University of Southern California Dissertations and Theses

A genome wide association study of multiple sclerosis (MS) in Hispanics

(USC Thesis Other)

A genome wide association study of multiple sclerosis (MS) in Hispanics

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content

A GENOME WIDE ASSOCIATION STUDY OF MULTIPLE SCLEROSIS (MS)
IN HISPANICS

by
Shengzhi Wang

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(BIOSTATISTICS)

Aug 2013

Copyright 2013 Shengzhi Wang

i

Acknowledgements
I would like to thank people who helped me over the course of my Master’s training at
University of Southern California.
First, I want express my deepest gratitude to my mentor, Dr. David Conti for his
invaluable guidance and tremendous help to complete this thesis.
I also want to thank my committee members, Dr. James Gauderman, Dr. Lilyana
Amezcua and Dr. Wendy Cozen for their thoughtful suggestions and comments for this
thesis.
I want specifically thank Mr. Christopher Edlund for performing genetic ancestry
estimation as well as preparing the data for GWAS study.
Last but not least, I feel so blessed to have my wife, Jinghua Liu as well as other family
members always standing by me. It’s them that make my life full.

ii

Table of Contents
Acknowledgements .............................................................................................................. i
List of Figures ...................................................................................................................... iii
List of Tables ........................................................................................................................ iv
Abstract.................................................................................................................................... v
Chapter 1: Introduction .................................................................................................... 1
1.1 Overview of Genome Wide Association Study(GWAS)............................... 1
1.2 Multiple Sclerosis (MS) in Hispanics ................................................................ 2
Chapter 2: Methods ............................................................................................................. 6
2.1 Study population .................................................................................................................. 6
2.2 Data Collection and assessments .................................................................................... 6
2.3 Statistical Analysis and model building ....................................................................... 9
Chapter 3: Results ............................................................................................................. 11
3.1 Participants Characteristics ......................................................................................... 11
3.2 Association of MS clinical features with MS subtypes ......................................... 12
3.3 Global ancestry estimation and test of association with clinical variables . 14
3.4 Effect estimation of genetic ancestries for the risk of OSMS ............................. 19
3.5 GWAS scan comparing OSMS vs classical MS ........................................................... 21
Chapter 4: Discussion ...................................................................................................... 27
Bibliography ....................................................................................................................... 31

iii

List of Figures
Figure 1: Global ancestry estimation of MS patients using STRUCTURE. ................... 15
Figure 2: Plot of first eigenvector against the rest of top ten eigenvector of
EIGENSTRAT result for MS patients. ........................................................................... 17
Figure 3: Comparison of distributions of global genetic ancestry estimation of between
OSMS and classical MS patients for Asian, European and African ancestries,
respectively.. ..................................................................................................................... 18
Figure 4: Quantile-Quantile plot for GWAS comparing OSMS to classical MS ............. 21
Figure 5: GWAS scan result comparing OSMS vs classical MS.. ................................... 22
iv

List of Tables
Table 1: MS Classification................................................................................................ 11
Table 2: Participants Characteristics ................................................................................. 12
Table 3: Association of Clinical Variables with MS Types ............................................. 13
Table 4: Parameters estimates of logistic regression model for the odds of OSMS vs
classical MS ...................................................................................................................... 19
Table 5: TOP 10 SNPs with the lowest p value from the GWAS scan ............................ 23
Table 6: Examination of SNPs previously shown to associate with MS .......................... 25

v

Abstract

Genetic factors are postulated to contribute to the difference of susceptibility as well as
clinical outcomes of multiple sclerosis (MS) in different populations. The Hispanic
population offers a unique potential to identify novel genetic variants involved in
differential disease presentation of MS (e.g. the site of lesions in the central nervous
system, opticospinal (OSMS) vs classical MS) due to the genetic diversities in this
admixed population. While MS overall is more common in Whites of European ancestry,
if and how the heterogeneity of genetic admixture in Hispanics contributes to
characteristics of the disease is unknown.
Methods: 127 Hispanics with MS were genotyped and their global ancestries were
estimated. They were then classified into either classical MS or OSMS based on clinical
and radiological assessment. For statistical testing of the association of genetic ancestry
to OSMS vs. classical MS and to clinical characteristics, linear regression or t-tests were
used for continuous outcomes and logistic regression or chi-square tests were used for
binary outcomes. For the genome-wide investigation of SNP associations, logistic
regression was used with adjustment by age, gender and global ancestry to control for
potential confounding due to population admixture. The Wald test is used to determine
the statistical significance.
Results: For the 142 MS patients participated in the study, Asian characteristic of the
disease, such as OSMS was noted in 25 patients (17.6% of total patients) while classical
MS was observed in 102 patients (71.8% of the total patients). There was no significant
vi

difference of age, gender, ethnicity, migration history, age at diagnosis or disease
duration between OSMS and classical MS patients. However, increased disability was
significantly noted in OSMS patients (Mean score of disability measurement ± SD,
4.64± 2.05) as compared to classical MS patients (2.47± 1.92) (p=3.0e-05). In addition,
age at the first symptom onset is significantly younger in OSMS (26.36± 11.6) compared
with classical MS (31.47± 11.9) (p=0.057). Interestingly, the migration history of the
patients (early migration vs late migration) as well as their neurological disability severity
(EDSS score) are statistically significant associated with the risk of OSMS as compared
to classical MS (odds ratio =3.63, p value=0.055 for migration history and odds
ratio=1.99, p value<0.001 for disability severity). Global ancestry estimation showed
that both European and Asian genetic ancestries were the most common background
followed by African in these Hispanic MS patients. No significant difference of these
ancestry proportions were found between OSMS and classical MS. Logistic regression
modeling suggest that European ancestry is negatively associated with the risk of OSMS
as compared to classical MS (OR=0.063, 95% confidence interval (0.001, 2.598)) after
adjusting for EDSS and age of 1
st
symptom onset. However, this association is not
statistically significant based on current model estimation (p=0.152). Finally as a pilot
investigation, to identify genomic regions associated with risk of OSMS, we performed a
genome wide scan comparing OSMS patients vs classical MS patients. None of the
SNPs have p values surpassing the genome wide level significance threshold (5 x 10
-8
).
The top SNPs with lowest p values (around 1x10
-5
) are discussed for their potential
involvement in the odds of OSMS vs classical MS.
vii

Conclusion: The results presented in this study provide pilot data to address whether and
how the heterogeneity of genetic admixture in Hispanics contributes to characteristics of
the MS. The GWAS result serves as an initial point which needs to be expanded as more
patients are recruited into the study in the future to increase the power of detecting
genomic regions that are associated with the differential MS presentations.

1

Chapter 1: Introduction

1.1 Overview of Genome Wide Association Study(GWAS)
GWAS sought to identify genetic risk factors for common diseases in human populations.
This is achieved by measuring and analyzing hundreds of thousands of DNA sequence
variations (primarily focused on single-nucleotide polymorphism (SNP)) across the
genome typically for hundreds of thousands of people with particular disease compared
with appropriate controls (Manolio, Brooks et al. 2008; McCarthy, Abecasis et al. 2008;
Hardy and Singleton 2009; Bush and Moore 2012). While linkage analysis is extremely
successful to reveal mutations responsible for Mendelian traits (e.g. rare single gene
disease), GWAS is much more advantageous to identify common genetic variants (with
minor allele frequencies > 0.05) underlying the common disorders whereas each variants
has a rather small to medium effect size (Botstein and Risch 2003; McCarthy, Abecasis et
al. 2008; Bush and Moore 2012). The past decade has witnessed the revolutionary
evolvement of GWAS techniques as well as its applications with tremendous success.
Since the first publications of GWAS study in 2005 (Klein, Zeiss et al. 2005; Dewan, Liu
et al. 2006), there are more than 2000 loci identified as genome-wide significant and
robustly associated with one or more common human diseases (GWAS Catalog,
www.genome.gov). These fruitful discoveries not only provide insight into the biological
mechanisms of disease, but also open the door to the potential for better clinical
prediction and diagnosis for individuals in the future.

