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Genome-wide association meta-analysis identifies novel variants associated with fasting plasma glucose in East Asians
Joo-Yeon Hwang1,43, Xueling Sim2,3,43, Ying Wu4,43, Jun Liang5,43, Yasuharu Tabara6,43, Cheng Hu7,43, Kazuo Hara8,9,43, Claudia H.T. Tam10,43, Qiuyin Cai11,43, Qi Zhao12,43, Sunha Jee13,43, Fumihiko Takeuchi14,43, Min Jin Go1, Rick Twee Hee Ong15, Takayoshi Ohkubo16,17, Young Jin Kim1, Rong Zhang7, Toshimasa Yamauchi8, Wing Yee So10, Jirong Long11, Dongfeng Gu18,19,20, Nanette R. Lee21, Soriul Kim13, Katsuya Tomohiro22, JiHee Oh1, JianJun Liu23, Satoshi Umemura24, Yeon-Jung Kim1, Feng Jiang7, Shiro Maeda25, Juliana C.N. Chan10, Wei Lu26, James E. Hixson27, Linda S. Adair28, KeumJi Jung13, Toru Nabika29, Jae-Bum Bae1, MiHee Lee1, Mark Seielstad30, Terri L Young31, Yik Ying Teo3,15,23,32,33, Yoshikuni Kita17, Naoyuki Takashima17, Haruhiko Osawa34, So-Hyun Lee1, Min-Ho Shin35, Dong Hoon Shin36, Bo Youl Choi37, Jiajun Shi11, Yu-Tang Gao38, Yong-Bing Xiang38, Wei Zheng11, Norihiro Kato14,44, Miwuk Yoon13,44, Jiang He12,44, Xiao Ou Shu11,44, Ronald C.W. Ma10,44, Takashi Kadowaki8,44, Weiping Jia7,44, Tetsuro Miki39,44, Lu Qi5,44, E Shyong Tai14,40,41,44, Karen L. Mohlke4,44, Bok-Ghee Han1,44, ¶, Yoon Shin Cho42,44, ¶, Bong-Jo Kim1,44, ¶
1. Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
2. Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
3. Centre for Molecular Epidemiology, National University of Singapore, Singapore 117597, Singapore
4. Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
5. Department of Nutrition, Harvard School of Public Health, Boston 02115, Massachusetts, USA
6. Center for Genomic Medicine, Kyoto University Graduate School of Medicine
7. Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233, PR China
8. Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, TheUniversity of Tokyo, Tokyo 113-8655, Japan
9. Department of IntegratedMolecular Science on Metabolic Diseases, 22nd Century Medical and ResearchCenter, the University of Tokyo, Tokyo 113-8655, Japan
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Diabetes Publish Ahead of Print, published online September 3, 2014 Page 3 of 66 Diabetes
10. Dept of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong
11. Division of Epidemiology, Department of Medicine; Vanderbilt Epidemiology Center; and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
12. Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA
13. Institute for Health Promotion, Graduate School of Public Health, Yonsei University
14. Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
15. Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117597, Singapore
16. Department of Planning for Drug Development and Clinical Evaluation, Tohoku UniversityGraduate School of Pharmaceutical Sciences, Sendai, Japan
17. Department of Health Science, Shiga University of Medical Science, Shiga, Japan
18. State Key Laboratory of Cardiovascular Disease, Department of Epidemiology
19. Population Genetics, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences
20. Peking Union Medical College, 167 Beilishi Road, Beijing 100037, China
21. USC-Office of Population Studies Foundation Inc., University of San Carlos, Cebu City, Philippines
22. Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan
23. Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore
24. Department of Medical Science and Cardiorenal Medicine, Yokohama City University School of Medicine, Yokohama, Japan
25. Laboratory for Endocrinology, Metabolism and Kidney Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
26. Shanghai Municipal Center for Disease Control & Prevention, Shanghai, China
27. Human Genetics Center, University of Texas School of Public Health, 1200 Herman Pressler St, Houston, TX 77030
28. Department of Nutrition, University of North Carolina, Chapel Hill, NC, USA
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29. Department of Functional Pathology, Shimane University School of Medicine, Izumo, Japan
30. Institute of Human Genetics, University of California,Box 0794, San Francisco, CA 94143-0794, USA
31. Center for Human Genetics, Duke University Medical Center, Durham, North Carolina, USA
32. Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546, Singapore
33. NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore 117456 Singapore
34. Department of Molecular and Genetic Medicine, Ehime University Graduate School of Medicine, Toon, Japan
35. Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, South Korea
36. Department of Occupational and Environmental Medicine, Keimyung University Dongsan Medical Center, Daegu, South Korea
37. Department of Preventive Medicine, College of Medicine, Hanyang University, Seoul, Korea
38. Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
39. Department of Geriatric Medicine, Ehime University Graduate School of Medicine, Toon, Japan
40.Department of Medicine, National University of Singapore, Singapore 119228 Singapore
41.Duke-National University of Singapore Graduate Medical School, Singapore 169857,Singapore
