Transferability of Type 2 Diabetes Implicated Loci in
Multi-Ethnic Cohorts from Southeast Asia
Xueling Sim
1
, Rick Twee-Hee Ong
1,2,3
, Chen Suo
1
, Wan-Ting Tay
4
, Jianjun Liu
3
, Daniel Peng-Keat Ng
5
,
Michael Boehnke
6
, Kee-Seng Chia
1,5
, Tien-Yin Wong
4,5,7,8
, Mark Seielstad
3¤
, Yik-Ying Teo
1,2,3,5,9
*,
E-Shyong Tai
5,10,11
*
1Centre for Molecular Epidemiology, National University of Singapore, Singapore, Singapore, 2NUS Graduate School for Integrative Science and Engineering, National
University of Singapore, Singapore, Singapore, 3Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore, 4Singapore Eye
Research Institute, Singapore National Eye Centre, Singapore, Singapore, 5Department of Epidemiology and Public Health, National University of Singapore, Singapore,
Singapore, 6Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America,
7Department of Ophthalmology, National University of Singapore, Singapore, Singapore, 8Centre for Eye Research Australia, University of Melbourne, Melbourne,
Australia, 9Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore, 10 Department of Medicine, National University of
Singapore, Singapore, Singapore, 11 Duke-National University of Singapore Graduate Medical School, Singapore, Singapore
Abstract
Recent large genome-wide association studies (GWAS) have identified multiple loci which harbor genetic variants
associated with type 2 diabetes mellitus (T2D), many of which encode proteins not previously suspected to be involved in
the pathogenesis of T2D. Most GWAS for T2D have focused on populations of European descent, and GWAS conducted in
other populations with different ancestry offer a unique opportunity to study the genetic architecture of T2D. We
performed genome-wide association scans for T2D in 3,955 Chinese (2,010 cases, 1,945 controls), 2,034 Malays (794 cases,
1,240 controls), and 2,146 Asian Indians (977 cases, 1,169 controls). In addition to the search for novel variants implicated in
T2D, these multi-ethnic cohorts serve to assess the transferability and relevance of the previous findings from European
descent populations in the three major ethnic populations of Asia, comprising half of the world’s population. Of the SNPs
associated with T2D in previous GWAS, only variants at CDKAL1 and HHEX/IDE/KIF11 showed the strongest association with
T2D in the meta-analysis including all three ethnic groups. However, consistent direction of effect was observed for many of
the other SNPs in our study and in those carried out in European populations. Close examination of the associations at both
the CDKAL1 and HHEX/IDE/KIF11 loci provided some evidence of locus and allelic heterogeneity in relation to the
associations with T2D. We also detected variation in linkage disequilibrium between populations for most of these loci that
have been previously identified. These factors, combined with limited statistical power, may contribute to the failure to
detect associations across populations of diverse ethnicity. These findings highlight the value of surveying across diverse
racial/ethnic groups towards the fine-mapping efforts for the casual variants and also of the search for variants, which may
be population-specific.
Citation: Sim X, Ong RT-H, Suo C, Tay W-T, Liu J, et al. (2011) Transferability of Type 2 Diabetes Implicated Loci in Multi-Ethnic Cohorts from Southeast Asia. PLoS
Genet 7(4): e1001363. doi:10.1371/journal.pgen.1001363
Editor: Greg Gibson, Georgia Institute of Technology, United States of America
Received July 26, 2010; Accepted March 4, 2011; Published April 7, 2011
Copyright: ß2011 Sim et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: We acknowledge the support of the Yong Loo Lin School of Medicine from the National University of Singapore, the Singapore National Medical
Research Council, and the Singapore Biomedical Research Council. The Singapore Prospective Study Program (SP2) was funded through grants from the
Biomedical Research Council of Singapore (BMRC 05/1/36/19/413 and 03/1/27/18/216) and the National Medical Research Council of Singapore (NMRC/1174/
2008). The Singapore Malay Eye Study (SiMES) was funded by the National Medical Research Council (NMRC 0796/2003 and NMRC/STaR/0003/2008) and
Biomedical Research Council (BMRC, 09/1/35/19/616). The Singapore Indian Eye Study (SINDI) was funded by grants from Biomedical Research Council of
Singapore (BMRC 09/1/35/19/616 and BMRC 08/1/35/19/550) and National Medical Research Council of Singapore (NMRC/STaR/0003/2008). Y-YT acknowledges
support from the Singapore National Research Foundation, NRF-RF-2010-05. E-ST also receives additional support from the National Medical Research Council
through a clinician scientist award. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: eshyong@pacific.net.sg (E-ST); statyy@nus.edu.sg (Y-YT)
¤ Current address: Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
Introduction
Type 2 diabetes mellitus (T2D) is a major chronic disease
worldwide, affecting more than 300 million people. The greatest
increase in the prevalence of T2D in the coming years is likely to
be in Asia, home to half of the world’s population with 3 billion
people [1–2]. It is estimated that in China alone, there are 100
million people with T2D [3].
T2D has been one of the major human diseases to benefit from
the advent of large-scale genetic studies that survey the entire
genomic landscape for variants correlating with disease onset or
severity. These genome-wide association studies (GWAS) have
identified a number of novel loci harboring common variants that
are associated with an increased risk of T2D [4–11], adding to the
loci previously identified by candidate gene studies (PPARG [12],
KCNJ11 [13–14], WFS1 [15–16]) and linkage studies TCF7L2
[17–19]). There have been fewer GWAS of T2D performed in non-
European populations, namely in the Japanese [20], Han Chinese
in Taiwan [21] and South Asians in the United Kingdom [22].
These latter studies, however, are important for the following
PLoS Genetics | www.plosgenetics.org 1 April 2011 | Volume 7 | Issue 4 | e1001363
reasons: (i) the frequencies of genuinely implicated variants may
differ across ethnic groups, and these studies may discover novel
regions that have been overlooked in previous studies due to lower
risk allele frequencies in populations of European ancestry; (ii)
ethnicity may modulate the associations between common variants
and T2D, such that the same locus may exert different effects in
other populations due to differences in genetic background or
environmental exposures; (iii) the pathogenesis of T2D may be
heterogeneous across populations, resulting in differing importance
of genetic susceptibility to a particular loci. Examples of novel
findings emerging from the Asian GWAS include variants in
KCNQ1 [8–9], PTPRD [21], SRR [21] and PEPD [20]. Of these,
only the association at KCNQ1 has been extensively replicated
[11,20–21,23–30]. The discovery of these genetic loci for T2D is
exciting, since it heralds the prospect of identifying novel
therapeutic targets for its treatment and prevention. For example,
PPARG and KCNJ11 both harbor common genetic variants
associated with T2D and are both therapeutic targets for drugs
used to lower blood glucose [12–13].