2

1.2 Multiple Sclerosis (MS) in Hispanics
Multiple sclerosis (MS) is a common autoimmune disorder of the central nervous system
(CNS) characterized by chronic inflammation, myelin loss, axonal pathology and
progressive neurological dysfunction (Oksenberg, Baranzini et al. 2008; Oksenberg and
Baranzini 2010; Baranzini 2011; Kemppinen, Sawcer et al. 2011). At disease onset,
about 80% of the people with MS follow a clinical disease course characterized by
intermittent relapses followed by periods of disease stability, called relapsing remitting
MS (RRMS) (Noseworthy, Lucchinetti et al. 2000). These relapses (disability) in RRMS
patients accumulates by incomplete recovery from relapses and/or movement into the
secondary progressive phase of the disease typically 10-20 years after symptom onset
(so-called secondary progressive MS or SPMS); where disability accumulates slowly and
independent of relapses (Noseworthy, Lucchinetti et al. 2000).
MS belongs to a large group of multifactorial disorders showing modest heritability
(Oksenberg and Barcellos 2005; Oksenberg and Hauser 2005). Previous studies have
shown that genetic variants, environmental factors as well as epigenetic mechanisms
contribute to the susceptibility and progression of multiple sclerosis (Oksenberg,
Baranzini et al. 2008; Oksenberg and Baranzini 2010; Baranzini 2011). It has been
observed that MS is more prevalent in European populations and their descendants
compared to African Americans, Hispanics and Asians (Pugliatti, Rosati et al. 2006;
Oksenberg and Baranzini 2010). Linkage studies and GWAS primarily in individuals of
European background have been helpful in confirming known susceptibility regions such
as HLA-DR2 and identifying additional novel loci with modest effects including
interleukin 7 receptor (IL-7R) and interleukin 2 receptor (IL-2R) (McElroy, Isobe et al.
3

2011; McElroy and Oksenberg 2011; Sawcer 2011). While MS is rare in Asians, small
Asian cohorts have been instrumental in identifying a poor prognostic subgroup of
RRMS: opticospinal MS (OSMS)(Kira 2003). OSMS is characterized by incomplete
recovery from relapses predominantly involving the optic nerves and spinal cord which
typically leads to early and severe disability. OSMS affects 15-40% of MS patients in
Japanese cohorts (Kira 2011) yet it is unclear if OSMS is the result of genetic,
environmental or lifestyle factors more common to Asians.
Hispanics are the largest minority population in US, with 50.5 million people accounting
for 16% of the nation’s total population in the United States in 2010 [U.S. Census
Bureau. 2010]. Consistent with the migration history of America, Hispanics are
primarily an admixed population mix of European, Amerindian(Asian related), and
African ancestries whereas the ancestry proportions are different among different
Hispanics individuals (Bertoni, Budowle et al. 2003; Salari, Choudhry et al. 2005;
Choudhry, Coyle et al. 2006; Choudhry, Seibold et al. 2007; Lee, Teitelbaum et al. 2010).
This leads to genetic heterogeneity (Bertoni, Budowle et al. 2003), as well as diversity in
environmental and socioeconomic factors among Hispanic populations (Reibman and Liu
2010).
Epidemiological data regarding MS in Hispanics note similarities with Asians as shown
by the high preponderance of optic nerve and spinal cord involvement suggesting that
OSMS could also be common to this population (Cordova, Vargas et al. 2007). An
observational study from Mexico found both classical and Asian features of MS; Asian as
suggested by a higher percentage of optic neuritis (ON) (33%), a demyelinating
syndrome in MS, in comparison to previous published studies on Caucasians (14-19%)
4

(Cordova, Vargas et al. 2007). A recent study of MS in Hispanics mirror these findings
and suggest that indeed, there exists a higher predilection for optic nerve (31.5%) and
spinal cord (25%) involvement at onset of disease for MS in Hispanics compared to non-
Hispanic whites (19.7%, 13% respectively)(Amezcua, Lund et al. 2011). It is unknown if
this may represent unique features to the cohorts studied reflecting elements which may
or may not be shared by Asians and Hispanics.
Because evolutionary history and migration patterns link Hispanics to both Asians and
Europeans (Rivera 2009), it is possible that ancestry could play a role. In a small pilot
study (n=45), Amezcua et al investigated whether global individual admixture is
associated with MS characteristics in Hispanics (Amezcua, Lund et al. 2011), specifically
with longitudinally extensive spinal cord lesions (LESCLs), an radiographic
characteristic of many OSMS patients seen on magnetic resonance imaging (MRI) (Kira
2011). An increasing proportion of non-European ancestry was associated with an
increased risk of LESCLs (p=0.03; unadjusted) in addition to increased disability
(p=0.05; unadjusted). This association suggests that recent admixture within the Hispanic
population may facilitate localization of genetic variants for OSMS through extended
regions of LD. Studying MS in Hispanics provides an ideal backdrop for identifying
relative contributions of ancestry, genotype and environmental factors to OSMS. While
admixed populations have been helpful in identifying genes responsible for both
susceptibility and disease characteristics, such as for Alzheimer’s disease (Lee, Cheng et
al. 2011), breast cancer (Slattery, Baumgartner et al. 2011; Fejerman, Chen et al. 2012)
and diabetes (Hayes, Pluzhnikov et al. 2007), their studies have been relatively limited in
MS. One example demonstrating the potential for success is a recent study that identified
5

a novel locus involved in susceptibility on Chromosome 1 using admixture mapping in a
sample of African Americans and non-Hispanic whites (NHW) with MS (Reich,
Patterson et al. 2005). Thus, utilizing Hispanics, a unique admixed population that are
affected with both Asian (OSMS) and European (classical MS) phenotypes for genetic
association studies will likely lead to discovery of novel variants involved in MS
susceptibility and prognosis.

6

Chapter 2: Methods
2.1 Study population
The primary goal of this thesis project is to examine genetic differences within MS
subtypes. Consequently, new recruits are limited to MS cases only. 142 self-identified
Hispanic MS cases were recruited from the University of Southern California (USC) MS
Comprehensive Care Center and the USC+ Los Angeles California medical center where
about 800 cases per year are calculated to be of Hispanic origin. All Hispanic patients
participated in the study have relapsing remitting MS, as defined by the McDonald
criteria(Polman, Reingold et al. 2011) gave informed consent.
2.2 Data Collection and assessments
Clinical data collection and the in-person questionnaire are approved IRB. The
structured interview has been developed using CAFÉ (Common Application Framework
Extensible- café .usc.edu) and lasts approximately 45 minutes. The questionnaire captured
clinical characteristics including type of relapses to allow for accurate classification of
phenotype (OSMS vs classical RRMS), predictors of poor prognosis (ie. incomplete
recovery from first attack, motor and sphincter symptoms at onset, early relapse
frequency, male sex, older age at onset, disease subtype) (Langer-Gould, Popat et al.
2006) as well as potential confounders, such as migration history ((place or birth, year of
migration and time in the US)) and smoking.
A full neurological exam to assess disability, patient current Expanded Disability Status
Scale (Kurtzke 1983) was calculated. EDSS is an ordinal scale in 0.5 increments ranging
from 0, being normal, to 10, being death.
7

Classification of MS or OSMS: The classification is based on clinical assessments as
well as radiological assessments of MS phenotype characteristics based on previous
OSMS study (Kira 2011). OSMS is defined by a history of relapses and clinical signs
restricted to the optic nerves and/or spinal cord, excluding patients positive for
neuromyelitis –IgG antibody. Spinal cord relapses- transverse myelitis(TM) - is defined
as partial sensory level with motor disturbance and with or without bladder or bowel
dysfunction, confirmed by a medical record and magnetic resonance imaging consistent
with TM. Optic nerve involvement is assessed as part of routine neurological exam
(includes fundoscopic exam, visual acuity and field testing). Spinal cord MRI were
assessed for the presence, location and extent of spinal cord lesions and noting segment
involved on both sagittal and axial planes independently by a neurologist and neuro-
radiologist. Brain MRI data are also evaluated and location of lesions are identified to
assist the classification of MS subtypes.
Race and Ethnicity: Patients involved in the current study self-identified as Mexican,
French-Mexican, Mexican-American, Mexican-Cuban, Mexican-Polish, Mexican-
Spanish are generally classified as “Mexican”. Patients that are self-identified as
Bolivian, Caribbean, Central American, Central American-Puerto Rican, Colombian,
Cuban-Greek, Nicaraguan-Guatemalan, Peruvian, PR mother/AA father, Puerto Rican,
Salvadoran, Salvadoran-American, Salvadoran-Asian-Spanish, South American, South/
Central American are classified as “South/Central American”. The rest are classified as
“Others”.
8

