiran Musunuru 1,2,3 , Michael Preuss 12,7 , Arne Schillert 12 , Gudmar Thorleifsson 16 , Benjamin F. Voight 2,3,24 , George A. Wells 25 Writing group: Themistocles L. Assimes 8 , Panos Deloukas 26 , Jeanette Erdmann 7,79 , Hilma Holm 16 , Sekar Kathiresan 1,2,3 , Inke R. König 12 , Ruth McPherson 14 , Muredach P. 169 Reilly 4 , Robert Roberts 14 , Nilesh J. Samani 5,6 , Heribert Schunkert 7,79 , Alexandre F. R. Stewart 14 ADVANCE: Devin Absher 18 , Themistocles L. Assimes 8 , Stephen Fortmann 8 , Alan Go 27 , Mark Hlatky 8 , Carlos Iribarren 27 , Joshua Knowles 8 , Richard Myers 18 , Thomas Quertermous 8 , Steven Sidney 27 , Neil Risch 28 , Hua Tang 29 CADomics: Stefan Blankenberg 30 *, Tanja Zeller 30 *, Arne Schillert 12 , Philipp Wild 30 , Andreas Ziegler 12 , Renate Schnabel 30 *, Christoph Sinning 30 *, Karl Lackner 31 , Laurence Tiret 32 , Viviane Nicaud 32 , Francois Cambien 32 , Christoph Bickel 30 , Hans J. Rupprecht 30 , Claire Perret 32 , Carole Proust 32 , Thomas Münzel 30 CHARGE: Maja Barbalic 33 , Joshua Bis 34 , Eric Boerwinkle 9 , Ida Yii‐Der Chen 35 , L. Adrienne Cupples 20,21 , Abbas Dehghan 36 , Serkalem Demissie‐Banjaw 37,21 , Aaron Folsom 38 , Nicole Glazer 39 , Vilmundur Gudnason 40,41 , Tamara Harris 42 , Susan Heckbert 43 , Daniel Levy 21 , Thomas Lumley 44 , Kristin Marciante 45 , Alanna Morrison 46 , Christopher J. O´Donnell 47 , Bruce M. Psaty 48 , Kenneth Rice 49 , Jerome I. Rotter 35 , David S. Siscovick 50 , Nicholas Smith 43 , Albert Smith 40,41 , Kent D. Taylor 35 , Cornelia van Duijn 36 , Kelly Volcik 46 , Jaqueline Whitteman 36 , Vasan Ramachandran 51 , Albert Hofman 36 , Andre Uitterlinden 52,36 deCODE: Solveig Gretarsdottir 16 , Jeffrey R. Gulcher 16 , Hilma Holm 16 , Augustine Kong 16 , Kari Stefansson 16,17 , Gudmundur Thorgeirsson 53,17 , Karl Andersen 53,17 , Gudmar Thorleifsson 16 , Unnur Thorsteinsdottir 16,17 170 GERMIFS I and II: Jeanette Erdmann 7,79 , Marcus Fischer 11 , Anika Grosshennig 12,7 , Christian Hengstenberg 11 , Inke R. König 12 , Wolfgang Lieb 54 , Patrick Linsel‐Nitschke 7 , Michael Preuss 12,7 , Klaus Stark 11 , Stefan Schreiber 55 , H.‐Erich Wichmann 56,58,59 , Andreas Ziegler 12 , Heribert Schunkert 7,79 GERMIFS III (KORA): Zouhair Aherrahrou 7,79 , Petra Bruse 7,79 , Angela Doering 56 , Jeanette Erdmann 7,79 , Christian Hengstenberg 11 , Thomas Illig 56 , Norman Klopp 56 , Inke R. König 12 , Patrick Diemert 7 , Christina Loley 12,7 , Anja Medack 7,79 , Christina Meisinger 56 , Thomas Meitinger 57,60 , Janja Nahrstedt 12,7 , Annette Peters 56 , Michael Preuss 12,7 , Klaus Stark 11 , Arnika K. Wagner 7 , H.‐Erich Wichmann 56,58,59 , Christina Willenborg ,7,79 , Andreas Ziegler 12 , Heribert Schunkert 7,79 LURIC/AtheroRemo: Bernhard O. Böhm 61 , Harald Dobnig 62 , Tanja B. Grammer 63 , Eran Halperin 22 , Michael M. Hoffmann 64 , Marcus Kleber 65 , Reijo Laaksonen 13 , Winfried März 63,66,67 , Andreas Meinitzer 66 , Bernhard R. Winkelmann 68 , Stefan Pilz 62 , Wilfried Renner 66 , Hubert Scharnagl 66 , Tatjana Stojakovic 66 , Andreas Tomaschitz 62 , Karl Winkler 64 MIGen: Benjamin F. Voight 2,3,24 , Kiran Musunuru 1,2,3 , Candace Guiducci 3 , Noel Burtt 3 , Stacey B. Gabriel 3 , David S. Siscovick 50 , Christopher J. O’Donnell 47 , Roberto Elosua 69 , Leena Peltonen 49 , Veikko Salomaa 70 , Stephen M. Schwartz 50 , Olle Melander 26 , David Altshuler 71,3 , Sekar Kathiresan 1,2,3 171 OHGS: Alexandre F. R. Stewart 14 , Li Chen 19 , Sonny Dandona 14 , George A. Wells 25 , Olga Jarinova 14 , Ruth McPherson 14 , Robert Roberts 14 PennCATH/MedStar: Muredach P. Reilly 4 , Mingyao Li 23 , Liming Qu 23 , Robert Wilensky 4 , William Matthai 4 , Hakon H. Hakonarson 72 , Joe Devaney 73 , Mary Susan Burnett 73 , Augusto D. Pichard 73 , Kenneth M. Kent 73 , Lowell Satler 73 , Joseph M. Lindsay 73 , Ron Waksman 73 , Christopher W. Knouff 74 , Dawn M. Waterworth 74 , Max C. Walker 74 , Vincent Mooser 74 , Stephen E. Epstein 73 , Daniel J. Rader 75,4 WTCCC: Nilesh J. Samani 5,6 , John R. Thompson 15 , Peter S. Braund 5 , Christopher P. Nelson 5 , Benjamin J. Wright 76 , Anthony J. Balmforth 77 , Stephen G. Ball 78 , Alistair S. Hall 10 , Wellcome Trust Case Control Consortium CARDIoGRAM Affiliations 1 Cardiovascular Research Center and Cardiology Division, Massachusetts General Hospital, Boston, MA, USA; 2 Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA; 3 Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; 4 The Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA, USA; 5 Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, UK; 6 Leicester National Institute for Health 172 Research Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, Leicester, LE3 9QP, UK; 7 Medizinische Klinik II, Universität zu Lübeck, Lübeck, Germany; 8 Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA; 9 University of Texas Health Science Center, Human Genetics Center and Institute of Molecular Medicine, Houston, TX, USA; 10 Division of Cardiovascular and Neuronal Remodelling, Multidisciplinary Cardiovascular Research Centre, Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, UK; 11 Klinik und Poliklinik für Innere Medizin II, Universität Regensburg, Regensburg, Germany; 12 Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany; 13 Science Center, Tampere University Hospital, Tampere, Finland; 14 The John & Jennifer Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada; 15 Department of Health Sciences, University of Leicester, Leicester, UK; 16 deCODE Genetics, 101 Reykjavik, Iceland; 17 University of Iceland, Faculty of Medicine, 101 Reykjavik, Iceland; 18 Hudson Alpha Institute, Huntsville, Alabama, USA; 19 Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, Ontario, Canada, K1Y 4W7; 20 Department of Biostatistics, Boston University School of Public Health, Boston, MA USA; 21 National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA; 22 The Blavatnik School of Computer Science and the Department of Molecular Microbiology and Biotechnology, Tel‐Aviv University, Tel‐Aviv, Israel, and the International Computer Science Institute, Berkeley, CA, USA; 23 Biostatistics and 173 Epidemiology, University of Pennsylvania, Philadelphia, PA, USA; 24 Department of Medicine, Harvard Medical School, Boston, MA, USA; 25 Research Methods, Univ Ottawa Heart Inst; 26 Department of Clinical Sciences, Hypertension and Cardiovascular Diseases, Scania University Hospital, Lund University, Malmö, Sweden; 27 Division of Research, Kaiser Permanente, Oakland, CA, USA; 28 Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA; 29 Dept Cardiovascular Medicine, Cleveland Clinic; 30 Medizinische Klinik und Poliklinik, Johannes‐Gutenberg Universität Mainz, Universitätsmedizin, Mainz, Germany; 31 Institut für Klinische Chemie und Laboratoriumsmediizin, Johannes‐ Gutenberg Universität Mainz, Universitätsmedizin, Mainz, Germany; 32 INSERM UMRS 937, Pierre and Marie Curie University (UPMC, Paris 6) and Medical School, Paris, France; 33 University of Texas Health Science Center, Human Genetics Center, Houston, TX, USA; 34 Cardiovascular Health Resarch Unit and Department of Medicine, University of Washington, Seattle, WA USA; 35 Cedars‐Sinai Medical Center, Medical Genetics Institute, Los Angeles, CA, USA; 36 Erasmus Medical Center, Department of Epidemiology, Rotterdam, The Netherlands; 37 Boston University, School of Public Health, Boston, MA, USA; 38 University of Minnesota School of Public Health, Division of Epidemiology and Community Health, School of Public Health (A.R.F.), Minneapolis, MN, USA; 39 University of Washington, Cardiovascular Health Research Unit and Department of Medicine, Seattle, WA, USA; 40 Icelandic Heart Association, Kopavogur Iceland; 41 University of Iceland, Reykjavik, Iceland; 42 Laboratory of Epidemiology, Demography, and Biometry, 174 Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda MD, USA; 43 University of Washington, Department of Epidemiology, Seattle, WA, USA; 44 University of Washington, Department of Biostatistics, Seattle, WA, USA; 45 University of Washington, Department of Internal Medicine, Seattle, WA, USA; 46 University of Texas, School of Public Health, Houston, TX, USA; 47 National Heart, Lung and Blood Institute, Framingham Heart Study, Framingham, MA and Cardiology Division, Massachusetts General Hospital, Boston, MA, USA; 48 Center for Health Studies, Group Health, Departments of Medicine, Epidemiology, and Health Services, Seattle, WA, USA; 49 The Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridge, UK; 50 Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle; 51 Boston University Medical Center, Boston, MA, USA; 52 Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands; 53 Department of Medicine, Landspitali University Hospital, 101 Reykjavik, Iceland; 54 Boston University School of Medicine, Framingham Heart Study, Framingham, MA, USA; 55 Institut für Klinische Molekularbiologie, Christian‐Albrechts Universität, Kiel, Germany; 56 Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany; 57 Institut für Humangenetik, Helmholtz Zentrum München, Deutsches Forschungszentrum für Umwelt und Gesundheit, Neuherberg, Germany; 58 Institute of Medical Information Science, Biometry and Epidemiology, Ludwig‐Maximilians‐Universität München, Germany; 175 59 Klinikum Grosshadern, Munich, Germany; 60 Institut für Humangenetik, Technische Universität München, Germany; 61 Division of Endocrinology and Diabetes, Graduate School of Molecular Endocrinology and Diabetes, University of Ulm, Ulm, Germany; 62 Division of Endocrinology, Department of Medicine, Medical University of Graz, Austria; 63 Synlab Center of Laboratory Diagnostics Heidelberg, Heidelberg, Germany; 64 Division of Clinical Chemistry, Department of Medicine, Albert Ludwigs University, Freiburg, Germany; 65 LURIC non profit LLC, Freiburg, Germany; 66 Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University Graz, Austria; 67 Institute of Public Health, Social and Preventive Medicine, Medical Faculty Manneim, University of Heidelberg, Germany; 68 Cardiology Group Frankfurt‐Sachsenhausen, Frankfurt, Germany; 69 Cardiovascular Epidemiology and Genetics Group, Institut Municipal d’Investigació Mèdica, Barcelona; Ciber Epidemiología y Salud Pública (CIBERSP), Spain; 70 Chronic Disease Epidemiology and Prevention Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland; 71 Department of Molecular Biology and Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, USA; 72 The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; 73 Cardiovascular Research Institute, Medstar Health Research Institute, Washington Hospital Center, Washington, DC 20010, USA; 74 Genetics Division and Drug Discovery, GlaxoSmithKline, King of Prussia, Pennsylvania 19406, USA; 75 The Institute for Translational Medicine and Therapeutics, School of 176 Medicine, University of Pennsylvania, Philadelphia, PA, USA; 76 Department of Cardiovascular Surgery, University of Leicester, Leicester, UK; 77 Division of Cardiovascular and Diabetes Research, Multidisciplinary Cardiovascular Research Centre, Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, Leeds, LS2 9JT, UK; 78 LIGHT Research Institute, Faculty of Medicine and Health, University of Leeds, Leeds, UK; 79 Deutsches Zentrum für Herz‐Kreislauf‐Forschung (DZHK), Universität zu Lübeck, Lübeck, Germany *Present Address: Department of General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany. CARDIoGRAM Sources of Funding The ADVANCE study was supported by a grant from the Reynold's Foundation and NHLBI grant HL087647. Genetic analyses of CADomics were supported by a research grant from Boehringer Ingelheim. Recruitment and analysis of the CADomics cohort was supported by grants from Boehringer Ingelheim and PHILIPS medical Systems, by the Government of Rheinland‐Pfalz in the context of the “Stiftung Rheinland‐Pfalz für Innovation”, the research program “Wissen schafft Zukunft” and by the Johannes‐Gutenberg 177 University of Mainz in the context of the “Schwerpunkt Vaskuläre Prävention” and the “MAIFOR grant”, by grants from the Fondation de France, the French Ministry of Research, and the Institut National de la Santé et de la Recherche Médicale. The deCODE CAD/MI Study was sponsored by NIH grant, National Heart, Lung and Blood Institute R01HL089650‐02. The German MI Family Studies (GerMIFS I‐III (KORA)) were supported by the Deutsche Forschungsgemeinschaft and the German Federal Ministry of Education and Research (BMBF) in the context of the German National Genome Research Network (NGFN‐2 and NGFN‐plus), the EU funded integrated project Cardiogenics (LSHM‐CT‐2006‐037593) and ENGAGE, and the bi‐national BMBF/ANR funded project CARDomics (01KU0908A). LURIC has received funding from the EU framework 6 funded Integrated Project “Bloodomics” (LSHM‐CT‐2004‐503485), the EU framework 7 funded Integrated Project AtheroRemo (HEALTH‐F2‐2008‐201668) and from Sanofi/Aventis, Roche, Dade Behring/Siemens, and AstraZeneca. The MIGen study was funded by the US National Institutes of Health (NIH) and National Heart, Lung, and Blood Institute’s STAMPEED genomics research program through R01 HL087676. Ron Do from the MIGen study is supported by a Canada Graduate Doctoral Scholarship from the Canadian Institutes of Health Research. Recruitment of PennCATH was supported by the Cardiovascular Institute of the University of Pennsylvania. Recruitment of the MedStar sample was supported in part by the MedStar Research Institute and the Washington Hospital Center and a research grant from GlaxoSmithKline. Genotyping of PennCATH and Medstar was performed at the Center for Applied Genomics at the Children’s Hospital of Philadelphia and supported by GlaxoSmithKline through an Alternate Drug 178 Discovery Initiative research alliance award (M. P. R. and D. J. R.) with the University of Pennsylvania School of Medicine. The Ottawa Heart Genomic Study was supported by CIHR #MOP‐‐82810 (R. R.), CFI #11966 (R. R.), HSFO #NA6001 (R. McP.), CIHR #MOP172605 (R. McP.), CIHR #MOP77682 (A. F. R. S.). The WTCCC Study was funded by the Wellcome Trust. Recruitment of cases for the WTCCC Study was carried out by the British Heart Foundation (BHF) Family Heart Study Research Group and supported by the BHF and the UK Medical Research Council. N. J. S. and S. G. B. hold chairs funded by the British Heart Foundation. N. J. S. and A.H.G are also supported by the Leicester NIHR Biomedical Research Unit in Cardiovascular Disease and the work described in this paper is part of the research portfolio of the Leicester NIHR Biomedical Research Unit. The Age, Gene/Environment Susceptibility Reykjavik Study has been funded by NIH contract N01‐AG‐12100, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). The Cleveland Clinic GeneBank study was supported by NHLBI grants P01HL098055, P01HL076491, R01HL103866, P20HL113452, and R01HL103931. The collection of clinical and sociodemographic data in the Dortmund Health Study was supported by the German Migraine‐ & Headache Society (DMKG) and by unrestricted grants of equal share from Astra Zeneca, Berlin Chemie, Boots Healthcare, Glaxo‐Smith‐Kline, McNeil Pharma (former Woelm Pharma), MSD Sharp & Dohme and Pfizer to the University of Muenster. Blood collection was done through funds from the Institute of Epidemiology and Social Medicine, University of Muenster. 179 The EPIC‐Norfolk study is supported by the Medical Research Council UK and Cancer Research UK. The EpiDREAM study is supported by the Canadian Institutes of Health Research, Heart and Stroke Foundation of Ontario, Sanofi‐Aventis, GlaxoSmithKline and King Pharmaceuticals. Funding for Andrew Lotery from the LEEDS study was provided by the T.F.C. Frost charity and the Macular Disease Society. The Rotterdam Study is supported by the Erasmus Medical Center and Erasmus University Rotterdam; the Netherlands Organization for Scientific Research; the Netherlands Organization for Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly; The Netherlands Heart Foundation; the Ministry of Education, Culture and Science; the Ministry of Health Welfare and Sports; the European Commission (DG XII); and the Municipality of Rotterdam. Support for genotyping was provided by the Netherlands Organization for Scientific Research (NWO) (175.010.2005.011, 911.03.012), the Netherlands Genomics Initiative (NGI)/ NWO project nr. 050‐060‐810 and Research Institute for Diseases in the Elderly (RIDE). Abbas Dehghan is supported by a grant from NWO (Vici, 918‐76‐ 619). The SAS study was funded by the British Heart Foundation. The Swedish Research Council, the Swedish Heart & Lung Foundation and the Stockholm County Council (ALF) supported the SHEEP study. SMILE was funded by the Netherlands Heart foundation (NHS 92345). Dr Rosendaal is a recipient of the Spinoza Award of the Netherlands Organisation for Scientific Research (NWO) which was used for part of this work. 180 The Verona Heart Study was funded by grants from the Italian Ministry of University and Research, the Veneto Region, and the Cariverona Foundation, Verona. The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts N01‐HC‐ 55015, N01‐HC‐55016, N01‐HC‐55018, N01‐HC‐55019, N01‐HC‐55020, N01‐HC‐ 55021, and N01‐HC‐55022. The authors thank the staff and participants of the ARIC study for their important contributions. The KORA (Kooperative Gesundheitsforschung in der Region Augsburg) research platform was initiated and financed by the Helmholtz Zentrum München ‐ National Research Center for Environmental Health, which is funded by the German Federal Ministry of Education, Science, Research and Technology and by the State of Bavaria. Part of this work was financed by the German National Genome Research Network (NGFN‐2 and NGFNPlus) and within the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ. Work described in this paper is part of the research portfolio supported by the Leicester NIHR Biomedical Research Unit in Cardiovascular Disease. This work forms part of the research themes contributing to the translational research portfolio of Barts and the London Cardiovascular Biomedical Research Unit which is supported and funded by the National Institute of Health Research. 181 Supplemental References 1. Bhattacharyya T, Nicholls SJ, Topol EJ, Zhang R, Yang X, Schmitt D, Fu X, Shao M, Brennan DM, Ellis SG, Brennan ML, Allayee H, Lusis AJ, Hazen SL (2008) Relationship of paraoxonase 1 (PON1) gene polymorphisms and functional activity with systemic oxidative stress and cardiovascular risk. JAMA. 299: 1265‐76. 2. Hartiala J, Li D, Conti DV, Vikman S, Patel Y, Tang WH, Brennan ML, Newman JW, Stephensen CB, Armstrong P, Hazen SL, Allayee H (2011) Genetic contribution of the leukotriene pathway to coronary artery disease. Hum Genet. 129:617‐27. 3. Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, Dugar B, Feldstein AE, Britt EB, Fu X, Chung YM, Wu Y, Schauer P, Smith JD, Allayee H, Tang WH, DiDonato JA, Lusis AJ, Hazen SL. (2011) Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. 472(7341):57–63. 4. Tang, W.H., Wu, Y., Hartiala, J., Fan, Y., Stewart, A.F., Roberts, R., McPherson, R., Fox, P.L., Allayee, H. and Hazen, S.L. (2012) Clinical and genetic association of serum ceruloplasmin with cardiovascular risk. Arterioscler. Thromb. Vasc. Biol., 32, 516‐522. 5. Friedman, G.D., Cutter, G.R., Donahue, R.P., Hughes, G.H., Hulley, S.B., Jacobs, D.R., Jr., Liu, K. and Savage, P.J. (1988) CARDIA: study design, recruitment, and some characteristics of the examined subjects. J. Clin. Epidemiol., 41, 1105‐1116. 6. Fried, L.P., Borhani, N.O., Enright, P., Furberg, C.D., Gardin, J.M., Kronmal, R.A., Kuller, L.H., Manolio, T.A., Mittelmark, M.B., Newman, A. et al. (1991) The Cardiovascular Health Study: design and rationale. Ann. Epidemiol., 1, 263‐276. 7. Dawber, T.R., Meadors, G.F. and Moore, F.E., Jr. (1951) Epidemiological approaches to heart disease: the Framingham Study. Am. J. Public Health Nations Health, 41, 279‐281. 8. Feinleib, M., Kannel, W.B., Garrison, R.J., McNamara, P.M. and Castelli, W.P. (1975) The Framingham Offspring Study. Design and preliminary data. Prev. Med., 4, 518‐525. 182 9. Holle, R., Happich, M., Lowel, H. and Wichmann, H.E. (2005) KORA‐‐a research platform for population based health research. Gesundheitswesen, 67 Suppl 1, S19‐25. 10. Zeller T, Wild P, Szymczak S, Rotival M, Schillert A, Castagne R, Maouche S,Germain M, Lackner K, Rossmann H, Eleftheriadis M, Sinning CR, Schnabel RB, Lubos E, Mennerich D, Rust W, Perret C, Proust C, Nicaud V, Loscalzo J, Hübner N, Tregouet D, Münzel T, Ziegler A, Tiret L, Blankenberg S, Cambien F (2010) Genetics and beyond‐‐the transcriptome of human monocytes and disease susceptibility. PLoS One. 5, e10693. 11. Winkelmann, B.R., Marz, W., Boehm, B.O., Zotz, R., Hager, J., Hellstern, P. and Senges, J. (2001) Rationale and design of the LURIC study‐‐a resource for functional genomics, pharmacogenomics and long‐term prognosis of cardiovascular disease. Pharmacogenomics, 2, S1‐73. 183 Supplemental Table 1. Detailed Description of Genotyping Data Prior to Imputation for the Cohorts in this Study. Cohort Ethnicity Genotype platform Genotype calling Sample call rate SNP call rate HWE p‐value MAF cutoff # of Genotyped SNPs (Imputation Software) Analysis Package GeneBank European Affymetrix 6.0 Birdseed 0.97 0.95 1.0x10 ‐4 0.01 562,554 (Mach 1.0.16) PLINK CHS European European AA Illumina 370 CNV IBCv2 IBCv2 Beadstudio Beadstudio Beadstudio 0.95 0.95 0.95 0.97 0.95 0.95 1.0x10 ‐5 1.0x10 ‐5 none * none none 306,655 (BIMBAM 0.99) 46,423 (N/A) 47,046 (N/A) R v2.12 PLINK PLINK FHS European European Affymetrix 500K IBCv2 BRLMM Beadstudio 0.97 0.95 0.95 0.95 1.0x10 ‐6 1.0x10 ‐5 0.01 none 378,163 (MACH 1.0.15) 46,930 R kinship GWAF CARDIA European European AA Affymetrix 6.0 IBCv2 IBCv2 Birdseed/BEAGLECALL Beadstudio Beadstudio 0.98 0.95 0.95 0.95 0.95 0.95 1.0x10 ‐4 1.0x10 ‐5 none 0.02 none none 578,568 (Beagle 3.2) 46,506 (N/A) 46,346 (N/A) ProbABEL PLINK PLINK MONICA/KORA European IBCv2 Beadstudio 0.95 0.90 1.0x10 ‐7 none 45,707 (N/A) PLINK GHS I European Affymetrix 6.0 Birdseed 0.97 0.98 1.0x10 ‐4 0.01 662,405 (IMPUTE2) R v2.12 GHS II European Affymetrix 6.0 Birdseed 0.97 0.98 1.0x10 ‐4 0.01 673,914 (IMPUTE2) R v2.12 LURIC1 European Affymetrix 500K BRLMM 0.95 0.95 1.0x10 ‐6 none 393,157 (MACH) PLINK LURIC2 European Affymetrix 6.0 Birdseed 0.95 0.95 1.0x10 ‐6 none 893,909 (MACH) PLINK AA, African American. All cohorts imputed SNPs according to NCBI Build 36 coordinates. *SNPs without observed heterozygotes were excluded. 184 Supplemental Table 2. Characteristics of Loci Demonstrating Association with Serum MPO in GWAS of Subjects with European Ancestry. SNP (Nearest Gene) Parameter GeneBank (n=2189) CHS (n=2667) FHS (n=2940) CARDIA (n=1509) Combined (n=9305) Effect Allele (Frequency) A (0.22) A (0.24) A (0.24) A (0.23) A (0.23) rs800292 (CFH) Beta (SE) ‐0.11 (0.03) ‐0.21 (0.02) ‐0.07 (0.02) ‐0.23 (0.03) ‐0.15 (0.01) p‐value 0.0003 2.60x10 ‐30 9.71x10 ‐5 2.33x10 ‐17 4.89x10 ‐41 Effect Allele (Frequency) C (0.26) C (0.31) C (0.26) C (0.25) C (0.27) rs3134931 (C2‐NOTCH4) Beta (SE) 0.05 (0.03) 0.06 (0.02) 0.07 (0.02) 0.09 (0.04) 0.05 (0.01) p‐value 0.08 0.0006 0.00026 0.02 1.49x10 ‐08 Effect Allele (Frequency) A (0.12) A (0.12) A (0.12) C (0.91) A (0.11) rs6042507 (SIRPB2) Beta (SE) ‐0.03 (0.04) ‐0.13 (0.03) ‐0.07 (0.02) 0.12 (0.06) ‐0.09 (0.02) p‐value 0.50 5.55x10 ‐7 0.0035 0.03 4.30x10 ‐08 Effect Allele (Frequency) C (0.40) C (0.38) C (0.40) C (0.39) C (0.39) rs2144300 (GALNT2) Beta (SE) ‐0.03 (0.03) ‐0.08 (0.02) ‐0.03 (0.02) ‐0.04 (0.02) ‐0.05 (0.01) p‐value 0.28 1.51x10 ‐6 0.09 0.09 2.52x10 ‐6 Effect Allele (Frequency) G (0.32) G (0.35) G (0.33) G (0.35) G (0.33) rs1390943 (ATP6V1B2‐ LZTS1) Beta (SE) ‐0.07 (0.03) ‐0.08 (0.02) ‐0.02 (0.02) ‐0.04 (0.02) ‐0.05 (0.01) p‐value 0.01 3.13x10 ‐6 0.33 0.09 9.38x10 ‐7 Units for betas are natural log transformed circulating MPO levels in pmol/L. 185 Supplemental Table 3. Previously Identified SNPs at the CFH Locus that Demonstrate Association with Serum MPO Levels. SNP Position Nearest Gene Previously Reported Association p‐value *Conditioned p‐value rs800292 194908856 CFH AMD 4.89x10 ‐41 ‐‐ rs1329424 194912799 CFH AMD 2.71x10 ‐24 1.06x10 ‐6 rs1061170 194925860 CFH AMD 1.18x10 ‐18 2.66x10 ‐6 rs10737680 194946078 CFH AMD 1.40x10 ‐30 5.30x10 ‐6 rs6677604 194953541 CFH IgA nephropathy 0.31 9.48x10 ‐5 rs1410996 194963556 CFH AMD 2.58x10 ‐30 7.04x10 ‐6 rs380390 194967674 CFH AMD 1.31x10 ‐23 1.07x10 ‐5 rs1329428 194969433 CFH AMD 4.61x10 ‐30 7.70x10 ‐6 rs426736 195027040 CFHR3 Meningococcal disease 0.151 0.54 SNP position along chromosome 1 is provided according to NCBI build 36 coordinates. AMD, age‐related macular degeneration. *conditioned on rs800892. LD (r 2 ) values between each listed SNP is shown in the figure below. 186 Supplemental Table 4. Characteristics of Loci Demonstrating Association with Serum MPO in Gene‐Centric Analyses of Subjects with European Ancestry (Units for betas are natural log transformed circulating MPO levels in pmol/L). SNP (Nearest Gene(s)) Parameter CHS (n=3085) FHS (n=2660) CARDIA (n=1262) MONICA/KORA (n=1328) Combined (n=8335) Effect Allele (Frequency) G (0.23) G (0.22) G (0.22) G (0.22) G (0.22) rs6680396 (CFH) Beta (SE) ‐0.23 (0.02) ‐0.08 (0.02) ‐0.23 (0.03) ‐0.05 (0.02) ‐0.13 (0.01) p‐value 5.10x10 ‐36 0.000045 4.10x10 ‐15 0.01 6.65x10 ‐43 Effect Allele (Frequency) C (0.041) C (0.043) C (0.042) C (0.049) C (0.044) rs9332739 (C2) Beta (SE) ‐0.18 (0.04) ‐0.13 (0.04) ‐0.21 (0.06) ‐0.03 (0.04) ‐0.12 (0.02) p‐value 5.30x10 ‐6 0.00061 0.00098 0.37 4.83x10 ‐10 Effect Allele (Frequency) T (0.012) T (0.011) T (0.008) T (0.024) T (0.014) rs28730837 (MPO) Beta (SE) ‐0.23 (0.07) ‐0.29 (0.08) ‐0.29 (0.14) ‐0.27 (0.05) ‐0.27 (0.04) p‐value 0.00097 0.00012 0.04 5.65x10 ‐7 5.21x10 ‐12 Effect Allele (Frequency) C (0.007) C (0.011) C (0.010) C (0.002) C (0.007) rs35897051 (MPO) Beta (SE) ‐0.15 (0.10) ‐0.30 (0.09) ‐0.69 (0.14) ‐0.26 (0.18) ‐0.31 (0.06) p‐value 0.12 0.00041 8.10x10 ‐8 0.14 3.32x10 ‐8 Effect Allele (Frequency) T (0.36) T (0.36) T (0.37) T (0.38) T (0.36) rs8081967 (TRIM37) Beta (SE) ‐0.03 (0.02) ‐0.04 (0.02) ‐0.04 (0.03) ‐0.07 (0.02) ‐0.05 (0.01) p‐value 0.10 0.015 0.09 1.11x10 ‐5 2.13x10 ‐6 Effect Allele (Frequency) C (0.39) C (0.39) C (0.39) C (0.41) C (0.39) rs2144300 (GALNT2) Beta (SE) ‐0.06 (0.02) ‐0.03 (0.02) ‐0.05 (0.03) ‐0.03 (0.02) ‐0.04 (0.01) p‐value 0.000016 0.10 0.03 0.03 2.52x10 ‐7 187 Supplemental Table 5. Characteristics of Loci Demonstrating Association with Serum MPO in Gene‐Centric Analyses of African American Subjects. SNP (Nearest Gene) Parameter CHS (n=643) CARDIA (n=1047) Combined (n=1690) Effect Allele (Frequency) A (0.30) A (0.28) A (0.29) rs505102 (CFH) Beta (SE) 0.12 (0.04) 0.16 (0.03) 0.15 (0.03) p‐value 0.0027 6.50x10 ‐7 1.05x10 ‐8 Effect Allele (Frequency) A (0.25) A (0.18) A (0.22) rs2814778 (DARC) Beta (SE) 0.09 (0.06) 0.28 (0.05) 0.21 (0.04) p‐value 0.13 1.0x10 ‐8 6.48x10 ‐8 Effect Allele (Frequency) C (0.15) C (0.014) C (0.014) rs9332739 (C2) Beta (SE) ‐0.22 (0.15) ‐0.30 (0.12) ‐0.27 (0.09) p‐value 0.15 0.01 0.0045 Effect Allele (Frequency) T (0.003) T (0.003) T (0.003) rs28730837 (MPO) Beta (SE) ‐0.15 (0.32) ‐0.50 (0.24) ‐0.37 (0.19) p‐value 0.65 0.04 0.061 Units for betas are natural log transformed circulating MPO levels in pmol/L. 188 Supplemental Table 6. Characteristics of Loci Demonstrating Association with Plasma MPO Levels in GWAS of Subjects with European Ancestry. SNP (Nearest Gene(s)) Parameter CCF (n=2191) GHS I (n=2996) GHS II (n=1178) LURIC1 (n=794) LURIC2 (n=2100) Combined (n=9260) rs6503905 (C17orf7) Effect Allele (Frequency) A (0.35) G (0.63) G (0.64) A (0.38) A (0.38) A (0.37) Beta (SE) ‐0.05 (0.03) 0.06 (0.01) 0.08 (0.02) ‐0.14 (0.05) ‐0.05 (0.03) ‐0.06 (0.01) p‐value 0.07 1.93x10 ‐7 1.98x10 ‐5 0.0087 0.06 2.94x10 ‐12 rs2680701 (RNF43) Effect Allele (Frequency) A (0.18) G (0.81) G (0.82) A (0.22) A (0.19) G (0.81) Beta (SE) 0.02 (0.04) ‐0.06 (0.01) ‐0.07 (0.02) 0.07 (0.06) 0.13 (0.03) ‐0.06 (0.01) p‐value 0.67 8.39x10 ‐7 0.0029 0.23 0.00014 4.98x10 ‐10 rs9911753 (PPM1E) Effect Allele (Frequency) G (0.39) G (0.41) G (0.41) G (0.34) G (0.39) G (0.40) Beta (SE) ‐0.03 (0.03) ‐0.04 (0.01) ‐0.07 (0.02) ‐0.11 (0.05) ‐0.05 (0.03) ‐0.05 (0.01) p‐value 0.30 1.89x10 ‐5 8.13x10 ‐6 0.04 0.05 1.51x10 ‐9 rs12940923 (MPO‐ BZRAP1) Effect Allele (Frequency) T (0.16) T (0.15) T (0.15) T (0.17) T (0.16) T (0.15) Beta (SE) 0.01 (0.04) 0.06 (0.01) 0.08 (0.02) 0.07 (0.06) 0.13 (0.04) 0.07 (0.01) p‐value 0.80 5.96x10 ‐6 0.0019 0.30 0.00014 3.85x10 ‐9 rs12049351 (ABCB10‐ TAF5L‐URB2‐GALNT2) Effect Allele (Frequency) G (0.22) G (0.21) G (0.21) G (0.22) G (0.22) G (0.21) Beta (SE) 0.06 (0.03) 0.05 (0.01) 0.05 (0.02) 0.09 (0.06) 0.02 (0.03) 0.05 (0.01) p‐value 0.06 2.93x10 ‐5 0.01 0.12 0.55 1.08x10 ‐6 Units for betas are natural log transformed circulating MPO levels in pmol/L. 189 Supplemental Table 7. Results of Conditional Analysis for SNPs Significantly Associated with Plasma MPO Levels. SNP rs12940923 rs2680701 rs9911753 rs6503905 rs12940923* ‐‐ 0.01 6.13x10 ‐6 9.07x10 ‐9 rs2680701* 0.11 ‐‐ 3.98x10 ‐5 7.38x10 ‐5 rs9911753* 1.46x10 ‐5 1.26x10 ‐5 ‐‐ 0.00016 rs6503905* 8.22x10 ‐6 7.22x10 ‐6 0.09 ‐‐ Results are shown as p‐values for the association of each SNP with plasma MPO levels, conditioned on the other SNPs (denoted by an asterix). LD (r 2 ) values between each listed SNP is shown in the figure below. 190 Supplemental Table 8. Comparison of Loci Demonstrating Association with Decreased Circulating MPO Levels in Subjects with European Ancestry. Serum MPO Levels Plasma MPO Levels Locus (Nearest Gene(s)) *SNP Analysis Effect/ Other Allele EAF n Direction p‐value n Direction p‐value 1q31.3 (CFH) rs800292 GWAS A/G 0.23 9305 ‐‐‐‐ 4.89x10 ‐41 9260 ++‐‐+ 0.82 6p21.32 (NOTCH4) rs3134931 GWAS T/C 0.73 9305 ‐‐‐‐ 1.49x10 ‐08 9260 ++‐+‐ 0.28 20p13 (SIRPB2) rs6042507 GWAS A/C 0.11 9305 ‐‐‐‐ 4.30x10 ‐08 9260 ‐+‐ 0.52 1q42.13 (GALNT2) rs2144300 GWAS C/T 0.39 9305 ‐‐‐‐ 2.52x10 ‐6 9260 ‐+‐‐‐ 0.57 8p21.3 (ATP6V1B2‐LZTS1) rs1390943 GWAS G/T 0.33 9305 ‐‐‐‐ 9.38x10 ‐7 9260 +‐‐‐+ 0.94 1q31.3 (CFH) rs6680396 IBC G/A 0.22 8335 ‐‐‐‐ 6.65x10 ‐43 9260 ‐‐++‐ 0.91 6p21.32 (C2) rs9332739 IBC C/G 0.044 8335 ‐‐‐‐ 4.83x10 ‐10 9260 +‐‐++ 0.30 17q22 (TRIM37) rs8081967 IBC T/C 0.36 8335 ‐‐‐‐ 2.13x10 ‐6 9260 ‐‐‐‐‐ 4.02x10 ‐7 1q42.13 (GALNT2) rs2144300 IBC C/T 0.39 8335 ‐‐‐‐ 2.52x10 ‐7 9260 ‐+‐‐‐ 0.57 17q22 (C17orf71) rs6503905 GWAS A/G 0.37 9305 ‐‐‐‐ 0.0006 9260 ‐‐‐‐‐ 2.94x10 ‐12 17q22 (RNF43) rs2680701 GWAS G/A 0.81 9305 ‐‐‐‐ 0.0094 9260 ‐‐‐‐‐ 4.98x10 ‐10 17q22 (PPM1E) rs9911753 GWAS G/A 0.39 9305 ‐‐‐‐ 3.99x10 ‐5 9260 ‐‐‐‐‐ 1.51x10 ‐9 17q22 (MPO‐BZRAP1) rs12940923 GWAS A/T 0.84 9305 ‐+‐‐ 0.060 9260 ‐‐‐‐‐ 3.85x10 ‐9 1q42.13 (ABCB10‐TAF5L‐ URB2‐GALNT2) rs12049351 GWAS C/G 0.79 9305 ‐‐‐‐ 0.0008 9260 ‐‐‐‐‐ 1.08x10 ‐6 *Association results are shown for only those SNPs that were available in the serum MPO GWAS/IBCv2 and plasma MPO GWAS datasets. Effect allele refers to the allele that lowers MPO levels. EAF, effect allele frequency. 191 FUTURE CHALLENGES IN CVD GENETICS Concerns related to GWAS Over the last several years, GWAS strategies have been applied to understanding the genetic architecture of hundreds of complex traits, including CVD and CVD‐associated quantitative biomarkers. The fundamental premise behind these studies is that common variants explain a significant fraction of the heritability of CVD since this disease is common in the population. However, there are some additional inherent assumptions in GWAS 1 beyond the common disease‐ common variant hypothesis. First, single SNP associations capture the maximum amount of variation at loci by virtue of its LD with an unknown causal SNP(s), and other nearby SNPs show association because of the LD with the lead SNP. However, allelic heterogeneity and imperfect tagging may result in a scenario where the above assumptions do not hold. Even in the case where there is only single causal variant at a particular locus, a single SNP may not capture the overall variation in that region. Secondly, multiple causal variants may be present at the locus, and thus a single SNP is unlikely to capture all of the LD between the unknown causal variants and those genotyped or imputed SNPs at the locus. Thus, total variation at the locus may be underestimated, if only the lead SNP is considered 1 . For most association studies, causal variants and the underlying mechanisms remain unknown 2‐4 . For example, a chromosome 9p21 locus near the cyclin‐dependent kinase inhibitor 2A and 2B (CDKN2A and CDKN2B, respectively) genes was the first 192 genetic risk factor identified for coronary artery disease (CAD) 5 and myocardial infarction (MI) 6 using an unbiased GWAS approach in subjects of European descent. The association with the 9p21 locus has been replicated with other vascular phenotypes, such as intracranial aneyrysms, intra‐abdominal aortic aneyrysms and stroke. Subsequently, the association between 9p21 and CAD has been replicated in Asians (Japanese, Korean and South Asian populations), but not in African‐ Americans 4 . Although the 9p21 locus is the most strongly associated genetic risk factor for CAD, the mechanism(s) by which it increases risk has yet to be described 4 . Moreover, GWAS to date have identified 50 genetic risk variants for CAD or MI, but the molecular basis for these associations remains unknown for the vast majority of them 4 . Despite the large number of genes identified for CVD using the GWAS approach, none have yet to materialize as novel therapeutic targets. For example, the primary class of drugs used to treat high LDL‐C levels are HMG‐CoA (3‐hydroxy‐ 3‐methylglutaryl‐CoA) reductase inhibitors, collectively referred to as statins 7,8 . These drugs were developed based on a series of studies by Goldstein and Brown 9 , who initially identified mutations in LDL receptor (LDLR) gene as the cause of elevated lipid levels in individuals with familial hypercholesterolemia (FH). Subsequent studies led to the discovery of HMG‐CoA reductase inhibitors, which have been in use for over two decades to reduce the risk of heart attacks and prolonging life in CVD patients by selectively lowering plasma LDL‐C 10 . Although 193 variants of HMD‐CoA reductase were identified for total cholesterol and LDL‐C levels in GWAS, these findings only provided further validation for the role of this enzyme as a drug target to modulate lipid metabolism and CVD risk. More recently, the proprotein convertase subtilisin/kexin type 9 (PCSK9) gene has become the latest pharmacological focal point because loss‐of‐function variants of this gene decrease degradation of LDL receptors, which lower circulating LDL‐C levels 4 . Although PCSK9 variants were also identified through GWAS 3,12,13 , it was first identified by Abifadel et al. 14 through a positional cloning study for a Mendelian form of hyperlipidemia. Clinical trials are underway to validate the efficacy and safety of compounds that inhibit PCSK9, with some promising initial results in lowering LDL levels 11 . Even though LDLR and PCSK9 loci are consistently replicated in GWAS for CVD related traits, they were both identified preceding the GWAS era. Until the mechanisms and biological pathways have been described for newly discovered risk loci, translational applications based on GWAS findings for reducing risk of CVD remains in its infancy. In addition, the identified variants identified usually explain only a small fraction of the variability. For example, heritability estimates for CVD based on epidemiological and family studies vary between be 40% and 60% 8 . Since the variants identified to date explain only ~20% of the expected heritability 4 , fundamental questions that remain unanswered are, where is the rest of the variation or ‘missing heritability’? And the heritability estimates from family studies 194 accurate? Are we ignoring undetected shared environmental factors? Or can the missing heritability be hiding in plain sight? These questions are discussed further below. Small effect size Although ~50 risk loci for CVD have been identified over the past decade through GWAS, the causal variants and underlying mechanisms remain unknown 4 . The effect size of the associated variant at any particular locus is usually very small and explains only a small fraction of the variability at the locus. To overcome the lack of power in many GWAS for identifying variants with small effect size, meta‐ analysis has proven to be an effective tool. Meta‐analyses of GWAS data has identified many more variants for CVD (and other complex traits as well). As an example, the Coronary Artery Disease Genome wide Replication and Meta‐analysis (CARDIoGRAM) consortium performed a meta‐analysis of 14 GWAS for CVD with ~22,000 cases and ~65,000 controls of European descent 3 . This represented the largest GWAS for CVD phenotypes to date and identified 13 novel loci, with confirmation of the 10 previously known loci, including 9p21. Interestingly, only one third of those loci appear to act through traditional risk factors, such as lipid metabolism and hypertension. Some of the risk loci were associated with inflammatory pathways, while the majority are working through unknown 195 mechanisms. Some of the risk variants also had pleiotropic effects and were associated with other human disease traits 3 . However, SNPs discovered using meta‐ GWAS approaches do not explain considerably more of the genetic variation for the risk of developing CVD. Moreover, it is likely that there are multiple independent risk alleles at the known loci. Even under an ideal scenario with near infinite sample sizes, it is not likely that all of the genetic variation associated with CVD will be identified with current methods. For example, GWAS still effectively evaluate association one variant at a time. Another approach that has been used to extend GWAS results involves genetic risk score (GRS) analysis, based on cumulative effects of pre‐selected risk alleles. This approach was described by Kathiresan et al. 12 , and demonstrated that a GRS, based on 8 risk alleles previously identified through a GWAS, was linearly associated with early‐onset MI. Another example of using the GRS method is a study by Patel et al. 15 . They reported that a GRS comprising of 11 predefined CAD and/or MI risk alleles was associated with MI before the age of 70, but did not significantly predict incident adverse events. Alternatively, methods could be applied that allow unbiased multi‐locus associations, beyond genetic risk score analysis. Specifically, ‘lower Manhattan plot’ signals, which do not exceed the threshold for genome‐wide significant association, are currently not considered further in a typical GWAS, but could still represent true associations. Although such variants are not significantly associated individually, it is 196 possible that their contribution to risk of CVD could be detected using methods that take their effects into account simultaneously and in combination. Multiple independent variants Another hypothesis regarding identification of additional susceptibility variants postulates the existence of multiple, independent variants at a single locus. Currently, there are multiple methods available to test unbiased multi‐locus associations, which recent studies have used to support this notion. In the case of lipid levels, Tada et al. 16 identified additional variants at known GWAS loci that account for almost 50% more of the heritability. The method used for this analysis is based on a software package within the Genome‐wide Complex Trait Analysis (GCTA) program, (http://www.complextraitgenomics.com/software/gcta/massoc.html), which can estimate conditional and joint multiple‐SNP association on summary level data 1 . This method first identifies the most significant SNP at each locus in the summary level data, and tests all the remaining variants conditioned on the lead SNP. Secondly, the method selects additional variants, one SNP at a time, based on the conditional analysis and jointly fits them in a linear model, after which the joint multi‐SNP effects can be estimated. A correction for the LD between the SNPs that are tested can be obtained using a reference sample, where individual level data 197 are available 1 . Such methods can detect multiple variants in large‐scale meta‐ analysis throughout the genome, and potentially increase the variance explained based on GWAS data. Another interesting set of software tools are currently being developed by Microsoft Research, termed LIMIX, which is a flexible and efficient framework for genetic analysis of multiple traits. In addition to multi‐locus association, this software is able to perform a joint GWAS on multiple related phenotypes, such as pro‐inflammatory biomarkers, or on multiple isoforms in expression quantitative loci (eQTL) analysis. LIMIX can increase power to detect associations by exploiting phenotype correlations, and use correlation to estimate hidden common causes. Joint GWAS on related traits can not only reveal common causes, but also provide functional evidence. LIMIX is based on linear mixed models, and it accounts for the relatedness (population stratification) in the study sample 17 . Multiple related inflammatory biomarkers are available in GeneBank, and it would be of interest to test whether some of associated alleles are common to these CVD‐associated quantitative variables, which could potentially reveal pathways to pursue in follow up studies. 