However, at present, there is limited information on the relevance
of the identified loci across multiple populations, as these discoveries
have primarily been made in populations of European ancestry.
Even if the same locus is causally implicated with T2D onset in
multiple populations, it is unclear whether the genetic effect
estimated from these studies is representative in other non-
European populations. One common strategy of evaluating the
transferability and the genetic effects of the associated loci is to
replicate the index SNPs that have been identified by the genome-
wide surveys. Many of these studies, conducted in individuals of
Han Chinese, Japanese, Asian Indians, exhibited replication for
many of the index SNPs that emerged from the first wave of T2D
GWAS [4–7] with consistent direction of effect [20,23–24,30–45].
However, replication efforts for the more recently identified SNPs
have been less successful [32,46–48]. Failure to replicate the original
associations at the index SNPs in heterogeneous populations does
not necessarily indicate that these loci are not involved in T2D
pathogenesis in these populations. As these SNPs are unlikely to be
the biologically functional polymorphisms but merely in linkage
disequilibrium (LD) with the underlying causal variants, the index
SNPs identified from European populations may be poorly
correlated with the causal variants in other populations such that
studies aiming to reproduce only the original associations are under-
powered and thus are unable to observe any statistical evidence at
these SNPs. GWAS carried out in non-European populations can
also address the two issues of transferability and consistency of the
genetic etiology between populations with differing ancestry.
The multi-ethnic demography of Singapore, consisting mainly
of Chinese, Malays and Asian Indians, possesses vast potential for
investigating the genetic etiology of T2D. Importantly, these
populations broadly capture the genetic diversity across Asia,
home to almost three billion people, and especially in a large
proportion of the populations that are likely to experience the
greatest increase in the burden of T2D in the near future [1].
Here we describe three separate genome-wide surveys of T2D in
the Chinese, Malay and Asian Indian populations assaying a total of
10,718 individuals, yielding a post-QC sample size of 3,781 cases
and 4,354 controls. With this resource, we set out: (i) to identify any
novel genetic variants that are associated with T2D in these ethnic
groups; (ii) to examine the genetic architecture of the previously
established T2D loci in the heterogeneous settings offered by the
three ethnic groups; and (iii) to estimate the magnitude of the effects
at variants that replicate in our populations.
Results
We performed three population-based case-control GWAS in
T2D in 10,718 individuals of Chinese, Malay and Asian Indian
ethnicities living in Singapore. A total of 3,955 Chinese (2,010
cases, 1,945 controls), 2,034 Malays (794 cases, 1,240 controls) and
2,146 Asian Indians (977 cases, 1,169 controls) remained after
sample quality control. The Chinese samples were genotyped on a
combination of Illumina610 and Illumina1M arrays, while the
Malays and Indians were entirely genotyped on the Illumina610
array (Figure S1). In general, cases were older than controls and
the Malays and Indians were more obese than Chinese irrespective
of case-control status. Amongst the Chinese, there were more men
genotyped on the Illumina1M array than on Illumina610 (Table 1).
The genomic inflation factors were 1.049 for Chinese on the
Illumina610 array, 1.058 for Chinese on the Illumina1M array
and 1.017 for the combined Chinese. The inflation factors were
1.035 and 1.030 for the Malays and Indians respectively, with an
overall genomic factor 1.007 for all populations combined.
Top regions emerging from genome-wide scans
The Indian GWAS identified a SNP (rs1048886) intronic to a
hypothetical protein (C6orf57) on chromosome 6 which exhibited
genome-wide significance (OR = 1.54, 95% CI = 1.32 – 1.80,
P= 3.48610
28
) although this was not statistically significant in the
Chinese (P= 0.995) or the Malays (P= 8.23610
22
). No SNP
achieved genome-wide significance in the individual Chinese and
Malays genome scans, or in the meta-analysis across all three
populations. SNPs that exhibited suggestive evidence of associa-
tion with T2D at P-value,10
25
in each ethnic group are shown in
Table S1. SNPs at 6 loci showed suggestive evidence of association
with T2D at P-value,10
25
after meta-analysis of the three ethnic
groups (Table 2 and Table S2). These include HMG20A,ZPLD1
and HUNK which showed no evidence of heterogeneity from I
2
statistics; C6orf57 which was driven primarily by the Indians; and
the well-established gene regions at CDKAL1 and KIF11 (Table 2,
Table S2 and Figure S5). More details are provided in Text S1.
Evaluating transferability of known loci across
populations
In assessing whether there was evidence in our GWAS scans to
support the associations established in previous studies on T2D
Author Summary
Type 2 diabetes mellitus (T2D) is a chronic disease which can
lead to complications such as heart disease, stroke, hyperten-
sion, blindness due to diabetic retinopathy, amputations from
peripheral vascular diseases, and kidney disease from diabetic
nephropathy. The increasing prevalence and complications of
T2D are likely to increase the health and economic burden of
individuals, families, health systems, and countries. Our study
carried out in three major Asian ethnic groups (Chinese,
Malays, and Indians) in Singapore suggests that the findings of
studies carried out in populations of European ancestry (which
represents most studies to date) may be relevant to popu-
lations in Asia. However, our study also raises the possibility
that different genes, and within the genes different variants,
may confer susceptibility to T2D in these populations. These
findings are particularly relevant in Asia, where the greatest
growth of T2D is expected in the coming years, and emphasize
the importance of studying diverse populations when trying
to localize the regions of the genome associated with T2D. In
addition, we may need to consider novel methods for
combining data across populations.
Type 2 Diabetes in Southeast Asia
PLoS Genetics | www.plosgenetics.org 2 April 2011 | Volume 7 | Issue 4 | e1001363
onset, we defined statistical significance as P-value,0.05. Even
with the reduced stringency, we noticed that only the SNPs in
CDKAL1 and HHEX/IDE/KIF11 replicated in the meta-analysis
across all three populations (Table 2). While the reported index
SNPs at KCNJ11 replicated in Chinese and Malays (Table S3), the
majority of the established associations in other genes were only
detected in one population, including TCF2/HNF1B,IGF2BP2,
CENTD2,C2CD4A-C2CD4B and FTO in the Chinese; KCNQ1 and
PRC1 in the Malays; and TCF7L2 and BCL11A in the Indians
(Table S3). In addition, the meta-analysis also supported the
reported associations at IRS1 and SLC30A8 despite none of the
SNPs achieving statistical significance in the single-population
analyses (Table S3).