Migration classification: Patients who are born in US or their age at migration to US
less or equal than 10 are classified as “early migration” whereas patients whose age at
migration to US larger than 10 are classified as “late migration”.
Blood Collection: DNA were extracted from PBMC and sent for whole-genome
amplification blinded to clinical data.
Genotyping and Assessment of genetic admixture using ancestral markers Analysis:
Individuals are genotyped using the Illumina Omni Express 700k SNP array chip. A
rigorous 30+ step quality control process was used and has been previously described for
other GWAS(Cozen, Li et al. 2012). To estimate the ancestral proportions and admixture
within the Hispanic cases, a comprehensive set of 2,193 ancestry informative markers
that have been previously reported to capture between and within continental
heterogeneity (Cozen, Li et al. 2012; Liu, Lewinger et al. 2013) are employed.
Global ancestry are estimated as previously described using both EIGENSTRAT (Price,
Patterson et al. 2006) and STRUCTURE (Falush, Stephens et al. 2003). In brief,
EIGENSTRAT applies principal components analysis to genotype data to infer
continuous axes of genetic variations. First, SNPs are centered and scaled. Then
eigenvectors are calculated from the covariance matrix between individuals based on the
genotype of the SNPs. Top continuous axes of variation (eigenvectors) are used to infer
substructures within the study samples.
As for STRUCTURE, it assumes K founder populations characterized by a set of allele
frequencies across a number of independent markers. In addition, Hardy-Weinberg
equilibrium is assumed within each population. Individuals are then originated from one
9

or more of the K populations, and are probabilistically assigned to populations. The
program models the likelihood of the observed genotypes based on the assigned
populations and allele frequency within each population, and uses the Markov chain
Monte Carlo (MCMC) algorithm to sample the posterior distribution. Ultimately,
STRUCTURE gives the estimated proportion of ancestry (average across the genome)
from each contributing population for each individual under study.
2.3 Statistical Analysis and model building
For effect estimation and statistical testing of the association of ancestry to OSMS vs.
classical MS and to clinical characteristics (e.g. transverse myelitis and ancestry), we use
linear regression or two sample t-tests for continuous outcomes and logistic regression or
chi-square tests for binary outcomes. For genome-wide association of genetic effects for
OSMS vs. classical MS, we use logistic regression as implemented in the program
PLINK (pngu.mgh.harvard.edu/~purcell/plink/). The Wald test is used to determine
statistical significance. We include appropriate adjustment variables such as age, gender
and, most importantly, global ancestry to control for confounding by population
substructure by using the top two principle components from EIGENSTRAT result.
For model building to estimate the effect of ancestry for the risk of MS outcome as
OSMS (compared to classical MS), we performed logistic regression with European
ancestry (percent_european) in the model first as explanatory variables. Then other
variables were added to the model one at a time and the change of coefficient estimate of
European ancestry was examined. Variables that cause a change of 10% or more were
considered as confounders and kept in the model. To avoid the colinearity, variables that
10

were significantly correlated were not included in the model at the same time. Finally,
the overall model fitting compared to null model is estimated and odds ratios (including
95% confidence intervals) were calculated.

11

Chapter 3: Results

3.1 Participants Characteristics
We first classified the 142 MS patients participated in the study into OSMS or classical
MS based on clinical and radiological assessments criteria (as described in the methods
section). As shown in Table 1, 102 patients are classified as classical MS (71.8% of total
patients) and 25 of them are classified as OSMS (17.6% of total patients). The rest of 15
patients are not classified into either group due to the lack of sufficient information (NA,
10.6% of the total patients) and consequently excluded from further analysis.
Table 1: MS Classification

Next, we characterized several variables of our interest for 127 MS patients left in the
study. As shown in Table 2, the ages of patients range from 17 to 68 with mean age
37.09 (standard deviation, SD= 12.14). The age of diagnosis also varies considerably
from 13 to 66, with the mean 32.25 (SD=11.80). Not surprisingly, the duration of MS
symptom among these patients also varied substantially with mean 4.93 and SD 6.42.
The measurement of patients disability (EDSS score) showed that the mean score is 2.9
with SD of 2.13. Among the 127 patients, there are 55 males (43.3% the participants)
and 72 females (56.7% of the participants) (Table 3). In terms of ethnicity, 99 of them
are Mexican (78.0% of the participants) and 27 of them are South or Central American
(21.3% of the participants) (Table 3). We also classified the patients into migration
MRI type Classical OSMS NA Total
Frequency 102 25 15 142
Percentage 71.8% 17.6% 10.6% 100%
12

group “Early Migration” if they are born in the United States or immigrate to US before
the age of 10 and into migration group “Late Migration” if they immigrate to US after the
age of 10. We found 80 of them are “Early Migration” (63.0% the participants) and 47 of
them are “Late Migration” (37% the participants) (Table3).
Table 2: Participants Characteristics

3.2 Association of MS clinical features with MS subtypes
Next we want to investigate whether any the variables we described above are associated
with the types of MS these patients belong to. As shown in table3, as for categorical
variables, neither the gender, ethnicity or migration pattern is significantly associated
with MS types (p value=0.15, 0.67 and 0.30 for gender, ethnicity and migration pattern,
respectively).
As for continuous variables, there is no statistically significant difference between
classical MS and OSMS for age (mean± SD , 37.2± 11.9 for MS and 36.5± 13.4 for
OSMS, p=0.81), for age of diagnosis (mean± SD , 33.1± 11.6 for MS and 28.7± 12.0 for
OSMS , p=0.10) and for the duration of MS symptoms (mean± SD , 4.10± 5.3 for MS and
7.84± 9.8 for OSMS, p=0.08). However, we found the age of first MS symptom onset is
Variable Number of
Observatio
n
Number of
Missing
Min Max Mean Std. Dev.
age 127 0 17 68 37.09 12.14
age_diagnosis 127 0 13 66 32.25 11.80
MS_duration 127 0 0 39 4.93 6.42
EDSS 127 0 0 7.5 2.90 2.13
13

marginally associated with MS types (mean± SD , 31.47± 11.9 for MS and 26.36± 11.6 for
OSMS, p=0.06). This data shows that OSMS patients typically have first MS symptom
onset at younger ages than classical MS patients. Furthermore, we found that EDSS
score is significantly higher in OSMS (mean± SD , 4.64± 2.05) compared with classical
MS (mean± SD , 2.47± 1.92) (p =3e-05) and suggests that the level of disability of
patients with OSMS is more severe compared with those with MS. This observation is
not unexpected considering the fact that OSMS by nature is more devastating than
classical MS.