198 Ethnicity differences in GWAS Populations of European ancestry have been utilized overwhelmingly in GWAS, along with other genetic studies. However, it is important to replicate GWAS using samples from different ethnic populations to confirm the associations, and to refine the LD regions, since LD blocks are shorter in older populations, such as those of African ancestry 18,19 . This is increasingly being carried out for complex traits and an approach we used in our recent studies, both with the PLA2G4A and CFH genes. Post GWAS SNP prioritizing Another important consideration in the post‐GWAS era is prioritization of associated variants for follow‐up functional studies. Such approaches can exploit other information, including biological features (eQTLs, functional predictions, DNase hypersensitive sites, transcription factor binding sites, histone modifications, differential gene expression, etc.). For example, a review by Hou and Zhao 20 cataloged web‐based tools to aid with SNP prioritization, such as SNPranker 2.0 21 and SPOT 22 . These methods contain two main steps. First, annotations or filters are applied based on whether the SNP of interest has anticipated biological features. Second, candidate SNPs are scored by incorporating information from multiple sources 20 . Web‐based tools for variant annotation are also highlighted, which could provide additional valuable information for SNP prioritization. Other annotation 199 tools use an in‐built database to annotate queried variants. As an example, HaploReg 23 provides a user‐friendly web‐interphase annotation tool for genetic variants by considering chromatin state, conservation across mammals, and computationally predicted transcription factor binding sites. Furthermore, HaploReg annotates all variation within a user‐specified LD threshold for the SNP of interest based on information from the 1000 Genomes Project. Another interesting annotation tool is ANNOVAR 24 , which can be used as a stand‐alone software package and easily incorporated into variant analysis pipelines. Additionally, a web‐ based version wANNOVAR 25 is suitable for users who are not familiar with programming skills. SNP prioritizing and annotation tools, such as those described above, can aid in selecting the most likely candidate causal SNPs for follow‐up functional characterization. Functional characterization and causal variant localization Functional experiments can provide evidence for the molecular mechanisms by which gene variants affect disease pathophysiology. Commonly used methods to assess variant functionality include eQTL studies in relevant tissues or cell populations, measuring intermediate inflammatory biomarker levels as function of genotype, and synteny mapping based on mouse QTL studies. However, even if the variant is considered functional, it does not necessarily mean that the variant is also 200 causal. Currently, it is a common practice to augment the resolution of GWAS identified loci with imputation, exome or targeted sequencing, or even utilizing whole sequencing data. Although next‐generation sequencing (NGS) is increasing the ability to detect rare variants not captured by GWAS, identification of causal variants remain challenging 25 . While not immediately obvious, combining GWAS with imputed genotypes or sequencing data can produce differential genotyping error among variants, biasing the evidence for association toward SNPs with better genotyping accuracy 26 . Additionally, if the discovery data (GWAS) is re‐used for fine mapping, this can lead to biased estimates towards the tag SNPs (or SNPs in LD with the tag SNP) and away from the causal SNP 25 . Fay et al. 26 developed a re‐ranking method, BR2, which accounts for these unwanted properties and aids in localizing a causal variant. This re‐ranking procedure provides bias‐reduced estimates based on Bootstrap resampling at the genome‐wide level. This method includes information throughout the entire GWAS, takes into account the LD structure, and ranks each SNP based on the bias. The probability that the causal SNP is the top ranked variant after utilizing the BR2 re‐ranking procedure may even be doubled, compared to traditional analysis methods, given the causal SNP is present in the data set. Since this method is fairly straightforward to use, it can provide another useful tool for localizing causal variants as part of a post‐GWAS evaluation. 201 Rare variants The reduced cost of NGS has enabled the detection of rare and low‐ frequency variants, which are not well captured by current genotyping array platforms. For example, exome sequencing is able to detect rare and common coding sequence variants, whereas the whole genome sequencing can identify coding sequence variants, structural alterations (insertions, deletions, copy number variants and other variation), as well as non‐coding regulatory variants. Although the cost of NGS is considerably lower than a decade ago, it is still relatively expensive to sequence the entire genome for thousands of samples to capture rare variants. For this reason, exome sequencing has been in more widespread use thus far, but it is likely that it will be replaced by whole genome sequencing as costs decrease further. While exome sequencing has been tremendously successful for rare Mendelian diseases or tumor profiling 27,28 , it does not appear to have yielded any further insight into the missing heritability of CVD. For example, a recent exome sequencing study with >9000 subjects with early‐onset MI and older controls, did not reveal any novel genes harboring rare variants that were associated with MI 29 . Similar observations have been reported for other diseases as well 30‐32 . Thus, although, exome and whole genome sequencing could facilitate localizing causal single variants, it is not likely that rare, low‐frequency, structural and non‐coding 202 regulatory variants will explain a significant fraction of the missing heritability for CVD or other complex diseases. Interactions Intuitively, it is plausible that a large proportion of unexplained genetic variance is hidden in various interactions or complex non‐additive effects; and that genetic susceptibility to CVD can arise from interactions between genes and environmental factors (GxE) and/or between genes themselves (GxG). In this regard, nearly all GxG or GxE analyses have focused on a few candidate SNPs that were selected based on previously observed main effect associations. By comparison, GxE and GxG interaction analyses have not been carried out on a genome‐wide basis due to their computationally intensive nature. More recently however, new methods to test genome‐wide interactions have been developed, such as GxEscan 33 for GxE interactions with dichotomous outcomes, and Genome Wide Interaction Search (GWIS) 34 for epistatic interactions with case‐control data. While these new methods have managed to make genome‐wide interaction analyses more feasible, many studies still suffer from the lack of power to detect even single variant GxE or GxG interactions. Since most sample sizes required to detect GxE and GxG interactions on a genome‐wide basis are simply not feasible to obtain, these type of studies will still remain challenging for the foreseeable future. Novel methods and approaches will need to be developed in order to overcome some of these limitations. 203 Gut microbiome A new emerging field for complex diseases is the genomic study of uncultured microorganisms, with a particular focus on the gut microbiome. Gut microbiota are comprised of trillions of typically non‐pathogenic microorganisms that reside in the intestine and affect host metabolism 35 . The gut microbiome serves several purposes, including the development and maintenance of a functional intestine, nutrient metabolism, development and maintenance of a well‐ balanced immune system, secretion of certain vitamins and hormones, and maintenance of an epithelial barrier against harmful pathogens in our intestine 36 . Early microbial exposure (route of delivery, feeding method of the newborn), diet, environment and genetic architecture of the host has been suggested to influence the diversity of the human microbiome 37 . Chronic disturbances (e.g. inflammation, particular dietary pattern, use of antibiotics) can cause lasting changes to gut microbiome composition, such as reduced diversity or specific devastation of some species, which in turn can lead to disease development 38 . Alterations in the gut microbiota have been implicated in the risk of inflammatory bowel disease, obesity, insulin resistance, alterations in immune responses, and, most recently, CVD and atherosclerosis 36,39‐44 . In a series of elegant studies, Hazen and colleagues 36, 44 demonstrated an obligatory role for the gut microbiome in generating a pro‐atherogenic metabolite, trimethylanine N‐oxide (TMAO), derived from either dietary choline or l‐carnitine. 204 These studies demonstrated that plasma TMAO levels in humans were positively associated with the presence of multiple CVD phenotypes, including atherosclerotic plaque burden and future risk of MI, stroke, or death in a dose‐dependent fashion. A similar relationship was observed between plasma TMAO levels and aortic lesion development among various inbred mouse strains 45,Appendix C . Moreover, TMAO supplementation in mice, or dietary supplementation of either choline or L‐carnitine, in the presence of intact gut microbiota led to alterations in cholesterol and sterol metabolism in multiple distinct compartments, including reduction in reverse cholesterol transport, providing a mechanistic rational for the association between TMAO levels and atherosclerotic cardiovascular phenotypes. Taken together, these studies support the notion that gut microbiota play an obligatory role in the formation pro‐atherogenic TMAO from dietary choline and L‐carnitine. They further imply that the interaction between environment, gut microbiome, and host genetic factors could jointly promote inflammation and the production of pro‐ atherogenic metabolites, and thus increase the risk of CVD related traits. Thus, it is possible that variation in gut microbiota represents another potential mechanism that could explain a fraction of the missing heritability for CVD. Although the composition and diversity of the gut microbiome is not completely defined 38,46 NGS technology has enabled sequencing of the gut microbiome, and allowing in‐depth analysis of the gut microbiota 46 . However, there are great inter‐individual differences, and it is presently not entirely clear which bacterial taxa are beneficial and which are harmful 38 . More advances in the field of metagenomics will be required in order to characterize the gut microbiome 205 in further detail, and to evaluate its role in human health. The Human Microbiome Project (HMP) in the United States, and Metagenomics of the Human Intestinal Tract (MetaHIT) in Europe have been established to study this phenomenon. Since bacterial genes in the gut microbiome outnumber genes in human genome by ~150 fold 46 , it is entirely plausible that the missing heritability can be explained, at least in part, by our ‘other genomes’ in the context of inflammation and CVD. 206 References 1. Yang J, Ferreira T, Morris AP, Medland SE; Genetic Investigation of ANthropometric Traits (GIANT) Consortium; DIAbetes Genetics Replication And Meta‐analysis (DIAGRAM) Consortium, Madden PA, Heath AC, Martin NG, Montgomery GW, Weedon MN, Loos RJ, Frayling TM, McCarthy MI, Hirschhorn JN, Goddard ME, Visscher PM (2012) Conditional and joint multiple‐SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet. Mar 18;44(4):369‐75, S1‐3. 2. Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, Nadeau JH (2010) Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet. Jun;11(6):446‐50. 207 3. Schunkert H, König IR, Kathiresan S, Reilly MP, Assimes TL, Holm H, Preuss M, Stewart AF, Barbalic M, Gieger C, Absher D, Aherrahrou Z, Allayee H, Altshuler D, Anand SS, Andersen K, Anderson JL, Ardissino D, Ball SG, Balmforth AJ, Barnes TA, Becker DM, Becker LC, Berger K, Bis JC, Boekholdt SM, Boerwinkle E, Braund PS, Brown MJ, Burnett MS, Buysschaert I; Cardiogenics, Carlquist JF, Chen L, Cichon S, Codd V, Davies RW, Dedoussis G, Dehghan A, Demissie S, Devaney JM, Diemert P, Do R, Doering A, Eifert S, Mokhtari NE, Ellis SG, Elosua R, Engert JC, Epstein SE, de Faire U, Fischer M, Folsom AR, Freyer J, Gigante B, Girelli D, Gretarsdottir S, Gudnason V, Gulcher JR, Halperin E, Hammond N, Hazen SL, Hofman A, Horne BD, Illig T, Iribarren C, Jones GT, Jukema JW, Kaiser MA, Kaplan LM, Kastelein JJ, Khaw KT, Knowles JW, Kolovou G, Kong A, Laaksonen R, Lambrechts D, Leander K, Lettre G, Li M, Lieb W, Loley C, Lotery AJ, Mannucci PM, Maouche S, Martinelli N, McKeown PP, Meisinger C, Meitinger T, Melander O, Merlini PA, Mooser V, Morgan T, Mühleisen TW, Muhlestein JB, Münzel T, Musunuru K, Nahrstaedt J, Nelson CP, Nöthen MM, Olivieri O, Patel RS, Patterson CC, Peters A, Peyvandi F, Qu L, Quyyumi AA, Rader DJ, Rallidis LS, Rice C, Rosendaal FR, Rubin D, Salomaa V, Sampietro ML, Sandhu MS, Schadt E, Schäfer A, Schillert A, Schreiber S, Schrezenmeir J, Schwartz SM, Siscovick DS, Sivananthan M, Sivapalaratnam S, Smith A, Smith TB, Snoep JD, Soranzo N, Spertus JA, Stark K, Stirrups K, Stoll M, Tang WH, Tennstedt S, Thorgeirsson G, Thorleifsson G, Tomaszewski M, Uitterlinden AG, van Rij AM, Voight BF, Wareham NJ, Wells GA, Wichmann HE, Wild PS, Willenborg C, Witteman JC, Wright BJ, Ye S, Zeller T, Ziegler A, Cambien F, Goodall AH, Cupples LA, Quertermous T, März W, Hengstenberg C, Blankenberg S, Ouwehand WH, Hall AS, Deloukas P, Thompson JR, Stefansson K, Roberts R, Thorsteinsdottir U, O'Donnell CJ, McPherson R, Erdmann J; CARDIoGRAM Consortium, Samani NJ (2011) Large‐scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet. Mar 6;43(4):333‐8. 4. Dandona S, Roberts R (2014) The role of genetic risk factors in coronary artery disease. Curr Cardiol Rep. May;16(5):479. 5. McPherson R, Pertsemlidis A, Kavaslar N, Stewart A, Roberts R, Cox DR, Hinds DA, Pennacchio LA, Tybjaerg‐Hansen A, Folsom AR, Boerwinkle E, Hobbs HH, Cohen JC (2007) A common allele on chromosome 9 associated with coronary heart disease. Science. Jun 8;316(5830):1488‐91. 208 6. Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T, Jonasdottir A, Jonasdottir A, Sigurdsson A, Baker A, Palsson A, Masson G, Gudbjartsson DF, Magnusson KP, Andersen K, Levey AI, Backman VM, Matthiasdottir S, Jonsdottir T, Palsson S, Einarsdottir H, Gunnarsdottir S, Gylfason A, Vaccarino V, Hooper WC, Reilly MP, Granger CB, Austin H, Rader DJ, Shah SH, Quyyumi AA, Gulcher JR, Thorgeirsson G, Thorsteinsdottir U, Kong A, Stefansson K (2007) A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science. Jun 8;316(5830):1491‐3. 7. Endo, A,, M. Kuroda, and K. Tanzawa. (1976). Competitive inhibition of 3‐ hydroxy‐3‐methylglutaryl coenzyme A reductase by ML‐236A and ML‐236B, fungal metabolites, having hypocholesterolemic activity. FEBS Lett. 72: 323‐ 326. 8. Roberts R, Stewart AF (2012) Genes and coronary artery disease: where are we? J Am Coll Cardiol. Oct 30;60(18):1715‐21. 9. Goldstein JL, Brown MS (1973) Familial hypercholesterolemia: identification of a defect in the regulation of 3‐hydroxy‐3‐methylglutaryl coenzyme A reductase activity associated with overproduction of cholesterol. Proc Natl Acad Sci U S A. Oct;70(10):2804‐8. 10. Goldstein JL, Brown MS (2009) The LDL receptor. Arterioscler Thromb Vasc Biol. Apr;29(4):431‐8. 11. Roth EM, Diller P (2014) Alirocumab for hyperlipidemia: physiology of PCSK9 inhibition, pharmacodynamics and Phase I and II clinical trial results of a PCSK9 monoclonal antibody. Future Cardiol. Mar;10(2):183‐99. 209 12. Myocardial Infarction Genetics Consortium, Kathiresan S, Voight BF, Purcell S, Musunuru K, Ardissino D, Mannucci PM, Anand S, Engert JC, Samani NJ, Schunkert H, Erdmann J, Reilly MP, Rader DJ, Morgan T, Spertus JA, Stoll M, Girelli D, McKeown PP, Patterson CC, Siscovick DS, O'Donnell CJ, Elosua R, Peltonen L, Salomaa V, Schwartz SM, Melander O, Altshuler D, Ardissino D, Merlini PA, Berzuini C, Bernardinelli L, Peyvandi F, Tubaro M, Celli P, Ferrario M, Fetiveau R, Marziliano N, Casari G, Galli M, Ribichini F, Rossi M, Bernardi F, Zonzin P, Piazza A, Mannucci PM, Schwartz SM, Siscovick DS, Yee J, Friedlander Y, Elosua R, Marrugat J, Lucas G, Subirana I, Sala J, Ramos R, Kathiresan S, Meigs JB, Williams G, Nathan DM, MacRae CA, O'Donnell CJ, Salomaa V, Havulinna AS, Peltonen L, Melander O, Berglund G, Voight BF, Kathiresan S, Hirschhorn JN, Asselta R, Duga S, Spreafico M, Musunuru K, Daly MJ, Purcell S, Voight BF, Purcell S, Nemesh J, Korn JM, McCarroll SA, Schwartz SM, Yee J, Kathiresan S, Lucas G, Subirana I, Elosua R, Surti A, Guiducci C, Gianniny L, Mirel D, Parkin M, Burtt N, Gabriel SB, Samani NJ, Thompson JR, Braund PS, Wright BJ, Balmforth AJ, Ball SG, Hall A; Wellcome Trust Case Control Consortium, Schunkert H, Erdmann J, Linsel‐Nitschke P, Lieb W, Ziegler A, König I, Hengstenberg C, Fischer M, Stark K, Grosshennig A, Preuss M, Wichmann HE, Schreiber S, Schunkert H, Samani NJ, Erdmann J, Ouwehand W, Hengstenberg C, Deloukas P, Scholz M, Cambien F, Reilly MP, Li M, Chen Z, Wilensky R, Matthai W, Qasim A, Hakonarson HH, Devaney J, Burnett MS, Pichard AD, Kent KM, Satler L, Lindsay JM, Waksman R, Knouff CW, Waterworth DM, Walker MC, Mooser V, Epstein SE, Rader DJ, Scheffold T, Berger K, Stoll M, Huge A, Girelli D, Martinelli N, Olivieri O, Corrocher R, Morgan T, Spertus JA, McKeown P, Patterson CC, Schunkert H, Erdmann E, Linsel‐Nitschke P, Lieb W, Ziegler A, König IR, Hengstenberg C, Fischer M, Stark K, Grosshennig A, Preuss M, Wichmann HE, Schreiber S, Hólm H, Thorleifsson G, Thorsteinsdottir U, Stefansson K, Engert JC, Do R, Xie C, Anand S, Kathiresan S, Ardissino D, Mannucci PM, Siscovick D, O'Donnell CJ, Samani NJ, Melander O, Elosua R, Peltonen L, Salomaa V, Schwartz SM, Altshuler D (2009). Genome‐wide association of early‐onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nat Genet. Mar;41(3):334‐41. 13. Kathiresan S, Melander O, Guiducci C, Surti A, Burtt NP, Rieder MJ, Cooper GM, Roos C, Voight BF, Havulinna AS, Wahlstrand B, Hedner T, Corella D, Tai ES, Ordovas JM, Berglund G, Vartiainen E, Jousilahti P, Hedblad B, Taskinen MR, Newton‐Cheh C, Salomaa V, Peltonen L, Groop L, Altshuler DM, Orho‐ Melander M (2008) Six new loci associated with blood low‐density lipoprotein cholesterol, high‐density lipoprotein cholesterol or triglycerides in humans. Nat Genet. Feb;40(2):189‐97. 210 14. Abifadel M, Varret M, Rabès JP, Allard D, Ouguerram K, Devillers M, Cruaud C, Benjannet S, Wickham L, Erlich D, Derré A, Villéger L, Farnier M, Beucler I, Bruckert E, Chambaz J, Chanu B, Lecerf JM, Luc G, Moulin P, Weissenbach J, Prat A, Krempf M, Junien C, Seidah NG, Boileau C (2003) Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat Genet. Jun;34(2):154‐ 6. 15. Patel RS, Sun YV, Hartiala J, Veledar E, Su S, Sher S, Liu YX, Rahman A, Patel R, Rab ST, Vaccarino V, Zafari AM, Samady H, Tang WH, Allayee H, Hazen SL, Quyyumi AA (2012). Association of a genetic risk score with prevalent and incident myocardial infarction in subjects undergoing coronary angiography. Circ Cardiovasc Genet. Aug 1;5(4):441‐9. 16. Tada H, Won HH, Yang J, Peloso G, and Kathiresan S (2014) Multiple associated variants increase the heritability explained for plasma lipids and coronary artery disease. Circ Cardiovasc Genet. In Press. 17. Lippert C, Francesco PC, Rakitsch F, Stegle O (2014) LIMIX: genetic analysis of multiple traits. bioRxiv May 21. http://dx.doi.org/10.1101/003905. 18. International HapMap Consortium (2007). A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861. 19. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TF, McCarroll SA, Visscher PM. (2009) Finding the missing heritability of complex diseases. Nature Oct 8;461(7265):747‐53. 20. Hou L, Zhao H. (2013) A review of post‐GWAS prioritization approaches. Front Genet, 9;4:28. 21. Merelli, I., Calabria, A., Cozzi, P., Viti, F., Mosca, E., and Milanesi, L. (2013). SNPranker 2.0: a gene‐centric data mining tool for diseases associated SNP prioritization in GWAS. BMC Bioinformatics 14:S9. 22. Saccone SF, Bolze R, Thomas P, Quan J, Mehta G, Deelman E, Tischfield JA, Rice JP (2010) SPOT: a web‐based tool for using biological databases to prioritize SNPs after a genome‐wide association study. Nucleic Acids Res. Jul;38(Web Server issue):W201‐9. 211 23. Ward, L. D., and Kellis, M. (2012a). HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934. 24. Wang, K., Li, M., and Hakonarson, H. (2010). ANNOVAR: functional annotation of genetic variants from high‐throughput sequencing data. Nucleic Acids Res. 38, e164. 25. Chang X, Wang K (2012) wANNOVAR: annotating genetic variants for personal genomes via the web. J Med Genet. Jul;49(7):433‐6. 26. Faye LL, Machiela MJ, Kraft P, Bull SB, Sun L (2013) Re‐ranking sequencing variants in the post‐GWAS era for accurate causal variant identification. PLoS Genet. 9(8):e1003609. 27. Rabbani B, Mahdieh N, Hosomichi K, Nakaoka H, Inoue I (2012) Next‐ generation sequencing: impact of exome sequencing in characterizing Mendelian disorders. J Hum Genet. Oct;57(10):621‐32. 28. Maher B (2009). Exome sequencing takes centre stage in cancer profiling. Nature. May 14;459(7244):146‐7. 29. Do R, Stitziel NO, Hong HH, Jørgensen AB, Duga S, Merlini PA, Kiezun K, Farrall M, Goel A, Zuk O, Guella I, Asselta R, Lange LA, Peloso GM, Auer PL, NHLBI Exome Sequencing Project, Girelli D, Martinelli N, Farlow DN, DePristo MA, Roberts R, Stewart AFR, Saleheen D, Danesh J, Epstein SE, Sivapalaratnam S, Hovingh GK, Kastelein JJ, Samani NJ, Schunkert H, Erdmann J, Shah SH, Krauss WE, Davies R, Nikpay M, Johansen CT, Wang J, Hegele RA, Hechter E, Marz W, Kleber ME, Huang J, Johnson AD, Li M, Burke GL, Gross M, Liu Y, Assimes TL, Heiss G, Lange EM, Folsom AR, Taylor HA, Olivieri O, Hamsten A, Clarke R, Reilly DF, Yin W, Rivas MA, Donnelly P, Rossouw JE, Psaty BM, Herrington D, Wilson JG, Rich SS, Bamshad MJ, Tracy RP, Cupples LA, Rader DJ, Reilly MP, Spertus JA, Cresci S, Hartiala J, Tang WH, Hazen SL, Allayee H, Reiner AP, Carlson CS, Kooperberg C, Jackson RD, Boerwinkle E, Lander ES, Schwartz SM, Siscovick DS, McPherson R, Tybjaerg‐ Hansen A, Abecasis GR, Watkins H, Nickerson DA, Ardissino D, Sunyaev SR, O’Donnell CJ, Altshuler D, Gabriel S, Kathiresan S (2014) A rare variant association study with exome sequencing in >9,700 early‐onset myocardial infarction cases and controls. Nature. In Press. 212 30. Cruchaga C, Karch CM, Jin SC, Benitez BA, Cai Y, Guerreiro R, Harari O, Norton J, Budde J, Bertelsen S, Jeng AT, Cooper B, Skorupa T, Carrell D, Levitch D, Hsu S, Choi J, Ryten M; UK Brain Expression Consortium, Hardy J, Ryten M, Trabzuni D, Weale ME, Ramasamy A, Smith C, Sassi C, Bras J, Gibbs JR, Hernandez DG, Lupton MK, Powell J, Forabosco P, Ridge PG, Corcoran CD, Tschanz JT, Norton MC, Munger RG, Schmutz C, Leary M, Demirci FY, Bamne MN, Wang X, Lopez OL, Ganguli M, Medway C, Turton J, Lord J, Braae A, Barber I, Brown K; Alzheimer’s Research UK Consortium, Passmore P, Craig D, Johnston J, McGuinness B, Todd S, Heun R, Kölsch H, Kehoe PG, Hooper NM, Vardy ER, Mann DM, Pickering‐Brown S, Brown K, Kalsheker N, Lowe J, Morgan K, David Smith A, Wilcock G, Warden D, Holmes C, Pastor P, Lorenzo‐Betancor O, Brkanac Z, Scott E, Topol E, Morgan K, Rogaeva E, Singleton AB, Hardy J, Kamboh MI, St George‐Hyslop P, Cairns N, Morris JC, Kauwe JS, Goate AM (2014) Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer's disease. Nature. Jan 23;505(7484):550‐4. 31. Suls A, Jaehn JA, Kecskés A, Weber Y, Weckhuysen S, Craiu DC, Siekierska A, Djémié T, Afrikanova T, Gormley P, von Spiczak S, Kluger G, Iliescu CM, Talvik T, Talvik I, Meral C, Caglayan HS, Giraldez BG, Serratosa J, Lemke JR, Hoffman‐Zacharska D, Szczepanik E, Barisic N, Komarek V, Hjalgrim H, Møller RS, Linnankivi T, Dimova P, Striano P, Zara F, Marini C, Guerrini R, Depienne C, Baulac S, Kuhlenbäumer G, Crawford AD, Lehesjoki AE, de Witte PA, Palotie A, Lerche H, Esguerra CV, De Jonghe P, Helbig I; EuroEPINOMICS RES Consortium (2013) De novo loss‐of‐function mutations in CHD2 cause a fever‐sensitive myoclonic epileptic encephalopathy sharing features with Dravet syndrome. Am J Hum Genet. Nov 7;93(5):967‐75. 32. Rosti RO, Sadek AA, Vaux KK, Gleeson JG. The genetic landscape of autism spectrum disorders. Dev Med Child Neurol. Jan;56(1):12‐8. 33. Gauderman WJ, Zhang P, Morrison JL, Lewinger JP (2014) Finding novel genes by testing G×E interactions in a genome‐wide association study. Genet Epidemiol. 2013 Sep;37(6):603‐13. 34. Goudey B, Rawlinson D, Wang Q, Shi F, Ferra H, Campbell RM, Stern L, Inouye MT, Ong CS, Kowalczyk A (2013) GWIS‐‐model‐free, fast and exhaustive search for epistatic interactions in case‐control GWAS. BMC Genomics. 14 Suppl 3:S10. 213 35. Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, Dugar B, Feldstein AE, Britt EB, Fu X, Chung YM, Wu Y, Schauer P, Smith JD, Allayee H, Tang WH, DiDonato JA, Lusis AJ, Hazen SL. (2011) Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. 472(7341):57–63. 36. Sekirov I, Russell SL, Antunes LC, Finlay BB (2010) Gut microbiota in health and disease. Physiol Rev. 90(3):859–904. 37. Human Microbiome Project Consortium (2012) Structure, function and diversity of the healthy human microbiome. Nature. Jun 13;486(7402):207‐ 14. 38. Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R (2012) Diversity, stability and resilience of the human gut microbiota. Nature. Sep 13;489(7415):220‐30. 39. Sokol H, Seksik P, Rigottier‐Gois L, Lay C, Lepage P, Podglajen I, Marteau P, Doré J (2006) Specificities of the fecal microbiota in inflammatory bowel disease.Inflamm Bowel Dis. 12(2):106–111. 40. Ley RE, Turnbaugh PJ, Klein S, Gordon JI (2006) Microbial ecology: human gut microbes associated with obesity. Nature. Dec 21;444(7122):1022‐3. 41. Reigstad CS, Lundén GO, Felin J, Bäckhed F (2009) Regulation of serum amyloid A3(SAA3) in mouse colonic epithelium and adipose tissue by the intestinal microbiota. PLoS One. Jun 9;4(6):e5842. 42. Dumas ME, Barton RH, Toye A, Cloarec O, Blancher C, Rothwell A, Fearnside J, Tatoud R, Blanc V, Lindon JC, Mitchell SC, Holmes E, McCarthy MI, Scott J, Gauguier D, Nicholson JK (2006) Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin‐resistant mice. Proc Natl Acad Sci U S A. Aug 15;103(33):12511‐6. 43. Li M, Wang B, Zhang M, Rantalainen M, Wang S, Zhou H, Zhang Y, Shen J, Pang X, Zhang M, Wei H, Chen Y, Lu H, Zuo J, Su M, Qiu Y, Jia W, Xiao C, Smith LM, Yang S, Holmes E, Tang H, Zhao G, Nicholson JK, Li L, Zhao L (2008) Symbiotic gut microbes modulate human metabolic phenotypes. Proc Natl Acad Sci U S A. Feb 12;105(6):2117‐22. 214 44. Koeth RA, Wang Z, Levison BS, Buffa JA, Org E, Sheehy BT, Britt EB, Fu X, Wu Y, Li L, Smith JD, DiDonato JA, Chen J, Li H, Wu GD, Lewis JD, Warrier M, Brown JM, Krauss RM, Tang WH, Bushman FD, Lusis AJ, Hazen SL (2013) Intestinal microbiota metabolism of L‐carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med. May;19(5):576‐85. 45. Hartiala J, Bennett BJ, Tang WH, Wang Z, Stewart AF, Roberts R, McPherson R; CARDIoGRAM Consortium, Lusis AJ, Hazen SL, Allayee H (2014) Comparative Genome‐Wide Association Studies in Mice and Humans for Trimethylamine N‐Oxide, a Proatherogenic Metabolite of Choline and L‐ Carnitine. Arterioscler Thromb Vasc Biol. Jun;34(6):1307‐13. 46. Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, Sicheritz‐Ponten T, Turner K, Zhu H, Yu C, Li S, Jian M, Zhou Y, Li Y, Zhang X, Li S, Qin N, Yang H, Wang J, Brunak S, Doré J, Guarner F, Kristiansen K, Pedersen O, Parkhill J, Weissenbach J; MetaHIT Consortium, Bork P, Ehrlich SD, Wang J (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature. Mar 4;464(7285):59‐65. 215 REFERENCES AND BIBLIOGRAPHY Abifadel M, Varret M, Rabès JP, Allard D, Ouguerram K, Devillers M, Cruaud C, Benjannet S, Wickham L, Erlich D, Derré A, Villéger L, Farnier M, Beucler I, Bruckert E, Chambaz J, Chanu B, Lecerf JM, Luc G, Moulin P, Weissenbach J, Prat A, Krempf M, Junien C, Seidah NG, Boileau C (2003) Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat Genet. Jun;34(2):154‐ 6. Abu‐Soud HM, Hazen SL (2000) Nitric oxide is a physiological substrate for mammalian peroxidases. J Biol Chem. 275:37524‐32. Abrera‐Abeleda MA, Nishimura C, Smith JL, Sethi S, McRae JL, Murphy BF, Silvestri G, Skerka C, Józsi M, Zipfel PF, Hageman GS, Smith RJ (2006) Variations in the complement regulatory genes factor H (CFH) and factor H related 5 (CFHR5) are associated with membranoproliferative glomerulonephritis type II (dense deposit disease). J Med Genet. 43, 582‐589. Ahluwalia N, Lin AY, Tager AM, Pruitt IE, Anderson TJ, Kristo F, Shen D, Cruz AR, Aikawa M, Luster AD, Gerszten RE (2007) Inhibited aortic aneurysm formation in BLT1‐deficient mice. J Immunol 179: 691‐7. Aiello RJ, Bourassa PA, Lindsey S, Weng W, Freeman A, Showell HJ (2002) Leukotriene B4 receptor antagonism reduces monocytic foam cells in mice. Arterioscler Thromb Vasc Biol 22: 443‐9. Alberghina M. Phospholipase A(2): new lessons from endothelial cells. Microvasc Res 2010;80:280‐5. Allayee H, Baylin A, Hartiala J, Wijesuriya H, Mehrabian M, Lusis AJ, Campos H (2008) Nutrigenetic association of the 5‐lipoxygenase gene with myocardial infarction. Am J Clin Nutr. 88:934–940. Allayee H, Roth N, Hodis HN (2009) Polyunsaturated fatty acids and cardiovascular disease: implications for nutrigenetics. J Nutrigenet Nutrigenomics. 2(30): 140‐ 8. Asselbergs, F.W., Reynolds, W.F., Cohen‐Tervaert, J.W., Jessurun, G.A. and Tio, R.A. (2004) Myeloperoxidase polymorphism related to cardiovascular events in coronary artery disease. Am. J. Med., 116, 429‐430. 216 Assimes TL, Knowles JW, Priest JR, Basu A, Volcik KA, Southwick A, Tabor HK, Hartiala J, Allayee H, Grove ML, Tabibiazar R, Sidney S, Fortmann SP, Go A, Hlatky M, Iribarren C, Boerwinkle E, Myers R, Risch N, Quertermous T (2008) Common polymorphisms of ALOX5 and ALOX5AP and risk of coronary artery disease. Hum Genet 123: 399‐408. Assmann, G., Cullen, P., Jossa, F., Lewis, B. & Mancini, M (1999) Coronary heart disease: reducing the risk. Arterioscl. Thromb. Vasc. Biol. 19, 1819–1824. Arakawa S, Takahashi A, Ashikawa K, Hosono N, Aoi T, Yasuda M, Oshima Y, Yoshida S, Enaida H, Tsuchihashi T, Mori K, Honda S, Negi A, Arakawa A, Kadonosono K, Kiyohara Y, Kamatani N, Nakamura Y, Ishibashi T, Kubo M (2011) Genome‐ wide association study identifies two susceptibility loci for exudative age‐ related macular degeneration in the Japanese population. Nat. Genet., 43, 1001‐1004. Arnhold, J. and Flemmig, J. (2010) Human myeloperoxidase in innate and acquired immunity. Arch. Biochem. Biophys., 500, 92‐106. Back M, Hansson GK (2006) Leukotriene receptors in atherosclerosis. Ann Med. 38:493–502. Baldus S, Heeschen C, Meinertz T, Zeiher AM, Eiserich JP, Munzel T, Simoons ML, Hamm CW (2003) Myeloperoxidase serum levels predict risk in patients with acute coronary syndromes. Circulation. 108:1440‐5. Baehner, R.L. (1975) Microbe ingestion and killing by neutrophils: normal mechanisms and abnormalities. Clin. Haematol., 4, 609‐633. Baylin A, Kabagambe EK, Ascherio A, Spiegelman D, Campos H. Adipose tissue alpha‐linolenic acid and nonfatal acute myocardial infarction in Costa Rica. Circulation 2003;107:1586‐91. Bevan S, Dichgans M, Wiechmann HE, Gschwendtner A, Meitinger T, Markus HS (2008) Genetic variation in members of the leukotriene biosynthesis pathway confer an increased risk of ischemic stroke: a replication study in two independent populations. Stroke 39: 1109‐14. Bhattacharyya T, Nicholls SJ, Topol EJ, Zhang R, Yang X, Schmitt D, Fu X, Shao M, Brennan DM, Ellis SG, Brennan ML, Allayee H, Lusis AJ, Hazen SL (2008) Relationship of paraoxonase 1 (PON1) gene polymorphisms and functional activity with systemic oxidative stress and cardiovascular risk. Jama 299: 1265‐76. 217 Boxer, L.A. and Smolen, J.E. (1988) Neutrophil granule constituents and their release in health and disease. Hematol. Oncol. Clin. North Am., 2, 101‐134. Brennan ML, Penn MS, Van Lente F, Nambi V, Shishehbor MH, Aviles RJ, Goormastic M, Pepoy ML, McErlean ES, Topol EJ, Nissen SE, Hazen SL (2003) Prognostic value of myeloperoxidase in patients with chest pain. N Engl J Med. 349:1595‐ 1604. Burdon KP, Rudock ME, Lehtinen AB, Langefeld CD, Bowden DW, Register TC, Liu Y, Freedman BI, Carr JJ, Hedrick CC, Rich SS (2010) Human lipoxygenase pathway gene variation and association with markers of subclinical atherosclerosis in the diabetes heart study. Mediators Inflamm. 170153. Caprioli J, Noris M, Brioschi S, Pianetti G, Castelletti F, Bettinaglio P, Mele C, Bresin E, Cassis L, Gamba S, Porrati F, Bucchioni S, Monteferrante G, Fang CJ, Liszewski MK, Kavanagh D, Atkinson JP, Remuzzi G, International Registry of Recurrent and Familial HUS/TTP (2006) Genetics of HUS: the impact of MCP, CFH, and IF mutations on clinical presentation, response to treatment, and outcome. Blood, 108, 1267‐1279. Carlson CS, Heagerty PJ, Nord AS, Pritchard DK, Ranchalis J, Boguch JM, Duan H, Hatsukami TS, Schwartz SM, Rieder MJ, Nickerson DA, Jarvik GP (2007) TagSNP evaluation for the association of 42 inflammation loci and vascular disease: evidence of IL6, FGB, ALOX5, NFKBIA, and IL4R loci effects. Hum Genet. 121: 65‐75. Chang X, Wang K (2012) wANNOVAR: annotating genetic variants for personal genomes via the web. J Med Genet. Jul;49(7):433‐6. Chen W, Stambolian D, Edwards AO, Branham KE, Othman M, Jakobsdottir J, Tosakulwong N, Pericak‐Vance MA, Campochiaro PA, Klein ML, Tan PL, Conley YP, Kanda A, Kopplin L, Li Y, Augustaitis KJ, Karoukis AJ, Scott WK, Agarwal A, Kovach JL, Schwartz SG, Postel EA, Brooks M, Baratz KH, Brown WL; Complications of Age‐Related Macular Degeneration Prevention Trial Research Group, Brucker AJ, Orlin A, Brown G, Ho A, Regillo C, Donoso L, Tian L, Kaderli B, Hadley D, Hagstrom SA, Peachey NS, Klein R, Klein BE, Gotoh N, Yamashiro K, Ferris Iii F, Fagerness JA, Reynolds R, Farrer LA, Kim IK, Miller JW, Cortón M, Carracedo A, Sanchez‐Salorio M, Pugh EW, Doheny KF, Brion M, Deangelis MM, Weeks DE, Zack DJ, Chew EY, Heckenlively JR, Yoshimura N, Iyengar SK, Francis PJ, Katsanis N, Seddon JM, Haines JL, Gorin MB, Abecasis GR, Swaroop A (2010) Genetic variants near TIMP3 and high‐ density lipoprotein‐associated loci influence susceptibility to age‐related macular degeneration. Proc Natl Acad Sci USA. 107, 7401‐7406. 218 Crosslin DR, Shah SH, Nelson SC, Haynes CS, Connelly JJ, Gadson S, Goldschmidt‐ Clermont PJ, Vance JM, Rose J, Granger CB, Seo D, Gregory SG, Kraus WE, Hauser ER (2009) Genetic eVects in the leukotriene biosynthesis pathway and association with atherosclerosis. Hum Genet. 125:217–229. Cruchaga C, Karch CM, Jin SC, Benitez BA, Cai Y, Guerreiro R, Harari O, Norton J, Budde J, Bertelsen S, Jeng AT, Cooper B, Skorupa T, Carrell D, Levitch D, Hsu S, Choi J, Ryten M; UK Brain Expression Consortium, Hardy J, Ryten M, Trabzuni D, Weale ME, Ramasamy A, Smith C, Sassi C, Bras J, Gibbs JR, Hernandez DG, Lupton MK, Powell J, Forabosco P, Ridge PG, Corcoran CD, Tschanz JT, Norton MC, Munger RG, Schmutz C, Leary M, Demirci FY, Bamne MN, Wang X, Lopez OL, Ganguli M, Medway C, Turton J, Lord J, Braae A, Barber I, Brown K; Alzheimer’s Research UK Consortium, Passmore P, Craig D, Johnston J, McGuinness B, Todd S, Heun R, Kölsch H, Kehoe PG, Hooper NM, Vardy ER, Mann DM, Pickering‐Brown S, Brown K, Kalsheker N, Lowe J, Morgan K, David Smith A, Wilcock G, Warden D, Holmes C, Pastor P, Lorenzo‐Betancor O, Brkanac Z, Scott E, Topol E, Morgan K, Rogaeva E, Singleton AB, Hardy J, Kamboh MI, St George‐Hyslop P, Cairns N, Morris JC, Kauwe JS, Goate AM (2014) Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer's disease. Nature. Jan 23;505(7484):550‐4. Dandona S, Roberts R (2014) The role of genetic risk factors in coronary artery disease. Curr Cardiol Rep. May;16(5):479. Daugherty A, Dunn JL, Rateri DL, Heinecke JW (1994) Myeloperoxidase, a catalyst for lipoprotein oxidation, is expressed in human atherosclerotic lesions. J Clin Invest. 94:437‐44. Davila S, Wright VJ, Khor CC, Sim KS, Binder A, Breunis WB, Inwald D, Nadel S, Betts H, Carrol ED, de Groot R, Hermans PW, Hazelzet J, Emonts M, Lim CC, Kuijpers TW, Martinon‐Torres F, Salas A, Zenz W, Levin M, Hibberd ML, International Meningococcal Genetics Consortium (2010) Genome‐wide association study identifies variants in the CFH region associated with host susceptibility to meningococcal disease. Nat. Genet., 42, 772‐776. Dawber, T.R., Meadors, G.F. and Moore, F.E., Jr. (1951) Epidemiological approaches to heart disease: the Framingham Study. Am J Public Health Nations Health. 41, 279‐281. de Bakker PI, Yelensky R, Pe'er I, Gabriel SB, Daly MJ, Altshuler D (2005) Efficiency and power in genetic association studies. Nat Genet. 37: 1217‐23. 219 Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, Thompson JR, Ingelsson E, Saleheen D, Erdmann J, Goldstein BA, Stirrups K, Konig IR, Cazier JB, Johansson A, Hall AS, Lee JY, Willer CJ, Chambers JC, Esko T, Folkersen L, Goel A, Grundberg E, Havulinna AS, Ho WK, Hopewell JC, Eriksson N, Kleber ME, Kristiansson K, Lundmark P, Lyytikainen LP, Rafelt S, Shungin D, Strawbridge RJ, Thorleifsson G, Tikkanen E, Van Zuydam N, Voight BF, Waite LL, Zhang W, Ziegler A, Absher D, Altshuler D, Balmforth AJ, Barroso I, Braund PS, Burgdorf C, Claudi‐Boehm S, Cox D, Dimitriou M, Do R, Doney AS, Mokhtari NE, Eriksson P, Fischer K, Fontanillas P, Franco‐Cereceda A, Gigante B, Groop L, Gustafsson S, Hager J, Hallmans G, Han BG, Hunt SE, Kang HM, Illig T, Kessler T, Knowles JW, Kolovou G, Kuusisto J, Langenberg C, Langford C, Leander K, Lokki ML, Lundmark A, McCarthy MI, Meisinger C, Melander O, Mihailov E, Maouche S, Morris AD, Muller‐Nurasyid M, Nikus K, Peden JF, Rayner NW, Rasheed A, Rosinger S, Rubin D, Rumpf MP, Schafer A, Sivananthan M, Song C, Stewart AF, Tan ST, Thorgeirsson G, Schoot CE, Wagner PJ, Wells GA, Wild PS, Yang TP, Amouyel P, Arveiler D, Basart H, Boehnke M, Boerwinkle E, Brambilla P, Cambien F, Cupples AL, de Faire U, Dehghan A, Diemert P, Epstein SE, Evans A, Ferrario MM, Ferrieres J, Gauguier D, Go AS, Goodall AH, Gudnason V, Hazen SL, Holm H, Iribarren C, Jang Y, Kahonen M, Kee F, Kim HS, Klopp N, Koenig W, Kratzer W, Kuulasmaa K, Laakso M, Laaksonen R, Lind L, Ouwehand WH, Parish S, Park JE, Pedersen NL, Peters A, Quertermous T, Rader DJ, Salomaa V, Schadt E, Shah SH, Sinisalo J, Stark K, Stefansson K, Tregouet DA, Virtamo J, Wallentin L, Wareham N, Zimmermann ME, Nieminen MS, Hengstenberg C, Sandhu MS, Pastinen T, Syvanen AC, Hovingh GK, Dedoussis G, Franks PW, Lehtimaki T, Metspalu A, Zalloua PA, Siegbahn A, Schreiber S, Ripatti S, Blankenberg SS, Perola M, Clarke R, Boehm BO, O'Donnell C, Reilly MP, Marz W, Collins R, Kathiresan S, Hamsten A, Kooner JS, Thorsteinsdottir U, Danesh J, Palmer CN, Roberts R, Watkins H, Schunkert H, Samani NJ (2013) Large‐scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 45:25‐33. 