Our single-population analyses and meta-analysis failed to
detect associations at several loci, including PPARG,WFS1 and
several regions that were identified through T2D GWAS in
European GWAS. This may be attributed to the lower statistical
power in our three GWAS as many of these regions possess only
modest effects on T2D pathogenesis and have only been
successfully identified in large-scale meta-analyses involving tens
of thousands of samples. To detect ORs exhibited in the European
ranging from 1.10 to 1.25 [11], our studies are not sufficiently
Table 1. Summary characteristics of cases and controls stratified by their ethnic groups and genotyping arrays.
Characteristics Chinese Malay
a
Asian Indian
a
Illumina610quad Illumina1Mduov3 Illumina610quad Illumina610quad
Cases Controls Cases Controls Cases Controls Cases Controls
N 1,082 1,006 928 939 794 1240 977 1169
Sex Ratio M/F (%) 402/680
(37.15/62.85)
217/789
(21.57/78.43)
602/326
(64.87/35.13)
599/340
(63.79/36.21)
405/389
(51.01/48.99)
645/595
(52.02/47.98)
531/466
(54.35/45.65)
566/603
(48.42/51.58)
Age
b
(yr) 65.07 (9.70) 47.69 (11.07) 63.67 (10.81) 46.74 (10.23) 62.27 (9.90) 56.89 (11.39) 60.71 (9.85) 55.73 (9.72)
Age at diagnosis
(yr)
55.65 (11.96) -- 52.15 (14.40) -- 54.35 (11.19) -- 51.35 (10.63) --
Fasting glucose
(mmol/L)
-- 4.67 (0.45) -- 4.73 (0.46) -- -- -- --
HbA1C -- -- -- -- 8.05 (1.84) 5.60 (0.30) 7.56 (1.52) 5.55 (0.28)
BMI
1
(kg/m
2
) 25.27 (3.92) 22.30 (3.67) 25.42 (3.81) 22.84 (3.41) 27.82 (4.88) 25.13 (4.82) 27.06 (5.10) 25.33 (4.40)
aFor Malay and Asian Indian samples, diabetic samples are defined as either with history of diabetes or hba1c $6.5% while controls are defined as no history of
diabetes and hba1c,6%.
bMean(Standard Error).
doi:10.1371/journal.pgen.1001363.t001
Table 2. Statistical evidence of the top regions (defined as P,10
25
) that emerged from the fixed-effects meta-analysis of the
GWAS results across Chinese, Malays, and Asian Indians, with information on whether each SNP is a directly observed genotype (1)
or is imputed (0).
SNP Chr Pos (bp) Nearest gene
Risk
allele
Reference
allele
Genotyped
(1) or
imputed (0)
a
N
Chinese
+
Malays
+
Indians (3781 cases/4354
controls)
Risk allele
frequency
b
Fixed effects
OR (95% CI)
Fixed effects
P-
value
I
2
(%)
rs7119 15 75564687 HMG20A T C 1111 8135 0.188 1.24
(1.14–1.34)
5.24610
27
0
rs2063640 3 103685735 ZPLD1 A C 1111 8131 0.167 1.23
(1.13–1.34)
3.47610
26
0
rs2833610 21 32307057 HUNK A G 1111 8127 0.567 1.17
(1.09–1.24)
3.90610
26
0
rs6583826 10 94337810 KIF11 G A 1111 8134 0.259 1.18
(1.10–1.27)
7.38610
26
0
rs1048886 6 71345910 C6orf57 G A 1111 8135 0.110 1.26
(1.14–1.39)
9.70610
26
85.40
rs9295474 6 20760696 CKDAL1 G C 0000 8079 0.357 1.16
(1.09–1.24)
8.59610
26
33.46
Combined minor allele frequencies of each lead SNP is at least 5%. The I
2
statistic refers to the test of heterogeneity of the observed odds ratios for the risk allele in the
three populations, and is expressed here as a percentage.
aThis column shows whether each SNP is directly genotyped (1) or imputed (0) in each of the case control studies shown in Table 1. Each digit represents a case control
study in the following order from left to right: Chinese on Illumina610, Chinese on Illumina1M, Malays on Illumina610 and Indians on Illumina610.
bRisk allele frequencies are sample size weighted frequencies across the three ethnic groups.
doi:10.1371/journal.pgen.1001363.t002
Type 2 Diabetes in Southeast Asia
PLoS Genetics | www.plosgenetics.org 3 April 2011 | Volume 7 | Issue 4 | e1001363
powered even at risk allele frequencies of 0.30 and higher (Figure
S4). Thus, in evaluating the relevance of these established findings
in our populations, we took a number of approaches. Firstly, we
performed a binomial test on the number of loci expected to have
P-value less than 0.05 which showed evidence of an over-
representation of the European established loci in the Chinese
and Indians and combined meta-analysis with one sided P-values
given by 2.85610
24
for Chinese, 1.05610
201
for Malays,
2.22610
202
for Indians and 3.31610
207
for Meta-analysis. Next,
we observed that most of the associations observed in these genes
(with the exception of PEPD where the risk allele initially
discovered in a Japanese study [20] conferred a protective effect
in our populations instead) trended in the same direction, i.e. the
same allele conferred risk in all three populations as the published
results, with a binomial test for consistency of direction giving the
following p-values: Chinese: 5.92610
23
; Malay: 9.30610
22
;
Indian: 4.34610
23
; Meta-analysis: 1.49610
23
). Finally, at lead
SNPs reported in T2D studies in European populations, we also
compared the detected effect sizes of the risk alleles in our study
and those in European populations. Whenever possible, we used
the effect sizes from stage 2 of DIAGRAM+[11]. This approach
allows us to test the hypothesis that the observed effect sizes across
multiple populations should be comparable at a SNP that is
genuinely associated with T2D in these populations despite limited
power. There was a greater proportion of SNPs displaying
attenuated odds ratios in our populations when compared to the
effect sizes at the lead SNPs from DIAGRAM+consortium [11]
(with two-sided P-values given by Chinese: 5.22610
22
; Malay:
3.47610
22
; Indian: 8.55610
24
; Meta-analysis: 7.20610
23
and
Figure 1).