42.Department of Biomedical Science, Hallym University, Chuncheon, Korea
43. These authors contributed equally as co-first authors.
44. These authors jointly supervised this work.
¶Correspondence should be addressed to B-.G.H. ([email protected]), Y.S.C. ([email protected]) or B-.J. K. ([email protected])
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ABSTRACT
Fasting plasma glucose (FPG) has been recognized as an important indicator for the overall
glycemic state preceding the onset of metabolic diseases. Most genome-wide association loci
for FPG so far been identified were derived from populations with European ancestry with a
few exceptions. To extend a thorough catalog for FPG loci, we conducted meta-analyses of
13 genome-wide association studies in up to 24,740 non-diabetic subjects with East Asian
ancestry. Follow-up replication analyses in up to additional 21,345 participants identified
three new FPG loci reaching genome-wide significance in or near PDK1-RAPGEF4, KANK1
and IGF1R. Our results could provide additional insight into the genetic variation implicated
in fasting glucose regulation.
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Fasting plasma glucose (FPG) levels are tightly regulated as a part of metabolic homeostasis
(1). Failure in blood glucose regulation can lead to elevated FPG levels, representing an independent risk factor for T2D and a predictor of cardiovascular disease (2; 3). The fasting glucose level is a moderately heritable trait with the heritability around 30% (4-6). A considerable number of genetic determinants influencing fasting glucose levels have been identified from numerous genetic studies in the last few years. The total heritability of fasting glucose levels, however, is yet to be fully explained.
To date, 39 genetic loci harbouring variants associated with FPG have been identified from genome-wide association (GWA) studies and GWA meta-analyses that were conducted in populations of European ancestry (7; 8). The genetic basis of glycemic regulation has not been fully explored in non-European populations with only one study in
East Asians which identified a single locus associated with FPG (rs895636 at the SIX2-SIX3 loci) (9). Considering differences in the allele frequencies and linkage disequilibrium structures among ethnic groups, large scale genetic studies in populations of non-European ancestries may increase the chance to detect additional novel genetic loci for FPG.
Genome-wide association meta-analysis has an advantage to identify genetic variants with small effect size and low allele frequency that were hardly detected in a single
GWA study (10). Therefore, in this study, we aimed to identify novel loci influencing fasting glucose variation by conducting GWA meta-analysis in East Asian populations. We conducted a two-stage association study, comprising a discovery set (stage1) of 24,740 individuals from the Asian Genetic Epidemiology Network (AGEN) and follow-up de novo genotyping replication set (stage 2) of 21,345 individuals from independent East Asian populations (Table 1 and Supplementary Figure 1).
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RESEAECH DESIGN AND METHODS
Study subjects. Stage 1 subjects were drawn from 13 GWAS participating in the Asian
Genetic Epidemiology Network (AGEN) consortium, which was organized in 2010 to enable
genetic association studies of metabolic traits such as diabetes, hypertension and obesity.