220 Do R, Stitziel NO, Hong HH, Jørgensen AB, Duga S, Merlini PA, Kiezun K, Farrall M, Goel A, Zuk O, Guella I, Asselta R, Lange LA, Peloso GM, Auer PL, NHLBI Exome Sequencing Project, Girelli D, Martinelli N, Farlow DN, DePristo MA, Roberts R, Stewart AFR, Saleheen D, Danesh J, Epstein SE, Sivapalaratnam S, Hovingh GK, Kastelein JJ, Samani NJ, Schunkert H, Erdmann J, Shah SH, Krauss WE, Davies R, Nikpay M, Johansen CT, Wang J, Hegele RA, Hechter E, Marz W, Kleber ME, Huang J, Johnson AD, Li M, Burke GL, Gross M, Liu Y, Assimes TL, Heiss G, Lange EM, Folsom AR, Taylor HA, Olivieri O, Hamsten A, Clarke R, Reilly DF, Yin W, Rivas MA, Donnelly P, Rossouw JE, Psaty BM, Herrington D, Wilson JG, Rich SS, Bamshad MJ, Tracy RP, Cupples LA, Rader DJ, Reilly MP, Spertus JA, Cresci S, Hartiala J, Tang WH, Hazen SL, Allayee H, Reiner AP, Carlson CS, Kooperberg C, Jackson RD, Boerwinkle E, Lander ES, Schwartz SM, Siscovick DS, McPherson R, Tybjaerg‐Hansen A, Abecasis GR, Watkins H, Nickerson DA, Ardissino D, Sunyaev SR, O’Donnell CJ, Altshuler D, Gabriel S, Kathiresan S (2014) A rare variant association study with exome sequencing in >9,700 early‐onset myocardial infarction cases and controls. Nature. In Press. Drazen JM, Israel E, O’Byrne PM (1999) Treatment of asthma with drugs modifying the leukotriene pathway. N Engl J Med. 340:197‐206. [Errata, N Engl J Med 1999; 340:663, 341:1632.] Dumas ME, Barton RH, Toye A, Cloarec O, Blancher C, Rothwell A, Fearnside J, Tatoud R, Blanc V, Lindon JC, Mitchell SC, Holmes E, McCarthy MI, Scott J, Gauguier D, Nicholson JK (2006) Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin‐resistant mice. Proc Natl Acad Sci USA. Aug 15;103(33):12511‐6. Dwyer JH, Allayee H, Dwyer KM, Fan J, Wu H, Mar R, Lusis AJ, Mehrabian M (2004) Arachidonate 5‐lipoxygenase promoter genotype, dietary arachidonic acid, and atherosclerosis. N Engl J Med. 350:29‐37. Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, Nadeau JH (2010) Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet. Jun;11(6):446‐50. Eiserich JP, Baldus S, Brennan ML, Ma W, Zhang C, Tousson A, Castro L, Lusis AJ, Nauseef WM, White CR, Freeman BA (2002) Myeloperoxidase, a leukocyte‐ derived vascular NO oxidase. Science. 296:2391‐4. Endo, A, M. Kuroda, and K. Tanzawa. (1976). Competitive inhibition of 3‐hydroxy‐3‐ methylglutaryl coenzyme A reductase by ML‐236A and ML‐236B, fungal metabolites, having hypocholesterolemic activity. FEBS Lett. 72: 323‐326. 221 Ergen, A., Isbir, S., Timirci, O., Tekeli, A. and Isbir, T. (2011) Effects of myeloperoxidase ‐463 G/A gene polymorphism and plasma levels on coronary artery disease. Mol. Biol. Rep., 38, 887‐891. Faye LL, Machiela MJ, Kraft P, Bull SB, Sun L (2013) Re‐ranking sequencing variants in the post‐GWAS era for accurate causal variant identification. PLoS Genet. 9(8):e1003609. Feinleib, M., Kannel, W.B., Garrison, R.J., McNamara, P.M. and Castelli, W.P. (1975) The Framingham Offspring Study. Design and preliminary data. Prev. Med., 4, 518‐525. Fried, L.P., Borhani, N.O., Enright, P., Furberg, C.D., Gardin, J.M., Kronmal, R.A., Kuller, L.H., Manolio, T.A., Mittelmark, M.B., Newman, A. et al. (1991) The Cardiovascular Health Study: design and rationale. Ann. Epidemiol., 1, 263‐ 276. Freiberg JJ, Tybjaerg‐Hansen A, Sillesen H, Jensen GB, Nordestgaard BG (2008) Promotor polymorphisms in leukotriene C4 synthase and risk of ischemic cerebrovascular disease. Arterioscler Thromb Vasc Biol. 28:990‐6. Freiberg JJ, Dahl M, Tybjaerg‐Hansen A, Grande P, Nordestgaard BG (2009) Leukotriene C4 synthase and ischemic cardiovascular disease and obstructive pulmonary disease in 13,000 individuals. J Mol Cell Cardiol. 46:579‐86. Friedman, G.D., Cutter, G.R., Donahue, R.P., Hughes, G.H., Hulley, S.B., Jacobs, D.R., Jr., Liu, K. and Savage, P.J. (1988) CARDIA: study design, recruitment, and some characteristics of the examined subjects. J. Clin. Epidemiol. 41, 1105‐ 1116. Fu X, Kassim SY, Parks WC, Heinecke JW (2001) Hypochlorous acid oxygenates the cysteine switch domain of pro‐matrilysin (MMP‐7). A mechanism for matrix metalloproteinase activation and atherosclerotic plaque rupture by myeloperoxidase. J Biol Chem. 276:41279‐87. Gauderman WJ, Zhang P, Morrison JL, Lewinger JP (2014) Finding novel genes by testing G×E interactions in a genome‐wide association study. Genet Epidemiol. 2013 Sep;37(6):603‐13. 222 Gharavi AG, Kiryluk K, Choi M, Li Y, Hou P, Xie J, Sanna‐Cherchi S, Men CJ, Julian BA, Wyatt RJ, Novak J, He JC, Wang H, Lv J, Zhu L, Wang W, Wang Z, Yasuno K, Gunel M, Mane S, Umlauf S, Tikhonova I, Beerman I, Savoldi S, Magistroni R, Ghiggeri GM, Bodria M, Lugani F, Ravani P, Ponticelli C, Allegri L, Boscutti G, Frasca G, Amore A, Peruzzi L, Coppo R, Izzi C, Viola BF, Prati E, Salvadori M, Mignani R, Gesualdo L, Bertinetto F, Mesiano P, Amoroso A, Scolari F, Chen N, Zhang H, Lifton RP (2011) Genome‐wide association study identifies susceptibility loci for IgA nephropathy. Nat. Genet. 43, 321‐327. Goldstein, I.M., Roos, D., Kaplan, H.B. and Weissmann, G. (1975) Complement and immunoglobulins stimulate superoxide production by human leukocytes independently of phagocytosis. J. Clin. Invest., 56, 1155‐1163. Goldstein JL, Brown MS (1973) Familial hypercholesterolemia: identification of a defect in the regulation of 3‐hydroxy‐3‐methylglutaryl coenzyme A reductase activity associated with overproduction of cholesterol. Proc Natl Acad Sci USA. Oct;70(10):2804‐8. Goldstein JL, Brown MS (2009) The LDL receptor. Arterioscler Thromb Vasc Biol. Apr;29(4):431‐8. Gonzalez P, Reguero JR, Lozano I, Moris C, Coto E (2007) A functional Sp1/Egr1‐ tandem repeat polymorphism in the 5‐lipoxygenase gene is not associated with myocardial infarction. Int J Immunogenet. 34: 127‐30. Gordon, D. J. & Rifkind, B. M (1989) High‐density lipoprotein—the clinical implications of recent studies. N Engl J Med. 321, 1311–1316. Goudey B, Rawlinson D, Wang Q, Shi F, Ferra H, Campbell RM, Stern L, Inouye MT, Ong CS, Kowalczyk A (2013) GWIS‐‐model‐free, fast and exhaustive search for epistatic interactions in case‐control GWAS. BMC Genomics. 14 Suppl 3:S10. 223 Graham I, Atar D, Borch‐Johnsen K, Boysen G, Burell G, Cifkova R, Dallongeville J, De Backer G, Ebrahim S, Gjelsvik B, Herrmann‐Lingen C, Hoes A, Humphries S, Knapton M, Perk J, Priori SG, Pyorala K, Reiner Z, Ruilope L, Sans‐Menendez S, Scholte op Reimer W, Weissberg P, Wood D, Yarnell J, Zamorano JL, Walma E, Fitzgerald T, Cooney MT, Dudina A, Vahanian A, Camm J, De Caterina R, Dean V, Dickstein K, Funck‐Brentano C, Filippatos G, Hellemans I, Kristensen SD, McGregor K, Sechtem U, Silber S, Tendera M, Widimsky P, Altiner A, Bonora E, Durrington PN, Fagard R, Giampaoli S, Hemingway H, Hakansson J, Kjeldsen SE, Larsen ML, Mancia G, Manolis AJ, Orth‐Gomer K, Pedersen T, Rayner M, Ryden L, Sammut M, Schneiderman N, Stalenhoef AF, Tokgozoglu L, Wiklund O, Zampelas A (2007) European guidelines on cardiovascular disease prevention in clinical practice: executive summary. Eur Heart J. 28:2375‐414. Hadad N, Tuval L, Elgazar‐Carmom V, Levy R (2011) Endothelial ICAM‐1 protein induction is regulated by cytosolic phospholipase A2alpha via both NF‐ kappaB and CREB transcription factors. J Immunol. 186:1816‐27. Hadley, T.J. and Peiper, S.C. (1997) From malaria to chemokine receptor: the emerging physiologic role of the Duffy blood group antigen. Blood, 89, 3077‐ 3091. Hansson GK, Hermansson A (2011) The immune system in atherosclerosis. Nat Immunol. 12:204‐12. Hartiala J, Li D, Conti DV, Vikman S, Patel Y, Tang WH, Brennan ML, Newman JW, Stephensen CB, Armstrong P, Hazen SL, Allayee H (2011) Genetic contribution of the leukotriene pathway to coronary artery disease. Hum Genet. 129:617‐27. Hartiala J, Bennett BJ, Tang WH, Wang Z, Stewart AF, Roberts R, McPherson R; CARDIoGRAM Consortium, Lusis AJ, Hazen SL, Allayee H (2014) Comparative Genome‐Wide Association Studies in Mice and Humans for Trimethylamine N‐Oxide, a Proatherogenic Metabolite of Choline and L‐Carnitine. Arterioscler Thromb Vasc Biol. Jun;34(6):1307‐13. Heinecke J (2011) HDL and cardiovascular disease risk ‐ time for a new approach? N Engl J Med. 364:170‐1. 224 Helgadottir A, Manolescu A, Thorleifsson G, Gretarsdottir S, Jonsdottir H, Thorsteinsdottir U, Samani NJ, Gudmundsson G, Grant SF, Thorgeirsson G, Sveinbjornsdottir S, Valdimarsson EM, Matthiasson SE, Johannsson H, Gudmundsdottir O, Gurney ME, Sainz J, Thorhallsdottir M, Andresdottir M, Frigge ML, Topol EJ, Kong A, Gudnason V, Hakonarson H, Gulcher JR, Stefansson K (2004) The gene encoding 5‐lipoxygenase activating protein confers risk of myocardial infarction and stroke. Nat Genet. 36: 233‐9. Helgadottir A, Gretarsdottir S, St Clair D, Manolescu A, Cheung J, Thorleifsson G, Pasdar A, Grant SF, Whalley LJ, Hakonarson H, Thorsteinsdottir U, Kong A, Gulcher J, Stefansson K, MacLeod MJ (2005) Association between the gene encoding 5‐lipoxygenase‐activating protein and stroke replicated in a Scottish population. Am J Hum Genet. 76: 505‐9. Helgadottir A, Manolescu A, Helgason A, Thorleifsson G, Thorsteinsdottir U, Gudbjartsson DF, Gretarsdottir S, Magnusson KP, Gudmundsson G, Hicks A, Jonsson T, Grant SF, Sainz J, O'Brien S J, Sveinbjornsdottir S, Valdimarsson EM, Matthiasson SE, Levey AI, Abramson JL, Reilly MP, Vaccarino V, Wolfe ML, Gudnason V, Quyyumi AA, Topol EJ, Rader DJ, Thorgeirsson G, Gulcher JR, Hakonarson H, Kong A, Stefansson K (2006) A variant of the gene encoding leukotriene A4 hydrolase confers ethnicity‐specific risk of myocardial infarction. Nat Genet. 38: 68‐74. Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T, Jonasdottir A, Jonasdottir A, Sigurdsson A, Baker A, Palsson A, Masson G, Gudbjartsson DF, Magnusson KP, Andersen K, Levey AI, Backman VM, Matthiasdottir S, Jonsdottir T, Palsson S, Einarsdottir H, Gunnarsdottir S, Gylfason A, Vaccarino V, Hooper WC, Reilly MP, Granger CB, Austin H, Rader DJ, Shah SH, Quyyumi AA, Gulcher JR, Thorgeirsson G, Thorsteinsdottir U, Kong A, Stefansson K (2007) A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science. Jun 8;316(5830):1491‐3. Heller EA, Liu E, Tager AM, Sinha S, Roberts JD, Koehn SL, Libby P, Aikawa ER, Chen JQ, Huang P, Freeman MW, Moore KJ, Luster AD, Gerszten RE (2005) Inhibition of atherogenesis in BLT1‐deWcient mice reveals a role for LTB4 and BLT1 in smooth muscle cell recruitment. Circulation. 112:578–586. Holle, R., Happich, M., Lowel, H. and Wichmann, H.E. (2005) KORA‐‐a research platform for population based health research. Gesundheitswesen, 67 Suppl 1, S19‐25. Hou L, Zhao H. (2013) A review of post‐GWAS prioritization approaches. Front Genet, 9;4:28. 225 Hoy, A., Tregouet, D., Leininger‐Muller, B., Poirier, O., Maurice, M., Sass, C., Siest, G., Tiret, L. and Visvikis, S. (2001) Serum myeloperoxidase concentration in a healthy population: biological variations, familial resemblance and new genetic polymorphisms. Eur J Hum Genet. 9, 780‐786. Human Microbiome Project Consortium (2012) Structure, function and diversity of the healthy human microbiome. Nature. Jun 13;486(7402):207‐14. In KH, Asano K, Beier D, Grobholz J, Finn PW, Silverman EK, Silverman ES,Collins T, Fischer AR, Keith TP, Serino K, Kim SW, De Sanctis GT, Yandava C,Pillari A, Rubin P, Kemp J, Israel E, Busse W, Ledford D, Murray JJ, Segal A,Tinkleman D, Drazen JM (1997) Naturally occurring mutations in the human 5‐ lipoxygenase gene promoter that modify transcription factor binding and reporter gene transcription. J Clin Invest. 99:1130‐7. International HapMap Consortium (2007). A second generation human haplotype map of over 3.1 million SNPs. Nature. 449, 851–861. Iovannisci DM, Lammer EJ, Steiner L, Cheng S, Mahoney LT, Davis PH, Lauer RM, Burns TL (2007) Association between a leukotriene C4 synthase gene promoter polymorphism and coronary artery calcium in young women: the Muscatine Study. Arterioscler Thromb Vasc Biol. 27: 394‐9. Jawien J, Gajda M, Rudling M, Mateuszuk L, Olszanecki R, Guzik TJ, Cichocki T, Chlopicki S, Korbut R (2006) Inhibition of five lipoxygenase activating protein (FLAP) by MK‐886 decreases atherosclerosis in apoE/LDLR‐double knockout mice. Eur J Clin Invest. 36: 141‐6. Jawien J, Gajda M, Wolkow P, Zuranska J, Olszanecki R, Korbut R (2008) The effect of montelukast on atherogenesis in apoE/LDLR‐double knockout mice. J Physiol Pharmacol. 59: 633‐9. Kabagambe EK, Baylin A, Allan DA, Siles X, Spiegelman D, Campos H (2001) Application of the method of triads to evaluate the performance of food frequency questionnaires and biomarkers as indicators of long‐term dietary intake. Am J Epidemiol. 154:1126‐35. 226 Kathiresan S, Melander O, Guiducci C, Surti A, Burtt NP, Rieder MJ, Cooper GM, Roos C, Voight BF, Havulinna AS, Wahlstrand B, Hedner T, Corella D, Tai ES, Ordovas JM, Berglund G, Vartiainen E, Jousilahti P, Hedblad B, Taskinen MR, Newton‐Cheh C, Salomaa V, Peltonen L, Groop L, Altshuler DM, Orho‐ Melander M (2008) Six new loci associated with blood low‐density lipoprotein cholesterol, high‐density lipoprotein cholesterol or triglycerides in humans. Nat Genet. Feb;40(2):189‐97. Karakas M, Koenig W (2012) Myeloperoxidase production by macrophage and risk of atherosclerosis. Curr Atheroscler Rep. 14:277‐83. Keating BJ, Tischfield S, Murray SS, Bhangale T, Price TS, Glessner JT, GalverL, Barrett JC, Grant SF, Farlow DN, Chandrupatla HR, Hansen M, Ajmal S, Papanicolaou GJ, Guo Y, Li M, Derohannessian S, de Bakker PI, Bailey SD, Montpetit A, Edmondson AC, Taylor K, Gai X, Wang SS, Fornage M, Shaikh T, GroopL, Boehnke M, Hall AS, Hattersley AT, Frackelton E, Patterson N, Chiang CW, Kim CE, Fabsitz RR, Ouwehand W, Price AL, Munroe P, Caulfield M, Drake T, Boerwinkle E, Reich D, Whitehead AS, Cappola TP, Samani NJ, Lusis AJ, Schadt E, Wilson JG, Koenig W, McCarthy MI, Kathiresan S, Gabriel SB, Hakonarson H, Anand SS, Reilly M, Engert JC, Nickerson DA, Rader DJ, Hirschhorn JN, Fitzgerald GA (2008) Concept, design and implementation of a cardiovascular gene‐centric 50 k SNP array for large‐scale genomic association studies. PLoS One. 3, e3583. Khera AV, Cuchel M, de la Llera‐Moya M, Rodrigues A, Burke MF, Jafri K, French BC, Philips JA, Mucksavage ML, Wilensky RL, Mohler ER, Rothblat GH, Rader DJ (2011) Cholesterol efflux capasity, high‐density lipoprotein function, and atherosclerosis. N Engl J Med. Jan 13;364(2):127‐35. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, Bracken MB, Ferris FL, Ott J, Barnstable C,Hoh J ( 2005) Complement factor H polymorphism in age‐related macular degeneration. Science. 308, 385‐389. Koeth RA, Wang Z, Levison BS, Buffa JA, Org E, Sheehy BT, Britt EB, Fu X, Wu Y, Li L, Smith JD, DiDonato JA, Chen J, Li H, Wu GD, Lewis JD, Warrier M, Brown JM, Krauss RM, Tang WH, Bushman FD, Lusis AJ, Hazen SL (2013) Intestinal microbiota metabolism of L‐carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med. May;19(5):576‐85. 227 Koch W, Hoppmann P, Mueller JC, Schomig A, Kastrati A (2007) No association of polymorphisms in the gene encoding 5‐lipoxygenase‐activating protein and myocardial infarction in a large central European population. Genet Med. 9:123–129. Kopplin LJ, Igo RP Jr, Wang Y, Sivakumaran TA, Hagstrom SA, Peachey NS, Francis PJ, Klein ML, SanGiovanni JP, Chew EY, Pauer GJ, Sturgill GM, Joshi T, Tian L, Xi Q, Henning AK, Lee KE, Klein R, Klein BE, Iyengar SK (2010) Genome‐wide association identifies SKIV2L and MYRIP as protective factors for age‐related macular degeneration. Genes Immun. 11, 609‐621. Kutter, D., Devaquet, P., Vanderstocken, G., Paulus, J.M., Marchal, V. and Gothot, A. (2000) Consequences of total and subtotal myeloperoxidase deficiency: risk or benefit ? Acta Haematol. 104, 10‐15. Lemaitre RN, Rice K, Marciante K, Bis JC, Lumley TS, Wiggins KL, Smith NL, Heckbert SR, Psaty BM (2009) Variation in eicosanoid genes, non‐fatal myocardial infarction and ischemic stroke. Atherosclerosis. 204: e58‐63. Ley RE, Turnbaugh PJ, Klein S, Gordon JI (2006) Microbial ecology: human gut microbes associated with obesity. Nature. Dec 21;444(7122):1022‐3. Li M, Wang B, Zhang M, Rantalainen M, Wang S, Zhou H, Zhang Y, Shen J, Pang X, Zhang M, Wei H, Chen Y, Lu H, Zuo J, Su M, Qiu Y, Jia W, Xiao C, Smith LM, Yang S, Holmes E, Tang H, Zhao G, Nicholson JK, Li L, Zhao L (2008) Symbiotic gut microbes modulate human metabolic phenotypes. Proc Natl Acad Sci USA. Feb 12;105(6):2117‐22. Libby P, Ridker PM, Hansson GK (2011) Progress and challenges in translating the biology of atherosclerosis. Nature. 473:317‐25. Lima JJ, Zhang S, Grant A, Shao L, Tantisira KG, Allayee H, Wang J, Sylvester J, Holbrook J, Wise R, Weiss ST, Barnes K (2006) Influence of leukotriene pathway polymorphisms on response to montelukast in asthma. Am J Respir Crit Care Med. 173: 379‐85. Linsel‐Nitschke P, Gotz A, Medack A, Konig IR, Bruse P, Lieb W, Mayer B, Stark K, Hengstenberg C, Fischer M, Baessler A, Ziegler A, Schunkert H, Erdmann J (2008) Genetic variation in the arachidonate 5‐lipoxygenase‐activating protein (ALOX5AP) is associated with myocardial infarction in the German population. Clin Sci (Lond). 115: 309‐15. 228 Lippert C, Francesco PC, Rakitsch F, Stegle O (2014) LIMIX: genetic analysis of multiple traits. bioRxiv May 21. http://dx.doi.org/10.1101/003905. Livak KJ (1999) Allelic discrimination using fluorogenic probes and the 5' nuclease assay. Genet Anal. 14: 143‐9. Livak KJ (2003) SNP genotyping by the 5'‐nuclease reaction. Methods Mol Biol. 212: 129‐47. Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R (2012) Diversity, stability and resilience of the human gut microbiota. Nature. Sep 13;489(7415):220‐30. Lusis AJ (2000) Atherosclerosis. Nature. 407:233‐41. Maher B (2009). Exome sequencing takes centre stage in cancer profiling. Nature. May 14;459(7244):146‐7. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TF, McCarroll SA, Visscher PM. (2009) Finding the missing heritability of complex diseases. Nature. Oct 8;461(7265):747‐53. Marchetti, C., Patriarca, P., Solero, G.P., Baralle, F.E. and Romano, M. (2004) Genetic characterization of myeloperoxidase deficiency in Italy. Hum Mutat. 23, 496‐505. Maznyczka A, Braund P, Mangino M, Samani NJ (2008) Arachidonate 5‐lipoxygenase (5‐LO) promoter genotype and risk of myocardial infarction: a case‐control study. Atherosclerosis. 199: 328‐32. McPherson R, Pertsemlidis A, Kavaslar N, Stewart A, Roberts R, Cox DR, Hinds DA, Pennacchio LA, Tybjaerg‐Hansen A, Folsom AR, Boerwinkle E, Hobbs HH, Cohen JC (2007) A common allele on chromosome 9 associated with coronary heart disease. Science. Jun 8;316(5830):1488‐91. Mehrabian M, Allayee H, Wong J, Shi W, Wang XP, Shaposhnik Z, Funk CD, Lusis AJ (2002) Identification of 5‐lipoxygenase as a major gene contributing to atherosclerosis susceptibility in mice. Circ Res. 91: 120‐6. 229 Mehrabian M, Allayee H (2003) 5‐Lipoxygenase and atherosclerosis. Curr Opin Lipidol. 14: 447‐57. Mehrabian M, Allayee H, Stockton J, Lum PY, Drake TA, Castellani LW, Suh M, Armour C, Edwards S, Lamb J, Lusis AJ, Schadt EE (2005) Integrating genotypic and expression data in a segregating mouse population to identify 5‐lipoxygenase as a susceptibility gene for obesity and bone traits. Nat Genet. 37: 1224‐33. Mehrabian M, Schulthess FT, Nebohacova M, Castellani LW, Zhou Z, Hartiala J, Oberholzer J, Lusis AJ, Maedler K, Allayee H (2008) Identification of ALOX5 as a gene regulating adiposity and pancreatic function. Diabetologia. 51: 978‐ 88. Merelli, I., Calabria, A., Cozzi, P., Viti, F., Mosca, E., and Milanesi, L. (2013). SNPranker 2.0: a gene‐centric data mining tool for diseases associated SNP prioritization in GWAS. BMC Bioinformatics. 14:S9. Meuwese MC, Stroes ES, Hazen SL, van Miert JN, Kuivenhoven JA, Schaub RG, Wareham NJ, Luben R, Kastelein JJ, Khaw KT, Boekholdt SM (2007) Serum myeloperoxidase levels are associated with the future risk of coronary artery disease in apparently healthy individuals: the EPIC‐Norfolk Prospective Population Study. J Am Coll Cardiol. 50:159‐65. Mocatta TJ, Pilbrow AP, Cameron VA, Senthilmohan R, Frampton CM, Richards AM, Winterbourn CC (2007) Plasma concentrations of myeloperoxidase predict mortality after myocardial infarction. J Am Coll Cardiol. 49:1993‐2000. Musunuru K, Lettre G, Young T, Farlow DN, Pirruccello JP, Ejebe KG, Keating BJ, Yang Q, Chen MH, Lapchyk N, Crenshaw A, Ziaugra L, Rachupka A, Benjamin EJ, Cupples LA, Fornage M, Fox ER, Heckbert SR, Hirschhorn JN, Newton‐Cheh C, Nizzari MM, Paltoo DN, Papanicolaou GJ, Patel SR, Psaty BM, Rader DJ, Redline S, Rich SS, Rotter JI, Taylor HA Jr, Tracy RP, Vasan RS, Wilson JG, Kathiresan S, Fabsitz RR, Boerwinkle E, Gabriel SB, NHLBI Candidate Gene Association Resource (2010) Candidate gene association resource (CARe): design, methods, and proof of concept. Circ Cardiovasc Genet. 3, 267‐275. 230 Myocardial Infarction Genetics Consortium, Kathiresan S, Voight BF, Purcell S, Musunuru K, Ardissino D, Mannucci PM, Anand S, Engert JC, Samani NJ, Schunkert H, Erdmann J, Reilly MP, Rader DJ, Morgan T, Spertus JA, Stoll M, Girelli D, McKeown PP, Patterson CC, Siscovick DS, O'Donnell CJ, Elosua R, Peltonen L, Salomaa V, Schwartz SM, Melander O, Altshuler D, Ardissino D, Merlini PA, Berzuini C, Bernardinelli L, Peyvandi F, Tubaro M, Celli P, Ferrario M, Fetiveau R, Marziliano N, Casari G, Galli M, Ribichini F, Rossi M, Bernardi F, Zonzin P, Piazza A, Mannucci PM, Schwartz SM, Siscovick DS, Yee J, Friedlander Y, Elosua R, Marrugat J, Lucas G, Subirana I, Sala J, Ramos R, Kathiresan S, Meigs JB, Williams G, Nathan DM, MacRae CA, O'Donnell CJ, Salomaa V, Havulinna AS, Peltonen L, Melander O, Berglund G, Voight BF, Kathiresan S, Hirschhorn JN, Asselta R, Duga S, Spreafico M, Musunuru K, Daly MJ, Purcell S, Voight BF, Purcell S, Nemesh J, Korn JM, McCarroll SA, Schwartz SM, Yee J, Kathiresan S, Lucas G, Subirana I, Elosua R, Surti A, Guiducci C, Gianniny L, Mirel D, Parkin M, Burtt N, Gabriel SB, Samani NJ, Thompson JR, Braund PS, Wright BJ, Balmforth AJ, Ball SG, Hall A; Wellcome Trust Case Control Consortium, Schunkert H, Erdmann J, Linsel‐Nitschke P, Lieb W, Ziegler A, König I, Hengstenberg C, Fischer M, Stark K, Grosshennig A, Preuss M, Wichmann HE, Schreiber S, Schunkert H, Samani NJ, Erdmann J, Ouwehand W, Hengstenberg C, Deloukas P, Scholz M, Cambien F, Reilly MP, Li M, Chen Z, Wilensky R, Matthai W, Qasim A, Hakonarson HH, Devaney J, Burnett MS, Pichard AD, Kent KM, Satler L, Lindsay JM, Waksman R, Knouff CW, Waterworth DM, Walker MC, Mooser V, Epstein SE, Rader DJ, Scheffold T, Berger K, Stoll M, Huge A, Girelli D, Martinelli N, Olivieri O, Corrocher R, Morgan T, Spertus JA, McKeown P, Patterson CC, Schunkert H, Erdmann E, Linsel‐Nitschke P, Lieb W, Ziegler A, König IR, Hengstenberg C, Fischer M, Stark K, Grosshennig A, Preuss M, Wichmann HE, Schreiber S, Hólm H, Thorleifsson G, Thorsteinsdottir U, Stefansson K, Engert JC, Do R, Xie C, Anand S, Kathiresan S, Ardissino D, Mannucci PM, Siscovick D, O'Donnell CJ, Samani NJ, Melander O, Elosua R, Peltonen L, Salomaa V, Schwartz SM, Altshuler D (2009). Genome‐wide association of early‐onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nat Genet. Mar;41(3):334‐41. Neale BM, Fagerness J, Reynolds R, Sobrin L, Parker M, Raychaudhuri S, Tan PL, Oh EC, Merriam JE, Souied E, Bernstein PS, Li B, Frederick JM, Zhang K, Brantley MA Jr, Lee AY, Zack DJ, Campochiaro B, Campochiaro P, Ripke S, Smith RT, Barile GR, Katsanis N, Allikmets R, Daly MJ, Seddon JM (2010) Genome‐wide association study of advanced age‐related macular degeneration identifies a role of the hepatic lipase gene (LIPC). Proc Natl Acad Sci USA. 107, 7395‐ 7400. 231 Nicholls SJ, Hazen SL (2005) Myeloperoxidase and cardiovascular disease. Arterioscler Thromb Vasc Biol. 25: 1102‐1111. Nicholls, S.J., Zheng, L. and Hazen, S.L. (2005) Formation of dysfunctional high‐ density lipoprotein by myeloperoxidase. Trends Cardiovasc. Med. 15, 212‐ 219. Nicholls, S.J. and Hazen, S.L. (2009) Myeloperoxidase, modified lipoproteins, and atherogenesis. J. Lipid Res. 50 Suppl, S346‐351. Nicholls SJ, Tang WH, Scoffone H, Brennan DM, Hartiala J, Allayee H, Hazen SL (2010) Lipoprotein (a) levels and long‐term cardiovascular risk in the contemporary Era of statin therapy. J Lipid Res. Oct;51(10):3055‐61. Nikpoor, B., Turecki, G., Fournier, C., Theroux, P. and Rouleau, G.A. (2001) A functional myeloperoxidase polymorphic variant is associated with coronary artery disease in French‐Canadians. Am. Heart J. 142, 336‐339. Nordestgaard BG, Zacho J (2009) Lipids, atherosclerosis and CVD risk: is CRP an innocent bystander? Nutr Metab Cardiovasc Dis. Oct; 19(8):521‐4. Oldenborg, P.A., Gresham, H.D. and Lindberg, F.P. (2001) CD47‐signal regulatory protein alpha (SIRPalpha) regulates Fcgamma and complement receptor‐ mediated phagocytosis. J Exp Med. 193, 855‐862. Patel RS, Sun YV, Hartiala J, Veledar E, Su S, Sher S, Liu YX, Rahman A, Patel R, Rab ST, Vaccarino V, Zafari AM, Samady H, Tang WH, Allayee H, Hazen SL, Quyyumi AA (2012). Association of a genetic risk score with prevalent and incident myocardial infarction in subjects undergoing coronary angiography. Circ Cardiovasc Genet. Aug 1;5(4):441‐9. Peters‐Golden M, Henderson WR, Jr. (2007) Leukotrienes. N Engl J Med. 357: 1841‐ 54. Piccio, L., Vermi, W., Boles, K.S., Fuchs, A., Strader, C.A., Facchetti, F., Cella, M. and Colonna, M. (2005) Adhesion of human T cells to antigen‐presenting cells through SIRPbeta2‐CD47 interaction costimulates T‐cell proliferation. Blood. 105, 2421‐2427. Poeckel D, Funk CD (2010) The 5‐lipoxygenase/leukotriene pathway in preclinical models of cardiovascular disease. Cardiovasc Res. 86:243‐53. 232 Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, Sicheritz‐Ponten T, Turner K, Zhu H, Yu C, Li S, Jian M, Zhou Y, Li Y, Zhang X, Li S, Qin N, Yang H, Wang J, Brunak S, Doré J, Guarner F, Kristiansen K, Pedersen O, Parkhill J, Weissenbach J; MetaHIT Consortium, Bork P, Ehrlich SD, Wang J (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature. Mar 4;464(7285):59‐65. Qiu H, Gabrielsen A, Agardh HE, Wan M, Wetterholm A, Wong CH, Hedin U, Swedenborg J, Hansson GK, Samuelsson B, Paulsson‐Berne G, Haeggstrom JZ (2006) Expression of 5‐lipoxygenase and leukotriene A4 hydrolase in human atherosclerotic lesions correlates with symptoms of plaque instability. Proc Natl Acad Sci USA. 103: 8161‐6. Rabbani B, Mahdieh N, Hosomichi K, Nakaoka H, Inoue I (2012) Next‐generation sequencing: impact of exome sequencing in characterizing Mendelian disorders. J Hum Genet. Oct;57(10):621‐32. Raman K, Chong M, Akhtar‐Danesh GG, D'Mello M, Hasso R, Ross S, Xu F, Pare G (2013) Genetic markers of inflammation and their role in cardiovascular disease. Can J Cardiol. 29:67‐74. Reed KA, Tucker DE, Aloulou A, Adler D, Ghomashchi F, Gelb MH, Leslie CC, Oates JA, Boutaud O (2011) Functional characterization of mutations in inherited human cPLA deficiency. Biochemistry. 50:1731‐8. Reich D, Nalls MA, Kao WH, Akylbekova EL, Tandon A, Patterson N, Mullikin J, Hsueh WC, Cheng CY, Coresh J, Boerwinkle E, Li M, Waliszewska A, Neubauer J, Li R, Leak TS, Ekunwe L, Files JC, Hardy CL, Zmuda JM, Taylor HA, Ziv E, Harris TB, Wilson JG (2009) Reduced neutrophil count in people of African descent is due to a regulatory variant in the Duffy antigen receptor for chemokines gene. PLoS Genet. 5, e1000360. Reigstad CS, Lundén GO, Felin J, Bäckhed F (2009) Regulation of serum amyloid A3(SAA3) in mouse colonic epithelium and adipose tissue by the intestinal microbiota. PLoS One. Jun 9;4(6):e5842. Reynolds, W.F., Chang, E., Douer, D., Ball, E.D. and Kanda, V. (1997) An allelic association implicates myeloperoxidase in the etiology of acute promyelocytic leukemia. Blood. 90, 2730‐2737. 233 Riccioni G, Back M, Capra V (2010) Leukotrienes and atherosclerosis. Curr Drug Targets. 11:882‐7. Ridker PM, Hennekens CH, Buring JE, Rifai N (2000;) C‐reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med. 342:836‐43. Roberts R, Stewart AF (2012) Genes and coronary artery disease: where are we? J Am Coll Cardiol. Oct 30;60(18):1715‐21. Romano, M., Dri, P., Dadalt, L., Patriarca, P. and Baralle, F.E. (1997) Biochemical and molecular characterization of hereditary myeloperoxidase deficiency. Blood. 90, 4126‐4134. Romanoski CE, Lee S, Kim MJ, Ingram‐Drake L, Plaisier CL, Yordanova R, Tilford C, Guan B, He A, Gargalovic PS, Kirchgessner TG, Berliner JA, Lusis AJ (2010) Systems genetics analysis of gene‐by‐environment interactions in human cells. Am J Hum Genet. 86:399‐410. Rosti RO, Sadek AA, Vaux KK, Gleeson JG (2014) The genetic landscape of autism spectrum disorders. Dev Med Child Neurol. Jan;56(1):12‐8. Roth EM, Diller P (2014) Alirocumab for hyperlipidemia: physiology of PCSK9 inhibition, pharmacodynamics and Phase I and II clinical trial results of a PCSK9 monoclonal antibody. Future Cardiol. Mar;10(2):183‐99. Ruiz‐Narvaez EA, Bare L, Arellano A, Catanese J, Campos H (2010) West African and Amerindian ancestry and risk of myocardial infarction and metabolic syndrome in the Central Valley population of Costa Rica. Hum Genet. 127:629‐38. Saccone SF, Bolze R, Thomas P, Quan J, Mehta G, Deelman E, Tischfield JA, Rice JP (2010) SPOT: a web‐based tool for using biological databases to prioritize SNPs after a genome‐wide association study. Nucleic Acids Res. Jul;38(Web Server issue):W201‐9. Samuelsson B (1983) Leukotrienes: mediators of immediate hypersensitivity reactions and inflammation. Science. 220: 568‐75. Samuelsson B, Dahlen SE, Lindgren JA, Rouzer CA, Serhan CN (1987) Leukotrienes and lipoxins: structures, biosynthesis, and biological effects. Science. 237:1171‐6. 234 Sanak M, Pierzchalska M, Bazan‐Socha S, Szczeklik A (2000) Enhanced expression of the leukotriene C(4) synthase due to overactive transcription of an allelic variant associated with aspirin‐intolerant asthma. Am J Respir Cell Mol Biol. 23: 290‐296. Sarwar N, Butterworth AS, Freitag DF, Gregson J, Willeit P, Gorman DN, Gao P, Saleheen D, Rendon A, Nelson CP, Braund PS, Hall AS, Chasman DI, Tybjaerg‐ Hansen A, Chambers JC, Benjamin EJ, Franks PW, Clarke R, Wilde AA, Trip MD, Steri M, Witteman JC, Qi L, van der Schoot CE, de Faire U, Erdmann J, Stringham HM, Koenig W, Rader DJ, Melzer D, Reich D, Psaty BM, Kleber ME, Panagiotakos DB, Willeit J, Wennberg P, Woodward M, Adamovic S, Rimm EB, Meade TW, Gillum RF, Shaffer JA, Hofman A, Onat A, Sundstrom J, Wassertheil‐Smoller S, Mellstrom D, Gallacher J, Cushman M, Tracy RP, Kauhanen J, Karlsson M, Salonen JT, Wilhelmsen L, Amouyel P, Cantin B, Best LG, Ben‐Shlomo Y, Manson JE, Davey‐Smith G, de Bakker PI, O'Donnell CJ, Wilson JF, Wilson AG, Assimes TL, Jansson JO, Ohlsson C, Tivesten A, Ljunggren O, Reilly MP, Hamsten A, Ingelsson E, Cambien F, Hung J, Thomas GN, Boehnke M, Schunkert H, Asselbergs FW, Kastelein JJ, Gudnason V, Salomaa V, Harris TB, Kooner JS, Allin KH, Nordestgaard BG, Hopewell JC, Goodall AH, Ridker PM, Holm H, Watkins H, Ouwehand WH, Samani NJ, Kaptoge S, Di Angelantonio E, Harari O, Danesh J (2012) Interleukin‐6 receptor pathways in coronary heart disease: a collaborative meta‐analysis of 82 studies. Lancet. 379:1205‐13. Schrier RW (2006) Role of diminished renal function in cardiovascular mortality: marker or pathogenetic factor? J Am Coll Cardiol. 47:1‐8. Schnabel RB, Lunetta KL, Larson MG, Dupuis J, Lipinska I, Rong J, Chen MH,Zhao Z, Yamamoto JF, Meigs JB, Nicaud V, Perret C, Zeller T, Blankenberg S, Tiret L, Keaney JF Jr, Vasan RS, Benjamin EJ. (2009) The relation of genetic and environmental factors to systemic inflammatory biomarker concentrations. Circ Cardiovasc Genet. 2, 229‐237. Schnabel RB, Baumert J, Barbalic M, Dupuis J, Ellinor PT, Durda P, Dehghan A, Bis JC, Illig T, Morrison AC, Jenny NS, Keaney JF Jr, Gieger C, Tilley C,Yamamoto JF, Khuseyinova N, Heiss G, Doyle M, Blankenberg S, Herder C, Walston JD, Zhu Y, Vasan RS, Klopp N, Boerwinkle E, Larson MG, Psaty BM, Peters A, Ballantyne CM, Witteman JC, Hoogeveen RC, Benjamin EJ, Koenig W, Tracy RP (2010) Duffy antigen receptor for chemokines (Darc) polymorphism regulates circulating concentrations of monocyte chemoattractant protein‐1 and other inflammatory mediators. Blood. 115, 5289‐5299. 235 Schunkert H, König IR, Kathiresan S, Reilly MP, Assimes TL, Holm H, Preuss M, Stewart AF, Barbalic M, Gieger C, Absher D, Aherrahrou Z, Allayee H, Altshuler D, Anand SS, Andersen K, Anderson JL, Ardissino D, Ball SG, Balmforth AJ, Barnes TA, Becker DM, Becker LC, Berger K, Bis JC, Boekholdt SM, Boerwinkle E, Braund PS, Brown MJ, Burnett MS, Buysschaert I; Cardiogenics, Carlquist JF, Chen L, Cichon S, Codd V, Davies RW, Dedoussis G, Dehghan A, Demissie S, Devaney JM, Diemert P, Do R, Doering A, Eifert S, Mokhtari NE, Ellis SG, Elosua R, Engert JC, Epstein SE, de Faire U, Fischer M, Folsom AR, Freyer J, Gigante B, Girelli D, Gretarsdottir S, Gudnason V, Gulcher JR, Halperin E, Hammond N, Hazen SL, Hofman A, Horne BD, Illig T, Iribarren C, Jones GT, Jukema JW, Kaiser MA, Kaplan LM, Kastelein JJ, Khaw KT, Knowles JW, Kolovou G, Kong A, Laaksonen R, Lambrechts D, Leander K, Lettre G, Li M, Lieb W, Loley C, Lotery AJ, Mannucci PM, Maouche S, Martinelli N, McKeown PP, Meisinger C, Meitinger T, Melander O, Merlini PA, Mooser V, Morgan T, Mühleisen TW, Muhlestein JB, Münzel T, Musunuru K, Nahrstaedt J, Nelson CP, Nöthen MM, Olivieri O, Patel RS, Patterson CC, Peters A, Peyvandi F, Qu L, Quyyumi AA, Rader DJ, Rallidis LS, Rice C, Rosendaal FR, Rubin D, Salomaa V, Sampietro ML, Sandhu MS, Schadt E, Schäfer A, Schillert A, Schreiber S, Schrezenmeir J, Schwartz SM, Siscovick DS, Sivananthan M, Sivapalaratnam S, Smith A, Smith TB, Snoep JD, Soranzo N, Spertus JA, Stark K, Stirrups K, Stoll M, Tang WH, Tennstedt S, Thorgeirsson G, Thorleifsson G, Tomaszewski M, Uitterlinden AG, van Rij AM, Voight BF, Wareham NJ, Wells GA, Wichmann HE, Wild PS, Willenborg C, Witteman JC, Wright BJ, Ye S, Zeller T, Ziegler A, Cambien F, Goodall AH, Cupples LA, Quertermous T, März W, Hengstenberg C, Blankenberg S, Ouwehand WH, Hall AS, Deloukas P, Thompson JR, Stefansson K, Roberts R, Thorsteinsdottir U, O'Donnell CJ, McPherson R, Erdmann J; CARDIoGRAM Consortium, Samani NJ (2011) Large‐scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet. Mar 6;43(4):333‐8. Sekirov I, Russell SL, Antunes LC, Finlay BB (2010) Gut microbiota in health and disease. Physiol Rev. 90(3):859–904. Shabani F, McNeil J, Tippett L (1998) The oxidative inactivation of tissue inhibitor of metalloproteinase‐1 (TIMP‐1) by hypochlorous acid (HOCI) is suppressed by anti‐rheumatic drugs. Free Radic Res. 28:115‐23. Shah SH, Hauser ER, Crosslin D, Wang L, Haynes C, Connelly J, Nelson S, Johnson J, Gadson S, Nelson CL, Seo D, Gregory S, Kraus WE, Granger CB, Goldschmidt‐ Clermont P, Newby LK (2008) ALOX5AP variants are associated with in‐stent restenosis after percutaneous coronary intervention. Atherosclerosis. 201: 148‐54. 236 Spanbroek R, Grabner R, Lotzer K, Hildner M, Urbach A, Ruhling K, Moos MP, Kaiser B, Cohnert TU, Wahlers T, Zieske A, Plenz G, Robenek H, Salbach P, Kuhn H, Radmark O, Samuelsson B, Habenicht AJ (2003) Expanding expression of the 5‐ lipoxygenase pathway within the arterial wall during human atherogenesis. Proc Natl Acad Sci USA. 100:1238–1243. Sokol H, Seksik P, Rigottier‐Gois L, Lay C, Lepage P, Podglajen I, Marteau P, Doré J (2006) Specificities of the fecal microbiota in inflammatory bowel disease.Inflamm Bowel Dis. 12(2):106–111. Spite M, Serhan CN (2010) Novel lipid mediators promote resolution of acute inflammation: impact of aspirin and statins. Circ Res. 107:1170‐84. Subbarao K, Jala VR, Mathis S, Suttles J, Zacharias W, Ahamed J, Ali H, Tseng MT, Haribabu B (2004) Role of leukotriene B4 receptors in the development of atherosclerosis: potential mechanisms. Arterioscler Thromb Vasc Biol. 24: 369‐375. Suls A, Jaehn JA, Kecskés A, Weber Y, Weckhuysen S, Craiu DC, Siekierska A, Djémié T, Afrikanova T, Gormley P, von Spiczak S, Kluger G, Iliescu CM, Talvik T, Talvik I, Meral C, Caglayan HS, Giraldez BG, Serratosa J, Lemke JR, Hoffman‐ Zacharska D, Szczepanik E, Barisic N, Komarek V, Hjalgrim H, Møller RS, Linnankivi T, Dimova P, Striano P, Zara F, Marini C, Guerrini R, Depienne C, Baulac S, Kuhlenbäumer G, Crawford AD, Lehesjoki AE, de Witte PA, Palotie A, Lerche H, Esguerra CV, De Jonghe P, Helbig I; EuroEPINOMICS RES Consortium (2013) De novo loss‐of‐function mutations in CHD2 cause a fever‐sensitive myoclonic epileptic encephalopathy sharing features with Dravet syndrome. Am J Hum Genet. Nov 7;93(5):967‐75. Tada H, Won HH, Yang J, Peloso G, and Kathiresan S (2014) Multiple associated variants increase the heritability explained for plasma lipids and coronary artery disease. Circ Cardiovasc Genet. In Press. Tang, W.H., Tong, W., Troughton, R.W., Martin, M.G., Shrestha, K., Borowski, A., Jasper, S., Hazen, S.L. and Klein, A.L. (2007) Prognostic value and echocardiographic determinants of plasma myeloperoxidase levels in chronic heart failure. J. Am. Coll. Cardiol., 49, 2364‐2370. Tang WH, Katz R, Brennan ML, Aviles RJ, Tracy RP, Psaty BM, Hazen SL (2009) Usefulness of myeloperoxidase levels in healthy elderly subjects to predict risk of developing heart failure. Am J Cardiol. 103:1269‐74. 237 Tang, W.H., Wu, Y., Nicholls, S.J. and Hazen, S.L. (2011) Plasma myeloperoxidase predicts incident cardiovascular risks in stable patients undergoing medical management for coronary artery disease. Clin. Chem., 57, 33‐39. Tang, W.H., Wu, Y., Hartiala, J., Fan, Y., Stewart, A.F., Roberts, R., McPherson, R., Fox, P.L., Allayee, H. and Hazen, S.L. (2012) Clinical and genetic association of serum ceruloplasmin with cardiovascular risk. Arterioscler. Thromb. Vasc. Biol., 32, 516‐522. The Emerging Risk Factors Collaboration (2009) Major lipids, apolipoproteins, and risk of vascular disease. JAMA. 302:1993‐2000. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report (2002). Circulation. 106:3143‐421. Thygesen K, Alpert JS, White HD, Jaffe AS, Apple FS, Galvani M, Katus HA, Newby LK, Ravkilde J, Chaitman B, et al. Universal definition of myocardial infarction. Circulation. 2007;116:2634‐53. Tortajada, A., Montes, T., Martinez‐Barricarte, R., Morgan, B.P., Harris, C.L. and de Cordoba, S.R. (2009) The disease‐protective complement factor H allotypic variant Ile62 shows increased binding affinity for C3b and enhanced cofactor activity. Hum. Mol. Genet., 18, 3452‐3461. Tsai AK, Li N, Hanson NQ, Tsai MY, Tang W (2009) Associations of genetic polymorphisms of arachidonate 5‐lipoxygenase‐activating protein with risk of coronary artery disease in a European‐American population. Atherosclerosis 207: 487‐91. Tymchuk CN, Hartiala J, Patel PI, Mehrabian M, Allayee H (2006) Nonconventional genetic risk factors for cardiovascular disease. Curr Atheroscler Rep 8: 184‐ 92 Undurti, A., Huang, Y., Lupica, J.A., Smith, J.D., DiDonato, J.A. and Hazen, S.L. (2009) Modification of high density lipoprotein by myeloperoxidase generates a pro‐inflammatory particle. J Biol Chem. 284, 30825‐30835. Vikman S, Brena RM, Armstrong P, Hartiala J, Stephensen CB, Allayee H (2009) Functional Analysis of 5‐Lipoxygenase Promoter Repeat Variants. Hum Mol Genet. 18(23): 4521‐4529. 238 Vita JA, Brennan ML, Gokce N, Mann SA, Goormastic M, Shishehbor MH, Penn MS, Keaney JF, Jr., Hazen SL (2004) Serum myeloperoxidase levels independently predict endothelial dysfunction in humans. Circulation. 110:1134‐9. Wainstein, R.V., Wainstein, M.V., Ribeiro, J.P., Dornelles, L.V., Tozzati, P., Ashton‐ Prolla, P., Ewald, I.P., Vietta, G. and Polanczyk, C.A. (2010) Association between myeloperoxidase polymorphisms and its plasma levels with severity of coronary artery disease. Clin Biochem. 43, 57‐62. Walker, A.E., Seibert, S.M., Donato, A.J., Pierce, G.L. and Seals, D.R. (2010) Vascular endothelial function is related to white blood cell count and myeloperoxidase among healthy middle‐aged and older adults. Hypertension. 55, 363‐369. Wang, K., Li, M., and Hakonarson, H. (2010). ANNOVAR: functional annotation of genetic variants from high‐throughput sequencing data. Nucleic Acids Res. 38, e164. Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, Dugar B, Feldstein AE, Britt EB, Fu X, Chung YM, Wu Y, Schauer P, Smith JD, Allayee H, Tang WH, DiDonato JA, Lusis AJ, Hazen SL. (2011) Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. 472(7341):57–63. Ward, L. D., and Kellis, M. (2012a). HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934. Weismann D, Hartvigsen K, Lauer N, Bennett KL, Scholl HP, Charbel Issa P, Cano M, Brandstätter H, Tsimikas S, Skerka C, Superti‐Furga G, Handa JT, Zipfel PF, Witztum JL, Binder CJ (2011) Complement factor H binds malondialdehyde epitopes and protects from oxidative stress. Nature. 