Table 3: Association of Clinical Variables with MS Types

Variable Total CLASSICAL OSMS Test Statistics
Gender Male 55 41 14
Chi^2 = 2.04
P=0.15 Female 72 61 11
Ethnicity
Mexican 99 78 21
Chi^2 = 0.80
P=0.67
South/Central
American
27 23 4
Others 1 1 0
Migration Early 80 62 18
Chi^2 = 1.08
P=0.30
Late 47 40 7
Age (mean±s.d.) 37.2±11.9 36.5±13.4
t=-0.24, P=0.81
Age_of _Diagnosis(mean±s.d.) 33.1±11.6 28.7±12.0
t=-1.67, P=0.10
MS_duration (mean±s.d.) 4.10±5.3 7.84±9.8
t=-1.84, P=0.08
Age of 1
st
Symptom Onset(mean±s.d.) 31.47±11.9 26.36±11.6
t=-1.96, P=0.06*
EDSS(Disability measurement ) (mean±s.d.) 2.47±1.92 4.64±2.05
t=4.79, P=3e-05**
14

3.3 Global ancestry estimation and test of association with clinical
variables
Next we want to investigate whether genetic ancestry contributes to the risk of MS as
OSMS compared with classical MS. To do that, we estimated the global ancestries for
the Hispanic MS patients using STRUCTURE software (Falush, Stephens et al. 2003)
and EIGENSTRAT software (Price, Patterson et al. 2006).
Figure 1 shows the estimated global ancestry for MS patients using STRUCTURE. The
horizontal axis represents individual patient and the vertical axis represents the estimated
individual ancestry coefficients, which is a continuous variable between 0 and 1
indicating the percentage of different ancestries for the individual’s genome. Different
color represents different estimated ancestries, for example, red color represents African
ancestry, blue indicates Asian ancestry and green represents European ancestry. Overall,
European and Asian ancestries make up the majority of the global ancestry estimation for
the Hispanic MS patients while African ancestry only takes a small portion. In addition,
global ancestry estimation is highly heterogeneous among different MS patients:
individual located towards the left on the horizontal axis has relative higher proportion of
European ancestry and lower proportion of Asian ancestry, while the individuals located
more towards the right on the horizontal axis have decreasing proportion of European
ancestry and increasing proportion of Asian ancestry.

15

Figure 1: Global ancestry estimation of MS patients using STRUCTURE.
The horizontal axis represents each patient and the vertical axis represents the
estimated individual ancestry coefficients. Red color represents African
ancestry; blue indicates Asian ancestry and green represents European ancestry

We also evaluated the global ancestries of MS patients based on the ancestry information
markers as described in the methods section using EIGENSTRAT and extracted the top
ten principal components. HapMap III samples are also included in the analysis in
parallel to provide the reference for different ethnic populations. Figure 2 shows the
plots of the first eigenvector against the rest of top ten eigenvectors for MS patients as
well as HapMap III samples. Based on the clusters distribution of HapMap III samples,
we can see that that the first eigenvector was able to distinguishes two major continental
clusters, the African ancestry related groups (YRI, LWK, ASW and MKK, represented
by red color) and the other ethnic groups. The second eigenvector was able to clearly
identify the European cluster (TSI and CEU, represented by yellow and green color,
respectively), Asian cluster (CHB, JPT and CHD, represented by purple colors), and
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1
4
7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
55
58
61
64
67
70
73
76
79
82
85
88
91
94
97
100
103
106
109
112
115
118
121
124
127
130
133
136
percent_african
percent_asian
percent_european
Percent_african Percent_asian Percent_european
Ancestry
16

Eigenvector 1
Eigenvector 2
Eigenvector 1
Eigenvector 3
Eigenvector 1
Eigenvector 4
Eigenvector 1
Eigenvector 5
Eigenvector 1
Eigenvector 6
Eigenvector 1
Eigenvector 7
Eigenvector 1
Eigenvector 8
Eigenvector 1
Eigenvector 9
Eigenvector 1
Eigenvector 10
17

Figure 2: Plot of first eigenvector against the rest of top ten eigenvector of EIGENSTRAT result for
MS patients. HapMap III samples are also plotted from the same analysis as the reference. Black
color denotes MS patients involved in the study and other colors represent samples from HapMap III

largely identify the Indian cluster (GIH, blue color) as well Amerindian (MEX, pink)
ethnic groups. The remaining eigenvectors (eigenvectors three to ten) do not show good
pattern of clusters that are related to the ethnic groups for HapMap III samples. Thus the
top two eigenvectors (principle components) were used in the study as the adjustment for
population structures for GWAS study while other eigenvectors are not used.
Compared with HapMap III samples clusters on the plot of top two eigenvectors (figure
2), MS patients (black dots) primarily fall in the same clusters with Indian cluster (GIH,
blue color) as well Amerindian (MEX, pink) ethnic groups, although a small population
of points are in close proximity of European group (green and yellow) and a couple of
dots close to African group (red). This result is largely consistent with the findings from
STRUCTURE result.
Based on these global ancestry estimations, next we want to ask whether the global
ancestries are associated with clinical variables of our interest, in particular whether the
OSMS is found more often in patients with higher proportion of Asian ancestry.
We first compared the distribution of all three genetic ancestries between OSMS and
classical MS respectively. As shown in Figure 3, we did not find significant difference of
the mean ancestry proportion between OSMS and classical MS for Asian ancestry
(mean= 0.313 for OSMS, 0.303 for Classical MS, two sample t-test p value=0.657), for
European ancestry (mean=0.584 for OSMS, 0.604 for Classical MS, two sample t-test p
value=0.60) and for African ancestry (mean=0.102 for OSMS, 0.094 for Classical MS,
18

two sample t-test p value=0.79). Consistent with this result, when we performed logistic
regression analysis with OSMS as the outcome variable and the genetic ancestries as the
independent variables without adjustment of other variables, none of them are
significantly associated with the odds of MS as OSMS without any adjustment of other
variables (data not shown). In addition, we did not find any significant association of
global ancestries with any of the clinical variables (age, gender, age of diagnosis, MS
symptom duration, age of 1
st
symptom onset, EDSS score and migration time, data not
shown).

Figure 3: Comparison of distributions of global genetic ancestry estimation of
between OSMS and classical MS patients for Asian, European and African
ancestries, respectively. Y axis is the percentage of genetic ancestry for the
particular MS type.

OSMS Classical
OSMS Classical OSMS Classical
19

3.4 Effect estimation of genetic ancestries for the risk of OSMS
Finally we would like to build a model to estimate the effect of genetic ancestries for the
risk of MS occurrence as OSMS (as compared to Classical MS). As the three genetic
ancestries proportions (European, Asian and African ancestries) are negatively correlated
with each other (data not shown), we included European Ancestry only as an
representation for genetic ancestries in the model. We used logistic regression to build
the model (see methods for details) and ended up with the following model: Logit
(Pr(OSMS)) ~ Percent_European _ancestry + EDSS+ Age_1stSx (Table 4).
Table 4: Parameters estimates of logistic regression model for the odds of OSMS vs classical MS

Under this model, after adjusting for disability severeness (EDSS score) and age of 1
st

symptom onset (Age_1stsx), European ancestry is not significant associated with the
odds of MS as OSMS (compared to classical MS, p=0.152, OR=0.063). Our model
suggests that 1 unit increase of European ancestry percentage, the odds of getting OSMS
(as compared to classical MS) is decreased by 94%. This trend of negative association of
European ancestry with the risk of OSMS is consistent with our hypothesis; however,
there is lack of significance for this association under current model. One possibility is
there are other potential confounding variables that were not included in the current
model and leads to inaccurate effect estimate of European ancestry. Conversely, there is
no statistically significant association of genetic ancestry and the risk of OSMS
Coefficients Estimate Std. Error Odds Ratio(95% C.I.) Z value Pr(>|z|)
(Intercept) 0.119 1.372 1.126(0.076, 17.341) 0.087 0.931
Percent_European -2.772 1.935 0.063(0.001, 2.598) -1.432 0.152
Age_1stsx -0.065 0.026 0.937(0.887, 0.982) -2.547 0.011
EDSS 0.573 0.136 1.774(1.382, 2.369) 4.214 2.51e-05
20

(compared to classical MS). This remains to be further tested as more variables are
brought into considerations for future study.

21

3.5 GWAS scan comparing OSMS vs classical MS
Next we want to ask whether any of genetic regions are associated with risk of MS as
OSMS as compared to classical MS. To address this question, we performed a genome
wide scan using the 25 OSMS patients as cases and 102 classical MS patients as controls.
Association study using logistic regression as implemented in PLINK program was used
and statistical significance is determined by the Wald test. Adjustment variables include
age, gender and, most importantly, the top two principle components from
EIGENSTRAT result to control for confounding by population substructure.