These 13 studies consist of 24,740 subjects from the Korea Association REsource (KARE)
project, Health Examinee (HEXA) shared control study, Cardiovascular disease Association
Study (CAVAS), three Singapore Prospective Study Programs (SP2), Shanghai Breast Cancer
Study (SBCS), Shanghai Men's Health Study (SMHS), Genetic Epidemiology Network of
Salt Sensitivity (GenSalt), Cardio-metabolic Genome Epidemiology (CAGE), Cebu
Longitudinal Health and Nutritional Survey (CLHNS), Cardio metabolic Risk in Chinese
(CRC) and the Korea Cancer Prevention Study-II (KCPS-II). Stage 2 included 21,345
subjects from 5 independent studies for de novo replication analysis. Each study obtained an
approval from the appropriate institutional review board and all participants written informed
consent across studies. The information including the study design and descriptive
characteristics of each participating study was outlined in Supplementary Table 1 and
Supplementary Methods.
Phenotype measurement. Fasting glucose levels were measured from whole blood, plasma
or serum for each cohort. Fasting whole blood glucose levels were multiplied by 1.13 to
convert to fasting plasma glucose levels. Anthropometric measurements such as BMI were
obtained by standardized procedures.
Genotyping, imputation and quality control. Genotyping and quality control methods for
individual studies are outlined in Supplementary Table 2. A variety of genotyping platforms
from Affymetrix or Illumina were applied to each individual GWA analysis to obtain entire
genome scan data. Imputation of genotypes to the HapMap Phase 2 (CHB + JPT except for
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CLHNS which used HapMap CHB + JPT + CEU) as the reference panel was carried out using the program MACH, IMPUTE or BEAGLE. Imputed SNPs with high imputation quality (properinfo > 0.5 for IMPUTE, and Rsq > 0.3 for MACH and BEAGLE) were used for subsequent association analysis. Genotyping for de novo replication in stage 2 were carried out by TaqMan, multiplex-PCR-invader assay or Sequenom Mass ARRAY method.
Statistical analyses. Only non-diabetic individuals were tested for FPG by excluding diabetes patients, individuals using anti-diabetic medicine and individuals with fasting glucose ≥ 7 mmol/L. The rank-based inverse normal transformed (INT) FPG was tested for the association analyses to improve the normality of the FPG distribution and alleviate the impact of outliers. Association analyses were adjusted with sex and BMI (plus recruitment area in KARE and NCGM studies) to compensate for multivariate linear regression analyses in the additive genetic mode. For the family design of the GenSalt study, family relationship was adjusted using a linear mixed model in which family ID was used as a random effect.
Association analyses were performed using the program SNPTEST, MACH2QTL or PLINK
(Supplementary Table 2). The meta-analysis was conducted using an inverse-variance method assuming fixed effects with a Cochran’s Q test to assess heterogeneity between the thirteen studies. All meta-analyses were performed using the METAL software and study-specific genomic control adjustment was applied. Genomic control inflation factor (λ) was estimated from the median of the χ2 statistical divided by 0.456. The λ for the meta-analysis was 1.06
(and was less than 1.029 for individual studies), indicating that the results seen in stage 1 were probably not the results of population stratification. The Manhattan plot showing the negative log P-value distribution for stage 1 meta-analysis results was generated by the
WGAViewer software. The quantile-quantile (Q-Q) plot of trend test P-values showed deviations from the null distribution due to the strong associations observed for FPG.
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Regional association plots from genome-wide meta-analysis result were generated using the
LocusZoom software.
Gene relationships among implicated loci (GRAIL) analysis. To understand gene
relationship across implicated loci, a GRAIL analysis was conducted as described previously
(11; 12). A total of 43 FPG genes comprising 40 previously known genes (Supplementary
Table 3) and 3 genes newly implicated in this study (Table 2) were included for the analysis.
PubMed abstracts published after December 2006 were not included for the analysis to
reduce confounding by results from FPG GWAS.