The availability of three GWAS scans across three genetically
heterogeneous populations offers a unique opportunity to explore
the genetic architecture underlying the two loci CDKAL1 and
HHEX/IDE/KIF11 that showed the strongest evidence of
association with T2D in more than 1 population and in our
meta-analysis. We observed a cluster of SNPs displaying evidence
of association P,0.01 at the CDKAL1 locus in both the Chinese
(Figure 2A) and Indians (Figure 2C) scans. However, there was no
evidence of T2D association in the Malays (Figure 2B). The top
signal emerging from the meta-analysis (rs9295474, meta-analysis
P= 8.59610
26
) was located at 20.761Mb on chromosome 6, and
the risk allele frequency was 38.2%, 38.4% and 28.4% in the
Chinese, Malay and Indian populations, respectively. We
subsequently performed an analysis at this region conditioned on
the top SNP (rs7754840) that emerged from T2D studies in
populations of European descent. This conditional analysis
effectively removed any further evidence of T2D association at
this locus in the Chinese samples (Figure 2D), indicating that the
observed associations in the Chinese might be attributed to the
same functional polymorphism that is responsible for the
association signals in Europeans. Intriguingly, the conditional
analysis only partially attenuated the signal in Indians, and instead
Figure 1. Bivariate plots comparing odds ratios observed in each of the ethnic groups with odd ratios established in populations of
European ancestry. (A) Chinese, (B) Malays, (C) Indians, (D) Combined meta-analysis. Each SNP is plotted with a colour that indicates if the SNP was
identified through candidate gene studies (black) or linkage studies (red) or candidate-pathway analysis (green) or T2D genome-wide scans (blue).
doi:10.1371/journal.pgen.1001363.g001
Type 2 Diabetes in Southeast Asia
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Figure 2. Regional association plots of the index SNP in
CDKAL1
.For each ethnic group, the univariate analysis regional plot, A) Chinese B)
Malays C) Indians, is shown together with analysis conditioned on established index SNP rs7754840, D) Chinese E) Malays F) Indians, in populations of
Type 2 Diabetes in Southeast Asia
PLoS Genetics | www.plosgenetics.org 5 April 2011 | Volume 7 | Issue 4 | e1001363
appeared to strengthen the association evidence in the upstream
region of CDKAL1. This region was found to exhibit evidence of
regional LD variation between the Indians and CEU (varLD
monte carlo P= 1.16610
22
) (Figure 2F and Table S4), suggesting
the possibility of different biological mechanisms or causal signals.
The regional associations around the HHEX,IDE and KIF11
genes on chromosome 10 appeared to be considerably different
across the three populations, with suggestive statistical evidence
spanning all three genes in the Chinese (Figure 3A); marginal
evidence mainly around KIF11 in the Malays (Figure 3B); and
marginal evidence around HHEX in the Indians (Figure 3C). The
top SNP that emerged from our meta-analysis (rs6583826) is
located 115kb upstream of the SNP identified in European
populations (rs1111875), suggesting that either (i) this represents
only the combined signal at KIF11 across three populations and
may not be related to the associations observed at HHEX and IDE;
or (ii) the LD in this region is substantially different between our
populations and the European populations. To investigate the first
hypothesis, we performed a conditional analysis with respect to the
top SNP (rs6583826) from our meta-analysis. We observed that
the association signals in the Indians were attenuated significantly
(Figure 3F). This was not the case in the Chinese and Malays. In
particular, SNPs located in the IDE gene (Figure 3D–3E) were not
affected by conditioning on the top SNP. This suggests that there
may be different variants associated with T2D across these three
loci in different ethnic groups. We next performed a formal
assessment of the extent of LD variation between reference
populations for Europeans and the three ethnic groups in
Singapore (Materials and Methods) in a 400 kb region centered
on rs1111875 (Table S4). We found evidence of LD variation
between Europeans and both Singapore Chinese (varLD monte
carlo P= 3.00610
24
) and Singapore Malays (P= 1.21610
22
), but
not between the Europeans and Singapore Indians (P= 0.24). We
also performed conditional analysis with respect to the European
index SNP rs1111875 (Figure 3G–3I) and rs5015480 (Figure S6).
There were no perceptible changes in the regional signals for the
Chinese and Malays (Figure 3G and 3H), although the association
signals in the Indians appeared to have attenuated, consistent with
the lack of LD variation between Europeans and Indians
(Figure 3I).
Discussion
We have performed three genome-wide case-control surveys of
T2D in Chinese, Malays and Asian Indians in Singapore. To the
best of our knowledge, this is the first GWAS to be performed in
Malays, which forms the largest population group in Southeast
Asia, comprising more than 300 million people. A meta-analysis of
these three studies showed strong associations at the CDKAL1 and
HHEX/IDE/KIF11 loci that have consistently emerged in T2D
studies in multiple global populations. More importantly, the
availability of genome-wide data across these three major
population groups allowed us to evaluate the transferability and
relevance of the previously established genomic regions that are
involved in the pathogenesis of T2D. In addition, we investigated
the genetic architecture at CDKAL1 and HHEX/IDE/KIF11,
showing evidence of population-specific effects, allelic heteroge-
neity and LD variations at these loci across our three populations.
Of note, our study failed to detect statistically significant
associations for a number of the variants that had been discovered
and validated in previous studies, mostly in European populations.