Figure 4: Quantile-Quantile plot for GWAS comparing OSMS to classical MS

22

First, we evaluated the robustness of observed distribution of test statistic by examining
the quantile-quantile plot and calculated the genomic inflation factor (λ). As shown in
Figure4, we did not observe significant inflation for this GWAS (λ=1.03). Rather the
downwards deviation of observed test statistics (blue line) from identity line (black line)
at the upper tail suggests the study is quite conservative.

Figure 5: GWAS scan result comparing OSMS vs classical MS. Y axis
represents the –log10 transformed p value associated with each SNP which
are arranged according their chromosome location as laid out along the x
axis. Genomic locus for selected top SNPs are labeled.
Chromosome location
-log10(P value)
0
1
2
3
4
5
1 2 3 4 5 6 7 8 9 10 11 12 1314 ……………… 22
PTPRD
TNFRSF19
CNTNAP2
CRYGGP/LOC100131852
/ZNF863P
CRYGGP/LOC100131852
/ZNF863P
ELAVL2/SUMO2P2/
LOC402360
FAM189A1
23

We next generated Manhattan Plot for this GWAS whereas SNPs are plotted according to
their chromosome locations along the horizontal axis and their p values (-log 10
transformed) along the vertical axis. Due to the transformation, the lower the P value,
the higher the SNP will stand on the plot. As shown in Figure 5, we did not find SNPs
with p values that surpass the threshold of significance p=5 x 10
-8
for a typical GWAS.
This is not a surprise due to the small sample size we have in the current study.
However, we do find quite a few SNPs with P value around 1x10
-5
which are selectively
highlighted in the Manhattan plot (Figure 5).
Table 5: TOP 10 SNPs with the lowest p value from the GWAS scan

We further examined the top ten SNPs for their chromosome location, odds ratio, p value
and their genomic locus (table 5). The first SNP (rs324533) falls on the intron region of
PTPRD on the chromosome 9 (Odds ratio: 10.51, p value: 1.388e-05). PTPRD encode a
member of the protein tyrosine phosphatase (PTP) family and functions as signaling
molecules to regulate a variety of cellular processes including neurite growth, neurons
axon guidance, cell growth and differentiation (den Hertog, Blanchetot et al. 1999; Paul
Chr SNP Position Odds Ratio P value Locus(±200kb)
9 rs324533 9045011 10.51 1.388e-05 PTPRD
13 rs9510795 24216012 7.439 1.72e-05 TNFRSF19
2 rs12994529 52130099 15.84 2.353e-05 CRYGGP/LOC100131852/ZNF863P
7 rs17170936 147973957 10.32 3.118e-05 CNTNAP2
2 rs6733466 52137385 18.86 4.818e-05 CRYGGP/LOC100131852/ZNF863P
9 rs1036106 23677576 9.906 5.243e-05 ELAVL2/SUMO2P2/LOC402360
15 rs895250 29615111 5.423 5.364e-05 FAM189A1
1 rs922715 218858100 5.457 5.439e-05 TGFB2/LOC100130251
15 rs4842909 86499744 20.4 8.241e-05 AGBL1/MIR1276/KLHL25/MIR548AP
3 rs11925026 110453270 8.407 8.733e-05 RPSAP29/LOC151760/PVRL3
24

and Lombroso 2003; Aricescu, Siebold et al. 2008; Chien and Ryu 2013). It has been
shown to associate with a variety of human diseases, including coronary artery
disease(Saade, Cazier et al. 2011), type II diabetes(Tsai, Yang et al. 2010; Below,
Gamazon et al. 2011; Chang, Chiu et al. 2012) and restless leg syndrome(Yang, Li et al.
2011). Interestingly, recent research found that restless leg syndrome, which PTPRD is
associated with, had a higher prevalence in patients with multiple sclerosis (MS)
compared to healthy subjects (Auger, Montplaisir et al. 2005; Manconi, Ferini-Strambi et
al. 2008; Shaygannejad, Ardestani et al. 2013). If the association we found between
PTPRD and OSMS is true, it would be very interesting to study how PTPRD is linked to
MS and how it contribute to the increase in the odds outcome of MS as OSMS vs
classical MS.
The second highest SNP (rs9510795) falls on the 6
th
intron of TNFRSF19 on
chromosome 13 (Odds ratio: 7.439, p value: 1.72e-05). TNFRSF19 belongs to the tumor
necrosis factor receptor superfamily and is involved in death signaling via activating the
c-Jun N-terminal kinase pathway (Hu, Tamada et al. 1999; Eby, Jasmin et al. 2000;
Kojima, Morikawa et al. 2000). It is also the co-receptor for Nogo signaling, the
inhibitory signals that prevent axonal regeneration and myelination in the central nervous
system(Satoh, Tabunoki et al. 2007; McDonald, Bandtlow et al. 2011). In addition, it has
been reported that the expression of TNFRSF19 is elevated in astrocytes and
macrophages/microglia at the multiple sclerosis lesions site (Satoh, Tabunoki et al. 2007).
These observations highly suggest that TNFRSF19 is involved in multiple sclerosis.
How TNFRSF19 affects the odds of MS as OSMS will be a very interesting topic to
investigate.
25

Others SNPs among the top 10 list that directly hit the gene coding regions are:
rs17170936 which is located in the intron of CNTNAP2, a neurexin family of cell
adhesion molecules located at the juxtaparanodes of myelinated axons, and mediates
interactions between neurons and glia during nervous system development and implicated
in a variety of neurological disorders (Coman, Aigrot et al. 2006; Rodenas-Cuadrado, Ho
et al. 2013); rs895250 which is located in intron of FAM189A1, a transmembrane
protein with unknown function. Others SNPs do not directly hit any particular gene
coding regions. For those, genes in their close proximity are listed in table 5.
Table 6: Examination of SNPs previously shown to associate with MS

Chr SNP Position locus Odds Ratio
(current study)
P value
(current study)
P value
(reported)
1 rs2300747 117104215 CD58 0.8449 0.68470 6.46x10
-9
1 rs12122721 200984480 KIF21B 1.0860 0.85180 6.56 x 10
-10
2 rs882300 136976255 CXCR4 0.6972 0.40200 7.23 x10
-5
3 rs170934 28079085 EOMES 1.2040 0.59280 2x10
-8
3 rs1132200 119150836 TMEM39A 1.0960 0.86660 3.09 x 10
-8
5 rs6897932 35874575 IL7R 1.7040 0.17990 1.99x10
-6
5 rs2546890 158759900 IL12B 0.6526 0.23050 7.95x10
-8
5 rs10866713 158918894 IL12B 0.7679 0.58660 6.3x10
-7
6 rs3135388 32413051 HLA-DRB1 1.3500 0.58610 3.98x10
-225
6 rs3129889 32413545 HLA-DRB1 1.3500 0.58610 1.03x10-
206
6 rs9321619 137874408 OLIG3-TNFAIP3 1.3240 0.44370 9.34 x10
-4
6 rs1738074 159465977 TAGAP 1.2840 0.45860 3.72x10
-7
10 rs2104286 6099045 IL2RA 1.7900 0.25780 1.52 x10
-6
10 rs12722489 6102012 IL2RA 0.8641 0.83760 3.66x10
-8
12 rs1800693 6440009 TNFRSF1A 1.2050 0.62170 7.52 x10
-8
12 rs703842 58162739 METTL1 1.3630 0.36190 1.72x10
-5
16 rs7191700 11406803 TNP2/PRM3/PRM2
/PRM1/C16orf75
1.4120 0.46350 6.4x10
-7
16 rs17445836 86017663 IRF8 1.0920 0.87100 5.35x10
-10
17 rs744166 40514201 STAT3 1.6800 0.15910 6.35x10
-6
18 rs763361 67531642 CD226 0.4380 0.02803 7x10
-5
20 rs6074022 44740196 CD40 1.6670 0.18550 1.30x10
-7
26

It is worth noting that these SNPs are tag SNPs and they may not necessarily be the
causal SNP but rather are in LD with the real causal SNP. Since none of these SNPs
directly hit the on the exons, it is likely they exert their biological functions by regulating
gene expression for the genes where these SNPs are located or in close proximity with.
Finally, we examined the P values for SNPs that are previously published to be
associated with multiple sclerosis with genomic level or suggestive level of significance
(De Jager, Jia et al. 2009; Patsopoulos, Esposito et al. 2011) for the current GWAS study.
As shown in Table 6, none these SNPs show significant P value, including the SNPs for
HLA-DRB1, IL2RA and IL12B, which have well established association with MS
(Oksenberg, Baranzini et al. 2008; Oksenberg and Baranzini 2010; Patsopoulos, Esposito
et al. 2011). This is not unexpected since we are looking for SNPs that affect the
differential presentation of MS as OSMS as compared with MS. These typical MS genes
listed above may not necessarily contribute to this OSMS risk determination. Instead,
other unexpected genes might be involved in this process.
Nevertheless, current data will serve as a pilot study for future larger GWAS as more
patients are recruited. It will be interesting to see whether the current top SNPs remain
the top candidates or new SNPs will arise as the sample size is increased.