URLs
PLINK, http://pngu.mgh.harvard.edu/~purcell/plink ;
WGAViewer, http://compute1.lsrc.duke.efu/softwares/WGAViewer ;
METAL, http://www.sph.umich.edu/csg/abecasis/Metal/index.htmal ;
SNAP, http://www.broadinstitute.org/mpg/snap ;
HapMap, http://hapmap.ncbi.nlm.nih.gov/ ;
LocusZooms, http://csg.sph.umich.edu/locuszoom ;
GRAIL, http://www.broadinstitute.org/mpg/grail/
RESULTS
Our stage 1 meta-analyses from 24,740 AGEN subjects revealed signals showing strong
evidence for FPG associations. Most of them were in known FPG loci (Figure 1). Twenty-
three of 40 FPG loci that were detected mostly in the European populations were replicated in
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Table 3). Of these, 11 (GCKR, SIX2-SIX3, G6PC2-ABCC11, CDKAL1, TMEM195, GCK,
SLC30A8, GLIS3, CDKN2A/B, MTNR1B and FOXA2) reached genome-wide significance and showed similar direction of association as in the original reports (Table 2).
After removing signals within previously identified FPG loci, SNPs showing the deviation between the distributions of the observed and expected P-values were still observed on the quantile-quantile plot (Supplementary Figure 2). Those signals likely represent new
FPG loci that require the validation in additional investigation. For follow-up replication, we selected 3 independent signals (that is, with pairwise linkage disequilibrium (LD) statistics r2
< 0.2 and minor allele frequency (MAF) ≥ 0.05 within a 500-kb window of the genomic region) from the stage 1 meta-analysis based on our arbitrary inclusion threshold (P < 5 × 10-
7), heterogeneity P-value > 0.01 and at least 10 studies having been included in the meta- analysis. To consolidate genetic associations for promising 3 new variants, we conducted de novo genotyping. The stage 2 replication analysis (5 studies, up to 21,345 subjects) showed a statistically significant association of these three variants with FPG and the same direction of association as in the stage 1 analysis results (Table 2).
An overall meta-analysis of the total samples (18 studies, up to 46,085 subjects) identified three novel loci for FPG reaching genome-wide significance (P < 5 × 10-8). These
-11 FPG associated loci were located close to PDK1-RAPGEF4 (rs733331, Poverall = 6.98 × 10 ),
-9 -8 KANK1 (rs10815355, Poverall = 1.26 × 10 ) and IGF1R (rs2018860, Poverall = 2.99 × 10 )
(Table 2 and Figure 2). The newly identified loci in our study showed less significant association in European ancestry subjects studied by the MAGIC investigators (P < 5 × 10-3)
(2). Notably, two of the new loci (near PDK1-RAPGEF4 and KANK1) have very low minor allele frequencies (MAF < 0.01) in Europeans (Supplementary Table 4).
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DISCUSSION
SNP rs733331 is located on chromosome 2q31 between PDK1 (pyruvate dehydrogenase
kinase isozyme 1) and RAPGEF4 (Rap guanine nucleotide exchange factor 4). Pyruvate
dehydrogenase, a mitochondrial multi enzyme complex, is one of the important key enzymes
responsible for glucose homeostatic regulation. The enzyme activity by cyclic de-
phosphorylation cascades is regulated by a specific pyruvate dehydrogenase kinase (PDK). A
previous functional study reported that liver-specific Pdk1 deficiency in mice was associated
with postprandial hyperglycemia (13). In addition, a potential regulator of PDK1, the
pancrease-specific miR-375, was directly implicated in the regulation of glucose-induced
biological responses (14). RAPGEF4 has a role in initiating insulin secretion and mediating
cAMP-dependent pulsatile insulin release (15).
The rs10815355 signal on chromosome 9p24 is located in an intron of KANK1 (KN
motif and ankyrin repeat domains 1), which has a role in the formation of the cytoskeleton by
regulating actin polymerization. KANK1 negatively regulates the formation of actin stress
fibers and cell migration through the inhibition of Rho-associated kinase activity (16).