478, 76‐81. Wensley F, Gao P, Burgess S, Kaptoge S, Di Angelantonio E, Shah T, Engert JC, Clarke R, Davey‐Smith G, Nordestgaard BG, Saleheen D, Samani NJ, Sandhu M, Anand S, Pepys MB, Smeeth L, Whittaker J, Casas JP, Thompson SG, Hingorani AD, Danesh J (2011) Association between C reactive protein and coronary heart disease: mendelian randomisation analysis based on individual participant data. BMJ. 342:d548. 239 Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, Clarke R, Heath SC, Timpson NJ, Najjar SS, Stringham HM, Strait J, Duren WL, Maschio A, Busonero F, Mulas A, Albai G, Swift AJ, Morken MA, Narisu N, Bennett D, Parish S, Shen H, Galan P, Meneton P, Hercberg S, Zelenika D, Chen WM, Li Y, Scott LJ, Scheet PA, Sundvall J, Watanabe RM, Nagaraja R, Ebrahim S, Lawlor DA, Ben‐Shlomo Y, Davey‐Smith G, Shuldiner AR, Collins R, Bergman RN, Uda M, Tuomilehto J, Cao A, Collins FS, Lakatta E, Lathrop GM, Boehnke M, Schlessinger D, Mohlke KL, Abecasis GR (2008) Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet. 40, 161‐169. Willer, C.J., Li, Y. and Abecasis, G.R. (2010) METAL: fast and efficient meta‐analysis of genomewide association scans. Bioinformatics, 26, 2190‐2191. Willett W (1998) Nutritional Epidemiology. 2nd ed. New York: Oxford University Press. Winkelmann, B.R., Marz, W., Boehm, B.O., Zotz, R., Hager, J., Hellstern, P. and Senges, J. (2001) Rationale and design of the LURIC study‐‐a resource for functional genomics, pharmacogenomics and long‐term prognosis of cardiovascular disease. Pharmacogenomics. 2, S1‐73. Wu Z, Wagner MA, Zheng L, Parks JS, Shy JM, 3rd, Smith JD, Gogonea V, Hazen SL (2007) The refined structure of nascent HDL reveals a key functional domain for particle maturation and dysfunction. Nat Struct Mol Biol. 14:861‐868. Yang J, Ferreira T, Morris AP, Medland SE; Genetic Investigation of ANthropometric Traits (GIANT) Consortium; DIAbetes Genetics Replication And Meta‐analysis (DIAGRAM) Consortium, Madden PA, Heath AC, Martin NG, Montgomery GW, Weedon MN, Loos RJ, Frayling TM, McCarthy MI, Hirschhorn JN, Goddard ME, Visscher PM (2012) Conditional and joint multiple‐SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet. Mar 18;44(4):369‐75, S1‐3. 240 Yu Y, Bhangale TR, Fagerness J, Ripke S, Thorleifsson G, Tan PL, Souied EH, Richardson AJ, Merriam JE, Buitendijk GH, Reynolds R, Raychaudhuri S, Chin KA, Sobrin L, Evangelou E, Lee PH, Lee AY, Leveziel N, Zack DJ, Campochiaro B, Campochiaro P, Smith RT, Barile GR, Guymer RH, Hogg R, Chakravarthy U, Robman LD, Gustafsson O, Sigurdsson H, Ortmann W, Behrens TW, Stefansson K, Uitterlinden AG, van Duijn CM, Vingerling JR, Klaver CC, Allikmets R, Brantley MA Jr, Baird PN, Katsanis N, Thorsteinsdottir U, Ioannidis JP, Daly MJ, Graham RR, Seddon JM (2011) Common variants near FRK/COL10A1 and VEGFA are associated with advanced age‐related macular degeneration. Hum Mol Genet. 20, 3699‐3709. Zee RY, Cheng S, Hegener HH, Erlich HA, Ridker PM (2006) Genetic variants of arachidonate 5‐lipoxygenase‐activating protein, and risk of incident myocardial infarction and ischemic stroke: a nested case‐control approach. Stroke. 37: 2007‐11. Zeller T, Wild P, Szymczak S, Rotival M, Schillert A, Castagne R, Maouche S,Germain M, Lackner K, Rossmann H, Eleftheriadis M, Sinning CR, Schnabel RB, Lubos E, Mennerich D, Rust W, Perret C, Proust C, Nicaud V, Loscalzo J, Hübner N, Tregouet D, Münzel T, Ziegler A, Tiret L, Blankenberg S, Cambien F (2010) Genetics and beyond‐‐the transcriptome of human monocytes and disease susceptibility. PLoS One. 5, e10693. Zhao J, Wu H, Khosravi M, Cui H, Qian X, Kelly JA, Kaufman KM, Langefeld CD, Williams AH, Comeau ME, Ziegler JT, Marion MC, Adler A, Glenn SB, Alarcón‐ Riquelme ME; BIOLUPUS Network; GENLES Network, Pons‐Estel BA, Harley JB, Bae SC, Bang SY, Cho SK, Jacob CO, Vyse TJ, Niewold TB, Gaffney PM, Moser KL, Kimberly RP, Edberg JC, Brown EE, Alarcon GS, Petri MA, Ramsey‐ Goldman R, Vilá LM, Reveille JD, James JA, Gilkeson GS, Kamen DL, Freedman BI, Anaya JM, Merrill JT, Criswell LA, Scofield RH, Stevens AM, Guthridge JM, Chang DM, Song YW, Park JA, Lee EY, Boackle SA, Grossman JM, Hahn BH, Goodship TH, Cantor RM, Yu CY, Shen N, Tsao BP (2011) Association of genetic variants in complement factor H and factor H‐related genes with systemic lupus erythematosus susceptibility. PLoS Genet. 7, e1002079. Zheng L, Nukuna B, Brennan ML, Sun M, Goormastic M, Settle M, Schmitt D, Fu X, Thomson L, Fox PL, Ischiropoulos H, Smith JD, Kinter M, Hazen SL (2004) Apolipoprotein A‐I is a selective target for myeloperoxidase‐catalyzed oxidation and functional impairment in subjects with cardiovascular disease. J Clin Invest. 114, 529‐541. 241 Zintzaras E, Rodopoulou P, Sakellaridis N (2009) Variants of the arachidonate 5‐ lipoxygenase‐activating protein (ALOX5AP) gene and risk of stroke: a HuGE gene‐disease association review and meta‐analysis. Am J Epidemiol. 169: 523‐32. Zhang R, Brennan ML, Fu X, Aviles RJ, Pearce GL, Penn MS, Topol EJ, Sprecher DL, Hazen SL (2001) Association between myeloperoxidase levels and risk of coronary artery disease. JAMA. 286:2136‐42. Zheng L, Nukuna B, Brennan ML, Sun M, Goormastic M, Settle M, Schmitt D, Fu X, Thomson L, Fox PL, Ischiropoulos H, Smith JD, Kinter M, Hazen SL (2004) Apolipoprotein A‐I is a selective target for myeloperoxidase‐catalyzed oxidation and functional impairment in subjects with cardiovascular disease. J Clin Invest. 114:529‐41. 242 APPENDICES 243 Clinical and Genetic Association of Serum Ceruloplasmin With Cardiovascular Risk W.H. Wilson Tang, Yuping Wu, Jaana Hartiala, Yiying Fan, Alexandre F.R. Stewart, Robert Roberts, Ruth McPherson, Paul L. Fox, Hooman Allayee, Stanley L. Hazen Objective—Ceruloplasmin (Cp) is an acute-phase reactant that is increased in inflammatory diseases and in acute coronary syndromes. Cp has recently been shown to possess nitric oxide (NO) oxidase catalytic activity, but its impact on long-term cardiovascular outcomes in stable cardiac patients has not been explored. Methods and Results—We examined serum Cp levels and their relationship with incident major adverse cardiovascular events (MACE; death, myocardial infarction [MI], stroke) over 3-year follow-up in 4177 patients undergoing elective coronary angiography. We also carried out a genome-wide association study to identify the genetic determinants of serum Cp levels and evaluate their relationship to prevalent and incident cardiovascular risk. In our cohort (age 6311 years, 66% male, 32% history of MI, 31% diabetes mellitus), mean Cp level was 246 mg/dL. Serum Cp level was associated with greater risk of MI at 3 years (hazard ratio [quartile 4 versus 1] 2.35, 95% confidence interval [CI] 1.79–3.09, P0.001). After adjustment for traditional risk factors, high-sensitivity C-reactive protein, and creatinine clearance, Cp remained independently predictive of MACE (hazard ratio 1.55, 95% CI 1.10–2.17, P0.012). A 2-stage genome-wide association study identified a locus on chromosome 3 over the CP gene that was significantly associated with Cp levels (lead single-nucleotide polymorphism rs13072552; P1.9010 11 ). However, this variant, which leads to modestly increased serum Cp levels (1.5–2 mg/dL per minor allele copy), was not associated with coronary artery disease or future risk of MACE. Conclusion—In stable cardiac patients, serum Cp provides independent risk prediction of long-term adverse cardiac events. Genetic variants at the CP locus that modestly affect serum Cp levels are not associated with prevalent or incident risk of coronary artery disease in this study population. (Arterioscler Thromb Vasc Biol. 2012;32:516-522.) Key Words: coronary artery disease nitric oxide ceruloplasmin C eruloplasmin (Cp) is a circulating ferroxidase enzyme able to oxidize ferrous ions to less toxic ferric forms. 1 It is the major carrier of circulating copper and is synthesized and secreted by the liver. Cp not only acts as a mediator of iron oxidation but also is an acute phase reactant in the setting of inflammation (such as infections or inflammatory arthri- tis). In contrast, inherited liver disorders such as Wilson disease may present with lower than normal levels of circu- lating Cp. Recent studies support a role of Cp in regulating nitric oxide (NO) homeostasis. 2 Isolated Cp was shown capable of catalytically consuming NO through NO oxidase activity, and plasma NO oxidase activity was decreased after Cp immu- nodepletion, in Cp knockout mice, and in people with congenital aceruloplasminemia. 2 A mechanistic role for Cp in vascular disease beyond its association as an acute phase protein is thus suggested. Interestingly, myocardial uptake of Cp has been demonstrated in animal models, 3 and epidemi- ological studies have linked Cp levels with cardiovascular risk both in apparently healthy individuals 4–7 and in the setting of acute coronary syndromes. 8–10 Despite the relative ease and affordability of Cp testing, few studies have exam- ined Cp and its association with cardiovascular outcomes. The clinical prognostic value of Cp levels is not well understood, particularly in the contemporary era with statin therapy. Methods Study Population The Cleveland Clinic GeneBank study is a large, prospective cohort study, run from 2001 to 2006, that established a well-characterized clinical repository with clinical and longitudinal outcomes data Received on: August 16, 2011; final version accepted on: October 17, 2011. From the Center for Cardiovascular Diagnostics and Prevention, Department of Cell Biology, Lerner Research Institute, Cleveland Clinic, Cleveland, OH (W.H.W.T., P.L.F., S.L.H.); Department of Mathematics, Cleveland State University, Cleveland, OH (Y.W., Y.F.); Department of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, CA (J.H., H.A.); John and Jennifer Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Ontario, Canada (A.F.R.S., R.R., R.M.). This manuscript was sent to Hugh Watkins, Consulting Editor, for review by expert referees, editorial decision, and final disposition. Correspondence to W.H. Wilson Tang, MD, Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, 9500 Euclid Ave, Desk J3-4, Cleveland, OH 44195. E-mail tangw@ccf.org © 2011 American Heart Association, Inc. Arterioscler Thromb Vasc Biol is available at http://atvb.ahajournals.org DOI: 10.1161/ATVBAHA.111.237040 516 244 composed of consenting subjects undergoing elective diagnostic cardiac catheterization procedure. All GeneBank participants gave written informed consent approved by the Cleveland Clinic Institu- tional Review Board. This analysis included 4177 consecutive subjects of white European ancestry without evidence of myocardial infarction (MI; cardiac troponin I 0.03 ng/mL) with plasma samples available for analysis. An estimate of creatinine clearance was calculated using the Cockcroft-Gault equation. The presence of coronary artery disease (CAD) was confirmed by luminal stenosis of at least 50% in any major coronary arteries. Major adverse cardio- vascular event (MACE) was defined as death, nonfatal MI, or nonfatal cerebrovascular accident following enrollment. Adjudicated outcomes were ascertained over the ensuing 3 years for all subjects following enrollment. Cp Assay Quantitative determination of Cp was performed using an immuno- turbidimetric assay (Architect ci8200, Abbott, Abbott Park IL), which provides highly sensitive measurement of Cp levels with an intraassay coefficient of variation of 3.7%, interassay precision of up to 4%, and a reference range of 20 to 60 mg/dL. High-sensitivity C-reactive protein (hsCRP), myeloperoxidase (MPO), uric acid, creatinine, and fasting blood glucose and lipid profiles were mea- sured on the same platform, as previously described. 11 Genotyping Genome-wide genotyping of single-nucleotide polymorphisms (SNPs) was performed on the Affymetrix Genome-Wide Human Array 6.0 chip in a subset of European ancestry patients in Gen- eBank. Using these data and those from 120 phased chromosomes from the HapMap CEU samples (HapMap r22 release, National Center for Biotechnology Information build 36), genotypes were imputed for untyped SNPs across the genome using MACH 1.0 software. All imputations were done on the forward () strand using 562 554 genotyped SNPs that had passed quality control filters. Quality control filters for the imputed data set excluded SNPs with Hardy-Weinberg equilibrium probability values 0.0001 or minor allele frequencies 1%, and individuals with less than 95% call rates. This resulted in 2 421 770 autosomal SNPs that were available for analysis. Statistical Analyses The Student t test or Wilcoxon-Rank sum test for continuous variables and 2 test for categorical variables were used to examine the difference between the groups. Kaplan-Meier analysis with Cox proportional hazards regression was used for time-to-event analysis to determine hazard ratio and 95% confidence intervals (95% CI) for 1-year and 3-year MACE. Levels of Cp were then adjusted for traditional cardiac risk factors in a multivariable model, including age, gender, diabetes mellitus, systolic blood pressure, low- and high-density lipoprotein, and triglyceride, as well as log-transformed hsCRP and creatinine clearance. Receiver operating characteristic curve analyses and 5-fold cross validation were used to determine the optimal cutoff. The improvement in model performance introduced by the inclusion of Cp was evaluated using net reclassification improvement index. The C statistic was calculated using the area under the receiver operating characteristic curve. To determine the optimal cutoff for Cp, we used a logistic regression model to estimate the risk of MACE. The 5-fold cross validation divides the data into 5 approximately equally sized portions, and a logistic regression model is trained on 4 parts of the data and then estimates the risk of MACE in the fifth part. This is repeated for each of the 5 parts, and the area under the curve with the estimated risk was calculated. This process is carried out for a grid of values of Cp cutoff values, ranging from 17.3 mg/dL (10th percentile) to 31.5 mg/dL (90th percentile) with an increment of 0.1 mg/dL. The optimal cutoff is chosen to maximize area under the curve values. Probability values0.05 were considered statistically significant. A genome-wide association study (GWAS) for serum Cp levels was carried out with adjustment for age and gender under an additive model. Linear regression analyses were carried out with PLINK (version 1.07) using log-transformed values. Unconditional multiple logistic regression was used to independently test for association of genetic variants with the presence and severity of CAD, with adjustment for age, gender, medication use (aspirin or statins), and Framingham Adult Treatment Panel III risk score (which includes smoking and diabetes status). Relative risk for experiencing a MACE was assessed using Cox proportional hazard models with adjustment for age, gender, medication use, and Framingham Adult Treatment Panel III risk score. These analyses were carried out assuming additive and dominant models. Adjusted odds or hazard ratios (OR or hazard ratio) with 95% CIs are reported with 2-sided probability values. All statistical analyses were performed using R version 9.2 (R Foundation for Statistical Computing, Vienna, Austria) and SAS version 9.2 (SAS Institute Inc, Cary, NC). Results Baseline characteristics of the study population are shown in Table 1. The mean and median serum Cp levels were 24 and 23 mg/dL (interquartile range 20–27 mg/dL), respectively. Patients with elevated Cp were more likely to be older and female, with more cardiovascular risk factors, including dyslipidemia, history of diabetes mellitus, and poorer renal function at baseline. There was strong direct correlation between Cp and hsCRP (r0.52, P0.001) but far weaker correlations with total leukocyte count (r0.15, P0.001) or Table 1. Baseline Characteristics Without Event With Event Demographics and cardiovascular risk factors Age, y 6311 6710 Male, % 67 64 Diabetes mellitus, % 29 41 Hypertension, % 70 75 Smokers, former/current, % 65 70 Prior myocardial infarction, % 31 44 Laboratory data LDL cholesterol, mg/dL 95 (77–116) 95 (77–116) HDL cholesterol, mg/dL 34 (28–41) 33 (27–40) Triglycerides, mg/dL 114 (83–162) 120 (84–163) Total leukocyte count,10 9 cells/L 6.04 (5.02–7.34) 6.21 (5.07–7.53) Uric acid, mg/dL 6.1 (5.1–7.2) 6.5 (5.5–7.8) hsCRP, mg/L 1.95 (0.90–4.36) 3.50 (1.46–7.22) Myeloperoxidase, pg/mL 102 (70–189) 115 (74–227) Creatinine clearance, mL/min1.73 m 2 102 (79–127) 86 (59–114) Baseline medications Aspirin, % 74 67 -Adrenergic blockers, % 61 63 Angiotensin converting enzyme inhibitors, % 48 58 Statin therapy, % 60 55 Events are defined as death, myocardial infarction, or stroke. Values are expressed as median (interquartile ranges). LDL indicates low-density lipopro- tein; HDL, high-density lipoprotein; hsCRP, high-sensitivity C-reactive protein. Tang et al Ceruloplasmin and Cardiovascular Risk 517 245 MPO (r0.12, P0.001) and no correlation with serum uric acid levels (r0.02, P0.09). Association of Serum Cp Levels With Future MACE Table 2 demonstrates the relationship between Cp levels in quartiles with 3-year risk for incident MACE. This graded risk was clearly illustrated in the Kaplan-Meier analysis (Figure 1A) when stratified according to quartile Cp ranges (quartiles 1 versus 4, hazard ratio: 2.35, 95% CI 1.79–3.09, P0.001). After adjustment for traditional risk factors, in- creased Cp levels remained significantly associated with incident major long-term major adverse cardiac events at 3 years (Table 2). The results were similar when stratified by gender, even though the cutoffs of the quartiles were higher in women (adjusted hazard ratio 1.77, 95% CI 1.13–2.77, P0.013; fourth quartile Cp 31.5 mg/dL) than in men (adjusted hazard ratio 2.55, 95% CI 1.82–3.57, P0.001; fourth quartile Cp 24.6 mg/dL). In particular, those with elevated Cp (cutoff at 22 mg/dL) and MPO (cutoff at 322 pg/mL), another known oxidase that contributes to catalytic consumption of NO within the vascular compartment, 12–14 experienced the highest risk of developing future MACE (Figure 1B). Such prognostic value remained significant in various subgroups stratified by age, gender, presence of diabetes mellitus, hypertension, or renal insufficiency (Figure 2). When results were stratified by hsCRP or with MPO, we observed synergistic prediction of future MACE (Figure 3 and Table 2). Inclusion of Cp resulted in a significant improvement in risk estimation, based on net reclassification improvement index (9.6%, P0.001) and C statistic (67.7% versus 65.2%, P0.003). GWAS for Serum Cp Levels We next performed a 2-stage GWAS for serum Cp levels in 4697 GeneBank subjects (all of European ancestry). In stage 1(n2647), the genomic inflation factor was 1.001, indicat- ing that the GWAS results are not affected by underlying population (Figure 4). Serum Cp levels were primarily controlled by a single locus on chromosome 3, which con- Table 2. Adjusted Hazard Ratio for Major Adverse Cardiac Events at 3-y Follow-Up According to Serum Ceruloplasmin Quartiles Serum Ceruloplasmin Level Quartile 1 Quartile 2 Quartile 3 Quartile 4 All subjects (n4177) Range, mg/dL 19.9 19.9–23.1 23.1–27.0 27.0 Event rate 74/1044 106/1044 133/1042 169/1047 Unadjusted 1 1.46 (1.09–1.97)* 1.91 (1.44–2.53)† 2.35 (1.79–3.09)† Adjusted Model 1 1 1.38 (1.02–1.87)* 1.66 (1.24–2.22)† 2.22 (1.64–3.00)† Model 2 1 1.40 (1.03–1.91)* 1.82 (1.35–2.45)† 2.47 (1.81–3.39)† Model 3 1 1.25 (0.93–1.69) 1.39 (1.03–1.88)* 1.50 (1.06–2.13)* Secondary prevention subjects (n3100) Event rate 60/772 93/774 109/779 143/775 Unadjusted 1 1.59 (1.16–2.2)† 1.84 (1.35–2.52)† 2.48 (1.84–3.35)† Adjusted Model 1 1 1.45 (1.04–2.02)* 1.54 (1.11–2.14)† 2.08 (1.48–2.91)† Model 2 1 1.49 (1.06–2.08)* 1.67 (1.19–2.34)† 2.42 (1.70–3.43)† Model 3 1 1.34 (0.97–1.86) 1.33 (0.95–1.87) 1.54 (1.05–2.25)* Primary prevention subjects (n1077) Event rate 12/269 14/269 23/268 18/271 Unadjusted 1 1.17 (0.55–2.53) 2.00 (1.00–4.00) 2.41 (1.23–4.72)* Adjusted 1 1.35 (0.62–2.93) 2.42 (1.20–4.87)* 3.19 (1.54–6.62)† Model 1 1 1.40 (0.64–3.05) 2.51 (1.24–5.09)* 3.54 (1.71–7.35)† Model 2 1 1.14 (0.52–2.52) 1.77 (0.87–3.62) 1.74 (0.76–4.00) Model 3 Model 1: adjusted for traditional risk factors (including age, gender, systolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, smoking, diabetes mellitus) and medications (angiotensin coverting enzyme inhibitors, beta-blockers, statin, aspirin). Model 2: adjusted for traditional risk factors plus myeloperoxidase. Model 3: adjusted for traditional risk factors plus high-sensitivity C-reactive protein and serum uric acid. HR indicates hazard ratio; MACE, major adverse cardiac events (death, myocardial infarction, stroke). *P0.05 (compared with first quartile). †P0.01 (compared with first quartile). 518 Arterioscler Thromb Vasc Biol February 2012 246 tains the CP gene itself. As shown in Table 3, the lead SNP at this locus (rs13072552) modestly but significantly in- creases of serum Cp levels by 2 to 3 mg/dL as a function of carrying 1 or 2 copies of the T allele (P3.6310 13 ). The rs13072552 SNP has a minor allele frequency of 0.08, is located in intron 9 of CP, and is in strong linkage disequi- librium (r 2 0.7) with the 2 other SNPs at this locus (rs11714000 and rs11921705) that exhibit similar association with Cp levels (data not shown). In stage 2, we genotyped 2050 additional GeneBank subjects in whom Cp levels were available and confirmed the association of rs13072552 (P2.4210 2 ). This association remained highly signifi- cant in a combined analysis with all 4697 subjects (Table 3), and an analysis stratified by gender did not suggest that the association was gender-specific (Supplemental Table I, avail- able online at http://atvb.ahajournals.org). Association of CP Variants With Prevalent and Incident Risk of CAD We next sought to determine whether the genetic factors controlling Cp levels were associated with prevalent and incident risk of CAD. In addition to the 4697 subjects used for the quantitative analyses described above, we genotyped rs13072552 in 3448 additional GeneBank subjects with available CAD phenotype data for these analyses (total n8145). Under an additive model, the T allele of rs13072552, which increases serum Cp levels, was not associated with the presence or severity of CAD (Table 4). We also did not observe an association with history of MI in subjects with CAD or with future risk of MACE (Table 4) or when males and females were analyzed separately for MACE (P0.39 for males; P0.51 for females). Given the small number of subjects and to increase power, we also carried out these analyses using a dominant model with TT homozygotes grouped together with GT heterozygotes. However, these analyses also did not reveal an association with CAD, history of MI, or MACE (Table 4). Discussion A key finding of this study is the strong independent prognostic value of circulating Cp levels in stable patients undergoing cardiac evaluation in the contemporary statin era, above and beyond traditional cardiac risk factors, as well as cardiac and inflammatory risk markers. However, the effect of underlying genetic determinants of serum Cp levels were modest, and there was no gender-specific difference in prognostic value despite higher serum levels of Cp observed in women versus in men. With the broad availability and economic testing for serum Cp measurements in the clinical setting, these findings highlight the need to gain further insights into underlying pathophysiologic process Figure 2. Forrest plot of risk prediction for serum ceruloplasmin levels according to subgroups. DM indicates diabetes mellitus; HTN, hypertension; CAD, coronary artery disease; hsCRP, high- sensitivity C-reactive protein; WBC, white blood cell; MPO, myeloperoxidase. Figure 1. Kaplan-Meier analysis for 3-year major adverse car- diac events stratified according to serum ceruloplasmin quartiles (A) and for groups stratified by high/low serum ceruloplasmin (22 mg/dL vs 22 mg/dL) and high/low plasma myeloperoxi- dase levels (322 pg/mL vs 322 pg/mL) (B). Cp indicates ceruloplasmin; Q, quartile; H, high; L, low; MPO, myeloperoxi- dase; Q, quartile. Tang et al Ceruloplasmin and Cardiovascular Risk 519 247 related to increased serum Cp levels, which can affect long-term outcomes. The precise role that Cp plays in the pathogenesis of cardiovascular morbidity and mortality has not been well described, although there has been extensive literature dating back to the 1970s describing the association between Cp and the heart, 3,15 as well as its important oxidase activities. 16 Patients with MI have higher observed serum Cp levels, which returned to normal ranges over time, suggesting its restrictive role as an acute phase reactant. 17 Nevertheless, recent mechanistic studies show that Cp functions as an NO oxidase in vivo, suggesting that Cp elevations may lead to decreased NO bioavailability and endovascular dysfunction. 2 Our findings are consistent with findings from prior epide- miological data in patients without known CAD. In the Helsinki Heart Study of dyslipidemic, middle-age men, higher serum Cp level (but not ferritin) was associated with graded increase in risk of cardiovascular events in a case- control comparison. 18 Another prospective cohort study of elderly subjects also demonstrated a relationship between serum Cp levels and subsequent development of MI, whereas adjustment for hsCRP and leukocyte count reduced the excess risk by 33%. 5 Unlike our present study population, the majority of patients in prior reports did not have prevalent cardiac diseases and were not treated with cardiac or lipid- lowering medications. It is interesting to observe that in our patient population with contemporary cardiac care, we still observed strong correlations between Cp and hsCRP levels but far weaker associations with MPO and leukocyte counts. Cytokines released from jeopardized tissues may stimulate the liver to synthesize acute phase proteins. Cp is one of these well-known inflammation-sensitive plasma proteins found to have protective effects in isolated rat hearts subjected to ischemia-reperfusion. 19,20 The exact mechanisms for the car- dioprotection are unclear but likely are due to a wide variety of extracellular antioxidative ferroxidase and reactive oxygen species scavenging effects, 1 combined with putative glutathione-peroxidase and NO-oxidase/S-nitrosating activi- ties. 21 Whether such potentially beneficial effects are modi- fied by oxidative stress 22,23 and progress into vasculopathic factors that promote disease progression has been debated. 24 Figure 3. Event rates for major adverse cardiac events (MACE) stratified according to tertiles of ceruloplasmin (Cp), high- sensitivity C-reactive protein (hsCRP) (top), and myeloperoxi- dase (MPO, bottom). Figure 4. Quantile-Quantile and Manhattan plots from genome-wide association study (GWAS) for serum ceruloplasmin levels. The probability values obtained from single-nucleotide polymorphisms (SNPs) in the GWAS analyses deviate from that expected by chance, suggesting that a subset of these signals indicate true associations (A). Serum ceruloplasmin levels in this study population (n2647) are controlled predominantly by a locus on chromosome 3 containing the CP gene (B). 520 Arterioscler Thromb Vasc Biol February 2012 248 In the present study, we also used a GWAS approach to investigate the genetic determinants of serum Cp levels, which, to our knowledge, has not been previously reported. These analyses revealed that common genetic determinants linked to serum Cp levels in this white patient population are predomi- nantly located within the CP locus. The 3 most significantly associated SNPs are all intronic and in strong linkage disequi- librium with each other. In addition, previous studies have shown that plasma concentrations of Cp are regulated at the posttranscriptional level. Specifically, a cis-regulatory element in the 3 untranslated region of the CP transcript called GAIT (interferon--activated inhibitor of translation) leads to selective translational silencing in myeloid cells. 25,26 Despite the modest cis effect of the CP locus, our evalu- ation of the lead SNP (rs13072552) did not reveal an association with either the risk of CAD or future MACE. The lack of such an association could be due to several factors. For example, rs13072552 explains less than 1% of the variation in serum Cp levels, decreasing the power for detecting an association with cardiovascular outcomes. In addition, serum lipid and inflammatory markers, other ge- netic factors, and dietary/behavioral factors also contribute to the complex pathophysiology of CAD, MI, and MACE. The subjects used in our study were also recruited from patients undergoing elective cardiac catheterization, which results in a study population skewed toward individuals mostly having documented CAD and being treated with medications to lower their risk of future MACE. Such confounders, taken together with the low minor allele frequency of rs13072552 and its modest effects on serum Cp levels, in combination with the recognition that posttranscriptional processes play a major role in Cp production, may explain, in part, the negative association of the CP locus with CAD and MACE. Our study is also limited by only including subjects of white European ancestry, and additional studies in other ethnicities and with increased power will be required to determine whether the negative genetic findings hold true. However, it is interesting to note that our observations with Cp are similar to other inflammation-sensitive plasma proteins, such as C-reactive protein, where consistent prognostic effects were not confirmed by genetic predisposition of cardiovascular risk in relatively large data sets. 27 Thus, further studies are also warranted to explore the molecular mechanism underly- ing the strong association between serum Cp levels and cardiovascular events beyond those observed with alternative acute phase proteins (eg, hsCRP), leukocyte counts, and traditional cardiac risk factors. The recent discovery of a potential role for Cp as a catalytic sink for NO consumption in vivo raises new and important questions about the vascular function(s) and clinical utility of this unusual copper-containing protein. The addition of Cp testing, a readily available and affordable assay, to clinical practice may afford synergistic prognostic value with tradi- tional cardiac risk factors and alternative inflammation mark- ers, such as hsCRP, MPO, and leukocyte count. In an era of cost effectiveness, additional studies are warranted to deter- mine whether Cp testing may provide value in prioritizing preventive interventions among those with unrecognized heightened cardiovascular risks. Conclusion In subjects undergoing elective cardiac evaluation, serum Cp provides additive risk prediction of long-term adverse cardiac events independent of traditional cardiac risk factors, hsCRP, MPO, and leukocyte count. Common genetic variants at the CP locus that are linked to serum Cp levels are not associated with prevalent or incident risk of CAD in this study population. Table 4. Association of rs13072552 With Prevalent and Incident Risk of CAD Trait GG (n6941) GT (n1130) TT (n74) P Value GT/TT (n1204) P Value No CAD vs CAD , OR (95% CI) 1 0.99 (0.84–1.17) 1.44 (0.72–2.87) 0.74 1.01 (0.86–1.19) 0.92 No disease vs mild disease, OR (95% CI)* 1 0.92 (0.78–1.09) 1.21 (0.67–2.19) 0.61 0.94 (0.80–1.10) 0.44 No disease vs severe disease, OR (95% CI)* 1 1.04 (0.87–1.24) 0.69 (0.35–1.39) 0.93 1.02 (0.86–1.21) 0.84 CAD /MI vs CAD /MI , OR (95% CI) 1 0.97 (0.83–1.13) 1.02 (0.60–1.74) 0.77 0.97 (0.84–1.13) 0.72 Future MACE, HR (95% CI) 1 1.08 (0.91–1.28) 1.32 (0.78–2.23) 0.22 1.09 (0.93–1.29) 0.28 ORs and HRs were calculated with adjustment for age, gender, medication use (aspirin and/or statins), and Framingham risk score. OR indicates odds ratio; HR, hazard ratio. *Disease severity was defined as having 50% stenosis in 1 or 2 (mild) or 3 (severe) major epicardial arteries. Table 3. Association of rs13072552 With Serum Cp Levels Stage MAF GG GT TT P Value* GWAS (n2647) 0.081 236(n2234) 256(n393) 288(n20) 3.6310 13 Replication (n2050) 0.077 256(n1774) 266(n263) 249(n13) 2.4210 2 Combined (n4697) 0.079 246(n4008) 256(n656) 269(n33) 1.9010 11 Mean serum Cp levels are shown as a function of genotype for rs13072552. MAF indicates minor allele frequency. *P values were obtained using log-transformed Cp values and after adjustment for age and gender. Tang et al Ceruloplasmin and Cardiovascular Risk 521 249 Sources of Funding This study was supported by National Institutes of Health Grant P01-HL076491. The study GeneBank was supported in part by National Institutes of Health Grants 1P01-HL098055 (to S.L.H.), 1R01-HL103866 (to S.L.H.), 1R01-DK080732 (to S.L.H.), 1R01- DK083359 (to P.L.F.), and 1R01-HL103931 (to W.H.T.). The John and Jennifer Ruddy Canadian Cardiovascular Genetics Centre inves- tigators are supported by Canadian Institute of Health Research (CIHR) #MOP-82810 (to R.R.), Canadian Foundation for Innovation (CFI) #11966 (to R.R.), Heart and Stroke Foundation of Ontario (HSFO) #NA6001 (to R.M.), CIHR #MOP172605 (to R.M.), and CIHR #MOP77682 (to A.F.R.S.). Supplies for Cp testing were provided for by Abbott Laboratories, Inc. Disclosures Dr Tang received research grant support from Abbott Laboratories and served as a consultant for Medtronic Inc and St. Jude Medical. Dr Hazen is named as coinventor on pending and issued patents held by the Cleveland Clinic relating to cardiovascular diagnostics. Dr Hazen has been paid as a consultant or speaker for the following companies: Cleveland Heart Lab, Inc.; Esperion; Liposciences Inc.; Merck & Co., Inc.; and Pfizer Inc. Dr Hazen has received research funds from Abbott; Cleveland Heart Lab, Inc.; Esperion; and Liposciences, Inc. Dr Hazen has the right to receive royalty pay- ments for inventions or discoveries related to cardiovascular diag- nostics from Abbott Laboratories; Cleveland Heart Lab, Inc.; Frantz Biomarkers, LLC; and Siemens. References 1. Fox PL, Mazumder B, Ehrenwald E, Mukhopadhyay CK. Ceruloplasmin and cardiovascular disease. Free Radic Biol Med. 2000;28:1735–1744. 2. Shiva S, Wang X, Ringwood LA, Xu X, Yuditskaya S, Annavajjhala V, Miyajima H, Hogg N, Harris ZL, Gladwin MT. Ceruloplasmin is a NO oxidase and nitrite synthase that determines endocrine NO homeostasis. Nat Chem Biol. 2006;2:486–493. 3. Orena SJ, Goode CA, Linder MC. Binding and uptake of copper from ceruloplasmin. Biochem Biophys Res Commun. 1986;139:822–829. 4. Gocmen AY, Sahin E, Semiz E, Gumuslu S. Is elevated serum cerulo- plasmin level associated with increased risk of coronary artery disease? Can J Cardiol. 2008;24:209–212. 5. Klipstein-Grobusch K, Grobbee DE, Koster JF, Lindemans J, Boeing H, Hofman A, Witteman JC. Serum caeruloplasmin as a coronary risk factor in the elderly: the Rotterdam Study. Br J Nutr. 1999;81:139–144. 6. Suciu A, Chirulescu Z, Zeana C, Pirvulescu R. Study of serum cerulo- plasmin and of the copper/zinc ratio in cardiovascular diseases. Rom J Intern Med. 1992;30:193–200. 7. Reunanen A, Knekt P, Aaran RK. Serum ceruloplasmin level and the risk of myocardial infarction and stroke. Am J Epidemiol. 1992;136: 1082–1090. 8. Brunetti ND, Pellegrino PL, Correale M, De Gennaro L, Cuculo A, Di Biase M. Acute phase proteins and systolic dysfunction in subjects with acute myocardial infarction. J Thromb Thrombolysis. 2008;26:196–202. 9. Correale M, Brunetti ND, De Gennaro L, Di Biase M. Acute phase proteins in atherosclerosis (acute coronary syndrome). Cardiovasc Hematol Agents Med Chem. 2008;6:272–277. 10. Brunetti ND, Correale M, Pellegrino PL, Cuculo A, Biase MD. Acute phase proteins in patients with acute coronary syndrome: correlations with diagnosis, clinical features, and angiographic findings. Eur J Intern Med. 2007;18:109–117. 11. Tang WH, Wu Y, Nicholls SJ, Hazen SL. Plasma myeloperoxidase predicts incident cardiovascular risks in stable patients undergoing medical management for coronary artery disease. Clin Chem. 2011;57: 33–39. 12. Vita JA, Brennan ML, Gokce N, Mann SA, Goormastic M, Shishehbor MH, Penn MS, Keaney JF Jr, Hazen SL. Serum myeloperoxidase levels independently predict endothelial dysfunction in humans. Circulation. 2004;110:1134–1139. 13. Eiserich JP, Baldus S, Brennan ML, Ma W, Zhang C, Tousson A, Castro L, Lusis AJ, Nauseef WM, White CR, Freeman BA. Myeloperoxidase, a leukocyte-derived vascular NO oxidase. Science. 2002;296:2391–2394. 14. Abu-Soud HM, Hazen SL. Nitric oxide modulates the catalytic activity of myeloperoxidase. J Biol Chem. 2000;275:5425–5430. 15. Marceau N, Aspin N. Distribution of ceruloplasmin-ceruloplasmin-bound 67 Cu in the rat. Am J Physiol. 1972;222:106–110. 16. Frieden E, Hsieh HS. The biological role of ceruloplasmin and its oxidase activity. Adv Exp Med Biol. 1976;74:505–529. 17. Singh TK. Serum ceruloplasmin in acute myocardial infarction. Acta Cardiol. 1992;47:321–329. 18. Manttari M, Manninen V, Huttunen JK, Palosuo T, Ehnholm C, Heinonen OP, Frick MH. Serum ferritin and ceruloplasmin as coronary risk factors. Eur Heart J. 1994;15:1599–1603. 19. Atanasiu R, Dumoulin MJ, Chahine R, Mateescu MA, Nadeau R. Anti- arrhythmic effects of ceruloplasmin during reperfusion in the ischemic isolated rat heart. Can J Physiol Pharmacol. 1995;73:1253–1261. 20. Chahine R, Mateescu MA, Roger S, Yamaguchi N, de Champlain J, Nadeau R. Protective effects of ceruloplasmin against electrolysis- induced oxygen free radicals in rat heart. Can J Physiol Pharmacol. 1991;69:1459–1464. 21. Paradis M, Gagne J, Mateescu MA, Paquin J. The effects of nitric oxide-oxidase and putative glutathione-peroxidase activities of cerulo- plasmin on the viability of cardiomyocytes exposed to hydrogen peroxide. Free Radic Biol Med. 2010;49:2019–2027. 22. Tapryal N, Mukhopadhyay C, Das D, Fox PL, Mukhopadhyay CK. Reactive oxygen species regulate ceruloplasmin by a novel mRNA decay mechanism involving its 3 -untranslated region: implications in neurode- generative diseases. J Biol Chem. 2009;284:1873–1883. 23. Mukhopadhyay CK, Mazumder B, Lindley PF, Fox PL. Identification of the prooxidant site of human ceruloplasmin: a model for oxidative damage by copper bound to protein surfaces. Proc Natl Acad Sci U S A. 1997;94:11546–11551. 24. Shukla N, Maher J, Masters J, Angelini GD, Jeremy JY. Does oxidative stress change ceruloplasmin from a protective to a vasculopathic factor? Atherosclerosis. 2006;187:238–250. 25. Mazumder B, Sampath P, Fox PL. Translational control of ceruloplasmin gene expression: beyond the IRE. Biol Res. 2006;39:59–66. 26. Sampath P, Mazumder B, Seshadri V, Fox PL. Transcript-selective trans- lational silencing by interferon is directed by a novel structural element in the ceruloplasmin mRNA 3 untranslated region. Mol Cell Biol. 2003; 23:1509–1519. 27. Dehghan A, Dupuis J, Barbalic M, Bis JC, Eiriksdottir G, Lu C, Pellikka N, Wallaschofski H, Kettunen J, Henneman P, Baumert J, Strachan DP, Fuchsberger C, Vitart V, Wilson JF, Pare G, Naitza S, Rudock ME, Surakka I, de Geus EJ, Alizadeh BZ, Guralnik J, Shuldiner A, Tanaka T, Zee RY, Schnabel RB, Nambi V, Kavousi M, Ripatti S, Nauck M, Smith NL, Smith AV, Sundvall J, Scheet P, Liu Y, Ruokonen A, Rose LM, Larson MG, Hoogeveen RC, Freimer NB, Teumer A, Tracy RP, Launer LJ, Buring JE, Yamamoto JF, Folsom AR, Sijbrands EJ, Pankow J, Elliott P, Keaney JF, Sun W, Sarin AP, Fontes JD, Badola S, Astor BC, Hofman A, Pouta A, Werdan K, Greiser KH, Kuss O, Meyer zu Schwabedissen HE, Thiery J, Jamshidi Y, Nolte IM, Soranzo N, Spector TD, Volzke H, Parker AN, Aspelund T, Bates D, Young L, Tsui K, Siscovick DS, Guo X, Rotter JI, Uda M, Schlessinger D, Rudan I, Hicks AA, Penninx BW, Thorand B, Gieger C, Coresh J, Willemsen G, Harris TB, Uitterlinden AG, Jarvelin MR, Rice K, Radke D, Salomaa V, Willems van Dijk K, Boerwinkle E, Vasan RS, Ferrucci L, Gibson QD, Bandinelli S, Snieder H, Boomsma DI, Xiao X, Campbell H, Hayward C, Pramstaller PP, van Duijn CM, Peltonen L, Psaty BM, Gudnason V, Ridker PM, Homuth G, Koenig W, Ballantyne CM, Witteman JC, Benjamin EJ, Perola M, Chasman DI. Meta-analysis of genome-wide association studies in 80 000 subjects identifies multiple loci for C-reactive protein levels. Circulation. 