One potential reason relates to the power of our studies to
detect these associations. Several things contribute to the limited
power in our studies. Firstly, the sample sizes in our individual
studies were relatively small, especially compared to the European
consortia. Secondly, at several of these loci, the allele frequencies
were lower in our studies than in European populations. The
impact of low allele frequencies is exemplified for variants at the
TCF7L2 locus, which exhibited the greatest effects on T2D risk in
European populations. The frequencies of the risk allele at
TCF7L2 (rs7903146) were 0.023 in the Chinese and 0.043 in the
Malays, compared to 0.285 in the Indians (which is similar to that
observed in European populations). While the direction and
magnitude of the effects sizes were similar in our populations and
previous reports [24,49–50], statistical significance was only
observed in the Indian GWAS. This impact of low allele frequency
is consistent with the finding in a Japanese population that found
associations with TCF7L2 variants with T2D in the same direction
in European populations, when the sample size was sufficiently
large [20]. Based on the allele frequencies observed in our studies
and the sample sizes available, we had at least 80% power to
detect the associations for only 8 (TCF7L2,HNF1B,UBE2E2,
CDKAL1,SLC30A8,HHEX,KCNQ1 and SRR) of the 36 variants in
the Chinese (the ethnic group with the largest sample size), 1 in the
Malays (SRR) and 2 in the Indians (TCF7L2 and SRR). A third
reason for the lack of power in our studies is that the effect sizes in
our study were generally smaller than those observed in the initial
studies that identified these variants. It is possible that the effect
estimates of the initial discoveries were over-estimates (winner’s
curse). Other potential reasons for this include allelic heterogeneity
or LD variation between populations which are discussed in the
following paragraphs. For several of the variants where we had at
least 80% power to detect an association at a= 0.05, the effect
sizes seen in our studies were smaller than those reported in the
initial European populations. These include variants at the
TCF7L2,SLC30A8 and KCNQ1 loci. It is noteworthy that the
variants for which we were able to detect an association in our
study were mostly discovered in the earlier wave of genome-wide
scans for T2D (including CDKAL1,HHEX/IDE/KIF11,IGFBP2,
SLC30A8 and FTO) [5–7,51]. These early studies had smaller
sample sizes and thus, variants identified generally had larger OR
than those that emerged when large number of individuals were
analyzed jointly (as is typical of meta-analyses of populations with
European ancestry).
Despite the limited power of our study to detect statistically
significant associations, our study showed that, of the variants
identified in European GWAS for T2D, there were more
statistically significant associations detected in our study than
could be expected by chance, and that the direction of association
was consistent in our study and in studies conducted in European
populations. This suggests that for many of the variants identified
in European populations, the findings are likely to be relevant in
Asian populations.
The second reason for the failure to detect these associations may
be due to the presence of allelic heterogeneity, where different and/
or multiple causal variants may be responsible for the association
Caucasian ancestry. In each panel, the index SNP is represented with a purple diamond and surrounding SNPs coloured based on their r
2
with the
index SNP. Estimated recombination rates reflect the local LD structure in the 500kb buffer around the index SNP and the proxies are plotted on
Hapmap values from the Hapmap JPT+CHB. Data for gene annotations are obtained from the RefSeq track of the UCSC Gene Browser (See
LocusZoom http://csg.sph.umich.edu/locuszoom/ for more details).
doi:10.1371/journal.pgen.1001363.g002
Type 2 Diabetes in Southeast Asia
PLoS Genetics | www.plosgenetics.org 6 April 2011 | Volume 7 | Issue 4 | e1001363
signals observed at a locus in different populations. There is
suggestive evidence from our study that CDKAL1 may harbour at
least two separate functional polymorphisms in the Indians where
adjustment for the lead SNP attenuated a set of signals but boosted
the evidence in an upstream intronic region. Similarly, at the
HHEX/IDE/KIF11 locus, there is evidence to suggest that the
associations seen in Chinese (and to a certain extent, in Malays)
originate from at least two separate functional polymorphisms,
where the lead SNPs from our study (rs6583826) and from European
studies (rs1111875) do not entirely account for the association signals
observed at this locus. This finding is consistent with evidence that
recently emerged from a large meta-analysis in populations of
European ancestry that also identified more than 1 independent
signal at several loci that were associated with T2D [11].
Figure 3. Regional association plots of the index SNP in
HHEXX/IDE/KIF11
.For each ethnic group, the univariate analysis regional plot, A)
Chinese B) Malays C) Indians, is shown together with analysis conditioned on the index SNP rs6583826, D) Chinese E) Malays, F) Indians, found in
meta-analysis across three ethnic groups and established index SNP rs1111875, G) Chinese H) Malays I) Indians, in populations of European ancestry.
doi:10.1371/journal.pgen.1001363.g003
Type 2 Diabetes in Southeast Asia
PLoS Genetics | www.plosgenetics.org 7 April 2011 | Volume 7 | Issue 4 | e1001363
Finally, even when the same causal variant at a locus is present
in different populations, heterogeneous patterns of LD between
the causal variant and the genotyped SNPs may result in different
variants emerging from GWAS in these populations. This can
critically confound meta-analyses, which fundamentally assume
that the same index SNP is implicated across multiple populations.
Our analyses using varLD show that, for most T2D associated loci,
significant LD variation exists between all pairs of ethnic groups,
except Chinese and Malays. Unfortunately, despite this evidence
of LD variation, our power calculations suggest that our studies are
not adequately powered to detect these associations in the
individual ethnic groups. As such, we are not able to examine
heterogeneity between ethnic groups for most of these loci (apart
from CDKAL1 and HHEX/IDE/KIF11). In such settings, statistical
methods for assessing regional evidence across multiple studies
without relying on observing phenotype associations at the same
index SNP may be increasingly relevant and important.
One important caveat of our study is the age profile of our
control samples, which are generally younger than the cases,
especially in the Chinese cohort. As some of the control individuals
may subsequently develop T2D, the use of young controls likely
has the effect of reducing statistical power and under-estimating
the effect sizes. However, the signals of association at CDKAL1 are
useful for calibrating the extent of this, and the effect sizes in our
Chinese and Indian populations were similar or even larger than
those reported in European studies despite comparable frequen-
cies of the implicated alleles.
The advent of GWAS allows an unbiased survey to be made
across the entire human genome for novel genetic loci that are
causally responsible for T2D onset. Presently, it is difficult to assess
the implications of these genetic discoveries to global public health,
primarily because these findings are typically identifying surrogate
markers that likely do not have any bearing on genetic etiology of
T2D. Fine-mapping the polymorphisms that are biologically
responsible will present a significant advancement in addressing
the relevance of any genetic discoveries in other populations.
However, genetic and/or environmental modifiers resulting in
population-specific effects and allelic heterogeneity can continue to
confound the situation, even when the causal variants have been
identified. This may have implications for subsequent studies that
attempt to uncover the genetic architecture of T2D. It highlights
the importance of conducting genetic surveys in T2D across
multiple populations, particularly those that are likely to
experience the greatest increase in T2D burden. This is even
more pertinent as interest in medical genetics gradually shifts
towards searching for rare variants, which are exactly those that
are even more likely to be exclusive to specific populations.
Materials and Methods
Ethics statement
Ethics approval has been granted for the sample recruitment by
the Singapore General Hospital Ethics Committee and the
Singapore Eye Research Institute Ethics Committee. In addition,
the genetic analysis was approved by the National University of
Singapore Institutional Review Board (Approval Certificate
NUS465).