Recently, the population-based Metabolic Syndrome in Men (METSIM) study
conducted exome array analysis in 8,229 nondiabetic Finnish males (17). This study
demonstrated that genetic variant rs3824420, encoding Arg667His in KANK1 and located 90
kb from rs10815355, was associated with circulating proinsulin levels and related insulin
processing and secretion traits (17). In the METSIM study, rs10815355 also exhibited
association with proinsulin levels at GWA significance (Supplementary Table 5). The
substantial attenuation in association for both SNPs in conditional analysis suggests that
rs10815355 likely represents the same signal as rs3824420 for the proinsulin levels. This
result is supported by the strong LD (r2 = 0.787, D’ = 1.000, unpublished data) calculated by
the genotypes in METSIM (Supplementary Table 5).
Both rs10815355 and rs3824420 were not significantly associated with fasting
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Diabetes Page 12 of 66 glucose level in METSIM (Supplementary Table 5). On the other hand, in addition to the strong association of rs10815355 for FPG, our AGEN stage 1 meta-analysis (7 studies, up to
11,822 subjects) demonstrated that rs3824420 was also marginally associated with fasting glucose (Pstage 1 = 0.035). Considering substantial differences in SNP minor allele frequency and LD between two ethnic groups, these discrepancies between AGEN and METSIM are not surprising (Supplementary Table 6). It is known that genetic variants with low allele frequency are hardly detected in the genetic association study (18).
Unlike the case of FPG, both SNPs, rs10815355 and rs3824420, were not associated with the insulin-related traits such as fasting insulin and homeostatic model for assessment of beta-cell function (HOMA-B) in one of the AGEN stage 1 studies (KARE study, up to 7,183 subjects) (Supplementary Table 7). To detect the evidence of association for these traits in the
East Asian populations, meta-analysis combining all AGEN stage 1 data (thus improving power by increasing the sample size) should be carried out (Supplementary Table 7).
Although two SNPs, rs10815355 and rs3824420, are weakly linked in East Asians (r2
= 0.087, D’ = 0.362 in HapMap CHB/JPT), the association strength of one SNP was moderately diminished after the adjustment for the other SNP in our conditional analyses using data from three AGEN stage 1 studies including KARE, HEXA and CAVAS (up to
12,178 subjects) (Supplementary Table 8). These results plausibly indicate the functional relevance of rs10815355 to rs3824420 in the KANK1 region for FPG association in the East
Asian populations. Further study will be needed to determine whether these signals share an underlying unrevealed causal variant for fasting plasma glucose levels. Other conditional analyses demonstrated that association signals of rs10815355 and rs3824420 for FPG remained after the adjustment for the insulin-related traits (Supplementary Table 9). These results suggested that the FPG association of the KANK1 region was not simply secondary to the association for insulin-related traits in our study.
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SNP rs2018860 in 15q26 is located in an intron of IGF1R (insulin-like growth factor
receptor) which is involved in cell growth, differentiation, migration, and metabolism, as well
as major aspect of glucose homeostasis (19). IGF1R, the protein encoded by IGF1R, has
tyrosine kinase activity that stimulates growth in many different cell types and blocks
apoptosis in multiple signaling pathways (20; 21). Recent GWA analyses detected the
association of IGF1R with higher serum uric acid (SUA) concentrations in European
populations (22). SUA levels as a potential biomarker have been reported in association with
impaired glucose metabolism (23). In addition, a two-stage study reported a putative role of
IGF1R variants on insulin resistance and arterial hypertension (24). Expression of a dominant
negative IGF1R in muscle leads to severely impaired insulin mediated glucose uptake (25).
Beta-cell specific knockout of IGF1R results in hyperinsulinemia and impaired glucose
tolerance (26). IGF1R is involved in mediating GLP-1 increase in glucose competence and
proliferation on the beta-cell (27).