27

Chapter 4: Discussion
Differences across populations in multiple sclerosis (MS) susceptibility, age of
onset, CNS site of injury and progression are proposed to be the result of a complex
interaction between genetic and environmental risk factors (Barcellos, Oksenberg et al.
2003; Ascherio and Munger 2007; Ascherio and Munger 2007). It has been observed
that MS is far more prevalent in Europeans and their descendants (Pugliatti, Rosati et al.
2006) compared to other ethnic groups. In fact, most of the previous Genome-wide
association studies (GWAS) on MS consisted of individuals with European ancestry and
successfully identified and confirmed several susceptibility locus, including HLADR2,
interleukin 7 receptor (IL-7R) and interleukin 2 receptor (IL-2R) (Oksenberg, Baranzini
et al. 2008; Oksenberg and Baranzini 2010; Baranzini 2011). While the European
population has the advantage of less genetic heterogeneity, there exists the potential to
leverage admixed populations to help identify variants involved further in disease. The
potential advantage comes from using both the genetic diversity within an admixed
population, as well as the differences in MS characteristics. Epidemiological data
regarding MS in Latin America have indicated a preponderance of combined optic nerve
and motor deficits in countries such as Colombia, Brazil, Cuba and recently Mexico
suggesting that this population may be at risk for opticospinal MS (OSMS), which is also
more common in Asians than in Caucasians (White). These population differences
provide an ideal backdrop for studying OSMS in the Hispanic population, which is
ancestrally linked to both Asians and Caucasians.
In this thesis, we first identified 25 OSMS and 102 classical MS among the 142
MS patients participated in the study based on clinical and radiological criteria. We
28

subsequently characterized the 127 patients with disenable MS types in terms of disease
risk, phenotype characteristics and progression. They showed a substantial heterogeneity
for the clinical features such as age, age of diagnosis, disability measurement and
migration history. Consistent with our expectation, we found increased disability
measurement in OSMS (mean± SD, 4.64± 2.05) compared with classical MS. (Mean± SD,
2.47± 1.92) (t-test, p=3e-05). In addition, age at the first symptom onset is significantly
younger in OSMS (26.36± 11.6) compared with classical MS (31.47± 11.9) (p=0.057).
We also noted that the migration history of the patients as well as their neurological
disability severity are statistically significant associated with the risk of MS presentation
as OSMS as compared with classical MS (odds ratio=3.63, p value=0.055 for migration
history and odds ratio=1.99, p value<0.001 for disability severity).
To address the question whether OSMS occurrence is correlated with Asian or
European ancestries, we estimated global ancestries of the individuals using
STRUCTURE and EIGENSTRAT. For this cohort mostly self-reported as Mexicans,
their overall genetic makeup was primarily composed of European and Asian ancestries
followed by a small proportion of African ancestries. Not surprisingly, the genetic
makeup is highly heterogeneous among different individuals. We did not see any
difference in the mean genetic ancestries between OSMS and classical MS. To estimate
the effect of genetic ancestries for the risk of OSMS (as compared to classical MS), we
built a multivariable logistical regression model whereas European ancestry is treated as
explanatory variable. After adjusting for disability severeness (EDSS score) and age of
1
st
symptom onset(Age_1stsx), our current model suggests that 1 unit increase of
European ancestry percentage will lead to 94% decrease in the odds of getting OSMS (as
29

compared to classical MS). This trend of negative association of European ancestry with
the risk of OSMS is consistent with our hypothesis; however, there is lack of statistical
significance for this association under current model (OR=0.063, 95% C.I.( 0.001,
2.598), p=0.152). It is possible that our current effect estimation of genetic ancestry for
the risk of OSMS is inaccurate due to other potential confounding variables not being
adjusted. This needs to be further tested as more variables are brought into
considerations for future study to better address the contribution of genetic ancestry to the
risk of OSMS (compared to classical MS).
Finally, we performed a pilot genome wide scan to identify genomic regions
associated with differential MS disease presentation by comparing OSMS vs classical
MS. We did not detect SNPs with p values above the significance threshold (5 x 10
-8
).
This is not unexpected due to fact that we have limited sample size in the current study
(25 case and 102 controls). We were aware of the lack of power for this genomic wide
scan but this preliminary data will provide support for a larger GWAS in the future as
more patients are recruited into the study. Despite of this, it is very interesting to see
some of top SNPs coming out from this pilot screen did show evidence associated with
multiple sclerosis. It will be interesting to check whether the current top SNPs remain the
top candidates or new SNPs will arise as the sample size is increased.
In conclusion, this study assessed the difference of phenotype characteristics and
progression within Hispanics between OSMS and classical MS and provided preliminary
data to address the contribution of genetic ancestries to the outcome of MS phenotype
(OSMS vs classical MS). The result presented here could potentially inform future
strategies to identify high risk subgroups of MS that may benefit from more aggressive
30

treatments. In addition, future study along this direction could have the potential to
improve our understanding of the biology and genetics of MS through the use of our
unique and diverse Hispanic population.

31

Bibliography

Amezcua, L., B. T. Lund, et al. (2011). "Multiple sclerosis in Hispanics: a study of
clinical disease expression." Mult Scler 17(8): 1010-1016.
Aricescu, A. R., C. Siebold, et al. (2008). "Receptor protein tyrosine phosphatase micro:
measuring where to stick." Biochem Soc Trans 36(Pt 2): 167-172.
Ascherio, A. and K. L. Munger (2007). "Environmental risk factors for multiple sclerosis.
Part I: the role of infection." Ann Neurol 61(4): 288-299.
Ascherio, A. and K. L. Munger (2007). "Environmental risk factors for multiple sclerosis.
Part II: Noninfectious factors." Ann Neurol 61(6): 504-513.
Auger, C., J. Montplaisir, et al. (2005). "Increased frequency of restless legs syndrome in
a French-Canadian population with multiple sclerosis." Neurology 65(10): 1652-
1653.
Baranzini, S. E. (2011). "Revealing the genetic basis of multiple sclerosis: are we there
yet?" Curr Opin Genet Dev 21(3): 317-324.
Barcellos, L. F., J. R. Oksenberg, et al. (2003). "HLA-DR2 dose effect on susceptibility
to multiple sclerosis and influence on disease course." Am J Hum Genet 72(3):
710-716.
Below, J. E., E. R. Gamazon, et al. (2011). "Genome-wide association and meta-analysis
in populations from Starr County, Texas, and Mexico City identify type 2 diabetes
susceptibility loci and enrichment for expression quantitative trait loci in top
signals." Diabetologia 54(8): 2047-2055.
Bertoni, B., B. Budowle, et al. (2003). "Admixture in Hispanics: distribution of ancestral
population contributions in the Continental United States." Hum Biol 75(1): 1-11.
Botstein, D. and N. Risch (2003). "Discovering genotypes underlying human phenotypes:
past successes for mendelian disease, future approaches for complex disease." Nat
Genet 33: 228-237.
Bush, W. S. and J. H. Moore (2012). "Chapter 11: Genome-wide association studies."
PLoS Comput Biol 8(12): e1002822.
Chang, Y. C., Y. F. Chiu, et al. (2012). "Replication of genome-wide association signals
of type 2 diabetes in Han Chinese in a prospective cohort." Clin Endocrinol 76(3):
365-372.
Chien, P. N. and S. E. Ryu (2013). "Protein tyrosine phosphatase sigma in proteoglycan-
mediated neural regeneration regulation." Mol Neurobiol 47(1): 220-227.
32