2011;123:731–738. 522 Arterioscler Thromb Vasc Biol February 2012 250 Supplement Material Supplemental Table I. Association of rs13072552 with Serum Cp Levels Stratified by Gender. Males Stage MAF GG GT TT *p-value Males GWAS (n = 1880) 0.081 22 ± 5 (n = 1586) 23 ± 5 (n = 283) 25 ± 7 (n = 11) 2.25 x 10 -8 Replication (n = 1288) 0.075 23 ± 5 (n = 1105) 24 ± 5 (n = 173) 23 ± 10 (n = 10) 0.15 Combined (n = 3168) 0.079 22 ± 5 (n = 2691) 23 ± 5 (n = 456) 24 ± 9 (n = 21) 2.92 x 10 -07 Females GWAS (n = 767) 0.083 27 ± 6 (n = 648) 30 ± 7 (n = 110) 32 ± 9 (n = 9) 1.72 x 10 -5 Replication (n = 762) 0.063 28 ± 6 (n = 669) 29 ± 6 (n = 90) 28 ± 4 (n = 3) 4.84 x 10 -02 Combined (n = 1529) 0.073 28 ± 6 (n = 1317) 30 ± 7 (n = 200) 31 ± 8 (n = 12) 7.36 x 10 -6 Mean serum Cp levels as a function of genotype for rs13072552. Minor allele frequency (MAF). *p-values obtained using log transformed Cp values and after adjustment for age. 251 2803 H eightened oxidative stress in the form of oxidation of lipids and proteins by reactive oxidant species adversely contributes to disease progression in cardiovascular disease. 1 Paraoxonase-1 (PON-1) belongs to a family of high-density lipoprotein−associated enzymes that show hydrolytic activity toward a variety of substrates, including toxins in the environment 2 and oxidized lipids in the body. 3 Consequently, diminished activities of PON-1 and other paraoxonases have been associated with the development of cardiovascular disease. 4,5 PON-1 (EC 3.1.1.2) activity in serum is classically named after the substrate used to monitor enzymatic function, namely, paraoxonase activity (using paraoxon as substrate) and arylesterase activity (using phenyl acetate as substrate). 6 Our group has recently observed the relationship between a specific PON-1 genotype and functional activity with multiple systemic measures of oxidative stress and cardiovascular disease risk in humans. 7,8 Based on these encouraging findings, we developed analytically validated semiautomated high throughput methods for arylesterase and paraoxonase activity assays amenable to large scale clinical and genetic studies. We sought to expand and validate our findings in an independent cohort of stable patients undergoing cardiac evaluation to examine and contrast the potential role of distinct circulating PON-1 activity measures to predict adverse disease progression. In addition, we sought to identify genetic loci controlling paraoxonase and arylesterase activity by carrying Received on: May 26, 2012; final version accepted on: August 29, 2012. From the Department of Cell Biology, Center for Cardiovascular Diagnostics and Prevention, Lerner Research Institute (W.H.W.T., S.L.H.), and Department of Cardiovascular Medicine, Heart and Vascular Institute (W.H.W.T., S.L.H.), Cleveland Clinic, Cleveland, OH; Department of Preventive Medicine and Institute for Genetic Medicine, University of Southern California Keck School of Medicine, Los Angeles, CA (J.H., H.A.); Department of Mathematics, Cleveland State University, Cleveland OH (Y.F., Y.W.); John and Jennifer Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada (A.F.R.S., R.R., R.M.); Medizinische Klinik II, Universität zu Lübeck, Lübeck, Germany (J.E.); Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA (S.K.). A full list of authors and affiliations for the CARDIoGRAM Consortium is provided in the appendix. The online-only Data Supplement is available with this article at http://atvb.ahajournals.org/lookup/suppl/doi:10.1161/A TVBAHA.112.253930/-/DC1. Correspondence to Stanley L. Hazen, MD, PhD, 9500 Euclid Avenue, NE-10, Cleveland, OH 44195. E-mail hazens@ccf.org © 2012 American Heart Association, Inc. Arterioscler Thromb Vasc Biol is available at http://atvb.ahajournals.org DOI: 10.1161/ATVBAHA.112.253930 Objective—Diminished serum paraoxonase and arylesterase activities (measures of paraoxonase-1 [PON-1] function) in humans have been linked to heightened systemic oxidative stress and atherosclerosis risk. The clinical prognostic use of measuring distinct PON-1 activities has not been established, and the genetic determinants of PON-1 activities are not known. Methods and Results—We established analytically robust high-throughput assays for serum paraoxonase and arylesterase activities and measured these in 3668 stable subjects undergoing elective coronary angiography without acute coronary syndrome and were prospectively followed for major adverse cardiovascular events (MACE= death, myocardial infarction, stroke) over 3 years. Low serum arylesterase and paraoxonase activities were both associated with increased risk for MACE, with arylesterase activity showing greatest prognostic value (quartile 4 versus quartile 1; hazard ratio 2.63; 95% CI, 1.97–3.50; P<0.01). Arylesterase remained significant after adjusting for traditional risk factors, C-reactive protein, and creatinine clearance (hazard ratio, 2.20; 95% CI, 1.60–3.02; P<0.01), predicted future development of MACE in both primary and secondary prevention populations, and reclassified risk categories incrementally to traditional clinical variables. A genome-wide association study identified distinct single nucleotide polymorphisms within the PON-1 gene that were highly significantly associated with serum paraoxonase (1.18×10 -303 ) or arylesterase (4.99×10 −116 ) activity but these variants were not associated with either 3-year MACE risk in an angiographic cohort (n=2136) or history of either coronary artery disease or myocardial infarction in the Coronary Artery Disease Genome-Wide Replication and Meta- Analysis consortium (n≈80 000 subjects). Conclusion—Diminished serum arylesterase activity , but not the genetic determinants of PON-1 functional measures, provides incremental prognostic value and clinical reclassification of stable subjects at risk of developing MACE. (Arterioscler Thromb V asc Biol. 2012;32:2803-2812.) Key Words: paraoxonase 1 gene ◼ coronary artery disease ◼ oxidative stress ◼ arylesterase activity Clinical and Genetic Association of Serum Paraoxonase and Arylesterase Activities With Cardiovascular Risk W.H. Wilson Tang, Jaana Hartiala, Yiying Fan, Yuping Wu, Alexandre F.R. Stewart, Jeanette Erdmann, Sekar Kathiresan, The CARDIoGRAM Consortium, Robert Roberts, Ruth McPherson, Hooman Allayee, Stanley L. Hazen November 2012 252 2804 Arterioscler Thromb Vasc Biol November 2012 out an unbiased genome-wide association study (GWAS) and determine whether these genetic factors were associated with incident risks of adverse cardiac events or prevalent coronary artery disease (CAD). Patients and Methods Study Population The Cleveland Clinic GeneBank study is a large, prospective cohort study from 2001 to 2006 that established a well-characterized clini- cal repository with clinical and longitudinal outcomes data obtained from consenting subjects undergoing an elective diagnostic cardiac catheterization procedure. All the GeneBank participants gave writ- ten informed consent approved by the Cleveland Clinic Institutional Review Board. This study involved a total of 3668 subjects in the GeneBank study who underwent coronary angiography in the absence of acute coro- nary syndrome and confirmed by including only those with cardiac troponin I <0.03 ng/mL, with no history of revascularization within 30 days before enrollment, and with at least 3 years of long-term follow- up. Adjudicated outcomes were ascertained over the ensuing 3 years for all subjects after enrollment. Framingham risk factors age, sex, cigarette smoking, low-density lipoprotein and high-density lipopro- tein cholesterol, systolic blood pressure, and diabetes mellitus were identified. We defined secondary prevention cohort by a known his- tory of CAD (including stenosis of any coronary artery ≥50% at the time of catheterization), prior myocardial infarction (MI), known his- tory of peripheral artery disease, history of transient ischemic attack, stroke, or known cerebrovascular disease, or previous percutaneous or surgical revascularization. Those who did not fulfill secondary pre- vention cohort criteria were assigned to primary prevention cohort. An estimate of creatinine clearance was calculated using the Cockcroft−Gault equation, because a majority of the patients have preserved renal function. High-sensitivity C-reactive protein (hsCRP), cardiac troponin I, serum creatinine, fasting blood glucose, and lipid profiles were also measured on the Architect ci8200 platform (Abbott Laboratories, Abbott Park, IL). Absolute neutrophil counts were ana- lyzed by the Advia 120 Automated Hematology Analyzer (Siemens Healthcare Diagnostics, Deerfield, IL). Major adverse cardiovascular events (MACE) were defined as death, nonfatal MI, or nonfatal cere- brovascular accident after enrollment. Serum Paraoxonase Activity and Arylesterase Activity Assays Serum paraoxonase and arylesterase activities were measured by spec- trophotometry in an open channel on the aforementioned Architect ci8200 platform, and in a 96-well plate format (Spectramax 384 Plus, Molecular Devices, Sunnyvale, CA), respectively. For serum paraox- onase activity, the rate of generation of paranitrophenol was deter- mined at 405 nm in 40-fold diluted serum (final) in reaction mixtures composed of 1.5 mmol/L paraoxon (Sigma-Aldrich, St. Louis, MO), 10 mmol/L Tris hydrocholoride, pH 8, 1 mol/L sodium chloride, and 2 mmol/L calcium chloride at 24°C. An extinction coefficient (at 405 nm) of 17 000 mol/L –1 ·cm –1 was used for calculating units of paraox- onase activity, which is expressed as nanomoles of paranitrophenol produced per minute per milliliter of serum. The intra-assay and inter- assay coefficients of variance for the high-throughput paraoxonase activity assay were 1.9% and 3.3%, respectively, on 30 replicates per- formed on 15 different days. For serum arylesterase activity measure- ment, initial hydrolysis rates were determined at 270 nm in 50-fold diluted serum (final) in reactions mixtures composed of 3.4 mmol/L phenylacetate (Sigma-Aldrich), 9 mmol/L Tris hydrocholoride, pH 8, and 0.9 mmol/L calcium chloride at 24°C. An extinction coefficient (at 270 nm) of 1310 mol/L –1 ·cm –1 was used for calculating units of arylesterase activity, which are expressed as micromoles of phenyl acetate hydrolyzed per minute per milliliter of serum. The intra-assay and interassay coefficients of variance for arylesterase activity assay were 3.4% and 3.9%, respectively, on 20 replicates performed on 10 different days. Genotyping Genotyping was performed on the Affymetrix Genome-Wide Human single nucleotide polymorphism (SNP) Array 6.0 platform. Using these data and those from 120 phased chromosomes from the HapMap CEU samples (HapMap r22 release, National Center for Biotechnology Information build 36), genotypes were imputed for untyped SNPs across the genome using MACH 1.0 software. 9 All imputations were done on the forward (+) strand using 562 554 geno- typed SNPs that had passed quality control filters. QC filters for the imputed data set excluded SNPs with Hardy–Weinberg equilibrium P values <0.0001 or minor allele frequencies <1%, and individuals with <95% call rates. This resulted in 2 421 770 autosomal SNPs that were available for analysis. Statistical Analyses The Student t test or Wilcoxon-Rank sum test for continuous vari- ables and χ 2 test for categorical variables were used to examine the difference between the groups. Kaplan–Meier analysis with Cox pro- portional hazards regression was used for time-to-event analysis to determine hazard ratio (HR) and 95% CI for MACE. Adjustments were made for individual traditional cardiac risk factor (including age, sex, diabetes mellitus, hypertension, former or current cigarette smoking, prior CAD), log-transformed hsCRP, and creatinine clear- ance. The R package Mclust was used for discriminant analysis. The clustering process is based on multivariate normal mixture models. The optimal model parameters and the number of clusters were deter- mined via Bayesian information criterion. All analyses were per- formed using R 2.13.1 (Vienna, Austria) and P values <0.05 were considered statistically significant. Genome-wide linear regression analyses were used to identify loci associated with serum paraoxonase and arylesterase activity after adjustment for age and sex under an additive model. Genetic analy- ses were carried out with PLINK (v1.07) using both untransformed (arylesterase activity) and inverse-normal transformed (paraoxonase activity) values. Relative risk for experiencing a MACE as a func- tion of genotype was assessed using Cox proportional hazard models with adjustment for age, sex, Framingham ATP-III risk score (which includes diabetes mellitus status), and medication use (aspirin and statins). Adjusted HR and 95% CI are reported with 2-sided P values. A haplotype score test was also used to test all haplotypes with >1% frequency, as implemented in the Haplo.Stats package. All genetic analyses were performed using SAS version 9.2 (SAS Institute Inc, Cary, NC) or R 2.10.1 (http://www.R-project.org). Associations With CAD The Coronary Artery Disease Genome-Wide Replication and Metanalysis (CARDIoGRAM) Consortium represents a GWAS meta-analysis of CAD comprising a discovery set of ≈22 000 cases and ≈65 000 controls. 10 For each cohort in CARDIoGRAM, logistic regression was first used to test for association with CAD using a log-additive model with adjustment for age and sex and taking into account the uncertainty of possibly imputed genotypes. Subsequently, a meta-analysis was performed separately for every SNP from each study that passed the quality control criteria using a fixed effect model with inverse variance weighting. 10 The results of this default meta- analysis were used to determine whether SNPs influencing PON-1 functional activity measures were associated with CAD. Results Study Population Table 1 describes the baseline characteristics of our pri- mary study population of 3668 subjects. Serum arylesterase activity levels were normally distributed, with a mean of 104±25 μmol·min −1 ·mL −1 . However, serum paraoxonase activity levels were not normally distributed, with a median 253 Tang et al Paraoxonase-1 Activities and Cardiovascular Risk 2805 of 562 μmol·min −1 ·mL −1 (interquartile range, 315–1045 μmol·min −1 ·mL −1 ). Both serum paraoxonase and arylester- ase levels were lower in men than in women. Compared with those in the highest quartile, subjects in the lowest quartile of serum paraoxonase and arylesterase activity were more likely to have significantly obstructive (≥50% stenosis) CAD (odds ratio, 1.86 [95% CI, 1.53–2.27] P<0.01 for arylester- ase, and 1.48 [95% CI, 1.22–1.81] P<0.01 for paraoxonase). However on adjustment of cardiovascular risk factors, such differences were no longer apparent. In addition, there were very weak (but statistically significant) correlations between serum arylesterase activity and hsCRP (r=−0.09; P<0.001), estimated creatinine clearance (r=0.15; P<0.001), and abso- lute lymphocyte count (r=0.09; P<0.001). In contrast, there was no statistically significant relationship between serum arylesterase activity and leukocyte count (r=0.02; P=0.28) or absolute neutrophil count (r=0.002; P=0.91). These findings were similar when correlations were performed within the Q192R genotype subgroups (data not shown). PON-1 Activities and Major Adverse Cardiac Outcomes In the 3668 subjects, a total of 417 cardiac events were recorded within the 3-year period of follow-up. Lower serum paraoxonase and arylesterase activity levels were associated with poorer long-term outcomes when stratified by quartiles (Table 2, Figure 1). After adjusting for Framingham risk factors, estimated creatinine clearance, diabetes mellitus and log-transformed hsCRP, lower serum arylesterase activity (HR 2.20 [95% CI, 1.60–3.02]; P<0.01) and to a lesser extent lower serum paraoxonase activity (HR 1.39 [95% CI, 1.04–1.85]; P<0.05) demonstrated increased risk in developing future MACE. Even when cardiac troponin I levels were added to the model (within the normal range of 0.001–0.029 mg/dL), lower serum arylesterase levels still maintain a 2-fold increased risk in MACE at 3 years (HR, 2.04 [95% CI, 1.49–2.79]; P<0.01). The separation is particularly apparent between the lowest quartile and the upper 3 quartiles at the cutoff of 87 μmol·min −1 ·mL −1 (Figure 1). The addition of serum arylesterase activity or serum paraoxonase activity to the model results in significant improvement in risk classification with net reclassification index of 7.9% for arylesterase activity (P=0.003) and 7.2% for paraoxonase activity (P=0.002). The prognostic value of serum arylesterase activity was observed within the secondary prevention cohort (Table 2), as well as within subjects who demonstrated a recent normal catheterization (ie, no significant [>50%] angiographic evidence of CAD in any major vessel or preceding history of CAD, the primary prevention cohort; Table 2). Serum arylesterase activity also remained a prognostic indicator within each Framingham risk factor subcohort and among those with low hsCRP (Figure 2), or in those without statin therapy (n=1512). In addition to serum arylesterase remaining a significant predictor of MACE after addition of higher sensitivity troponin testing to traditional risk factors and laboratory risk markers in the models, we further observed an increased risk of developing subclinical myocardial necrosis (troponin levels that are detectable but remain below the 99th percentile diagnostic cutoff among healthy subjects used to define cutoff for MI) with decreasing quartiles of serum arylesterase levels (odds ratio 2.01 [95% CI, 1.53–2.64]; P<0.001; Figure 3); this trend was not observed with serum paraoxonase activity levels (Figure 3). GWAS for PON-1 Activities We next performed a GWAS for serum paraoxonase and arylesterase activity in 2136 GeneBank subjects (all of Caucasian ancestry) for whom both genotype and PON-1 functional data were available. The genomic inflation factors for paraoxonase and arylesterase activity were 1.015 and 1.013, respectively, indicating that the GWAS results were not confounded by underlying population stratification, and the Q-Q plots are shown in Figure I in the online-only Data Supplement. Serum paraoxonase activity was controlled by a major locus on chromosome 7 containing the PON-1, PON- 2, and PON-3 genes (Figure 4A). The lead SNP for serum paraoxonase activity at this locus (rs2057681) is located within the PON-1 gene and yielded a highly significant P value of 1.18×10 –303 (Figure 4A, Table 3). Based on our genotype data, rs2057681 is in near complete linkage disequilibrium (LD) (r 2 =0.99) with a functional amino acid substitution in PON-1 (rs662; Q192R), which is also associated with increased paraoxonase activity (P=3.31×10 –295 ; Table 3). Of interest, while rs2057681 and rs662 are associated with significant Table 1. Baseline Subject Characteristics From GeneBank Cohort (n=3668) Variable Value Age, y 63±11 Sex, male, % 65 Body mass index, kg/m 2 29.6±6 Race,(Caucasian), % 97 Diabetes mellitus, % 29 Hypertension, % 70 Smokers (former/current), % 65/11 Prior coronary artery disease, % 67 LDL cholesterol, mg/dL 95 (78, 117) HDL cholesterol, mg/dL 34 (28, 41) Triglycerides, mg/dL 114 (83, 162) hsCRP, mg/L 2.00 (0.91, 4.51) Creatinine clearance, mL/min 99.9 (76.8, 126.3) Total leukocyte count, ×10 9 cells/L 6.1 (5, 7.4) Absolute neutrophil count, cells/μL 3.9 (3.1, 5) Baseline medications, % ACE inhibitors/ARBs 49 β-blockers 61 Statin 59 Aspirin 73 Serum paraoxonase activity, nmol·min −1 ·mL −1 562 (315–1045) Serum arylesterase activity, μmol·min −1 ·mL −1 104±25 LDL indicates low-density lipoprotein cholesterol; HDL, high-density lipopro- tein cholesterol; hsCRP, high-sensitivity C-reactive protein; ACE, angiotensin converting enzyme; ARB, angiotensin receptor blocker. Values expressed in mean±SD or median (interquartile range). 254 2806 Arterioscler Thromb Vasc Biol November 2012 increase in paraoxonase activity, they are also associated with significant decrease in arylesterase activity (Table 3). Another known coding SNP in PON-1 (rs854560; L55M) exhibited significant association with paraoxonase activity as well (P=1.27×10 –140 ; Table 3) but is in relatively weak LD (r 2 =0.20) with the aforementioned lead SNPs (rs2057681 and rs662). The GWAS analysis for serum arylesterase activity also revealed that this chromosome 7 region was the major locus controlling this measure of PON-1 functional activity (Figure 4B). For example, the lead SNP for serum arylesterase activity, rs854572, is located in the promoter region of PON-1 and yielded a highly significant P value of 4.99×10 –116 (Table 3). This SNP (rs854572) also was associated with the increase in paraoxonase activity. Of note, the 4 lead SNPs for arylesterase activity differ from those identified for paraoxonase activity, although they do exhibit some association with paraoxonase activity and vice versa (Table 3). However, the lead SNPs for arylesterase and paraoxonase activity are in weak LD (r 2 <0.23), suggesting that the reciprocal associations are for the most part independent. Further, the lead SNPs for both paraoxonase (rs2057681) and arylesterase (rs854572) activities are localized in the PON-1 gene and not in LD with variants in either PON-2 or PON-3 genes. We next determined whether the lead SNPs on chromosome 7 influenced other CAD risk factors but did not observe any significant evidence for such associations (Table I in the online-only Data Supplement). Because body mass index and statin use have been shown to potentially affect serum paraoxonase activity, 11–13 we also adjusted the genetic analyses for these potential confounders. However, the strength of the association of the lead SNPs with paraoxonase and arylesterase activities was not diminished when either body mass index or statin use alone was included with age and sex in the genetic models or in a fully adjusted model that included all 4 covariates (Table II in the online- only Data Supplement). Thus, the effect of the lead SNPs on paraoxonase and arylesterase activities are robust and independent of traditional cardiovascular risk factors. Given the strong effect of the chromosome 7 locus on PON-1 function, we also carried out GW AS analyses for paraoxonase and arylesterase activity conditioned on the lead SNP for each respective trait. These analyses did not reveal other loci in the genome that were significantly associated with either mea- sure of PON-1 activity. To gain further insight into the effect of the chromosome 7 variants on PON-1 function, we also examined the relationship between serum paraoxonase and arylesterase activities in our cohort. As illustrated in Figure 5, Table 2. Unadjusted and Adjusted HR for Major Adverse Cardiovascular Events at 3-Year Follow-Up According to Serum Arylesterase Activity and Paraoxonase Activity Quartiles, Stratified According to Primary Versus Secondary Prevention Serum Arylesterase Activity, μmol·min −1 ·mL −1 Serum Paraoxonase Activity, μmol·min −1 ·mL −1 Quartile 4 Quartile 3 Quartile 2 Quartile 1 Quartile 4 Quartile 3 Quartile 2 Quartile 1 All subjects (n=3668) Range ≥121 103–121 87–103 <87 ≥1045 562–1045 315–562 <315 Unadjusted HR 1 1.50 (1.1–2.05)* 1.45 (1.06–1.99)* 2.63 (1.97–3.50)** 2.20 (1.60–3.02)** 1 1.39 (1.04–1.84)* 1.19 (0.89–1.6) 1.1 (0.82–1.48) 1.63 (1.24–2.14)** 1.39 (1.04–1.85)* Adjusted HR † 1 1.44 (1.05–1.97)* 1.34 (0.96–1.86) 1 1.04 (0.77–1.4) Adjusted HR ‡ 1 1.40 (1.02–1.92)* 1.27 (0.91–1.77) 1.85 (1.35–2.55)** 1 1.1 (0.82–1.49) 1.00 (0.74–1.36) 1.27 (0.95–1.70) Secondary prevention subjects (n=2636) Range ≥119 101–119 86–101 <86 ≥1022 549–1022 308–549 <308 Unadjusted HR 1 1.24 (0.89–1.73) 1.26 (0.9–1.75) 2.01 (1.49–2.72)** 1.78 (1.28–2.48)** 1 1.35 (0.98–1.84) 1.2 (0.87–1.65) 1.45 (1.07–1.97)* 1.30 (0.94–1.79) Adjusted HR † 1 1.23 (0.88–1.72) 1.21 (0.86–1.72) 1 1.2 (0.87–1.66) 1.14 (0.83–1.57) Adjusted HR ‡ 1 1.17 (0.83–1.63) 1.13 (0.80–1.61) 1.51 (1.08–2.11)* 1 1.12 (0.81–1.56) 1.12 (0.81–1.55) 1.18 (0.86–1.64) Primary prevention subjects (n=1032) Range ≥125 107–125 91–107 <91 ≥1107 644–1107 335–644 <335 Unadjusted HR 1 0.91 (0.35–2.35) 2.63 (1.21–5.72)* 4.38 (2.11–9.09)** 4.00 (1.82–8.82)** 1 1.20 (0.61–2.38) 0.71 (0.33–1.55) 2.15 (1.16–3.97)* 1.85 (0.99–3.45) Adjusted HR † 1 0.83 (0.32–2.18) 2.50 (1.08–5.79)* 1 1.09 (0.55–2.18) 0.71 (0.33–1.51) Adjusted HR ‡ 1 0.84 (0.32–2.21) 2.44 (1.02–5.84)* 3.02 (1.35–6.80)** 1 0.92 (0.46–1.84) 0.65 (0.30–1.42) 1.68 (0.88–3.19) HR indicates hazard ratio. † Model 1: Adjusted for traditional risk factors (include age, sex, systolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, cigarette smoking, and diabetes mellitus). ‡ Model 2: Adjusted for traditional risk factors (Model 1), plus log-transformed high-sensitivity C-reactive protein levels, creatinine clearance, race, body mass index, and statin. *P<0.05 and **P<0.01. 255 Tang et al Paraoxonase-1 Activities and Cardiovascular Risk 2807 both activities were significantly correlated overall (r=0.30; P<0.001) and exhibited a striking grouping of 3 distinct pat- terns when plotted against each other. These 3 clusters were found to correspond nearly exactly with the 3 genotype groups of the Q192R (rs662) polymorphism (Figure 5A), with 97.2% with the genotype QQ in the top cluster, 97.2% with genotype QR in the middle cluster, and 91.3% with genotype RR in the bottom cluster. However, such a pattern was not observed with the 3 genotype groups of the lead SNP for serum arylesterase activity (rs854572; G>C, Figure 5B). Quantitative analysis of the separation of the 3 genotypes, GG, GC, and CC, across the 3 clusters showed 50.7% with genotype CG in the top cluster, 48.4% with genotype CG in the middle cluster, and 51.4% with genotype CG in the bottom cluster. Figure 1. Kaplan–Meier analysis for long-term major adverse cardiac events stratified by serum arylesterase (left column) and paraoxonase (right column) activities quartiles in overall (top row), secondary prevention (middle row), and primary prevention (bottom row) populations. 256 2808 Arterioscler Thromb Vasc Biol November 2012 Association of PON-1 Variants With Incident MACE and Prevalent Cardiovascular Disease We next sought to determine whether the lead variants influencing paraoxonase and arylesterase activity were associated with the development of MACE. These analyses included the 2136 subjects used in the quantitative association described above plus an additional 567 subjects for whom genotype and MACE data were also available (total n=2703). As shown in Table 4, there were no individual effects of the Q192R (rs662) and L55M (rs854560) substitutions on incident risk of MACE in GeneBank subjects. Given the differential effects of the associated variants on PON-1 function (eg, rs662 is associated with increase in paraoxonase activity but decrease in arylesterase activity), we also constructed haplotypes with rs662, rs854560, rs854570, and rs854572 and specifically tested the A TAG haplotype, which leads to modest decreases in both paraoxonase and arylesterase activity (data not shown). However, risk of future MACE was not significantly increased in subjects carrying 1 (HR=0.96; 95% CI, 0.68–1.35) or 2 (HR=0.91; 95% CI, 0.50–1.63) copies of this haplotype (P=0.72; Table 4). Similarly, an analysis using a haplotype score test that included all haplotypes with >1% frequency also did not reveal any associations with MACE (data not shown). To further evaluate the genetic contribution of these SNPs to cardiovascular risk, we used the large CARDIoGRAM con- sortium, which comprises ≈80 000 case–controls subjects. As shown in Table 5, no significant evidence for association of these SNPs was observed with risk of prevalent CAD. A sub- analysis with only cases that had a positive history for MI also did not reveal any associations (Table 5). Discussion PON-1 is an atherosclerosis protective enzyme associated with high-density lipoprotein and systemic antioxidant func- tion. Its catalytic activity within crude serum mixtures has tra- ditionally been measured by quantifying enzymatic hydrolysis rates of 2 known in vitro substrates, with functional activities named paraoxonase and arylesterase activities. 4,5 There are several novel findings in this report that describe the clinical and genetic associations of PON-1 activities with cardiovas- cular risks that differ from prior observations from smaller cohorts. First, we demonstrated the prognostic value of serum arylesterase activity (and to a lesser extent serum paraoxonase activity) in predicting long-term cardiovascular risk in a wide range of subjects already treated with contemporary medi- cal therapy. We observed that diminished serum arylesterase activity, particularly within the lowest quartile range, was predictive of adverse long-term cardiac events independent of standard clinical and biochemical risk factors and provided incremental value in reclassifying subjects who are at higher risk of long-term MACE. Furthermore, the prognostic value of serum arylesterase activity was evident in both primary as well as secondary prevention subjects. The fact that aryles- terase activity provided prognostic value consistently within subjects with or without underlying angiographic evidence of significant CAD implies that a lack of systemic antioxidant defense mechanisms (a primary function of PON-1) may both promote greater vulnerability to oxidative stress, as well as increase risk for development and progression of CAD in sub- jects. Moreover, the ability of low serum arylesterase activity Figure 2. Forest plot regarding hazard ratios of serum arylesterase and paraox- onase activities according to traditional cardiac risk factors in subgroups of patients. hsCRP indicates high-sensitivity C-reactive protein. Figure 3. Adjusted odds ratio across serum arylesterase and paraoxonase activity quartiles with prevalent subclinical myocar- dial necrosis (defined as cardiac troponin I ≥0.009 ng/mL). 257 Tang et al Paraoxonase-1 Activities and Cardiovascular Risk 2809 to identify those at significant increased risk of MACE, even among primary prevention subjects with recent coronary angiographic data showing <50% stenosis in all major coro- nary vessels, suggests this assay has prognostic value and can identify a vulnerable cohort of subjects who otherwise are not identified as being at high risk. Taken together, the present results suggest that enzymatic activity measures with aryles- terase, more so than paraoxonase, serve as a powerful prog- nostic indicator of cardiovascular risk in a broad spectrum of subjects. Studies on PON-1 activities have relied on quantifying its wide range of enzymatic activities in breaking down in vitro substrates like paraoxon (paraoxonase activity) and phenylacetate (arylesterase activity). These activities are often reported together and the findings are commonly con- cordant. Hence, the distinction between serum paraoxonase and arylesterase activities in predicting future adverse car- diac events is somewhat unexpected, even though histori- cally the correlation between these 2 measures has not been particularly tight. The unique relationship between the 2 PON-1 activity measurements is largely the result of a very strong association between serum paraoxonase activity and its underlying genetic determinants, which may also explain why serum arylesterase activity is normally distributed whereas serum paraoxonase activity was not. Meanwhile, the strong association between the PON gene cluster and serum arylesterase activity confirms the long-standing assumption that this locus harbors important genetic determinants of serum arylesterase activity. The significant genetic associations revealed by the GWAS analyses for paraoxonase and arylesterase activities are of further interest for several reasons. First, there was a stronger relationship between serum paraoxonase activity levels and its genetic determinants than those with serum arylesterase activity. Second, our results confirm the strong association of PON-1 variants with serum paraoxonase and arylesterase activities and demonstrate that the 2 measured enzymatic functions are associated in a Mendelian- like fashion by a single, major locus on chromosome 7 containing the PON gene cluster. This is supported by the Figure 4. Manhattan plots for Genome-Wide Asso- ciation Studies identifying highest single nucleotide polymorphisms associated with serum paraoxonase (A), and arylesterase (B) activity levels. 258 2810 Arterioscler Thromb Vasc Biol November 2012 GWAS analyses conditioned on the lead SNPs, which did not identify other genomic regions associated with either paraoxonase or arylesterase activity. Furthermore, the lead SNPs for either enzymatic activity localize to the PON-1 gene, are distinct from each other, and are not in LD with SNPs in the neighboring PON-3 and PON-2 genes. However, although the PON-1 variants exhibited strong (if not opposed) effects on PON function, they were not associated with future risk of MACE, either individually or as a haplotype that modestly decreased both paraoxonase and arylesterase activity. These results are consistent with our analyses from the CARDIoGRAM consortium, which did not reveal an association of these SNPs with either prevalent CAD or history of MI in ≈80 000 subjects. It should be noted, however, that because the lead SNPs only contributed to an estimated 15% of the PON-1 activity variation, other processes, such as posttranslational modifications, could also play a role in determining the ultimate functionality of PON-1. For example, PON-1 is known to be sensitive to posttranslational oxidative modification and inactivation. 14 It is also possible that our study was underpowered to detect genetic effects on prospective risk of MACE because only 311 subjects out of the ≈2700 subjects included in these analyses experienced a MACE over 3 years of follow-up. Table 3. Mean Serum Paraoxonase and Arylesterase Activity as a Function of Genotype for Lead SNPs on Chromosome 7 Position (bp)* SNP Alleles † MAF ‡ Serum Paraoxonase Activity, nmoles·min −1 ·mL −1 Serum Arylesterase Activity, μmoles·min −1 ·mL −1 01 2 P Value ‡ 01 2 P Value ‡ 94 774 065 rs2269829 A/G 0.28 389±269 (n=1078) 934±361 (n=905) 1434±492 (n=153) 3.27×10 −288 105±25 (n=1106) 100±24 (n=930) 93±20 (n=157) 4.22×10 −11 94 775 382 rs662 (Q192R) A/G 0.29 382±261 (n=1062) 930±358 (n=914) 1424±497 (n=160) 3.31×10 −295 105±25 (n=1088) 100±24 (n=940) 94±21 (n=165) 9.43×10 −11 94 776 193 rs2057681 A/G 0.29 377±253 (n=1055) 929±353 (n=919) 1433±501 (n=162) 1.18×10 −303 105±25 (n=1080) 100±24 (n=946) 94±21 (n=167) 2.11×10 −10 94 784 020 rs854560 (L55M) A/T 0.36 906±489 (n=877) 620±398 (n=1020) 311±280 (n=269) 1.27×10 −140 109±23 (n=868) 99±24 (n=1053) 90±22 (n=272) 2.03×10 −38 94 790 628 rs854570 A/C 0.36 670±470 (n=898) 711±469 (n=987) 721±455 (n=251) 2.90×10 −09 91±21 (n=920) 107±23 (n=1014) 122±21 (n=259) 5.10×10 −106 94 792 632 rs854572 G/C 0.46 572±407 (n=645) 716±474 (n=1075) 831±494 (n=416) 1.23×10 −35 88±21 (n=659) 104±22 (n=1104) 119±22 (n=430) 4.99×10 −116 94 793 157 rs705382 G/C 0.36 670±470 (n=897) 710±469 (n=989) 723±455 (n=250) 2.92×10 −09 91±21 (n=919) 107±23 (n=1016) 122±21 (n=258) 1.98×10 −106 94 793 464 rs757158 C/T 0.42 569±403 (n=725) 731±478 (n=1053) 843±501 (n=358) 3.97×10 −38 89±22 (n=741) 105±22 (n=1084) 120±22 (n=368) 1.04×10 −104 SNPs indicate single nucleotide polymorphisms; MAF, minor allele frequency. Data are shown as mean±SD as a function of carrying 0, 1, or 2 copies of the minor alleles for lead genome-wide association study SNPs. * Base pair positions on chromosome 7 are given according to National Center for Biotechnology Information build 36.1 of the reference human genome sequence. † Major/minor alleles are given for Caucasians based on the forward (+) DNA strand. ‡P values are obtained from multiple linear regression using inverse normal-transformed values for paraoxonase activity and untransformed values for arylesterase activity, adjusted for age and sex. Figure 5. Relationship between serum arylesterase and paraoxonase activity levels stratified according to (A) the lead single nucleotide polymorphism (SNP) for serum paraoxonase activity (rs662; Q192R); (B) the lead SNP for serum arylesterase activity (rs854572; G>C). Percentages reflect separation of the 3 genotypes within each stratified cluster of each genotype. 259 Tang et al Paraoxonase-1 Activities and Cardiovascular Risk 2811 The lack of associations between the lead SNPs for PON and arylesterase activities with CAD and MI risks in humans in our study are in direct contrast to what has been observed in mice where PON-1 deficiency leads to increased aortic lesion formation 3 and transgenic mice over expressing PON-1 are protected. 15,16 It is of interest that the PON-1 transgenic mouse models demonstrating protective effects have 50% to 400% increased serum arylesterase activity, 15,16 far in excess of the modest changes in activity associated with the peak SNP iden- tified in the arylesterase GWAS. If arylesterase activity is the more important atheroprotective aspect of PON-1 function, as our clinical associations with MACE suggest, then it may not be surprising that an association was not observed with the SNPs identified. For example, the lead SNP for aryles- terase activity (rs854572) only increases activity by ≈16% per minor allele copy. It is, thus, possible that the genetic effects on arylesterase activity from this SNP are too weak to observe, especially if a minimum biological threshold of activity change is needed to influence risk of prevalent CAD, history of MI, or incident risk of MACE. The ability for serum arylesterase activity to identify a high-risk population in the primary prevention cohort that just underwent cardiac catheterization and showed no significant evidence of stenoses in any major vessel may have important clinical implications. Even though our sub- set analysis was limited by the relatively smaller sample size and low event rates, a significant 4-fold increase risk in long-term MACE in these otherwise low risk subjects was identified. Use of serum arylesterase activity may thus have clinical use and help identify an important patient cohort that may warrant more aggressive risk factor reduction treatment strategies who might otherwise not be targeted for aggressive preventive intervention. In addition to low serum arylesterase activity predicting increased risk for MACE in primary prevention subjects, there was a direct association also noted between lower serum arylesterase activity level and increased prevalence of significantly obstructive CAD by angiography. Of note, we did not find a strong relationship between systemic inflammatory bio- markers, such as CRP or leukocyte parameters, with serum arylesterase levels in this group of stable cardiac patients. This observation is consistent with previous reports that distinguished systemic inflammatory from oxidative stress processes. Several limitations of our study should be noted. Serum paraoxonase and arylesterase activities used substrates that are not the endogenous substrates for PON-1, but are used because they are not readily influenced by other esterases/ lactonases in serum, and are presumed to reflect the underlying catalytic activity of PON-1. The GWAS results observed demonstrate these assumptions are reasonable, as only genetic variations in the PON-1 gene were observed to be associated with variations in paraoxonase and arylesterase activities. A further potential limitation is that the measurements were only made under fasting conditions at a single time point. Hence, we are unable to determine the variability and prognostic value of level changes over time and the impact of dietary or therapeutic interventions on serum arylesterase activity level. Selection bias may also be present for those undergoing cardiac catheterization for symptomatic evaluation and management of cardiac diseases at a tertiary care setting, but the large sample size and event rates of the patient population, together with careful phenotypic evaluation, including angiographic data, provides unique insights and adequate power to adjust for clinical and biomarker variables. Table 4. Association of Variants Controlling Plasma Paraoxonase or Arylesterase Activity With MACE SNP/Haplotype HR (95% CI) 01 2 P Value Rs2057681 1 (n=1341) 1.18 (0.94–1.49) (n=1155) 0.94 (0.60–1.49) (n=207) 0.48 Rs854572 1 (n=794) 1.18 (0.91–1.54) (n=1358) 1.12 (0.81–1.56) (n=551) 0.44 ATAG 1 (n=1212) 0.96 (0.68–1.35) (n=1236) 0.91 (0.50–1.63) (n=255) 0.72 MACE indicates major adverse cardiovascular events; SNPs indicate single nucleotide polymorphisms; HR, hazard ratio. HRs are shown as a function of carrying 0, 1, or 2 copies of the minor allele for the lead SNP for paraoxonase (rs2057681) and arylesterase (rs854572) activity or the ATAG haplotype of rs662, rs854560, rs854570, and rs854572. HR are adjusted for age, sex, Framingham ATP-III risk score, and medication use (aspirin and statins). 