Given the knowledge that the substantial elevation of FPG is one of typical signs of
type 2 diabetes (T2D), we investigated the relevance of three new FPG signals to T2D risk
from AGEN-T2D meta-analysis data (11). None showed the evidence for association with
T2D (Supplementary Table 10). These results indicate that three new variants influencing
FPG likely have limited impact on T2D risk as exemplified by MADD and SLC2A2 loci in
the previous report (2).
We performed the Gene Relationships Across Implicated Loci (GRAIL) literature-
based annotation analysis (12) to investigate functional connectivity among the 3 new FPG
genes from this study and the 40 known genes from previous studies (Supplementary Table
11). The strongest connections were observed in biological pathways such as insulin secretion,
circadian rhythm, and carbohydrate digestion along with the most frequently connecting
terms including insulin, glucose, circadian and growth. The results highlighted biological
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Diabetes Page 14 of 66 functions of newly identified loci in the regulation of glucose metabolism (Supplementary
Table 12 and Supplementary Figure 3).
This study is the largest GWAS meta-analysis, to our knowledge, conducted for FPG in East Asians. In conclusion, our meta-analysis identified three novel loci in or near the
PDK1, KANK1 and IGF1R genes at genome-wide significance levels. This study was also able to replicate many of FPG-risk loci that were previously established in Europeans. The identification of these loci provides the possibility to further the functional connection and the causal evidence in fasting glucose regulation and related diseases.
FIGURE AND TABLE LEGENDS
Figure 1. Genome-wide Manhattan plot of the meta-analysis for fasting plasma glucose in East Asian populations. Shown are the –log10 P values using the trend test for SNPs distributed across the entire autosomal genome (NCBI build 37). The red dots at each locus indicate the signals with P < 10-6 detected in the genome-wide meta-analysis. Approximately
2.4 M SNPs that were present in at least thirteen stage 1 studies were used to generate the plot.
Figure 2. Regional association plots of three newly discovered FPG loci. A-C The SNP positions are shown at the top and the regional association results from the genome-wide meta-analysis are shown in the middle. The trend test -log10P values are shown for SNPs distributed in a 0.8-Mb genomic region centered on the most strongly associated signal, which is depicted as a purple diamond for the combined stage 1 and 2 results. The locations of known genes in the region are shown at the bottom. The genetic information was from the
Human Genome build hg 19, and the LD structure was based on the 1,000 Genomes ASN data (Mar 2012). 13
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Supplementary Figure 1. Overall study work flow.
Supplementary Figure 2. Quantile-quantile plot for FPG. The observed P-values (y axis)
were compared with the expected P-values under the null distribution (x axis) for FPG. The
expected null distribution is plotted along the black diagonal. The entire distribution of
observed P-values is plotted in red. The distribution after excluding the previously reported
FPG loci is plotted in blue. The distribution after excluding all known and new loci is plotted
in green.
Supplementary Figure 3. Gene relationships among implicated loci (GRAIL)-VIZ plot
of functional connections.
Table 1. Study design and samples
Table 2. Identified genetic loci associated with fasting plasma glucose at genome-wide
significance in East Asian populations
Supplementary Table 1. Descriptive characteristics of cohort studies
Supplementary Table 2. Information for genotyping, Imputation and association
analysis
Supplementary Table 3. Association results of stage 1 meta-analysis for established FPG
loci
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Supplementary Table 4. Evidence of association of 3 new loci with fasting plasma glucose in Europeans from the MAGIC
Supplementary Table 5. Associations for two KANK1 variants with glucose and proinsulin levels in 8,633 METSIM samples
Supplementary Table 6. Comparison of SNP minor allele frequency and linkage disequilibrium among ethnic groups
Supplementary Table 7. Association of SNPs rs10815355 and rs3824420 with insulin- related traits such as fasting insulin and homeostatic model for assessment of beta-cell function (HOMA-B) in KARE study
Supplementary Table 8. Conditional analyses for SNPs, rs10815355 and rs3824420.