Choudhry, S., N. E. Coyle, et al. (2006). "Population stratification confounds genetic
association studies among Latinos." Hum Genet 118(5): 652-664.
Choudhry, S., M. A. Seibold, et al. (2007). "Dissecting complex diseases in complex
populations: asthma in latino americans." Proc Am Thorac Soc 4(3): 226-233.
Coman, I., M. S. Aigrot, et al. (2006). "Nodal, paranodal and juxtaparanodal axonal
proteins during demyelination and remyelination in multiple sclerosis." Brain
129(Pt 12): 3186-3195.
Cordova, J., S. Vargas, et al. (2007). "Western and Asian features of multiple sclerosis in
Mexican Mestizos." Clin Neurol Neurosurg 109(2): 146-151.
Cozen, W., D. Li, et al. (2012). "A genome-wide meta-analysis of nodular sclerosing
Hodgkin lymphoma identifies risk loci at 6p21.32." Blood 119(2): 469-475.
De Jager, P. L., X. Jia, et al. (2009). "Meta-analysis of genome scans and replication
identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci."
Nat Genet 41(7): 776-782.
den Hertog, J., C. Blanchetot, et al. (1999). "Receptor protein-tyrosine phosphatase
signalling in development." Int J Dev Biol 43(7): 723-733.
Dewan, A., M. Liu, et al. (2006). "HTRA1 promoter polymorphism in wet age-related
macular degeneration." Science 314(5801): 989-992.
Eby, M. T., A. Jasmin, et al. (2000). "TAJ, a novel member of the tumor necrosis factor
receptor family, activates the c-Jun N-terminal kinase pathway and mediates
caspase-independent cell death." J Biol Chem 275(20): 15336-15342.
Falush, D., M. Stephens, et al. (2003). "Inference of population structure using multilocus
genotype data: linked loci and correlated allele frequencies." Genetics 164(4):
1567-1587.
Fejerman, L., G. K. Chen, et al. (2012). "Admixture mapping identifies a locus on 6q25
associated with breast cancer risk in US Latinas." Hum Mol Genet 21(8): 1907-
1917.
Hardy, J. and A. Singleton (2009). "Genomewide association studies and human disease."
N Engl J Med 360(17): 1759-1768.
Hayes, M. G., A. Pluzhnikov, et al. (2007). "Identification of type 2 diabetes genes in
Mexican Americans through genome-wide association studies." Diabetes 56(12):
3033-3044.
Hu, S., K. Tamada, et al. (1999). "Characterization of TNFRSF19, a novel member of the
tumor necrosis factor receptor superfamily." Genomics 62(1): 103-107.
Kemppinen, A., S. Sawcer, et al. (2011). "Genome-wide association studies in multiple
sclerosis: lessons and future prospects." Brief Funct Genomics 10(2): 61-70.
33

Kira, J. (2003). "Multiple sclerosis in the Japanese population." Lancet Neurol 2(2): 117-
127.
Kira, J. (2011). "Autoimmunity in neuromyelitis optica and opticospinal multiple
sclerosis: astrocytopathy as a common denominator in demyelinating disorders." J
Neurol Sci 311(1-2): 69-77.
Klein, R. J., C. Zeiss, et al. (2005). "Complement factor H polymorphism in age-related
macular degeneration." Science 308(5720): 385-389.
Kojima, T., Y. Morikawa, et al. (2000). "TROY, a newly identified member of the tumor
necrosis factor receptor superfamily, exhibits a homology with Edar and is
expressed in embryonic skin and hair follicles." J Biol Chem 275(27): 20742-
20747.
Kurtzke, J. F. (1983). "Rating neurologic impairment in multiple sclerosis: an expanded
disability status scale (EDSS)." Neurology 33(11): 1444-1452.
Langer-Gould, A., R. A. Popat, et al. (2006). "Clinical and demographic predictors of
long-term disability in patients with relapsing-remitting multiple sclerosis: a
systematic review." Arch Neurol 63(12): 1686-1691.
Lee, J. H., R. Cheng, et al. (2011). "Identification of novel loci for Alzheimer disease and
replication of CLU, PICALM, and BIN1 in Caribbean Hispanic individuals."
Arch Neurol 68(3): 320-328.
Lee, Y. L., S. Teitelbaum, et al. (2010). "Comparing genetic ancestry and self-reported
race/ethnicity in a multiethnic population in New York City." J Genet 89(4): 417-
423.
Liu, J., J. P. Lewinger, et al. (2013). "Confounding and heterogeneity in genetic
association studies with admixed populations." Am J Epidemiol 177(4): 351-360.
Manconi, M., L. Ferini-Strambi, et al. (2008). "Multicenter case-control study on restless
legs syndrome in multiple sclerosis: the REMS study." Sleep 31(7): 944-952.
Manolio, T. A., L. D. Brooks, et al. (2008). "A HapMap harvest of insights into the
genetics of common disease." J Clin Invest 118(5): 1590-1605.
McCarthy, M. I., G. R. Abecasis, et al. (2008). "Genome-wide association studies for
complex traits: consensus, uncertainty and challenges." Nat Rev Genet 9(5): 356-
369.
McDonald, C. L., C. Bandtlow, et al. (2011). "Targeting the Nogo receptor complex in
diseases of the central nervous system." Curr Med Chem 18(2): 234-244.
McElroy, J. P., N. Isobe, et al. (2011). "SNP-based analysis of the HLA locus in Japanese
multiple sclerosis patients." Genes Immun 12(7): 523-530.
34

McElroy, J. P. and J. R. Oksenberg (2011). "Multiple sclerosis genetics 2010." Neurol
Clin 29(2): 219-231.
Noseworthy, J. H., C. Lucchinetti, et al. (2000). "Multiple sclerosis." N Engl J Med
343(13): 938-952.
Oksenberg, J. R. and S. E. Baranzini (2010). "Multiple sclerosis genetics--is the glass half
full, or half empty?" Nat Rev Neurol 6(8): 429-437.
Oksenberg, J. R., S. E. Baranzini, et al. (2008). "The genetics of multiple sclerosis: SNPs
to pathways to pathogenesis." Nat Rev Genet 9(7): 516-526.
Oksenberg, J. R. and L. F. Barcellos (2005). "Multiple sclerosis genetics: leaving no
stone unturned." Genes Immun 6(5): 375-387.
Oksenberg, J. R. and S. L. Hauser (2005). "Genetics of multiple sclerosis." Neurol Clin
23(1): 61-75.
Patsopoulos, N. A., F. Esposito, et al. (2011). "Genome-wide meta-analysis identifies
novel multiple sclerosis susceptibility loci." Ann Neurol 70(6): 897-912.
Paul, S. and P. J. Lombroso (2003). "Receptor and nonreceptor protein tyrosine
phosphatases in the nervous system." Cell Mol Life Sci 60(11): 2465-2482.
Polman, C. H., S. C. Reingold, et al. (2011). "Diagnostic criteria for multiple sclerosis:
2010 revisions to the McDonald criteria." Ann Neurol 69(2): 292-302.
Price, A. L., N. J. Patterson, et al. (2006). "Principal components analysis corrects for
stratification in genome-wide association studies." Nat Genet 38(8): 904-909.
Pugliatti, M., G. Rosati, et al. (2006). "The epidemiology of multiple sclerosis in
Europe." Eur J Neurol 13(7): 700-722.
Reibman, J. and M. Liu (2010). "Genetics and asthma disease susceptibility in the US
Latino population." Mt Sinai J Med 77(2): 140-148.
Reich, D., N. Patterson, et al. (2005). "A whole-genome admixture scan finds a candidate
locus for multiple sclerosis susceptibility." Nat Genet 37(10): 1113-1118.
Rivera, V. M. (2009). "Multiple sclerosis in Latin America: reality and challenge."
Neuroepidemiology 32(4): 294-295.
Rodenas-Cuadrado, P., J. Ho, et al. (2013). "Shining a light on CNTNAP2: complex
functions to complex disorders." Eur J Hum Genet 29(10): 100.
Saade, S., J. B. Cazier, et al. (2011). "Large scale association analysis identifies three
susceptibility loci for coronary artery disease." PLoS One 6(12): 27.
Salari, K., S. Choudhry, et al. (2005). "Genetic admixture and asthma-related phenotypes
in Mexican American and Puerto Rican asthmatics." Genet Epidemiol 29(1): 76-
86.
35