2703 subjects were used in these analyses, of which 311 experienced a MACE (death, MI, or stroke) over 3 y of follow-up. Table 5. Association of Identified SNPs With Risk of Prevalent CAD and Risk of MI in the CARDIoGRAM Consortium SNP Allele Frequency Risk of CAD Risk of MI OR (95% CI) P Value n OR (95% CI) P Value N rs2269829 G 0.28 0.99 (0.97–1.03)) 0.74 83 324 0.97 (0.94–1.01) 0.17 52 973 rs662 (Q192R) G 0.30 0.99 (0.96–1.02) 0.60 79 262 0.98 (0.94–1.02) 0.24 52 306 rs2057681 G 0.28 0.99 (0.96–1.02) 0.44 84 106 0.97 (0.93–1.00) 0.09 53 649 rs854560 (L55M) N/A N/A N/A N/A N/A N/A N/A N/A rs854570 C 0.41 1.02 (0.99–1.05) 0.14 81 019 1.01 (0.98–1.05) 0.46 51 143 rs854572 C 0.52 1.01 (0.99–1.04) 0.33 83 486 1.00 (0.97–1.04) 0.74 53 204 rs705382 C 0.41 1.02 (1.0–1.05) 0.09 83 367 1.02(0.98–1.05) 0.40 53 035 rs757158 C 0.54 0.99 (0.96–1.02) 0.50 78 275 0.99 (0.96–1.03) 0.71 51 559 SNP indicates single nucleotide polymorphism; CAD, coronary artery disease; MI, myocardial infarction; OR, odds ratio; N/A, not available; CARDIoGRAM, Coronary Artery Disease Genome-Wide Replication And Meta-Analysis. 260 2812 Arterioscler Thromb Vasc Biol November 2012 Conclusions Diminished serum arylesterase activity can provide incre- mental prognostic value and clinical reclassification of stable patients at risk of developing MACE, even among primary prevention subjects who just demonstrated no significant cor- onary stenoses by angiography and might otherwise be dis- missed as low risk. Despite the strong genetic effects of the PON locus on serum paraoxonase and arylesterase activities, the identified variants were not associated with risk of incident MACE or prevalent CAD. Sources of Funding This research was supported by National Institutes of Health grants P01HL076491-055328, 5P01HL103453, and the Fondation Leducq. The GeneBank study has been supported by National Institutes of Health grants P01HL098055, R01HL103866, 1P20HL113452, 1R01HL103931, R01ES021801 (Dr Allayee), and the Cleveland Clinic Clinical Research Unit of the Case Western Reserve University Clinical and Translational Sciences Award (UL1TR 000439-06). Dr Hazen is also partially supported by a gift from the Leonard Krieger Fund. The John and Jennifer Ruddy Canadian Cardiovascular Genetics Centre investigators are supported by Canadian Institutes of Health Research (CIHR) #Medical Research Council Operating Grant Program (MOP)-82810, Canada Foundation for Innovation (CFI) #11966, Heart and Stroke Foundation of Ontario (HSFO) #NA6001, CIHR #MOP172605, and CIHR #MOP77682. Disclosures Dr Tang has received research grant support from Abbott Laboratories, Inc. Dr Hazen reports being listed as coinventor on pending and issued patents held by the Cleveland Clinic relating to cardiovascular diagnos- tics and therapeutics. Dr Hazen reports having been paid as a consultant or speaker for the following companies: Abbott Diagnostics, Cleveland Heart Lab, Esperion, Lilly, Liposcience Inc, Merck & Co, Inc, and Pfizer Inc. Dr Hazen reports receiving research funds from Abbott, Cleveland Heart Lab, Liposcience Inc, and Pfizer Inc. Dr Hazen reports having the right to receive royalty payments for inventions or discover- ies related to cardiovascular diagnostics or therapeutics from the com- panies shown below: Abbott Laboratories, Inc, Cleveland Heart Lab, Esperion, Frantz Biomarkers, LLC, Liposcience Inc, and Siemens. The other authors have no conflicts to report. There are no medical writers or editors involved in the preparation of the manuscript. References 1. Libby P , Ridker PM, Hansson GK. Progress and challenges in translating the biology of atherosclerosis. Nature. 2011;473:317–325. 2. Costa LG, Li WF, Richter RJ, Shih DM, Lusis A, Furlong CE. The role of paraoxonase (PON1) in the detoxication of organophosphates and its human polymorphism. Chem Biol Interact. 1999;119-120:429–438. 3. Shih DM, Gu L, Xia YR, Navab M, Li WF, Hama S, Castellani LW, Furlong CE, Costa LG, Fogelman AM, Lusis AJ. Mice lacking serum paraoxonase are susceptible to organophosphate toxicity and atheroscle- rosis. Nature. 1998;394:284–287. 4. Shih DM, Lusis AJ. The roles of PON1 and PON2 in cardiovascular disease and innate immunity. Curr Opin Lipidol. 2009;20:288–292. 5. Durrington PN, Mackness B, Mackness MI. Paraoxonase and athero- sclerosis. Arterioscler Thromb Vasc Biol. 2001;21:473–480. 6. Furlong CE, Richter RJ, Seidel SL, Costa LG, Motulsky AG. Spectrophotometric assays for the enzymatic hydrolysis of the active metabolites of chlorpyrifos and parathion by plasma paraoxonase/aryles- terase. Anal Biochem. 1989;180:242–247. 7. Tang WH, Wu Y , Mann S, Pepoy M, Shrestha K, Borowski AG, Hazen SL. Diminished antioxidant activity of high-density lipoprotein-associated proteins in systolic heart failure. Circ Heart Fail. 2011;4:59–64. 8. Bhattacharyya T, Nicholls SJ, Topol EJ, Zhang R, Yang X, Schmitt D, Fu X, Shao M, Brennan DM, Ellis SG, Brennan ML, Allayee H, Lusis AJ, Hazen SL. Relationship of paraoxonase 1 (PON1) gene polymorphisms and functional activity with systemic oxidative stress and cardiovascular risk. JAMA. 2008;299:1265–1276. 9. Willer CJ, Sanna S, Jackson AU, et al. Newly identified loci that influ- ence lipid concentrations and risk of coronary artery disease. Nat Genet. 2008;40:161–169. 10. Preuss M, König IR, Thompson JR,et al; CARDIoGRAM Consortium. Design of the Coronary ARtery DIsease Genome-Wide Replication And Meta-Analysis (CARDIoGRAM) Study: A Genome-wide association meta-analysis involving more than 22 000 cases and 60 000 controls. Circ Cardiovasc Genet. 2010;3:475–483. 11. Deakin S, Guernier S, James RW. Pharmacogenetic interaction between paraoxonase-1 gene promoter polymorphism C-107T and statin. Pharmacogenet Genomics. 2007;17:451–457. 12. Ferretti G, Bacchetti T, Masciangelo S, Bicchiega V. HDL-paraoxonase and membrane lipid peroxidation: a comparison between healthy and obese subjects. Obesity (Silver Spring). 2010;18:1079–1084. 13. Himbergen TM, van Tits LJ, Voorbij HA, de Graaf J, Stalenhoef AF, Roest M. The effect of statin therapy on plasma high-density lipoprotein cholesterol levels is modified by paraoxonase-1 in patients with familial hypercholesterolaemia. J Intern Med. 2005;258:442–449. 14. Besler C, Heinrich K, Rohrer L, et al. Mechanisms underlying adverse effects of HDL on eNOS-activating pathways in patients with coronary artery disease. J Clin Invest. 2011;121:2693–2708. 15. She ZG, Zheng W, Wei YS, Chen HZ, Wang AB, Li HL, Liu G, Zhang R, Liu JJ, Stallcup WB, Zhou Z, Liu DP, Liang CC. Human paraoxonase gene cluster transgenic overexpression represses atherogenesis and promotes atherosclerotic plaque stability in ApoE-null mice. Circ Res. 2009;104:1160–1168. 16. Tward A, Xia YR, Wang XP, Shi YS, Park C, Castellani LW, Lusis AJ, Shih DM. Decreased atherosclerotic lesion formation in human serum paraoxonase transgenic mice. Circulation. 2002;106:484–490. 261 Supplement Material Clinical and Genetic Association of Serum Paraoxonase and Arylesterase Activities with Cardiovascular Risk W. H. Wilson Tang, MD 1,2 , Jaana Hartiala, MS 3 , Yiying Fan PhD 4 , Yuping Wu, PhD 4 , Alexandre F.R. Stewart PhD 5 , Jeanette Erdmann PhD 6 , Sekar Kathiresan MD 7 , The CARDIoGRAM Consortium 8 , Robert Roberts MD 5 , Ruth McPherson MD PhD 5 , Hooman Allayee PhD 3 , and Stanley L. Hazen, MD PhD. 1,2 1 Center for Cardiovascular Diagnostics & Prevention, Department of Cell Biology, Lerner Research Institute, and 2 Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH. 3 Department of Preventive Medicine and Institute for Genetic Medicine, University of Southern California Keck School of Medicine, Los Angeles, CA. 4 Department of Mathematics, Cleveland State University, Cleveland OH. 5 John and Jennifer Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada. 6 Medizinische Klinik II, Universität zu Lübeck, 23538 Lübeck, Germany. 7 Center for Human Genetic Research, Massachusetts General Hospital, Boston MA. 8 A full list of authors and affiliations for the CARDIoGRAM Consortium is provided in the supplemental data. Address for Correspondence: E-mail: hazens@ccf.org 262 Supplemental Table I Association of Lead SNPs on Chromosome 7 with Adjustment for BMI and Statin Use. Serum Paraoxonase Activity Serum Arylesterase Activity SNP p-value P a p-value P b p-value P c p-value P d p-value P a p-value P b p-value P c p-value P d rs2269829 3.27x10 P -288 4.02x10P -288 2.83 x10 P -288 3.49 x10P -288 4.22x10 P -11 3.54x10 P -11 3.96x10 P -11 3.33x10 P -11 rs662 (Q192R) 3.31x10 P -295 4.36 x10P -295 2.43 x10 P -295 3.20 x10P -295 9.43x10 P -11 8.19x10 P -11 8.63 x10 P -11 7.50 x10 P -11 rs2057681 1.18x10 P -303 1.56 x10P -303 9.92 x10 P -304 1.32 x10P -303 2.11x10 P -10 1.83 x10 P -10 1.98 x10 P -10 1.71 x10 P -10 rs854560 (L55M) 1.27x10 P -140 1.22 x10P -140 1.31 x10 P -140 1.26 x10P -140 2.03x10 P -38 2.48 x10 P -38 2.14 x10 P -38 2.61 x10 P -38 rs854570 2.90x10 P -09 2.87 x10 P -09 3.00 x10 P -09 2.97 x10P -09 5.10x10 P -106 4.98 x10 P -106 3.12 x10 P -106 3.07 x10 P -106 rs854572 1.23x10 P -35 1.18 x10 P -35 1.32 x10 P -35 1.27 x10P -35 4.99x10 P -116 7.21 x10 P -116 3.73 x10 P -116 5.42 x10 P -116 rs705382 2.92x10 P -09 2.90 x10 P -09 3.03 x10 P -09 3.00 x10P -09 1.98x10 P -106 1.92 x10 P -106 1.15 x10 P -106 1.12 x10 P -106 rs757158 3.97x10 P -38 3.87 x10 P -38 4.28 x10 P -38 4.17 x10P -38 1.04x10 P -104 1.28 x10 P -104 8.10 x10 P -105 1.00 x10 P -104 P a Pp-values obtained with adjustment for age and gender. P b Pp-values obtained with adjustment for age, gender, and BMI. P c Pp-values obtained with adjustment for age gender, and statin use. P d Pp-values obtained with adjustment for age, gender, BMI, and statin use. 263 Supplemental Table II Association of TIdentified SNPs with CAD Biomarkers TT. SNP Total Cholesterol LDL cholesterol HDL cholesterol P b PC-reactive Protein Creatinine clearance rs2269829 0.12 0.12 0.77 0.29 0.19 rs662 (Q192R) 0.13 0.11 0.76 0.31 0.28 rs2057681 0.13 0.12 0.92 0.35 0.30 rs854560 (L55M) 0.76 0.54 0.40 0.49 0.15 rs854570 0.58 0.17 0.57 0.31 0.87 rs854572 0.83 0.33 0.52 0.70 0.82 rs705382 0.57 0.17 0.55 0.32 0.86 rs757158 0.94 0.43 0.78 0.68 0.88 P a Pp-values are shown for association of lead SNPs on chromosome 7 with CAD biomarkers, with adjustment for age and gender. P b Plog-transformed prior to analysis. 264 Paraoxonase Activity Arylesterase Activity 6XSSOHPHQWDO)LJXUH, Q-Q plots for GWAS of paraoxonase and arylesterase activity. 265 Supplement: The CARDIoGRAM Consortium Executive Committee: Sekar Kathiresan 1,2,3 , Muredach P. Reilly 4 , Nilesh J. Samani 5,6 , Heribert Schunkert 7,79 Executive Secretary: Jeanette Erdmann 7,79 Steering Committee: Themistocles L. Assimes 8 , Eric Boerwinkle 9 , Jeanette Erdmann 7,79 Alistair Hall 10 , Christian Hengstenberg 11 , Sekar Kathiresan 1,2,3 , Inke R. König 12 , Reijo Laaksonen 13 , Ruth McPherson 14 , Muredach P. Reilly 4 , Nilesh J. Samani 5,6 , Heribert Schunkert 7,79 , John R. Thompson 15 , Unnur Thorsteinsdottir 16,17 , Andreas Ziegler 12 Statisticians: Inke R. König 12 (chair), John R. Thompson 15 (chair), Devin Absher 18 , Li Chen 19 , L. Adrienne Cupples 20,21 , Eran Halperin 22 , Mingyao Li 23 , Kiran Musunuru 1,2,3 , Michael Preuss 12,7 , Arne Schillert 12 , Gudmar Thorleifsson 16 , Benjamin F. Voight 2,3,24 , George A. Wells 25 Writing group: Themistocles L. Assimes 8 , Panos Deloukas 26 , Jeanette Erdmann 7,79 , Hilma Holm 16 , Sekar Kathiresan 1,2,3 , Inke R. König 12 , Ruth McPherson 14 , Muredach P. Reilly 4 , Robert Roberts 14 , Nilesh J. Samani 5,6 , Heribert Schunkert 7,79 , Alexandre F. R. Stewart 14 ADVANCE: Devin Absher 18 , Themistocles L. Assimes 8 , Stephen Fortmann 8 , Alan Go 27 , Mark Hlatky 8 , Carlos Iribarren 27 , Joshua Knowles 8 , Richard Myers 18 , Thomas Quertermous 8 , Steven Sidney 27 , Neil Risch 28 , Hua Tang 29 CADomics: Stefan Blankenberg 30 , Tanja Zeller 30 , Arne Schillert 12 , Philipp Wild 30 , Andreas Ziegler 12 , Renate Schnabel 30 , Christoph Sinning 30 , Karl Lackner 31 , Laurence Tiret 32 , Viviane Nicaud 32 , Francois Cambien 32 , Christoph Bickel 30 , Hans J. Rupprecht 30 , Claire Perret 32 , Carole Proust 32 , Thomas Münzel 30 CHARGE: Maja Barbalic 33 , Joshua Bis 34 , Eric Boerwinkle 9 , Ida Yii-Der Chen 35 , L. Adrienne Cupples 20,21 , Abbas Dehghan 36 , Serkalem Demissie-Banjaw 37,21 , Aaron Folsom 38 , Nicole 266 Glazer 39 , Vilmundur Gudnason 40,41 , Tamara Harris 42 , Susan Heckbert 43 , Daniel Levy 21 , Thomas Lumley 44 , Kristin Marciante 45 , Alanna Morrison 46 , Christopher J. O´Donnell 47 , Bruce M. Psaty 48 , Kenneth Rice 49 , Jerome I. Rotter 35 , David S. Siscovick 50 , Nicholas Smith 43 , Albert Smith 40,41 , Kent D. Taylor 35 , Cornelia van Duijn 36 , Kelly Volcik 46 , Jaqueline Whitteman 36 , Vasan Ramachandran 51 , Albert Hofman 36 , Andre Uitterlinden 52,36 deCODE: Solveig Gretarsdottir 16 , Jeffrey R. Gulcher 16 , Hilma Holm 16 , Augustine Kong 16 , Kari Stefansson 16,17 , Gudmundur Thorgeirsson 53,17 , Karl Andersen 53,17 , Gudmar Thorleifsson 16 , Unnur Thorsteinsdottir 16,17 GERMIFS I and II: Jeanette Erdmann 7,79 , Marcus Fischer 11 , Anika Grosshennig 12,7 , Christian Hengstenberg 11 , Inke R. König 12 , Wolfgang Lieb 54 , Patrick Linsel-Nitschke 7 , Michael Preuss 12,7 , Klaus Stark 11 , Stefan Schreiber 55 , H.-Erich Wichmann 56,58,59 , Andreas Ziegler 12 , Heribert Schunkert 7,79 GERMIFS III (KORA): Zouhair Aherrahrou 7,79 , Petra Bruse 7,79 , Angela Doering 56 , Jeanette Erdmann 7,79 , Christian Hengstenberg 11 , Thomas Illig 56 , Norman Klopp 56 , Inke R. König 12 , Patrick Diemert 7 , Christina Loley 12,7 , Anja Medack 7,79 , Christina Meisinger 56 , Thomas Meitinger 57,60 , Janja Nahrstedt 12,7 , Annette Peters 56 , Michael Preuss 12,7 , Klaus Stark 11 , Arnika K. Wagner 7 , H.-Erich Wichmann 56,58,59 , Christina Willenborg ,7,79 , Andreas Ziegler 12 , Heribert Schunkert 7,79 LURIC/AtheroRemo: Bernhard O. Böhm 61 , Harald Dobnig 62 , Tanja B. Grammer 63 , Eran Halperin 22 , Michael M. Hoffmann 64 , Marcus Kleber 65 , Reijo Laaksonen 13 , Winfried März 63,66,67 , Andreas Meinitzer 66 , Bernhard R. Winkelmann 68 , Stefan Pilz 62 , Wilfried Renner 66 , Hubert Scharnagl 66 , Tatjana Stojakovic 66 , Andreas Tomaschitz 62 , Karl Winkler 64 267 MIGen: Benjamin F. Voight 2,3,24 , Kiran Musunuru 1,2,3 , Candace Guiducci 3 , Noel Burtt 3 , Stacey B. Gabriel 3 , David S. Siscovick 50 , Christopher J. O’Donnell 47 , Roberto Elosua 69 , Leena Peltonen 49 , Veikko Salomaa 70 , Stephen M. Schwartz 50 , Olle Melander 26 , David Altshuler 71,3 , Sekar Kathiresan 1,2,3 OHGS: Alexandre F. R. Stewart 14 , Li Chen 19 , Sonny Dandona 14 , George A. Wells 25 , Olga Jarinova 14 , Ruth McPherson 14 , Robert Roberts 14 PennCATH/MedStar: Muredach P. Reilly 4 , Mingyao Li 23 , Liming Qu 23 , Robert Wilensky 4 , William Matthai 4 , Hakon H. Hakonarson 72 , Joe Devaney 73 , Mary Susan Burnett 73 , Augusto D. Pichard 73 , Kenneth M. Kent 73 , Lowell Satler 73 , Joseph M. Lindsay 73 , Ron Waksman 73 , Christopher W. Knouff 74 , Dawn M. Waterworth 74 , Max C. Walker 74 , Vincent Mooser 74 , Stephen E. Epstein 73 , Daniel J. Rader 75,4 WTCCC: Nilesh J. Samani 5,6 , John R. Thompson 15 , Peter S. Braund 5 , Christopher P. Nelson 5 , Benjamin J. Wright 76 , Anthony J. Balmforth 77 , Stephen G. Ball 78 , Alistair S. Hall 10 , Wellcome Trust Case Control Consortium Affiliations 1 Cardiovascular Research Center and Cardiology Division, Massachusetts General Hospital, Boston, MA, USA; 2 Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA; 3 Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; 4 The Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA, USA; 5 Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, UK; 6 Leicester National Institute for Health Research Biomedical Research Unit in Cardiovascular Disease, Glenfield 268 Hospital, Leicester, LE3 9QP, UK; 7 Medizinische Klinik II, Universität zu Lübeck, Lübeck, Germany; 8 Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA; 9 University of Texas Health Science Center, Human Genetics Center and Institute of Molecular Medicine, Houston, TX, USA; 10 Division of Cardiovascular and Neuronal Remodelling, Multidisciplinary Cardiovascular Research Centre, Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, UK; 11 Klinik und Poliklinik für Innere Medizin II, Universität Regensburg, Regensburg, Germany; 12 Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany; 13 Science Center, Tampere University Hospital, Tampere, Finland; 14 The John & Jennifer Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada; 15 Department of Health Sciences, University of Leicester, Leicester, UK; 16 deCODE Genetics, 101 Reykjavik, Iceland; 17 University of Iceland, Faculty of Medicine, 101 Reykjavik, Iceland; 18 Hudson Alpha Institute, Huntsville, Alabama, USA; 19 Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, Ontario, Canada, K1Y 4W7; 20 Department of Biostatistics, Boston University School of Public Health, Boston, MA USA; 21 National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA; 22 The Blavatnik School of Computer Science and the Department of Molecular Microbiology and Biotechnology, Tel-Aviv University, Tel-Aviv, Israel, and the International Computer Science Institute, Berkeley, CA, USA; 23 Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA; 24 Department of Medicine, Harvard Medical School, Boston, MA, USA; 25 Research Methods, Univ Ottawa Heart Inst; 26 Department of Clinical Sciences, Hypertension and Cardiovascular Diseases, Scania University Hospital, Lund University, Malmö, Sweden; 27 Division of Research, Kaiser Permanente, Oakland, CA, USA; 28 Institute 269 for Human Genetics, University of California, San Francisco, San Francisco, CA, USA; 29 Dept Cardiovascular Medicine, Cleveland Clinic; 30 Medizinische Klinik und Poliklinik, Johannes- Gutenberg Universität Mainz, Universitätsmedizin, Mainz, Germany; 31 Institut für Klinische Chemie und Laboratoriumsmediizin, Johannes-Gutenberg Universität Mainz, Universitätsmedizin, Mainz, Germany; 32 INSERM UMRS 937, Pierre and Marie Curie University (UPMC, Paris 6) and Medical School, Paris, France; 33 University of Texas Health Science Center, Human Genetics Center, Houston, TX, USA; 34 Cardiovascular Health Resarch Unit and Department of Medicine, University of Washington, Seattle, WA USA; 35 Cedars- Sinai Medical Center, Medical Genetics Institute, Los Angeles, CA, USA; 36 Erasmus Medical Center, Department of Epidemiology, Rotterdam, The Netherlands; 37 Boston University, School of Public Health, Boston, MA, USA; 38 University of Minnesota School of Public Health, Division of Epidemiology and Community Health, School of Public Health (A.R.F.), Minneapolis, MN, USA; 39 University of Washington, Cardiovascular Health Research Unit and Department of Medicine, Seattle, WA, USA; 40 Icelandic Heart Association, Kopavogur Iceland; 41 University of Iceland, Reykjavik, Iceland; 42 Laboratory of Epidemiology, Demography, and Biometry, Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda MD, USA; 43 University of Washington, Department of Epidemiology, Seattle, WA, USA; 44 University of Washington, Department of Biostatistics, Seattle, WA, USA; 45 University of Washington, Department of Internal Medicine, Seattle, WA, USA; 46 University of Texas, School of Public Health, Houston, TX, USA; 47 National Heart, Lung and Blood Institute, Framingham Heart Study, Framingham, MA and Cardiology Division, Massachusetts General Hospital, Boston, MA, USA; 48 Center for Health Studies, Group Health, Departments of Medicine, Epidemiology, and Health Services, Seattle, WA, USA; 49 The 270 Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridge, UK; 50 Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle; 51 Boston University Medical Center, Boston, MA, USA; 52 Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands; 53 Department of Medicine, Landspitali University Hospital, 101 Reykjavik, Iceland; 54 Boston University School of Medicine, Framingham Heart Study, Framingham, MA, USA; 55 Institut für Klinische Molekularbiologie, Christian-Albrechts Universität, Kiel, Germany; 56 Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany; 57 Institut für Humangenetik, Helmholtz Zentrum München, Deutsches Forschungszentrum für Umwelt und Gesundheit, Neuherberg, Germany; 58 Institute of Medical Information Science, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Germany; 59 Klinikum Grosshadern, Munich, Germany; 60 Institut für Humangenetik, Technische Universität München, Germany; 61 Division of Endocrinology and Diabetes, Graduate School of Molecular Endocrinology and Diabetes, University of Ulm, Ulm, Germany; 62 Division of Endocrinology, Department of Medicine, Medical University of Graz, Austria; 63 Synlab Center of Laboratory Diagnostics Heidelberg, Heidelberg, Germany; 64 Division of Clinical Chemistry, Department of Medicine, Albert Ludwigs University, Freiburg, Germany; 65 LURIC non profit LLC, Freiburg, Germany; 66 Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University Graz, Austria; 67 Institute of Public Health, Social and Preventive Medicine, Medical Faculty Manneim, University of Heidelberg, Germany; 68 Cardiology Group Frankfurt-Sachsenhausen, Frankfurt, Germany; 69 Cardiovascular Epidemiology and Genetics Group, Institut Municipal d’Investigació Mèdica, Barcelona; Ciber Epidemiología y Salud Pública (CIBERSP), Spain; 70 Chronic Disease 271 Epidemiology and Prevention Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland; 71 Department of Molecular Biology and Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, USA; 72 The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; 73 Cardiovascular Research Institute, Medstar Health Research Institute, Washington Hospital Center, Washington, DC 20010, USA; 74 Genetics Division and Drug Discovery, GlaxoSmithKline, King of Prussia, Pennsylvania 19406, USA; 75 The Institute for Translational Medicine and Therapeutics, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; 76 Department of Cardiovascular Surgery, University of Leicester, Leicester, UK; 77 Division of Cardiovascular and Diabetes Research, Multidisciplinary Cardiovascular Research Centre, Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, Leeds, LS2 9JT, UK; 78 LIGHT Research Institute, Faculty of Medicine and Health, University of Leeds, Leeds, UK; 79 Deutsches Zentrum für Herz- Kreislauf-Forschung (DZHK), Universität zu Lübeck, Lübeck, Germany Sources of Funding The ADVANCE study was supported by a grant from the Reynold's Foundation and NHLBI grant HL087647. Genetic analyses of CADomics were supported by a research grant from Boehringer Ingelheim. Recruitment and analysis of the CADomics cohort was supported by grants from Boehringer Ingelheim and PHILIPS medical Systems, by the Government of Rheinland-Pfalz in the context of the “Stiftung Rheinland-Pfalz für Innovation”, the research program “Wissen schafft Zukunft” and by the Johannes-Gutenberg University of Mainz in the context of the “Schwerpunkt Vaskuläre Prävention” and the “MAIFOR grant 2001”, by grants from the Fondation de France, 272 the French Ministry of Research, and the Institut National de la Santé et de la Recherche Médicale. The deCODE CAD/MI Study was sponsored by NIH grant, National Heart, Lung and Blood Institute R01HL089650-02. The German MI Family Studies (GerMIFS I-III (KORA)) were supported by the Deutsche Forschungsgemeinschaft and the German Federal Ministry of Education and Research (BMBF) in the context of the German National Genome Research Network (NGFN-2 and NGFN-plus), the EU funded integrated project Cardiogenics (LSHM-CT-2006-037593) and ENGAGE, and the bi-national BMBF/ANR funded project CARDomics (01KU0908A). LURIC has received funding from the EU framework 6 funded Integrated Project “Bloodomics” (LSHM-CT-2004-503485), the EU framework 7 funded Integrated Project AtheroRemo (HEALTH-F2-2008-201668) and from Sanofi/Aventis, Roche, Dade Behring/Siemens, and AstraZeneca. The MIGen study was funded by the US National Institutes of Health (NIH) and National Heart, Lung, and Blood Institute’s STAMPEED genomics research program through R01 HL087676. Ron Do from the MIGen study is supported by a Canada Graduate Doctoral Scholarship from the Canadian Institutes of Health Research. Recruitment of PennCATH was supported by the Cardiovascular Institute of the University of Pennsylvania. Recruitment of the MedStar sample was supported in part by the MedStar Research Institute and the Washington Hospital Center and a research grant from GlaxoSmithKline. Genotyping of PennCATH and Medstar was performed at the Center for Applied Genomics at the Children’s Hospital of Philadelphia and supported by GlaxoSmithKline through an Alternate Drug Discovery Initiative research alliance award (M. P. R. and D. J. R.) with the University of Pennsylvania School of Medicine. 273 The Ottawa Heart Genomic Study was supported by CIHR #MOP--82810 (R. R.), CFI #11966 (R. R.), HSFO #NA6001 (R. McP.), CIHR #MOP172605 (R. McP.), CIHR #MOP77682 (A. F. R. S.). The WTCCC Study was funded by the Wellcome Trust. Recruitment of cases for the WTCCC Study was carried out by the British Heart Foundation (BHF) Family Heart Study Research Group and supported by the BHF and the UK Medical Research Council. N. J. S. and S. G. B. hold chairs funded by the British Heart Foundation. N. J. S. and A.H.G are also supported by the Leicester NIHR Biomedical Research Unit in Cardiovascular Disease and the work described in this paper is part of the research portfolio of the Leicester NIHR Biomedical Research Unit. The Age, Gene/Environment Susceptibility Reykjavik Study has been funded by NIH contract N01-AG-12100, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). The Cleveland Clinic GeneBank study was supported by NIH grants P01 HL098055, P01HL076491-06, R01DK080732, P01HL087018, and 1RO1HL103931-01. The collection of clinical and sociodemographic data in the Dortmund Health Study was supported by the German Migraine- & Headache Society (DMKG) and by unrestricted grants of equal share from Astra Zeneca, Berlin Chemie, Boots Healthcare, Glaxo-Smith-Kline, McNeil Pharma (former Woelm Pharma), MSD Sharp & Dohme and Pfizer to the University of Muenster. Blood collection was done through funds from the Institute of Epidemiology and Social Medicine, University of Muenster. The EPIC-Norfolk study is supported by the Medical Research Council UK and Cancer Research UK. The EpiDREAM study is supported by the Canadian Institutes fo Health Research, Heart and Stroke Foundation of Ontario, Sanofi-Aventis, GlaxoSmithKline and King Pharmaceuticals. 274 Funding for Andrew Lotery from the LEEDS study was provided by tha T.F.C. Frost charity and the Macular Disease Society. The Rotterdam Study is supported by the Erasmus Medical Center and Erasmus University Rotterdam; the Netherlands Organization for Scientific Research; the Netherlands Organization for Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly; The Netherlands Heart Foundation; the Ministry of Education, Culture and Science; the Ministry of Health Welfare and Sports; the European Commission (DG XII); and the Municipality of Rotterdam. Support for genotyping was provided by the Netherlands Organization for Scientific Research (NWO) (175.010.2005.011, 911.03.012), the Netherlands Genomics Initiative (NGI)/ NWO project nr. 050-060-810 and Research Institute for Diseases in the Elderly (RIDE). Abbas Dehghan is supported by a grant from NWO (Vici, 918-76-619). The SAS study was funded by the British Heart Foundation. The Swedish Research Council, the Swedish Heart & Lung Foundation and the Stockholm County Council (ALF) supported the SHEEP study. SMILE was funded by the Netherlands Heart foundation (NHS 92345). Dr Rosendaal is a recipient of the Spinoza Award of the Netherlands Organisation for Scientific Research (NWO) which was used for part of this work. The Verona Heart Study was funded by grants from the Italian Ministry of University and Research, the Veneto Region, and the Cariverona Foundation, Verona. The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts N01-HC-55015, N01-HC- 55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, and N01-HC-55022. The authors thank the staff and participants of the ARIC study for their important contributions. 275 The KORA (Kooperative Gesundheitsforschung in der Region Augsburg) research platform was initiated and financed by the Helmholtz Zentrum München - National Research Center for Environmental Health, which is funded by the German Federal Ministry of Education, Science, Research and Technology and by the State of Bavaria. Part of this work was financed by the German National Genome Research Network (NGFN-2 and NGFNPlus) and within the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ. 276 1307 C holine is a key nutrient with various metabolic roles in lipid metabolism and cell membrane structure, and it serves as a precursor for the synthesis of the neurotransmitter acetylcholine. 1–3 Dietary choline is also an important source of methyl groups that are required for proper metabolism of certain amino acids, such as homocysteine and methionine. 3 A variety of animal studies have shown that choline deficiency adversely affects brain and cognitive development during fetal and neonatal life, 1,4–6 which has led to specific nutritional guidelines recommending adequate intake of choline for infants and pregnant or lactating women. 7,8 One route for the initial catabolism of dietary choline (in the form of phosphotidylcholine) is mediated by intestinal microbes and leads to the formation of trimethylamine (TMA). TMA is efficiently absorbed from the gastrointestinal tract and subsequently oxidized by the liver to form TMA N-oxide (TMAO). This latter reaction is catalyzed by one or more of the flavin monooxygenase (FMO) enzymes, of which there are 6 gene family members in higher mammals. 9 Interestingly, muta- tions of FMO3 that result in deficiency of this enzyme are the cause of trimethylaminuria, otherwise known as fish malodor syndrome. 10 This relatively rare recessive disorder is character- ized by the near absence of plasma TMAO levels and highly elevated TMA levels, depending on the functional severity of the mutation in FMO3. The pungent odor of rotting fish that characterizes trimethylaminuria is because of the release of the volatile gas TMA through the breath, skin, and urine. Recently, we uncovered a novel mechanism through which gut microbiota and hepatic-mediated metabolism of dietary choline promote atherosclerosis and increase the risk of © 2014 American Heart Association, Inc. Arterioscler Thromb Vasc Biol is available at http://atvb.ahajournals.org DOI: 10.1161/ATVBAHA.114.303252 Objective—Elevated levels of plasma trimethylamine N-oxide (TMAO), the product of gut microbiome and hepatic-mediated metabolism of dietary choline and L-carnitine, have recently been identified as a novel risk factor for the development of atherosclerosis in mice and humans. The goal of this study was to identify the genetic factors associated with plasma TMAO levels. Approach and Results—We used comparative genome-wide association study approaches to discover loci for plasma TMAO levels in mice and humans. A genome-wide association study in the hybrid mouse diversity panel identified a locus for TMAO levels on chromosome 3 (P=2.37×10 −6 ) that colocalized with a highly significant (P=1.07×10 −20 ) cis-expression quantitative trait locus for solute carrier family 30 member 7. This zinc transporter could thus represent 1 positional candidate gene responsible for the association signal at this locus in mice. A genome-wide association study for plasma TMAO levels in 1973 humans identified 2 loci with suggestive evidence of association (P=3.0×10 −7 ) on chromosomes 1q23.3 and 2p12. However, genotyping of the lead variants at these loci in 1892 additional subjects failed to replicate their association with plasma TMAO levels. Conclusions—The results of these limited observational studies indicate that, at least in humans, genes play a marginal role in determining TMAO levels and that any genetic effects are relatively weak and complex. Variation in diet or the repertoire of gut microbiota may be more important determinants of plasma TMAO levels in mice and humans, which should be investigated in future studies. (Arterioscler Thromb Vasc Biol. 2014;34:1307-1313.) Key Words: atherosclerosis ◼ genetics ◼ humans ◼ mice ◼ trimethylamine N-oxide Received on: January 13, 2014; final version accepted on: March 18, 2014. From the Department of Preventive Medicine (J.H., H.A.) and Institute for Genetic Medicine (J.H., H.A.), Keck School of Medicine of the University of Southern California, Los Angeles; Department of Genetics (B.J.B.) and Nutrition Research Institute (B.J.B.), University of North Carolina, Chapel Hill, Kannapolis; Departments of Cardiovascular Medicine (W.H.W.T., Z.W., S.L.H.) and Cellular and Molecular Medicine (W.H.W.T., Z.W., S.L.H.) and Center for Cardiovascular Diagnostics and Prevention (W.H.W.T., Z.W., S.L.H.), Cleveland Clinic, OH; John and Jennifer Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Ontario, Canada (A.F.R.S., R.R., R.M.); and Departments of Medicine (A.J.L.), Human Genetics (A.J.L.), and Microbiology, Immunology, and Molecular Genetics (A.J.L.), David Geffen School of Medicine of UCLA. *A full list of authors and affiliations for the CARDIoGRAM Consortium is provided in the online-only Data Supplement. This manuscript was sent to Robert A. Hegele, Consulting Editor, for review by expert referees, editorial decision, and final disposition. The online-only Data Supplement is available with this article at http://atvb.ahajournals.org/lookup/suppl/doi:10.1161/A TVBAHA.114.303252/-/DC1. Correspondence to Hooman Allayee, PhD, Institute for Genetic Medicine, Keck School of Medicine of USC, 2250 Alcazar St, CSC202, Los Angeles, CA 90033. E-mail hallayee@usc.edu Comparative Genome-Wide Association Studies in Mice and Humans for Trimethylamine N-Oxide, a Proatherogenic Metabolite of Choline and L-Carnitine Jaana Hartiala, Brian J. Bennett, W.H. Wilson Tang, Zeneng Wang, Alexandre F.R. Stewart, Robert Roberts, Ruth McPherson, CARDIoGRAM Consortium,* Aldons J. Lusis, Stanley L. Hazen, Hooman Allayee at USC Norris Medical Library on June 12, 2014 http://atvb.ahajournals.org/ Downloaded from 277 1308 Arterioscler Thromb Vasc Biol June 2014 coronary artery disease (CAD). 11,12 These studies demonstrated that plasma TMAO levels in humans were positively associ- ated with the presence of multiple CAD phenotypes, including atherosclerotic plaque burden and future risk of myocardial infarction, stroke, or death in a dose-dependent fashion. A similar relationship was observed between plasma TMAO lev- els and aortic lesion development among various inbred mouse strains. 13 More recently, we also demonstrated that L-carnitine, a trimethylamine abundant in red meat, is also metabolized by intestinal microbiota to produce TMAO in mice and humans and that L-carnitine supplementation accelerated atheroscle- rosis in mice. 14 Notably, short-term administration of broad- spectrum antibiotics eliminated the production of TMAO in both mice and humans and decreased atherosclerosis in mice. Furthermore, TMAO supplementation in mice, or dietary sup- plementation of either choline or L-carnitine, in the presence of intact gut microbiota led to alterations in cholesterol and sterol metabolism in multiple distinct compartments, including reduction in reverse cholesterol transport, providing a mecha- nistic rationale for the association between TMAO levels and atherosclerotic cardiovascular phenotypes. 14 Taken together, these studies provide evidence consistent with the proath- erogenic role of TMAO in mammals and support the notion that gut microbiota plays an obligatory role in the formation TMAO from dietary choline and L-carnitine. It is reasonable to assume that variation in plasma TMAO lev- els could also be affected by intrinsic genetic factors of the host. However, with the exception of FMO3, the genes that control plasma TMAO levels are not known. Therefore, the aim of the present study was to use comparative genome- wide association study (GW AS) approaches in mice and humans to identify novel genetic determinants associated with plasma TMAO levels. Materials and Methods Materials and Methods are available in the online-only Supplement. Results Association of the FMO Cluster With FMO3 Gene Expression, Plasma TMAO Levels, and CAD in Humans In previous studies, we reported that Fmo3 expression var- ied significantly among inbred strains from the hybrid mouse diversity panel (HMDP) and that a major locus regulating its expression mapped directly over Fmo3, suggesting cis-acting regulation in mice. 13 Furthermore, Fmo3 expression was posi- tively correlated with both plasma TMAO levels and athero- sclerosis in mice. Based on these observations, we first used a targeted approach to evaluate whether genetic associations could specifically be observed with the human FMO locus on chromosome 1q24.3. To evaluate the association of the FMO cluster with hepatic FMO3 mRNA levels, we used a previously published liver gene expression data set. 15 These analyses were performed in a subset of 151 white subjects for whom complete gene expression and genotype data were publicly available. Fifty-seven single nucleotide polymor- phisms (SNPs) were available for analysis in a specified ≈451-kb region containing FMO3, FMO6P, FMO2, FMO1, and FMO4, including 200 kb of flanking sequence (100 kb from each end). As shown in Figure 1A, 1 SNP (rs2075988) yielded age- and sex-adjusted association with FMO3 mRNA levels (P=4.5×10 −4 ) that remained significant after correc- tion for multiple testing (0.05/57; Bonferroni-corrected P=8.8×10 −4 ). Cis-expression quantitative trait loci (QTL) were not observed for any other members of the FMO gene family at this locus (data not shown). We next determined whether variation in the FMO cluster influenced plasma TMAO levels using the GW AS results from the GeneBank study, a cohort of patients undergoing elective cardiac evaluation at the Cleveland Clinic. Table 1 describes the clinical characteristics of the 3865 individuals used in the present study. As expected for a patient population undergo- ing coronary angiography as part of their clinical evaluation, a majority of these subjects were men, had prevalent CAD, and were taking lipid-lowering medication (Table 1). In this analysis, 471 SNPs were available, but none were sig- nificantly associated with plasma TMAO levels (Figure 1B). Finally, we evaluated whether the FMO locus was associated with risk of CAD in the Coronary Artery Disease Genome- wide Replication And Meta-Analysis (CARDIoGRAM) consortium, which represents a meta- analysis of GWAS data from a discovery set of ≈22 000 CAD cases and ≈65 000 controls. 16 In CARDIoGRAM, 388 SNPs were available for analyses, of which 21 yielded values of P<0.05 for associa- tion with CAD (Figure 1C). However, none of these associa- tions were significant at the Bonferroni-corrected significance threshold (P=1.3×10 −4 ; 0.05/388). Furthermore, the SNP that exhibited the strongest association with FMO3 mRNA lev- els (rs2075988) did not demonstrate evidence for association with either plasma TMAO levels or risk of CAD (Figure 1). GWAS for Plasma TMAO Levels in Mice To identify novel genetic factors associated with plasma TMAO levels in mammals, we next used the HMDP to per- form an unbiased GWAS in mice. This newly developed genetic platform consists of ≈100 classic inbred and recombi- nant inbred mouse strains that are maximally informative for association analysis and have been used to perform GWAS for other quantitative traits relevant to human diseases, including atherosclerosis, metabolites, and hepatic mRNA levels. 