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POLYGENIC ANALYSES OF COMPLEX TRAITS IN
COMPLEX POPULATIONS
by
Fang Chen
_________________________________________________________
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOSTATISTICS)
December 2012
Copyright 2012 Fang Chen
Object Description
| Title | Polygenic analyses of complex traits in complex populations |
| Author | Chen, Fang |
| Author email | fangchen@usc.edu;silencecf@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Biostatistics |
| School | Keck School of Medicine |
| Date defended/completed | 2012-10-05 |
| Date submitted | 2012-11-29 |
| Date approved | 2012-11-29 |
| Restricted until | 2013-05-29 |
| Date published | 2013-05-29 |
| Advisor (committee chair) | Stram, Daniel O. |
| Advisor (committee member) |
Haiman, Christopher A. Gauderman, William James Lewinger, Juan Pablo Coetzee, Gerhard (Gerry) A. |
| Abstract | Genome-wide association scans (GWAS) have identified numerous common variants associated with hundreds of complex diseases. In this dissertation, I investigated the properties of the GWAS-identified common SNPs in multiple populations, and estimated their aggregate effects on complex diseases. In the first Chapter, I assessed the generalizability of a risk score derived from 12 SNPs known to be associated with breast cancer risk in European or Asian populations in the Multiethnic Cohort (MEC). I performed a case-control study with 2,224 cases and 2,827 controls nested in the MEC and found that when viewed as a summary risk score, the total number of risk alleles carried by women was significantly associated with breast cancer risk overall (OR per allele: 1.09; 95% CI: 1.06-1.12; p=2.0×10-10) and in all populations except African Americans, in which no significant association was observed (OR, 1.03; 95% CI, 0.98-1.08). These results emphasized the need for large-scale association studies in multiple racial/ethnic groups, especially in populations of African ancestry. ❧ Since body mass index and type 2 diabetes (T2D) are established risk factors for (post-menopausal) breast cancer, I tested for the pleiotropic effects of 31 common variants for T2D and obesity in a case-control study of 1,915 breast cancer cases and 2,884 controls nested within the MEC. However, following adjustment for multiple tests, we found no significant association between any variant and breast cancer risk, as in shown in Chapter Two. ❧ In Chapter Three I analyzed a large study of breast cancer in African American women (3,016 cases and 2,745 controls), where I tested 19 known risk variants identified by GWAS and replicated associations (P<0.05) with only 4 variants. Through fine-mapping, markers in that better capture the association with breast cancer risk in African Americans were identified in 4 regions (2q35, 5q11, 10q26 and 19p13). Statistically significant associations were also identified with markers in 4 separate regions (8q24, 10q22, 11q13 and 16q12) that are independent of the index signals and may represent putative novel risk variants. This detailed analysis of the known breast cancer risk loci has validated and improved upon markers of risk that better characterize their association with breast cancer in women of African ancestry. ❧ In the last Chapter, I genotyped and analyzed 966,578 autosomal SNPs across the entire genome using a variance components approach in a large sample of African ancestry (N=14,419), and estimated an additive heritability of 44.7% (se: 3.7%) for this phenotype in a sample of evidently unrelated individuals. Using simulation, I concluded that the additive heritability estimate is not necessarily the heritability proportion directly explained by the genotyped SNPs. I then explored the performance of the variance components approach in an unrelated sample and found that the approach fails when a large number of independent variables are included, indicating that some relatedness between subjects is required for the method to perform well using large number of SNPs. In two samples of close relatives defined by probability of identical-by-descent (IBD) alleles sharing (Pr (IBD=1)>=0.3, n=1,415 and Pr (IBD=1)>=0.4, n=575), the additive heritability estimate increased to 76.5% (se: 11.7%) and 75.1% (13.3%), respectively which is consistent with the view (Zuk et al PNAS 2011) that the additive component of genetic variation for height may have been overestimated in earlier studies (80%) and the proportion could also include variation due to epistatic effects. ❧ This dissertation contributes to the polygenic analyses of complex traits in two aspects: first, it emphasizes the necessity of using genetic markers that are specific to the populations of interest for disease prediction in different populations. Second, analyses performed in this dissertation add to the investigation of the “missing heritability” problem. I concluded from this dissertation that the hypothesis that common variants explain a large proportion of heritability remains unproven, and that studies of additional genetic markers such as rare variants, and investigations of non-linear effects of genetic markers including epistatic effects are needed in order to gain a better understanding of the genetic characteristics of complex traits. |
| Keyword | common variants; GWAS; heritability; polygenes |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Contributing entity | University of Southern California |
| Rights | Chen, Fang |
| Physical access | 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 author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@lib.usc.edu |
| Archival file | uscthesesreloadpub_Volume6/etd-ChenFang-1362.pdf |
Description
| Title | Page 1 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | POLYGENIC ANALYSES OF COMPLEX TRAITS IN COMPLEX POPULATIONS by Fang Chen _________________________________________________________ A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (BIOSTATISTICS) December 2012 Copyright 2012 Fang Chen |