Satoh, J., H. Tabunoki, et al. (2007). "TROY and LINGO-1 expression in astrocytes and
macrophages/microglia in multiple sclerosis lesions." Neuropathol Appl
Neurobiol 33(1): 99-107.
Sawcer, S. (2011). "The major cause of multiple sclerosis is environmental: genetics has
a minor role--no." Mult Scler 17(10): 1174-1175.
Shaygannejad, V., P. E. Ardestani, et al. (2013). "Restless legs syndrome in Iranian
multiple sclerosis patients: a case-control study." Int J Prev Med 4(Suppl 2):
S189-193.
Slattery, M. L., K. B. Baumgartner, et al. (2011). "Replication of five GWAS-identified
loci and breast cancer risk among Hispanic and non-Hispanic white women living
in the Southwestern United States." Breast Cancer Res Treat 129(2): 531-539.
Tsai, F. J., C. F. Yang, et al. (2010). "A genome-wide association study identifies
susceptibility variants for type 2 diabetes in Han Chinese." PLoS Genet 6(2):
1000847.
Yang, Q., L. Li, et al. (2011). "Family-based and population-based association studies
validate PTPRD as a risk factor for restless legs syndrome." Mov Disord 26(3):
516-519.

Abstract (if available)

Abstract Genetic factors are postulated to contribute to the difference of susceptibility as well as clinical outcomes of multiple sclerosis (MS) in different populations. The Hispanic population offers a unique potential to identify novel genetic variants involved in differential disease presentation of MS (e.g. the site of lesions in the central nervous system, opticospinal (OSMS) vs classical MS) due to the genetic diversities in this admixed population. While MS overall is more common in Whites of European ancestry, if and how the heterogeneity of genetic admixture in Hispanics contributes to characteristics of the disease is unknown. ❧ Methods: 127 Hispanics with MS were genotyped and their global ancestries were estimated. They were then classified into either classical MS or OSMS based on clinical and radiological assessment. For statistical testing of the association of genetic ancestry to OSMS vs. classical MS and to clinical characteristics, linear regression or t-tests were used for continuous outcomes and logistic regression or chi-square tests were used for binary outcomes. For the genome-wide investigation of SNP associations, logistic regression was used with adjustment by age, gender and global ancestry to control for potential confounding due to population admixture. The Wald test is used to determine the statistical significance. ❧ Results: For the 142 MS patients participated in the study, Asian characteristic of the disease, such as OSMS was noted in 25 patients (17.6% of total patients) while classical MS was observed in 102 patients (71.8% of the total patients). There was no significant difference of age, gender, ethnicity, migration history, age at diagnosis or disease duration between OSMS and classical MS patients. However, increased disability was significantly noted in OSMS patients (Mean score of disability measurement ±SD, 4.64±2.05) as compared to classical MS patients (2.47±1.92) (p=3.0e-05). In addition, age at the first symptom onset is significantly younger in OSMS (26.36±11.6) compared with classical MS (31.47±11.9) (p=0.057). Interestingly, the migration history of the patients (early migration vs late migration) as well as their neurological disability severity (EDSS score) are statistically significant associated with the risk of OSMS as compared to classical MS (odds ratio=3.63, p value=0.055 for migration history and odds ratio=1.99, p value<0.001 for disability severity). Global ancestry estimation showed that both European and Asian genetic ancestries were the most common background followed by African in these Hispanic MS patients. No significant difference of these ancestry proportions were found between OSMS and classical MS. Logistic regression modeling suggest that European ancestry is negatively associated with the risk of OSMS as compared to classical MS (OR=0.063, 95% confidence interval (0.001, 2.598)) after adjusting for EDSS and age of 1st symptom onset. However, this association is not statistically significant based on current model estimation (p=0.152). Finally as a pilot investigation, to identify genomic regions associated with risk of OSMS, we performed a genome wide scan comparing OSMS patients vs classical MS patients. None of the SNPs have p values surpassing the genome wide level significance threshold (5 x 10⁻⁸). The top SNPs with lowest p values (around 1x10⁻⁵) are discussed for their potential involvement in the odds of OSMS vs classical MS. ❧ Conclusion: The results presented in this study provide pilot data to address whether and how the heterogeneity of genetic admixture in Hispanics contributes to characteristics of the MS. The GWAS result serves as an initial point which needs to be expanded as more patients are recruited into the study in the future to increase the power of detecting genomic regions that are associated with the differential MS presentations.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Hispanic ethnicity in multiple sclerosis

PDF

Medical comorbidities and multiple sclerosis in the Hispanic population: linking healthcare disparities

PDF

Native American ancestry among Hispanic Whites is associated with higher risk of childhood obesity: a longitudinal analysis of Children’s Health Study data

PDF

Pharmacogenetic association studies and the impact of population substructure in the women's interagency HIV study

PDF

Bayesian model averaging methods for gene-environment interactions and admixture mapping

PDF

Bayesian hierarchical models in genetic association studies

PDF

The association between sun exposure and multiple sclerosis

PDF

Extending genome-wide association study methods in African American data

PDF

Characterization and discovery of genetic associations: multiethnic fine-mapping and incorporation of functional information

PDF

Disparities in exposure to traffic-related pollution sources by self-identified and ancestral Hispanic descent in participants of the USC Children’s Health Study

PDF

Association testing of polymorphisms in the vitamin D metabolism and signaling pathway with multiple sclerosis across multiple populatuions

PDF

Environmental risk factors of Multiple Sclerosis: a twin study

PDF

Two-step testing approaches for detecting quantitative trait gene-environment interactions in a genome-wide association study

PDF

Genomic risk factors associated with Ewing Sarcoma susceptibility

PDF

The effect of renal function on toxicity of E7389 (eribulin) among patients with bladder cancer

PDF

The impact of global and local Polynesian genetic ancestry on complex traits in Native Hawaiians

PDF

The role of genetic ancestry in estimation of the risk of age-related degeneration (AMD) in the Los Angeles Latino population

PDF

Prediction and feature selection with regularized regression in integrative genomics

PDF

Population substructure and its impact on genome-wide association studies with admixed populations

PDF

Hierarchical regularized regression for incorporation of external data in high-dimensional models

Asset Metadata

Creator Wang, Shengzhi (author)

Core Title A genome wide association study of multiple sclerosis (MS) in Hispanics

School Keck School of Medicine

Degree Master of Science

Degree Program Biostatistics

Publication Date 07/16/2013

Defense Date 06/21/2013

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

Tag genome wide association study,Hispanics,logistic regression,multiple sclerosis,OAI-PMH Harvest

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Conti, David V. (committee chair), Amezcua, Lilyana (committee member), Cozen, Wendy (committee member), Gauderman, William James (committee member)

Creator Email shengzhi.wang@gmail.com,wszcas@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-290074

Unique identifier UC11287941

Identifier etd-WangShengz-1783.pdf (filename),usctheses-c3-290074 (legacy record id)

Legacy Identifier etd-WangShengz-1783.pdf

Dmrecord 290074

Document Type Thesis

Rights Wang, Shengzhi

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