17–20 For the present study, we performed a GWAS for plasma TMAO levels in male mice on a chow diet and identified 1 locus on mouse chromosome 3 between 110 and Nonstandard Abbreviations and Acronyms CAD coronary artery disease CARDIoGRAM Coronary Artery Disease Genome-wide Replication And Meta-Analysis FMO flavin monooxygenase GWAS genome-wide association study HMDP hybrid mouse diversity panel QTL quantitative trait loci SNPs single nucleotide polymorphisms Slc30a7 solute carrier family 30 member 7 TMA trimethylamine TMAO trimethylamine N-oxide at USC Norris Medical Library on June 12, 2014 http://atvb.ahajournals.org/ Downloaded from 278 Hartiala et al GWAS for TMAO Levels in Mice and Humans 1309 115 Mb that exceeded the genome-wide significance thresh- old for association in the HMDP (P=2.37×10 −6 ; Figure 2A and 2B). The 10-Mb region centered around the lead SNP on chromosome 3 contained several genes and exhibited a highly significant cis-expression QTL (P=1.07×10 −20 ) for the gene encoding solute carrier family 30 member 7 (Slc30a7; Figure 2C). The colocalization of QTLs for plasma TMAO and Slc30a7 mRNA levels suggests that this zinc transporter could represent 1 positional candidate gene responsible for the association signal at this locus. Suggestive evidence for association of plasma TMAO levels (P=7.62×10 −6 ) was also observed with a region on mouse chromosome 1 at 184 Mb (Figure 2A), although this locus did not achieve genome- wide significance. The lead SNP on chromosome 1 maps to within 40 kb of the lamin β-receptor gene but ≈20 Mb distal from the Fmo gene cluster (162–163 Mb). GWAS for Plasma TMAO Levels in Humans To complement the mouse studies, we performed a 2-stage GW AS in GeneBank. In the first stage, ≈2.4 million genotyped and imputed autosomal SNPs were evaluated for association with plasma TMAO levels in 1973 subjects with adjustment for age and sex. The quantile-quantile plot for these analy- ses is shown in Figure 3A, and the observed genomic infla- tion factor (λ) was 1.007, indicating that the GWAS results are not confounded by underlying population stratification. As shown by the Manhattan plot in Figure 3B, 2 loci with sugges- tive evidence of association were identified on chromosomes 1q23.3 and 2p12. The lead SNP at the chromosome 1 locus (rs17359359; P=2.8×10 −7 ) is located ≈47 kb telomeric of NUF2, which is a component of the kinetochore complex that is required for chromosome segregation but, to our knowl- edge, has no known relationship to TMAO metabolism. This locus is also located ≈8 Mb telomeric to the FMO gene cluster and is clearly distinct because there is no apparent long-range linkage disequilibrium between these 2 loci. By compari- son, the lead SNP at the chromosome 2p12 locus (rs885187; P=2.8×10 −7 ) does not map near any known gene. Based on previously observed sex differences in plasma TMAO levels, we also performed a GWAS in men and women separately. However, these analyses did not reveal sex-specific effects on chromosomes 1q23.3 and 2p12 or identify other loci (Figure I in the online-only Data Supplement). In stage 2, we evaluated the chromosome 1 locus further by genotyping rs17359359 in 1892 additional GeneBank subjects for whom plasma TMAO levels were available. These analyses failed to replicate the association of rs17359359 with plasma TMAO levels in stage 2 (P=0.85), and a combined analysis with all subjects attenuated the overall association (P=1.8×10 – 4 ; Table 2). Based on the chromosome 3 locus identified in the Figure 1. Association of the flavin monooxygenase (FMO) locus with FMO3 mRNA levels, plasma trimethylamine N-oxide (TMAO) levels, and risk of coronary artery disease (CAD) in humans. Using a publicly available expression quantitative trait loci liver data set, 57 single nucleo- tide polymorphisms (SNPs) were tested for association with hepatic FMO3 mRNA levels, one of which (rs2075988) yielded a significant P value (4.5×10 −4 ) after Bonferroni correction for multiple testing (A). In the GeneBank cohort, none of the 471 SNPs tested in the FMO locus yielded significant association with plasma TMAO levels (B). Evaluation of the FMO locus with risk of CAD using 388 SNPs available from the results of the Coronary Artery Disease Genome-wide Replication And Meta-Analysis (CARDIoGRAM) consortium did not reveal any signifi- cant associations (C). The same genomic interval spanning ≈451 kb across the FMO cluster on chromosome 1q24.3 is shown for all 3 plots, and the variant most strongly associated with FMO3 mRNA levels is given as the reference SNP (rs2075988). Chr indicates chromosome. Table 1. Clinical Characteristics of the Study Population Trait n=3865 Age, y 64±11 Male/female 6372/2789 Number with CAD at baseline, % 6776 (76) CAD severity 0 vessels, % 2766 (30) 1 or 2 vessels, % 3392 (37) ≥3 vessels, % 3003 (33) No. of MACE, % 1285 (14) BMI, kg/m 2 29.6±6.2 Total cholesterol, mg/dL 170±41.1 HDL cholesterol, mg/dL 40.0±13.5 LDL cholesterol, mg/dL 99.0±33.5 Triglycerides, mg/dL 151.5±110.1 TMAO, μmol/L 6.2±13.0 Taking lipid-lowering medication (%) 5751 (63) Data are shown as mean±SD or numbers of individuals (%). BMI indicates body mass index; CAD, coronary artery disease; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MACE, major adverse cardiac events; and TMAO, trimethylamine N-oxide. at USC Norris Medical Library on June 12, 2014 http://atvb.ahajournals.org/ Downloaded from 279 1310 Arterioscler Thromb Vasc Biol June 2014 HMDP (Figure 2A and 2B), we used synteny mapping and evaluated the association of plasma TMAO levels with SNPs in the 1-Mb genomic region centered on the human SLC30A7 ortholog located on chromosome 1p21.2. In the GWAS data set (n=1973), 1 SNP located ≈225 kb telomeric to SLC30A7 (rs12402441) demonstrated nominal association (P=0.006) with plasma TMAO levels (Table 2). However, the associa- tion of rs12402441 with plasma TMAO levels did not replicate in the stage 2 samples, and a combined analysis with all sub- jects was not significant (Table 2). In the combined data set, there was also no evidence for an interaction between sex and either SNP on plasma TMAO levels (rs17359359, P int =0.33; rs12402441, P int =0.11). The sex- specific effects of rs17359359 and rs12402441 when men and women were analyzed sepa- rately are shown in Table I in the online-only Data Supplement. Discussion Using a combined mouse–human GW AS approach, we sought to identify the genetic determinants of plasma TMAO levels in mammals. Several factors served as the motivation for these studies, including recent studies demonstrating that TMAO can be generated from gut microbiota–mediated metabo- lism of either dietary choline or L-carnitine and that elevated plasma levels are strongly proatherogenic in both mice and humans. 11,12,14 Subsequent reports further showed that plasma TMAO levels in mice are regulated by both sex hormones, which could account, in part, for the observed dimorphism between male and female mice and increased Fmo3 gene expression via bile acid–mediated activation of the farnesoid X receptor. 13 Of note, in humans, no differences in plasma TMAO levels were observed between men and women. 12 The collective results of these comprehensive, albeit limited, observational studies indicate that genes play a marginal role in determining TMAO levels and that any genetic effects are either complex or relatively weak. This is particularly true in humans and raises the possibility that variation in dietary composition or the repertoire of gut microbiota may be more important determinants of plasma TMAO levels. Using the HMDP, we identified 1 locus on chromosome 3, which contains Slc30A7 and was associated with plasma TMAO levels in male mice at the genome-wide significance threshold. This locus also exhibited evidence for cis gene regu- lation of Slc30a7, which is a subfamily member of the cation diffusion facilitator family of transporters and has essential functions in dietary zinc absorption. 21 Although a biological mechanism for how Slc30a7 would regulate plasma TMAO levels is not directly evident, it has been reported that the zinc finger protein, YY1, regulates the expression of both rab- bit and human FMO1. 22 Interestingly, the activity of certain bacterial monoooxygenases has also been shown to use zinc Figure 2. Manhattan plot for genome-wide association study (GWAS) of plasma trimethylamine N-oxide (TMAO) levels in mice. A GWAS for plasma TMAO levels in the hybrid mouse diversity panel (HMDP) identifies a significant locus over the solute carrier family 30 mem- ber 7 (Slc30a7) gene (red dot) at 110 to 115 Mb on chromosome 3 and a suggestive locus on chromosome 1 ≈40 kb away from the Lbr gene (A). A regional plot for chromosome 3 shows the location and transcriptional orientation of Slc30a7 (indicated by red arrow) in rela- tion to the peak single nucleotide polymorphisms (SNPs) in this region (B). Of the genes in this locus, a highly significant (P=1.07×10 −20 ) cis-acting expression quantitative trait loci is observed for Slc30a7 (C). The red line indicates the genome-wide threshold for significance in the HMDP (P=4.1×10 −6 ). Plasma TMAO and hepatic mRNA levels were quantified in male mice from ≈100 HMDP strains (n=3–8 mice per strain) and analyzed for association with ≈107 000 SNPs, after correcting for population structure using the EMMA algorithm. at USC Norris Medical Library on June 12, 2014 http://atvb.ahajournals.org/ Downloaded from 280 Hartiala et al GWAS for TMAO Levels in Mice and Humans 1311 as a cofactor. 23 However, more detailed functional studies will be required to determine whether Slc30a7 could affect TMAO levels by influencing zinc-mediated activity of ≥1 of the FMOs in mice. We also note that although this locus on mouse chromosome 3 also yielded a highly significant cis- expression QTL for Slc30a7, we cannot exclude the possibility that another gene in this interval harboring functional coding variation is the causal genetic factor for plasma TMAO levels. Because our GW AS with the HMDP was only with male mice, it is also possible that inclusion of females would provide addi- tional support for the association of the Slc30a7 locus, as well as identify other genomic regions controlling plasma TMAO levels that are potentially specific to females. For example, we previously reported that plasma TMAO levels are several- fold higher in female mice compared with males, a portion of which is attributable to differences in sex hormones. 13 As a comparative analysis to our studies with the HMDP, we also performed a GWAS for plasma TMAO levels in the GeneBank cohort. This analysis identified 2 suggestive loci in the discovery phase, but our attempt to replicate the NUF2 locus on chromosome 1 was unsuccessful. Based on the Slc30a7 locus identified in the mouse GWAS, we also evalu- ated the syntenic region on human chromosome 1p21.2 for association with plasma TMAO levels. Although 1 SNP in this region yielded nominal association with plasma TMAO lev- els in humans, this signal also did not replicate in the stage 2 samples. Given the high concordance rate (>98.8%) for geno- types of the same DNA samples used in stages 1 and 2, we do not think technical variability to have been a factor for the lack of replication in stage 2 and conclude that these loci likely represent false-positive signals. However, despite the lack of genetic variation around the human SLC30A7 ortholog being associated with plasma TMAO levels, it is possible that this transporter still plays a biological role in regulating TMAO levels in both species. Furthermore, we did not obtain any evidence for sex-specific effects at these loci or identify any others when the GWAS was performed in men and women separately. Taken together, these results suggest that variation in plasma TMAO levels in humans may be because of weak genetic effects and that larger sample sizes will be required to identify the underlying regulatory factors. To date, FMO3 is the only genetic factor known to affect plasma TMAO levels in humans. FMO3 is composed of 10 exons spanning 26.9 kb on chromosome 1q24.3 and encodes a 532-residue enzyme. At the amino acid level, FMO3 shares ≥79% homology with the mouse Fmo3 protein and other members of the human FMO family. Interestingly, we previ- ously demonstrated that FMO1, FMO2, and FMO3 were able Figure 3. Results of a genome-wide asso- ciation study (GWAS) for plasma trimeth- ylamine N-oxide (TMAO) levels in humans. The quantile-quantile plot of the GWAS results for plasma TMAO levels in humans (n=1973) shows slight deviation of the observed P values from the expected distri- bution under the null hypothesis of no asso- ciation (A). The observed genomic control factor in these analyses was 1.007, indicat- ing that the results are not confounded by underlying population stratification. A GWAS analysis in humans identifies 2 loci on chromosomes 1 and 2 exhibiting sug- gestive evidence of association with plasma TMAO levels but no locus that exceeds the genome-wide threshold for significance (indicated by the horizontal red line; B). at USC Norris Medical Library on June 12, 2014 http://atvb.ahajournals.org/ Downloaded from 281 1312 Arterioscler Thromb Vasc Biol June 2014 to generate TMAO from TMA but that FMO3 was by far the most active family member. 13 Because the Mendelian disease trimethylaminuria is caused by rare mutations that lead to FMO3 deficiency, we leveraged our own data in GeneBank and those from public sources to evaluate whether common variants at the FMO locus were associated with FMO3 gene expression, plasma TMAO levels, and risk of CAD. However, these analyses in humans did not reveal any strong associa- tions with SNPs surrounding FMO3. It is possible that the imputed genotypes from the GWAS data we used did not pro- vide sufficient coverage of the variation around FMO3 (or the entire FMO locus). Based on data for subjects of European ancestry from the 1000 Genomes Project, 59 tagging SNPs with minor allele frequencies ≥1% would cover FMO3 at an r 2 ≥0.8. However, only 15 tagging SNPs across FMO3 were present in our analyses of plasma TMAO levels in GeneBank. In addition, rare variants in FMO3 that could influence gene expression, TMAO production, and risk of CAD would also not necessarily be represented by our imputed GW AS data. By comparison, our previous studies in mice revealed a relatively strong cis-expression QTL for Fmo3 expression over the Fmo locus. However, the present analyses for plasma TMAO levels in the HMDP did not yield association with the Fmo locus at the genome-wide level (data not shown). These observations suggest that the relationship between FMO3 gene expression and plasma TMAO levels in both mice and humans is complex and that other regulatory mechanisms, including post-tran- scriptional and post-translational modifications, may exist. The discordance between rare mutations in FMO3 that dra- matically reduce plasma TMAO levels and the lack of common genetic determinants associated with this metabolite implies that variation in TMAO levels in humans and mice may be influenced by other factors, such as gut microbial and dietary composition. For example, we previously defined the relative abundances of bacteria at each taxonomic level in relation to the production of TMAO through pyrosequencing of 16S rRNA genes in both mice and humans. One notable difference in these analyses was the source of gut bacteria because the contents of the cecum were used for mice, whereas stool samples were used for the human analyses. This may explain, at least in part, why a direct comparison of bacterial taxa associated with plasma TMAO concentrations did not identify any genre common to both species. This observation is consistent with previous reports indicating that microbes identified from the distal gut of the mouse do not necessarily represent those typically detected in humans. 24,25 Thus, although sharing many taxa, the microbial composition observed in mice is architecturally and globally different than in humans. Despite these differences, we were still able to demonstrate associations between dietary patterns (eg, vegan/vegetarian versus omnivore or normal chow versus choline/carnitine supplemented) and both plasma TMAO levels and proportions of specific taxa of fecal microbes in humans and cecal microbes in mice. 14 These observations suggest that high dietary intake of L-carnitine or choline would lead to increased plasma TMAO levels, particularly if specific bacte- rial taxa that metabolize these nutrients to TMA are present in the gut. It is possible that the effects of host genetic factors would also manifest under such dietary conditions. However, compared with mice housed under standardized environmental conditions, the diet in free-ranging humans is far more hetero- geneous, which would add further complexity and diversity to any potential interactions with the gut microbiome. Despite our comprehensive efforts to identify loci associated with plasma TMAO levels, we also note several potential limi- tations of our study. First, we used GWAS approaches in mice and humans that mostly test association with common genetic variation, which would not necessarily detect the effects of rare variants on plasma TMAO levels. Second, our human GWAS was performed in subjects of European ancestry, and it is pos- sible that genetic variants that are either specific to or present at higher frequency in other ethnicities could influence TMAO levels. Third, although including ≈100 inbred strains, it is still possible that the HMDP does provide sufficient genetic varia- tion to capture all of the effects on plasma TMAO levels in mice compared with the substantially greater genetic diversity present in outbred human populations. In addition, the path- ways leading to variation in TMAO levels in mice and humans may not be entirely similar. Finally, as discussed above, vari- ability in dietary composition, particularly in humans, and the gut microbiome clearly factor into plasma TMAO levels and are thus likely to be strong confounding variables that our study did not take into consideration. Conclusions Our results indicate that Slc30a7 may represent a novel gene for TMAO levels in mice but that the contribution of genetic factors in humans is more complex. These observations Table 2. Effect of Single Nucleotide Polymorphisms Identified Through GWAS in Humans and Mice on Plasma TMAO Levels in the GeneBank Cohort Stage rs17359359 rs12402441 GG AG AA P Value* AA AG GG P Value* GWAS 5.3±8.0 (n=1727) 8.2±20.3 (n=238) 9.9±8.1 (n=8) 2.8×10 –7 5.8±10.8 (n=1773) 4.5±4.7 (n=186) 4.5±3.7 (n=14) 0.006 Replication 6.6±14.5 (n=1495) 8.3±23.2 (n=186) 4.0±2.9 (n=9) 0.71 6.6±13.3 (n=1598) 8.8±31.2 (n=158) 4.5±1.5 (n=10) 0.68 Combined 5.9±11.5 (n=3222) 8.3±21.6 (n=424) 6.8±6.5 (n=17) 1.1×10 –4 6.2±12.1 (n=3371) 6.5±21.5 (n=344) 4.5±2.9 (n=24) 0.14 Mean (±SD) plasma TMAO levels (μmol/L) are shown as a function of genotype. GWAS indicates genome-wide association study; and TMAO, trimethylamine N-oxide. *P values obtained using linear regression with natural log-transformed values after adjustment for age and sex. at USC Norris Medical Library on June 12, 2014 http://atvb.ahajournals.org/ Downloaded from 282 Hartiala et al GWAS for TMAO Levels in Mice and Humans 1313 suggest that the inter-relationships between dietary choline and L-carnitine levels with the composition of gut microbes are perhaps more likely determinants of variation in plasma TMAO levels. Exploring such interactions as part of future studies may help to identify the intrinsic genetic factors that influence plasma TMAO levels and their influence on the development of atherosclerosis. Sources of Funding This study was supported, in part, by National Institutes of Health (NIH) grants K99HL102223, P01HL30568, P01HL28481, R01HL103866, P20HL113452, R01ES021801, a pilot project award from the Southern California Clinical and Translational Science Institute through NIH grant UL1TR000130, and American Heart Association Scientist Development grant 12SDG12050473. GeneBank was supported in part by NIH grants P01HL098055, P01HL076491, and R01HL103931. R. Roberts has received research funding from Canadian Institutes of Health Research MOT82810 and Canada Foundation for Innovation 11966. S.L. Hazen is also partially supported by a gift from the Leonard Krieger Fund. Mass spectrometry instrumentation used was housed within the Cleveland Clinic Mass Spectrometry Facility with partial support through a Center of Innovation by AB SCIEX. Disclosures S.L. Hazen is named as coinventor on pending and issued patents held by the Cleveland Clinic relating to cardiovascular diagnostics. He reports having been paid as a consultant or speaker for the following companies: Cleveland Heart Laboratory, Inc, Esperion, Liposciences Inc, Merck & Co, Inc, and Pfizer Inc. He reports he has received research funds from Abbott, Cleveland Heart Laboratory, Esperion, and Liposciences, Inc. He has the right to receive royalty payments for inventions or discoveries related to cardiovascular diagnostics from Abbott Laboratories, Cleveland Heart Laboratory, Inc, Frantz Biomarkers, LLC, and Siemens. The other authors report no conflicts. References 1. Zeisel SH, Blusztajn JK. Choline and human nutrition. Annu Rev Nutr. 1994;14:269–296. 2. Buchman AL, Ament ME, Sohel M, Dubin M, Jenden DJ, Roch M, Pownall H, Farley W, Awal M, Ahn C. Choline deficiency causes reversible hepatic abnormalities in patients receiving parenteral nutrition: proof of a human choline requirement: a placebo-controlled trial. JPEN J Parenter Enteral Nutr. 2001;25:260–268. 3. Hollenbeck CB. The importance of being choline. J Am Diet Assoc. 2010;110:1162–1165. 4. Meck WH, Williams CL. Metabolic imprinting of choline by its availabil- ity during gestation: implications for memory and attentional processing across the lifespan. Neurosci Biobehav Rev. 2003;27:385–399. 5. Cermak JM, Holler T, Jackson DA, Blusztajn JK. Prenatal availability of choline modifies development of the hippocampal cholinergic system. FASEB J. 1998;12:349–357. 6. Craciunescu CN, Albright CD, Mar MH, Song J, Zeisel SH. Choline availability during embryonic development alters progenitor cell mitosis in developing mouse hippocampus. J Nutr. 2003;133:3614–3618. 7. Yates AA, Schlicker SA, Suitor CW. Dietary Reference Intakes: the new basis for recommendations for calcium and related nutrients, B vitamins, and choline. J Am Diet Assoc. 1998;98:699–706. 8. Zeisel SH. Choline: an essential nutrient for humans. Nutrition. 2000;16:669–671. 9. Krueger SK, Williams DE. Mammalian flavin-containing monooxygen- ases: structure/function, genetic polymorphisms and role in drug metabo- lism. Pharmacol Ther. 2005;106:357–387. 10. Phillips IR, Shephard EA. Flavin-containing monooxygenases: mutations, disease and drug response. Trends Pharmacol Sci. 2008;29:294–301. 11. Wang Z, Klipfell E, Bennett BJ, et al. Gut flora metabolism of phosphati- dylcholine promotes cardiovascular disease. Nature. 2011;472:57–63. 12. Tang WH, Wang Z, Levison BS, Koeth RA, Britt EB, Fu X, Wu Y, Hazen SL. Intestinal microbial metabolism of phosphatidylcholine and cardio- vascular risk. N Engl J Med. 2013;368:1575–1584. 13. Bennett BJ, de Aguiar Vallim TQ, Wang Z, Shih DM, Meng Y, Gregory J, Allayee H, Lee R, Graham M, Crooke R, Edwards PA, Hazen SL, Lusis AJ. Trimethylamine-N-oxide, a metabolite associated with athero- sclerosis, exhibits complex genetic and dietary regulation. Cell Metab. 2013;17:49–60. 14. Koeth RA, Wang Z, Levison BS, et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med. 2013;19:576–585. 15. Schadt EE, Molony C, Chudin E, et al. Mapping the genetic architecture of gene expression in human liver. PLoS Biol. 2008;6:e107. 16. Schunkert H, König IR, Kathiresan S, et al.; Cardiogenics; CARDIoGRAM Consortium. Large-scale association analysis identifies 13 new suscepti- bility loci for coronary artery disease. Nat Genet. 2011;43:333–338. 17. Bennett BJ, Farber CR, Orozco L, et al. A high-resolution association mapping panel for the dissection of complex traits in mice. Genome Res. 2010;20:281–290. 18. Farber CR, Bennett BJ, Orozco L, et al. Mouse genome-wide association and systems genetics identify Asxl2 as a regulator of bone mineral density and osteoclastogenesis. PLoS Genet. 2011;7:e1002038. 19. Ghazalpour A, Bennett B, Petyuk VA, et al. Comparative analysis of pro- teome and transcriptome variation in mouse. PLoS Genet. 2011;7:e1001393. 20. Park CC, Gale GD, de Jong S, Ghazalpour A, Bennett BJ, Farber CR, Langfelder P, Lin A, Khan AH, Eskin E, Horvath S, Lusis AJ, Ophoff RA, Smith DJ. Gene networks associated with conditional fear in mice identi- fied using a systems genetics approach. BMC Syst Biol. 2011;5:43. 21. Huang L, Yu YY, Kirschke CP, Gertz ER, Lloyd KK. Znt7 (Slc30a7)- deficient mice display reduced body zinc status and body fat accumula- tion. J Biol Chem. 2007;282:37053–37063. 22. Luo Z, Hines RN. Regulation of flavin-containing monooxygen- ase 1 expression by ying yang 1 and hepatic nuclear factors 1 and 4. Mol Pharmacol. 2001;60:1421–1430. 23. Ensign SA, Allen JR. Aliphatic epoxide carboxylation. Annu Rev Biochem. 2003;72:55–76. 24. Ley RE, Bäckhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. Obesity alters gut microbial ecology. Proc Natl Acad Sci U S A. 2005;102:11070–11075. 25. Ley RE, Hamady M, Lozupone C, Turnbaugh PJ, Ramey RR, Bircher JS, Schlegel ML, Tucker TA, Schrenzel MD, Knight R, Gordon JI. Evolution of mammals and their gut microbes. Science. 2008;320:1647–1651. Elevated plasma levels of trimethylamine N-oxide (TMAO), a metabolite generated from dietary choline and carnitine by intestinal bacteria, have recently been identified as a novel risk factor for coronary artery disease. Notably, elimination of bacteria in the gut through administra- tion of antibiotics reduced TMAO levels in mice and humans and decreased atherosclerosis in mice. However, the genes that control plasma TMAO levels are not well defined. The present study uses a comparative genome-wide association study approach in mice and humans to identify the genetic determinants of plasma TMAO levels. In mice, genetic variants near solute carrier family 30 member 7 were significantly associated with plasma TMAO levels, whereas no locus was identified in humans. Our findings suggest that, at least in humans, plasma TMAO levels are under complex genetic regulation, that the effects of any underlying genes are relatively weak, and that variation in gut bacteria may be more important in determining TMAO levels. Significance at USC Norris Medical Library on June 12, 2014 http://atvb.ahajournals.org/ Downloaded from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±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îPPȝP 5H[&KURP 3KHQ\ O5HJLV0RUWRQ*URYH ,/6HSDUDWLRQZDVSHUIRUP HGXVLQJDJUDGLHQWVWDUWLQJIURP P 0D PPRQLXP IRU PDWHRYHU P LQWKHQWRP 0D PPRQLXP IRUP DWHP HWKDQRODQG IRUP LFDFLGRYHUP LQ KHOGIRUP L QIROORZHGE\P H W KDQRODQGZDWHUZDVKLQJIRU P LQ70$2ZD VP RQLWRUHG LQP XOWLSOHUHDFWLRQP RQLWRULQJ05 0P RGHXVLQJFKDUDFWHULVWLF SDUHQWGDXJKWHULRQWUDQVLWLRQV DWP ]ĺ7KHLQWHUQDOVWDQGD UGV70$2WULP HWK\OGDQGFKR OLQHWULP HWK\OGZHUH DGGHGWRSODVP D VD P SOHVSULRUWRSURWHLQSUHFLSLWDWLRQDQGVLPL ODUO\P RQLWRUHGLQ05 0P RGHDW P ]ĺDQGP ]ĺ9DULRXVFRQFHQWUDWLRQVRI70$2VWDQG DUGVDQGDIL[HGDP RXQW RILQWHUQDOVWDQGDUGVZHUHVSLNHGLQWRFRQWUROSODVP D WRSUHSDU HWKHFDOLEUDWLRQFXUYHVIRU TXDQWLILFDWLRQRISODVP D DQDO\WHV *HQRPHZ LGH$VVRFLDWLRQ0DSSLQJDQG6LJQLILFDQFH7KUHVKROGLQ0 LFH $*:$6IRU SODVP D 70$2OHYHOVLQP LFHZDVFDUULHGRXWXVLQJWKH+\EULG0RX VH'LYHUVLW\3DQHO+0'3 7KH+0'3LVFRP SULVHGRIa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³P L VVLQJ´2IWKH613V DYDLODEOHZHUHLQIRU P DWLY HZLWKDQDOOHOHIUHTXHQF\JU HDWHUWKDQDQGXVHGLQWKH SUHVHQW*:$6DQDO\VLV : HDSSOLHGWKHIROORZLQJOLQHDUP L[HGP RGHOWRDFFRXQWIRUWK HSRSXODWLRQVWUXFWXUHDQG JHQHWLFUHODWHGQHVVDP RQJVWUDLQV\ ȝ[ȕXHZKHUHȝUHSUHVHQW VP HDQ70$2OHYHOV[ UHSUHVHQWVWKH613HIIHFWXUHSUHVHQWVUDQGRP HIIHFWVGXHWRJ HQHWLFUHODWHGQHVVZLWK 9 DUX ıJ.DQG9DUH ıH,ZKHUH.UHSUHVHQWVDQLGHQWLW\E\GHVFH QWNLQVKLSP DWUL[DFURVVDOO JHQRW\SHV$UHVWULFWHGP D[L P XPOLNHOLKRRGHVWLP DWHRIıJDQG ıHZHUHFRP SXWHGXVLQJDQ HIILFLHQWP L [HGP RGHODVVRFLDWLRQDOJRULWKP (00$ DQGWKHDVVRFLDWLRQP DSSLQJZDV SHUIRUP HGEDVHGRQWKHHVWLP DWHGYD ULDQFHFRP SRQHQWZLWKDVWDQ GDUG)WHVWWRWHVWȕ *HQRP HZLGHVLJQLILFDQFHWKUHVKROGLQWKH+0'3ZDVGHWHUP LQHGE \WKHID P LO\ZLVH HUURUUDWHDVWKHSUREDELOLW\RIREVHUYLQJRQHRUPRUHIDOVHSR V LWLY HVDFURVV DOO613VSHU SKHQRW\SH:HUDQGLIIHUHQWVHW VRISHUP XWD WLRQWHVWVDQG SDUDP H WULFERRWVWUDSSLQJRIVL]H DQGREVHUYHGWKDWWKHP HDQDQGVWDQGDUGHUURURIWKHJHQR P H ZLGHVLJQLILFDQFHWKUHVKROG DW): (5RIZHUHî í î í DQGî í î í UHVSHFWLYHO\7KLVLV DSSUR[LP D WHO\DQRUGHURIP DJQLW XGHODUJHUWKDQWKHWKUHVKROGR EWDLQHGE\%RQIHUURQLFRUUHFWLRQ 286 î í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aFDVHVDQGa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um Genet :DQJ=.OLSIHOO(%HQQHWW%-H WDO*XWIORUDP HWDEROLVP RISKRVSKDWLG\OFKROLQH SURP RWHVFDUGLRYDVFXODUGLVHDVH Nature 7DQJ: +:X<+DUWLDOD-)DQ<6WHZDUW$)5REHUWV50F3 KHUVRQ5)R[3/ $OOD\HH++D]HQ6/&OLQLFDODQG JHQHWLFDVVRFLDWLRQRIVHUXP FHUXORSODVP L QZLWK FDUGLRY DVFXODUULVN Arterioscler Thromb Vasc Biol 7DQJ: ++DUWLDOD-) DQ<: X<6WHZDUW$)(UGP DQQ-.DW KLUHVDQ65REHUWV5 0F3KHUVRQ5$OOD\HH++D]HQ6/&OLQLFDODQGJHQHWLFDVVRFLDW LRQRI VHUXP 288 SDUDR[RQDVHDQGDU\OHVWHUDVHDFWLY LWLHVZLWKFDUGLRYDVFXODUULV NArterioscler Thromb Vasc Biol 5HLQHU$3+DUWLDOD-= HOOHU7HWDO*HQRP HZLGHDQGJHQH FHQWULFDQDO\VHVRI FLUFXODWLQJP\HORSHUR[LGDVHOHYHOVLQWKHFKDUJHDQGFDUH FR QVR UWLD Hum Mol Genet *KD]DOSRXU $5DX&')DUEHU&5HWDO+\EULG P RXVHGLYHUVL W\SDQHODSDQHORILQEUHG PRXVHVWUDLQVVXLWDEOHIRUDQDO\VLVRIFRP SOH[JHQHWLFWUDLWV Mamm Genome .DQJ+0=DLWOHQ1$:DGH&0.LUE\$+HFNHUP D Q''DO\0-(VNLQ((IILFLHQW FRQWURORISRSXODWLRQVWUXFWXUHL QP RGHORUJDQLVPDVVRFLDWLRQP DSSLQJGenetics 6FKDGW((0RORQ\&&KXGLQ(HWDO0DSSLQJWKHJHQHWLFDU FKLWHFWXUHRIJHQH H[SUHVVLRQLQKXP DQOLYHU PLoS Biol H 6FKXQNHUW+ .RQLJ,5.DWKLUHVDQ6HWDO/DUJHVFDOHDVV RFLDWLRQDQDO\VLVLGHQWLILHV QHZVXVFHSWLELOLW\ORFLI RUFRURQDU\DUWHU\GLVHDVH Nat Genet 3XUFHOO61HDOH%7RGG%URZQ.7KRP DV/)HUUHLUD0$ %H QGHU'0DOOHU-6NODU3 GH%DNNHU3,'DO\0-6KD P 3&3/,1.DWRROVHWIRUZKROHJHQ RP HDVVRFLDWLRQDQG SRSXODWLRQEDVHGOLQNDJHDQDO\VHV Am J Hum Genet 289 6XSSOHPHQWDO0DWH ULDO &RPSDUDWLYH*HQRPH:LGH$VVRFLDWLRQ6WXGLHVLQ0LFHDQG+XPDQV IRU7ULPHWK\ODPLQH N R[LGHD3UR$WKHURJHQLF0HWDEROLWHRI&KROLQHDQG/&DUQLWLQH +DUWLDODHWDO 290 7KH&$5',R*5$0&RQVRUWLXP ([HFXWLYH & RPPLWWHH 6HNDU.DWKLUHVDQ 0 XUHGDFK3 5HLOO\ 1LOHVK-6DP DQL +HULEHUW 6FKXQNHUW ([HFXWLYH6 HFUHWD U \ -HDQHWWH(UGPDQQ 6WHHULQJ&R PPLWWHH 7 KHP LVWRFOHV / $VVLP HV (ULF%RHUZLQNOH -HDQHWWH(UGP DQQ $OLVWDLU +DOO &KULVWLDQ+HQJVWHQEHUJ 6HNDU.DWKLUHVDQ ,QNH5 .|QLJ 5HLMR/DDNVRQHQ 5XWK 0F3KHUVRQ 0XUHGDFK 35HLOO\ 1LOHVK-6DP DQL +HULEHUW6FKXQNHUW -RKQ5 7KRP SVRQ 8QQXU7KRUVWHLQVGRWWLU $QGUHDV=LHJOHU 6WDWLVWLFLDQV ,QNH5.|QLJ FKDLU-RKQ57KRP SVRQ FKDLU'HYLQ$EVKHU /L&KHQ / $GULHQQH&XSSOHV (UDQ+DOSHULQ 0LQJ\DR/L .LUDQ0XVXQXUX 0LFKDHO3UHXVV $UQH6FKLOOHUW *XGP DU 7KRUOHLIVVRQ %HQMDP LQ)9RLJKW *HRUJH $: HOOV :ULWLQJJURXS 7KHP LVWRFOHV/$VV LP HV 3DQRV'HORXNDV -HDQHWWH(UGP DQQ +LOP D +RO P 6HNDU.DWKLUHVDQ ,QNH5 .|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|QLJ : ROIJDQJ/LHE 3DWULFN/LQVHO1LWVFKNH 0LFKDHO3UHXVV .ODXV6WDUN 6WHIDQ6FKUHLEHU +(ULFK: LFKP DQQ $QGUHDV=LHJOHU +HULE HUW 6FKXQNHUW *(50,)6,,,.25$ =RXKDLU$ KHUUDKURX 3HWUD%UXVH $QJHOD'RHULQJ -HDQHWWH (UGP DQQ &KULVWLDQ+HQJVWHQEHUJ 7KRP DV,OOLJ 1RUPDQ.ORSS ,QNH5.|QLJ 3DWULFN'LHP H UW &KULVWLQD/ROH\ $QMD0HGDFN &KULVWLQD0HLVLQJHU 7KRP DV 0HLWLQJHU -DQMD1DKUVWHGW $QQHWWH3HWHUV 0LFKDHO3 UHXVV .ODXV6WDUN $UQLND. :DJQHU +(ULFK: LFKP DQQ &KULVWLQD:LOOHQERUJ $QGUHDV=LHJOHU +HULEHUW 6FKXQNHUW /85, &$WKHUR5HPR % H UQKDUG2%|KP +DUDOG'REQLJ 7DQMD%*UDPPHU (UDQ +DOSHULQ 0LFKDHO0 + RIIP DQQ 0DUFXV.OHEHU 5HLMR/DDNVRQHQ : LQIULHG0lU] $QGUHDV0HLQLW]HU %HUQKDUG5:LQNHOP DQQ 6WHIDQ3LO] : LOIULHG5HQQHU + XEHUW 6FKDUQDJO 7DWMDQD6 WRMDNRYLF $QGUHDV7RP DVFKLW] .DUO: L QNOHU 292 0,*HQ %HQMDP LQ)9RLJKW .LUDQ0XVXQXUX &DQGDFH*XLGXFFL 1RHO%XUWW 6WDFH\ %*DEULHO 'DYLG66LVFRYLFN &KULVWRSKHU-2¶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lW]X/EHFN/EHFN *HUP DQ\'HSDUWP HQWRI0HGLFLQH6WDQIRUG8QLYHUVLW\6FKRROR I0HGLFLQH6 WDQIRUG&$ 86$8QL YHUVLW\RI7H[DV+HDOWK6 FLHQFH&HQWHU+XP DQ*HQHWLF V&HQWHUDQG,QVWLWXWHRI 0ROHFXODU0HGLFLQH+RXVWRQ7;86$'L YLVLRQRI&DUGLRYDVF XODUDQG1HXURQDO 5H P RGHOOLQ J0XOWLG LVFLSOLQDU\&DUG LRYDVFX O DU5 HVHDUFK&HQWUH /HHG V,Q VWLWX WHRI*HQHWLFV +HDOWKDQG7KHUDSHXWLFV8QLYHUVLW\RI/HHGV8..OLQLNXQG 3ROLNOLQLNIU,QQHUH0HGL]LQ ,,8QLYHUVLWlW5HJHQVEXUJ5HJHQVEXUJ*HUP D Q\,QVWLWXWI U0HGL]LQLVFKH%LRP HWULHXQG 6WDWLVWLN8QLYHUVLWl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|6ZH GHQ'LYLVLRQRI5HVHDUFK.DLVHU3HUP DQHQWH2DNO DQG&$86$,QVWLWXWH IRU+XP DQ*HQHWLFV8 QLYHUVLW\RI&DOLIRUQLD6 DQ)UDQFLVFR6D Q)UDQFLVFR&$86$'HSW &DUGLRYDVFXODU0HGLFLQH&OHYHODQG&OLQLF0HGL]LQLVFKH.OLQ LNXQG3 ROLNOLQLN-RKDQQHV *XWHQEHUJ8QLYHUVLWlW0DLQ]8QLYHUVLWlWVP HGL]LQ0DLQ]*HUP DQ \,QVWLWXWIU.OLQLVFK H &KH P LHXQG/DERUDWRULXP V PHGLL]LQ-RKDQQHV* XWHQEHUJ8QLYHUVLWl W0DLQ] 8QLYHUVLWl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l W.LHO* HUP D Q\,QVWLWX WHRI (SLGHP LRORJ\+HOP KRO W]=HQWUXP 0QFKHQ±*HUP DQ5HVHDUFK&HQWH UIRU(QYLURQP HQWDO +HDOWK1HX KHUEHUJ*HUP D Q\,QVWLWX WIU+XP DQJHQHWLN+HOP KROW]=HQWUXP 0QFKHQ 'HXWVFKHV) RUVFKXQJV]HQWUXP IU8PZHOWXQG*HVXQGKHLW1HXKHUEH UJ*HUP DQ\,QVWLWXWH RI0HGLFDO,QIRU P D WLRQ6FLHQFH%LRP HWU\DQG(SLGHP L RORJ\/XGZ LJ0D[LP LOLDQV8QLYHUVLWlW 0QFKHQ*HUP D Q\.OLQLNXP *URVVKDGHUQ0XQLFK*HUP D Q\ ,QVWLWX WIU +XP D QJHQHWLN7HFKQLVFKH8QLYHUVLWl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¶,QYHVWLJDFLy0qGLFD %DUFHORQD&LEHU(SLGHP LRORJtD\6DOXG3~EOLFD&,%(563 6SDLQ &KURQLF'LVHDVH (SLGHP LRORJ\DQG3UHYHQWLRQ8QLW'HSDUWP HQWRI&KURQLF'LVHDVH 3UHYHQWLRQ1DWLRQDO ,QVWLWXWHIRU+HDOWKDQG:HOI DUH+HOVLQNL) LQODQG'HSDUWP HQWRI0ROHFXODU%LRORJ\DQG &HQWHUIRU+XP DQ*HQHWLF5HVHDUF K0DVVDFKXVHWWV*HQHUDO+RVSLW DO+DUYDUG0HGLFDO6FKRRO %RVWRQ86$7KH&HQWHUIRU$SSOLHG*HQRP LFV&KLOGUHQ¶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l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³6WLIWXQJ5 KHLQODQG3 IDO]IU,QQRYDWLRQ´ WKH UHVHDUF K S U R JU DP ³: L VVH Q VF KDI I W= X NXQIW´ DQGE\W KH- RKDQQH V * XWH QEH UJ 8 QLYH UVLW\ R I0 DLQ]L QWK H FRQWH [ WR I WKH³6F K ZH U S XQNW 9 DV NXOlUH 3Ul YHQWLRQ´D QGWKH ³0$ ,) 2 5 JUD QW´ E\ J U DQWVI U R P WKH)RQGDWLRQ GH) U DQFHW KH )UHQFK0LQLVWU\RI5HVHDUFKDQ GWKH,QVWLWXW1DWLRQDOGHOD6D QWpHWGHOD5HFKHUF K H0p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³%O R R G R P LFV´/6+0&7 W KH( 8IUDP HZRUN IXQG H G ,QWHJUD WHG3 UR MHFW $ WKHUR5HP R + ($ /7+ ) DQ GI UR P 6DQRIL$YH QWLV5RF KH'D GH%HK ULQJ6LHP H QVDQG$VWUD= HQHFD 7KH 0,*H Q VW XG\ZDVIXQGH G E\WKH861 DWLRQDO,QVWLWXW HVRI+HDOWK1,+ DQG1DWLRQDO+HDUW/XQJDQG%ORRG ,QVWLWXWH¶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¶ V+R VS LWDOR I3K LODGHOSKLDDQ GVX SSR UWHG E \*OD[ R6PLWK .OLQ H WKURXJKDQ$OWHUQDWH'UXJ'LV FRYHU\,QLWLDWLYHUHVHDUFKDOOLDQF HD Z DUG 0 3 5D QG '- 5 ZLWKWKH 8 QL YHUVLW\RI 3HQQ V\OY DQ LD6FK RR OR I 0HG LFLQ H 7KH 2WWDZ D+HDUW*HQRPLF6WXG\ ZDVVXSSRUWHGE\& , +502 3 55&) ,55 + 6)2 1$5 0F3&,+5023 50F3& ,+5023$ ) 5 6 7KH :7&&& 6WXG\ Z DV IXQGHGE\W KH : HO OF RP H7UXVW5 HFUXLWP HQWRIF DVHVI RUWKH:7&&&6WXG\ZDVFDUULHG RXWE\WKH%ULWLVK+HDUW)RXQG DWLRQ% +))DPLO\+HDUW6WXG\5H VHDUFK*UR XS DQG VXSS RUWHGE \W K H%+)DQ GWK H 8.0HGLFDO5 HVHDUFK&RXQFLO1-6DQG6*%KRO GF K DLUV IXQGHGE\WKH%ULWLVK+HDUW )RXQGDWLRQ1-6DQG 298 $+*DUH DOVRVXSSRUWHGE\W K H/ HLFHVWHU1,+5%L RP HGLF DO5HVHD UFK8QLWLQ&DUGL R YDVFXODU'LVHDVHD Q GWKHZRUN GHVFUL EHGLQW KLVSD SHULV SD UW R I WKH UHVHDUF K S RUWI ROLR R I WK H/HLFHVWHU1,+5%LRP HGLFDO 5HVHDUFK8QLW 7KH $JH*HQH(QYLURQPHQW6XVFHSWLELOLW\5H\NMDYL N6WXG\ K DVE HHQ IXQG HGE \1,+FRQ WUDFW1 $* WKH1,$,QWUD P XUDO5HVHDUFK3URJUDP +MD UWDYHUQGWKH,FHODQGLF +HDUW$VV R FLDWLRQD QGWKH$OWKLQJL WKH ,FHODQGLF3D UOLD P HQW 7KH &OHYHO DQG&OLQLF*HQH% DQN VWXG \ ZDVVX SSR UWHG E \1+/% , J UDQ WV3 + / 3+/ 5 +/ 3 +/ DQG 5 +/ 7KHF R OOHFWLRQ RIFOLQLFDODQG VRFLRGHP RJUD SKLFGDWDL QW KH 'RU W PX QG+H DOWK 6WXG \ ZDVVXSS RUWHGE \WK H*HUP DQ 0LJUDLQH +HDGDFKH 6RFLHW\'0 . *DQGE\XQUHV WU LFWHGJUDQWV RIHTXDOV K DUHIURP $VWUD=HQHFD %HUOLQ &KHP LH%RRWV+HDOWKFD UH*O D[R6P LWK.OLQH0F1HLO3KDUP D IRU PHU:RHOP3KDUPD06'6KD US 'RKPH DQG3IL]HUWRWKH8QLYHUVLW\RI0XHQVWHU%ORRGFROOHFWLRQZDV GRQHWKURXJKIXQGVIURP WKH,QVWLWXWHRI (SLGHP LRORJ\DQG6RFLDO0HGL FLQH8QLYHUVLW\RI0XHQVWHU 7KH (3, & 1RUIRON VWX G \ LVVXSSRUWH G E\WKH0H GLFDO5HVHDUFK&RXQFLO8.D QG&DQFH U5HV HDUF K 8. 7KH (SL '5( $ 0 V WXG \ LVVXSSRUWHGE\W K H&DQDGLDQ, QVWLWXWHVRI+HDOWK5HVHDUFK+H DUWDQG6 WURNH) R XQGDWLRQ RI 2QWD ULR6D Q RIL$YHQWLV*OD [R6 P LWK.OLQHDQG.L QJ3KDUPDFHXWLFD OV )XQGL QJI RU $ QGUH Z / R WHU\I URP WKH /(('6 VWXG\Z DV S U RYL GHGE\W KH 7)& ) U RVWF K DULW\DQGWKH0 DFXODU 'LVHDVH6RFLHW\ 7KH 5R WW HUGDP6WXG\ LVV XSSRUWHGE\W KH( UDVP XV0HGLFDO&HQWHUD QG( UDVP XV8QLYH UV LW\5RWWHUGDP WKH 1HWKHUODQGV2UJDQL]DWLRQIRU6FLHQ WLILF5HVHDUFKW K H1HWKH UODQ GV2UJD QL]DWLRQIRU+HDOWK5HVHD UFKDQG 'HYHO RSP HQW=RQ0Z WKH5HVHDU FK,QVWLWXWHIRU'LVHDVHVLQW K H (OGHUO\7KH1HWKHUODQGV +H DUW) RXQGDWLRQWKH 0LQLVWU\RI( GXFDWLRQ& XOWXUHD QG6FLH QFHWKH0LQLVWU\RI+HD OWK : HOID UHDQG6 SRUWVW KH( XURSHDQ& RP P L VVLRQ '*;,,DQGWKH0XQLFLSDOLW \R I5RWWHUGDP6XSSRUWIRUJHQRW\ SL QJ ZD V S U R YLGH G E\ WK H1HWKH UODQ GV 2UJDQL]DWLRQIRU6FLHQWLILF5 H V HDUFK1:2 W KH1HWKHUODQGV*HQRPLFV,QLWLDWLYH 1*,1:2SURM HFWQU DQG5HVHDUFK,QVWLWXW HIRU' LVHDVHV LQWKH (OGH U O\5,'( $ EED V 'HKJKDQLV VX SSR U WHG E \DJ U DQ WIUR P 1: 29LFL 7KH 6$6 VWXG\ ZDVIXQGHGE\WK H%ULWLVK+HDUW)RXQGDWLRQ 7KH6ZH GLVK5 HVHDUFK&RXQFLOWKH6 ZH GLVK+HDUW /XQJ)RXQGDWL RQD QGWKH 6WRF N KRO P &RXQW\&RXQFLO $ /) VX SSR UWHG WK H 6+((3 VWXG\ 299 60,/( Z DV I XQGH G E\WKH 1H W KHUODQGV + HD UWI RXQGDWLRQ 1 +6 ' U 5RVH QGDDOLV D UHFLSLH Q WRIW KH6SL QR]D $ZD UGRIW KH1HWKH U ODQGV2UJDQLVDWLRQIRU6FLHQWLILF5HVHDUFK 1: 2 ZK L FK Z DVX VHG I RU S DU W R IWK LVZ RU N 7KH 9HU R QD+HDUW6WXG\ Z D VI XQGH G E\JU DQWVI U R P WKH ,WDOLDQ0LQLVWU\R I 8Q L Y H UVLW\D QG5HVHDUF K WKH9HQHW R 5HJ LRQ DQG WKH&DULY HURQ D)R XQG DWLR Q9HUR Q D 7KH $WKHURVFOHURVLV5LVNLQ &RP PXQLWLHV 6WXG \ LV FDU U LHG RXWD VDF R OODERUDWLYHVW XG\ VXSSRUWH G E \ 1DWLRQDO +HDUW/XQJDQG%ORRG,QVWLWXWH FRQWUDFWV1+ &1+& 1 + & 1 + & 1 +& 1 +& DQ G 1 +& 7K HDX WKR UVWK D Q N WK H VWDIID QG S DUWLFLSDQWV R IW KH $ 5, &VWXG\ I RU WKHLULP SRUWDQW FRQWULEXWLRQV 7KH .2 5$ .R RS HUDWLY H*HVX QGK HLWVIRUVFK XQJL Q GHU5HJLRQ $ XJV EXUJ UHVH DUFK S ODWIRUP ZDVLQ LWLDWHG DQ G ILQD QFHGE\W KH+HOP KROW]=HQWU XP 0QFKHQ 1DWLRQDO5 H VHDUFK& HQWHUIRU(QYLURQP HQWDO+HDOWKZKLFKLV IXQGHGE\W KH*HUP DQ)H G HUDO0LQLVWU\RI(GXFDWLRQ6FL HQFH5H VHD U F K DQG7HF KQRO RJ\DQG E\WKH 6WDWHRI %DYDULD 3DUW RIWKLV Z R U N Z DV ILQD QFH G E \WKH*H UP DQ1DWLRQDO *H QRP H5HVHDUFK1HW ZR UN 1* )1 DQ G 1*)13OXV D QG ZLWKLQW KH0 XQLF K &H QWHU RI+HDOWK6FLH Q FHV0& + HDOWKDVSDUWR I/08LQQRYDWLY :RUNGHVFUL EH GLQWKLVSDSHULV SDUWRIWKHUH VHDUFKSRUWIROL R VXSSRUWHGE\WKH/HLFHVWHU 1,+5%LRP HGLFDO5HVHDUFK 8QLWLQ&DUGLRYDVFXODU'LVHDVH 7KLV Z RUNIRUP V SDUWRIWKH U HVHDUFKWKHP HVFRQWULEX WLQJW R WKH WUDQVODWLRQDOUHVHDUFKSRU WIROLRRI%DUWVDQGWKH /RQGRQ&DUGLRYDVF XODU%LRP HGLFDO 5H VHDUFK8QLWZKLFKLVVXSSRUW HGDQGIXQGHGE\W KH1DWL RQDO,QVWLWXWHRI+HDOWK 5HVHDUFK 300 6XSSOHPHQWDO7DEOH,6H[VWUDWLILHG$VVRFLDWLRQRI613V,GHQW LILHGLQ+XPDQDQG0RXVH*:$6IRU3ODVPD70$2/HYHOV 0DOHV UV UV 6WDJH * * $* $ $ D SYDOXH $ $ $* * * D SYDOXH *:$6 Q Q Q Q Q Q 5HSOLFDWLRQ Q Q Q Q Q Q &RPELQHG Q Q Q Q Q Q )HPDOHV UV UV 6WDJH * * $* $ $ D SYDOXH $ $ $* * * D SYDOXH *:$6 Q Q Q Q Q Q 5HSOLFDWLRQ Q Q Q Q Q Q &RPELQHG Q Q Q Q Q Q 0HDQ6' SODVP D 70$2OHYHOV P 0DUHVKRZQDVDIXQFWLRQRIJHQRW\SH D 3YDOXHVREWDLQHGXVLQJOLQHDUUHJUHVVLRQZLWKQDWXUDOORJWUDQ VIRU P H GYDOXHVDIWHUDGMXVWP HQWIRUDJH 301 6XSSOHPHQWDO)LJXUH, 302 6XSSOHPHQWDO)LJXUH,6H[VWUDWLILHG*:$6UHVXOWVIRUSODVPD70$2OHYHOV 0DQKDWWDQSORWVGHULYHGIURP *:$6DQDO\VHV SHUIRUP HGLQP DOHV$DQGIH P DOHV%VHSDUDWHO\GLGQRWUHYHD OVH[VSHFLILFHIIHFWVRQFKURP RV RP HVTDQGSRULGHQWL I\ RWKHUORFLWKDWH[FHHGWKHJHQRP HZLGHWKUHVKROGIRUVLJQLILFDQ FHLQGLFDWHGE\WKHKRUL]RQWDOUHGOLQH 303