Study populations
The Singapore Diabetes Cohort Study (SDCS) is a research
initiative led by the National University of Singapore together with
the National Healthcare Group Polyclinics, National University
Hospital Singapore and Tan Tock Seng Hospital. Its primary aim
is to identify genetic and environmental risk factors for diabetic
complications especially diabetic nephropathy, and to develop
novel biomarkers for tracking disease progression. Since 2004, all
type 2 diabetes patients seen at the polyclinics and hospitals were
invited to be part of the cohort. Questionnaire data as well as
clinical data of consenting patients were obtained together with
bio-specimens such as blood and urine archived at 280uC. The
participation response rate is excellent at more than 90% and to
date, there are more than 5,000 patients in SDCS. For the purpose
of this study, 2,202 Chinese subjects were available for genome
wide analysis.
The Singapore Prospective Study Program (SP2) includes 6,968
participants from one of four previous cross-sectional studies:
Thyroid and Heart Study 1982–1984 [52], National Health
Survey 1992 [53], National University of Singapore Heart Study
1993–1995 [54] or National Health Survey 1998 [55]. All studies
involved a random sample of individuals from the Singapore
population, aged 24 to 95 years, with disproportionate sampling
stratified by ethnicity to increase the number of minority ethnic
groups (Malays and Asian Indians). From 2003–2007, 10,747
participants were invited to participate by linking their unique
national identification numbers with national registries, where
7,742 attended the interview and of these 7,742 participants, 5,163
attended the clinical examination. Detailed population selection
and methodology have been previously reported. A total of 5,499
Chinese, 1,405 Malays and 1,138 Asian-Indians were available at
the time of the study and only the Chinese were used for this study.
The Singapore Malay Eye Study (SiMES) is a population-based,
cross-sectional study of Malay adults (N = 3,280), aged 40 – 80
years living in Singapore. Of the 4,168 eligible participants invited,
3,280 participated in the study with a 78.7% response rate. Briefly,
age-stratified random sampling of all Malay adults aged from 40–
80 years residing in 15 residential districts in the southwestern part
of Singapore was performed. Details of the study participants and
methods have been published previously [56].
The Singapore Indian Eye Study (SINDI) is a population-based,
cross-sectional study of Asian Indian adults (N = 3,400), aged 40–
80+years residing in the South-Western part of Singapore, as part
of the Singapore Indian Chinese Cohort Eye Study. Age stratified
random sampling was used to select 6,350 eligible participants, of
which 3,400 participated in the study (75.6% response rate).
Detailed methodology has been published [57].
Chinese cases included individuals with diagnosis of T2D from
SDCS while Chinese controls were individuals with no prior
history of diabetes and had a fasting glucose level of not more than
6.0 mmol/L selected from SP2, giving a total of 2,010 cases from
SDCS and 1,945 controls from SP2 post genotype QC. In both
SiMES and SINDI, cases and controls were selected from the
population based cross sectional studies where diabetic cases were
defined as having either a history of diabetes or had HbA1c level
greater than or equal to 6.5%. Controls had no history of diabetes
and HbA1c level less than 6% [58]. This yielded 794 Malay
diabetic cases with 1,240 controls and 977 Indian diabetic cases
with 1,169 controls.
Genotyping
4,693 blood-derived samples, 2,210 cases from SDCS and 2,483
controls from SP2 study, were genotyped using Illumina
BeadStation, Illumina HumanHap 610 Quad, and 1Mduov3
Beadchips (http://www.illumina.com/), with 16 samples (8
random cases and 8 random controls) genotyped on both
Beadchips. The mean SNP concordance rate of 99.9% between
chips for the post-QC duplicated samples was computed based on
531,805 post-QC common SNPs between chips. 2,662 samples
were genotyped on the 610Quad and 2,031 samples on the
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1Mduov3. For each array in each cohort, a first round of
clustering was performed with the proprietary clustering files from
Illumina (GenCall). Samples achieving a 99% call rate were
subsequently used to generate local clusterfiles (GenTrain) which
were used for a final round of genotype calling. A threshold of 0.15
was implemented on the GenCall score to decide on the
confidence of the assigned genotypes.
For the SiMES study, 3,072 samples from the population based
study were genotyped on the Illumina HumanHap 610Quad. The
same procedure of genotype calling used for the Chinese was
implemented in the Malays.
For the SINDI study, 2,953 samples were genotyped on the
Illumina HumanHap 610Quad and identical genotype calling
procedures were applied.
Quality control (QC)
For each genotyping chip in individual cohorts, QC criteria
included a first round of SNP QC to obtain a pseudo-cleaned set
of genotypes for sample QC. SNPs that had missingness .5% or
gross departure from HWE (P,10
26
) or were monomorphic were
temporarily removed from the data. Samples were then removed
based on the following conditions: sample missingness, excessive
heterozygosity, cryptic relatedness, discordant ethnic membership
and gender discrepancy. Bivariate plots of sample call rates and
heterozygosity, defined as the proportion of heterozygous calls of
all valid autosomal genotypes in an individual, are used to assess
the overall distribution of missingness and heterozygosity across all
the samples. Cryptic relatedness by IBS computation for all
pairwise combinations of samples identified first degree relatives
such as monozygotic twins/duplicates, parent-offspring pairs and
full-sibling pairs and only one sample from each relationship will
be retained for further analysis. Samples with gender discrepancies
between the genetically inferred gender from Beadstudio and
clinical reported gender were removed. Population structure
ascertainment was carried out using principal components analysis
(PCA) [59] with 4 panels from International Hapmap [60] and the
Singapore Genome Variation Project [61] (http://www.nus-cme.
org.sg/SGVP/) which includes 96 Chinese, 89 Malays and 83
Asian Indians from Singapore. We used a thinned set of SNPs
evenly spaced across the genome to reduce LD. The PCA plots are
shown in Figure S2. Individuals who showed discordant ethnic
membership from their self-reported ethnicity were excluded from
the analysis. For the Malays and Indians which showed a
continuous cloud suggesting some degree of admixture, the
principal components were useful for correction of population
structure in association testing (Figure S2F–S2H). A final round of
SNP QC was then applied, removing SNPs that had missingness
.5% or gross departure from HWE (p-value,10
24
) or were
monomorphic. Minor allele frequency threshold is not used. We
visually assess clusterplots for every SNP with an association p-
value,10
24
in either the individual GWAS or the meta-analysis.
The above procedure led to the exclusion of 296 samples
genotyped on the 610Quad and 141 samples genotyped on the
1Mduov3 for the Chinese cohort. Samples were then checked for
cryptic relatedness and gender discrepancies across the two chips
resulting in another 139 samples removed. Among 542,201 post-
QC SNPs on the 610Quad samples and 944,144 post-QC SNPs
on the 1Mduov3 samples, common SNPs across the chips were
also checked for differences in allelic differences separately in the
cases and controls. 97 SNPs showing significant deviation from the
null of no difference in allele frequencies were removed. Lastly,
controls with fasting glucose .6 mmol/L were excluded from the
analysis. In summary, the post-QC dataset consists of 1,082 cases
and 1,006 controls genotyped on the 610Quad and 928 cases and
939 controls on the 1Mduov3. For the Malay study population,
530 samples were excluded due to sample call rates, cryptic
relatedness, issue with population structure ascertainment and
gender discrepancies. In all, 557,824 SNPs for 2,542 samples were
available for analysis after applying the above quality filter
procedures. For the Indian study population, 415 samples were
excluded due to sample call rates, cryptic relatedness, issue with
population structure ascertainment and gender discrepancies.
Finally, 559,119 SNPs in 2,538 samples were available for analysis
after applying the above quality filter procedures (Table S5).
Imputation
The final post-QC set of genotype data was used to impute
SNPs on the International HapMap Phase 2 panels using
IMPUTE v0.5.0 [62] (https://mathgen.stats.ox.ac.uk/impute/
impute.html). Imputation was carried out using the JPT+CHB
panel on build 36 release 22 for the Chinese, while all 4 panels of
Hapmap II were combined as a mixture reference set of the
Malays and Indians. The genotype data were split into chunks of
10 Mb and imputed with a buffer of 250 kb was implemented to
avoid edge effects. The effective population size of the YRI panel
(Ne = 17469) was used when imputing against the combined
reference panel. In addition, the known T2D regions (Table S3)
were imputed with IMPUTE v2.1.2 [63], incorporating popula-
tion-specific genotypes from SGVP. Specifically, for imputing the
Chinese samples, we have used the HapMap JPT+CHB
haplotypes as the base reference and the SGVP Chinese genotypes
as additional unphased reference. For imputing the Malays, we
have used the phased haplotypes from all three population panels
in HapMap2 as base reference, and the SGVP Malay genotypes as
additional unphased reference. For the Indian samples, we have
similarly used all the phased haplotypes in HapMap2, with the
addition of SGVP Indian genotypes as additional unphased
reference. A 5 Mb region was imputed around the lead SNP with
similar buffer size and effective population size as the genome-
wide imputation. Thresholded call rates and the information score
were used as the imputation metrics. For association testing, we
only included imputed SNPs that had a call rate of 95% when
thresholded at 0.90 posterior probability and proper_info .0.5.
Association tests
Logistic regression using the additive mode of inheritance was
performed to test the association between type 2 diabetes and the
SNPs on SNPTESTv1.1.5 [51] (http://www.stats.ox.ac.uk/
˜
marchini/software/gwas/snptest.html). Chromosome X was not
tested for association in each of the case control studies. The first
two principal components from PCA were used as covariates in
the association tests for the Malays to adjust for population
admixture while three principal components were required for the
Indians (Figure S2). Genotype imputation uncertainties were
incorporated in the association analyses with imputed data, using
the -proper option in SNPTEST. For SNPs typed on the
genotyping arrays, the experimentally-determined genotypes are
reported and imputed results are not used in association testing.
For the Chinese cohort, the association tests were carried out by
treating the samples from separate chips as independent studies
and the fixed-effects inverse-variance method of meta-analysis was
used to obtain an overall association result for the Chinese. We
have previously shown that SNPs associated with T2D in
European populations also show similar associations in popula-
tions of Asian ethnicity [24]. As such, to improve the power of our
study for discovery, the results from the combined Chinese analysis
were meta-analysed together with the Malays and Indians using
fixed effects inverse-variance modelling using METAL (http://
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www.sph.umich.edu/csg/abecasis/Metal/index.html). The com-
plete process from QC to association testing is depicted in Figure
S1. For each case control study, the manhattan plots of the
association P-values and bivariate plots of observed –log
10
(P-
values) against the expected –log
10
(P-values) are shown in Figure
S3. In addition, the genotype clustering of observed genotypes
with statistical significance,10
24
in the meta-analysis and top 10
regions of the individual GWAS are visually assessed, and SNPs
with indication of ambiguous genotype calls are subsequently
removed from the analyses. Genomic control was applied to the
individual cohorts and to the combined meta-analysis, by inflating
the standard error of the log-odds with the genomic inflation
factors. A P-value cutoff of 5610
28
was used to declare genome-
wide significance for any novel polymorphism associated with
T2D.
Comparing effect of risk alleles with SNPs reported in
T2D studies of European descent
The direction of effect for each SNP in each ethnic group and
combined meta-analysis were compared with those derived from
populations of European descent in established T2D lead SNPs. A
binomial test under the null hypothesis of probability of
concordance in direction of OR for the same allele at 0.5 was
performed to assess whether the observed concordance was due to
chance. A second binominal test was performed to investigate if
the number of observed nominally significant associated loci would
be expected by chance, under the null of P= 0.05. Lastly, in
comparing the effect sizes of our populations with those derived
from the European genome wide scans, a binomial test was
performed to investigate if the effect sizes observed in our
populations were smaller than those observed in European
populations by chance. For all the above binomial tests, the
meta-analysis results are only considered when information was
available for all four case control studies.
Conditional analysis
Conditional analysis was performed at CDKAL1 and HHEX/
IDE/KIF11 in two ways, (i) by including the genotypes of the index
SNPs as additional covariates to explore additional diabetes-
associated SNPs in the region and (ii) by including the genotypes of
the lead SNP that emerged out of T2D studies in populations of
European descent (rs7754840 for CDKAL1 and rs1111875/
rs5015480 for HHEX/IDE/KIF11) to assess the differences in
the LD between our populations and the European populations.
For (i), the lead observed SNPs with directly observed genotyped,
rs6583826, were used as covariates in the conditional analysis for
HHEX/IDE/KIF11.