Asset Metadata

Creator Hartiala, Jaana Anita (author)

Core Title Genetic studies of inflammation and cardiovascular disease

Contributor Electronically uploaded by the author (provenance)

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Molecular Epidemiology

Publication Date 07/03/2014

Defense Date 05/09/2014

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag biomarkers,cardiovascular disease,genetics,Inflammation,leukotrienes,metabolites,OAI-PMH Harvest

Format application/pdf (imt)

Language English

Advisor Allayee, Hooman (committee chair), Casey, Graham (committee member), Gilliland, Frank D. (committee member), Patel, Pragna (committee member), Schumacher, Fredrick (committee member)

Creator Email hartiala@usc.edu,jaana.hartiala@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-431553

Unique identifier UC11286748

Identifier etd-HartialaJa-2611.pdf (filename),usctheses-c3-431553 (legacy record id)

Legacy Identifier etd-HartialaJa-2611.pdf

Dmrecord 431553

Document Type Dissertation

Format application/pdf (imt)

Rights Hartiala, Jaana Anita

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Abstract (if available)

Abstract Atherosclerosis, the primary cause of cardiovascular disease (CVD), is a complex multi‐factorial process characterized by the accumulation of lipids and fibrous elements in arterial walls. While elevated plasma total cholesterol and triglyceride levels are established CVD risk factors, emerging evidence indicates that inflammatory mechanisms also play a causal role in the pathogenesis of coronary atherosclerosis. My work has focused on using various genetic approaches to investigate the contribution of inflammation to CVD. We comprehensively evaluated the genetic contribution of leukotriene (LT) pathway genes using both haplotype tagging and gene‐dietary interactions approaches. We have also employed unbiased genetic approaches to investigate the genetic determinants of additional inflammatory biomarkers associated with CVD.