Assessing linkage disequilibrium variation at the known
diabetes implicated loci
The varLD algorithm [64–65] was used to assess regional
patterns of LD variation between two populations. We considered
a 400kb region centred on each index SNP and used the targeted
varLD approach to evaluate the statistical significance that the
pattern of correlation between every pair of SNPs in this region is
similar between two populations. Briefly, a symmetric matrix of
the signed r
2
was calculated between all possible pairs of the SNPs
in the region. The extent of LD difference is then given by the
difference in the trace of the eigen-decomposition of the signed r
2
matrix in the two populations. We then generated a Monte Carlo
P-value by resampling from data combined across the two
populations under the null of no differences in regional LD. We
implemented 10,000 iterations for the Monte Carlo procedure.
We compared the European panel (CEU) from Phase 2 of the
International HapMap Project [60] with each of the three
populations from the Singapore Genome Variation Project [61].
Supporting Information
Figure S1 Flowchart summarising the study design and analysis
procedures for each of the three ethnic groups.
Found at: doi:10.1371/journal.pgen.1001363.s001 (0.42 MB TIF)
Figure S2 Principal components analysis (PCA) plots of genetic
diversity for each of the case control study and when superimposed
against the Singapore Genome Variation Project (SGVP)
populations. Each figure represents the genetic diversity across
each ethnic group, with each individual mapped onto a spectrum
of genetic variation represented by the first and second
eigenvectors of the PCA. Individuals from each SGVP population
is represented by a unique colour (Chinese CHS in red, Malays
MAS in green and Indians INS in blue) with cases and controls for
each ethnic group represented by grey and pink respectively. (A)
Chinese Type 2 Diabetes (T2D) case controls with SGVP; (B)
Malay T2D case controls with SGVP; (C and D) Indian T2D case
controls with SGVP and (E) Chinese T2D case controls, showing
first two components. No correction for population structure; (F)
Malay T2D case controls, showing first two principal components
used for population structure correction; (G and H) Indian T2D
case controls, showing first to third components. The first three
principal components were used for population structure correc-
tion.
Found at: doi:10.1371/journal.pgen.1001363.s002 (1.81 MB TIF)
Figure S3 Pairs of Manhattan and PP-plots of genome-wide
association with T2D for each case control study separately and
combined meta-analysis. Grey line on the PP-plots denotes the line
y = x and the upper 95% confidence interval. (A) Chinese
genotyped on the Illumina610 array (B) Chinese genotyped on
the Illumina1M array (C) Chinese combined on meta-analysis (D)
Malays genotyped on the Illumina610 array (E) Indians genotyped
on the Illumina610 array and (F) Combined meta-analysis for all
case control studies.
Found at: doi:10.1371/journal.pgen.1001363.s003 (1.42 MB TIF)
Figure S4 Power curves estimated for each ethnic group, based
on the sample sizes in the studies for odds ratios ranging from 1.0
to 1.5 in steps of 0.01 with allele frequencies of 0.05 and 0.10 in
increments of 0.05.
Found at: doi:10.1371/journal.pgen.1001363.s004 (0.52 MB TIF)
Figure S5 Regional plots of novel loci from meta-analysis
showing P-values,1610
25
at lead SNP, with buffer of 500kb
upstream and downstream of the index SNP, which are genotyped
in all the ethnic groups: (A) Chromosome 15, spanning genes
HMG20A and TSPAN3 (B) Chromosome 3 near ZPLD1 (C)
Chromosome 21 on the gene HUNK and (D) Chromosome 6 near
hypothetical protein C6orf57.
Found at: doi:10.1371/journal.pgen.1001363.s005 (0.92 MB TIF)
Figure S6 Regional association plots of the index SNP in
HHEX/IDE/KIF11 in each ethnic group. For each ethnic group,
the conditional analysis on the index SNP rs5015480 found in
DIAGRAM+are shown in populations our populations. (A)
Chinese (B) Malays (C) Indians.
Found at: doi:10.1371/journal.pgen.1001363.s006 (0.68 MB TIF)
Table S1 Statistical evidence of the top regions (defined as P-
value,10
25
) that emerged from the single-population GWAS for
Chinese, Malays and Asian Indians. For each region, the index
SNP with the strongest statistical evidence is reported along with
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PLoS Genetics | www.plosgenetics.org 10 April 2011 | Volume 7 | Issue 4 | e1001363
the number of SNPs within 500kb exhibiting evidence of P-
value,10
24
. Genomic control (GC) inflation factors for each
population are also reported.
Found at: doi:10.1371/journal.pgen.1001363.s007 (0.06 MB
DOC)
Table S2 Statistical evidence of the top regions (defined as P-
value,10
25
) that emerged from the fixed-effects meta-analysis of
the GWAS results across Chinese, Malays and Asian Indians
presented for each ethnic group.
Found at: doi:10.1371/journal.pgen.1001363.s008 (0.05 MB
DOC)
Table S3 Known Type 2 Diabetes susceptibility loci tested for
replication in the three Singapore populations separately and
combined meta-analysis. Published ORs are obtained from
European populations and correspond to the established ORs in
Figure 2. Risk alleles are in accordance with previously established
risk alleles and with information on whether each SNP is a directly
observed genotype (1) or is imputed (0) or (.) is not available for
analysis. Power (%) refers to the power of the individual studies to
detect the published ORs at an a-level 0.05, given the allele
frequency and sample sizes observed in our own studies.
Found at: doi:10.1371/journal.pgen.1001363.s009 (0.14 MB
DOC)
Table S4 Monte-Carlo P-values from varLD algorithm for the
36 established T2D susceptibility loci, comparing the European
panel of Hapmap II (CEU) with Chinese (CHS), Malays (MAS)
and Asian Indians (MAS) in Singapore and within the three ethnic
groups.
Found at: doi:10.1371/journal.pgen.1001363.s010 (0.11 MB
DOC)
Table S5 Number of samples excluded during quality control
and their reasons for exclusion. Note that the same sample may be
excluded for more than one reason and each sample falls into
exactly one of the exclusion reasons.
Found at: doi:10.1371/journal.pgen.1001363.s011 (0.05 MB
DOC)
Text S1 Description of results from the individual genome wide
association studies (GWAS) and meta-analysis.
Found at: doi:10.1371/journal.pgen.1001363.s012 (0.03 MB
DOC)
Acknowledgements
We acknowledge Genome Institute of Singapore for the genotyping for all
study populations. In addition, we would like to thank the three reviewers
for their insightful comments that have helped to improve this paper.
Author Contributions
Conceived and designed the experiments: JL DPKN MB KSC TYW MS
EST. Performed the experiments: XS. Analyzed the data: XS RTO CS
WTT. Contributed reagents/materials/analysis tools: JL DPKN TYW
EST. Wrote the paper: XS YYT EST.
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