Association analysis of one SNP for fasting glucose was adjusted with the other SNP.
Supplementary Table 9. Conditional analyses for fasting glucose SNPs. The association of SNPs with fasting glucose was adjusted with insulin-related traits.
Supplementary Table 10. Genetic association of 3 new FPG loci with type 2 diabetes.
Results of type 2 diabetes association were obtained from AGEN-T2D consortium.
Supplementary Table 11. List of regions contained in the 43 loci identified by genome- wide association meta-analysis (using published PubMed abstracts prior to December,
2006) with the gene identified by GRAIL analysis. GRAIL PRegion-value column presents the best significant PGene-value adjusted for the multiple comparisons within a locus with at
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least one gene. The right panel presents the keywords over-described in PubMed abstracts
featuring the most connected genes functionally.
Supplementary Table 12. List of connection results from the GRAIL analysis for
selected genes (PGene < 0.05) in Supplementary Table 11. GRAIL PGene-values are the
results for the test of the gene itself, and thus are not adjusted for the number of genes tested
within a locus. Analysis results present connections between previously known fasting
glucose associated genes based on their prior literature.
ACKNOWLEDGEMENTS
This work was supported by grants from Korea Centers for Disease Control and Prevention
(4845-301, 4851-302 and 4851-307) and an intramural grant from the Korea National
Institute of Health (2012-N73002-00), the Republic of Korea. Y.S.C. acknowledges support
from the National Research Foundation of Korea (NRF) grant funded by the Korea
government (MEST) (2012R1A2A1A03006155).
The Cebu Longitudinal Health and Nutrition Survey (CLHNS) was supported by
National Institutes of Health grants DK078150, TW05596, HL085144 and TW008288 and
pilot funds from RR20649, ES10126, and DK56350. We thank the Office of Population
Studies Foundation research and data collection teams and the study participants who
generously provided their time for this study.
We thank Markku Laakso, Michael Boehnke, Francis Collins and the METSIM study
investigators for providing access to unpublished data. METSIM data was generated and
analyzed with support from the Academy of Finland (contract 124243), the Finnish Heart
Foundation, the Finnish Diabetes Foundation, Tekes (contract 1510/31/06), the Commission
of the European Community (HEALTH-F2-2007-201681) and US National Institute of
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Health grants DK093757, DK072193, DK062370 and 1Z01 HG000024.
Shanghai Breast Cancer Study was supported by R01CA064277 and Shanghai Men’s
Health Study was supported by R01CA082729. GWA studies were supported by
R37CA070867, R01CA082729, R01CA124558, R01CA148667, R01CA122364,
R01CA122756, and R01CA137013, as well as Ingram Professorship and Research Reward funds from the Vanderbilt University School of Medicine. We want to thank Regina Courtney for DNA preparation and Jing He for data processing and analyses. The study was supported by grants from National 973 Program (2011CB504001), National Science Foundation of
China (81170735) and Excellent Young Medical Expert of Shanghai (XYQ2011041), China.
We thank all medical staff of the Shanghai Clinical Center for Diabetes.
We thank all the participants and the staff of the BioBank Japan project. We thank
Minoru Iwata and Kazuyuki Tobe (First Department of Internal Medicine, University of
Toyama), Kazuki Yasuda and Masato Kasuga (Diabetes Research Center, Research Institute,
National Center for Global Health and Medicine), and Hiroshi Hirose (Health Center, Keio
University School of Medicine) for the preparation of DNA and clinical information.
We thank the technical staff of the Laboratory for Endocrinology, Metabolism and Kidney diseases at the RIKEN Center for Integrative Medical Sciences for performing
SNP genotyping. We also thank Hayato Fujita (Department of Diabetes and Metabolic
Diseases, Graduate School of Medicine, The University of Tokyo) for statistical analysis.
This work was supported by a grant from the Leading Project of Ministry of Education,
Culture, Sports, Science and Technology, Japan.
This work was supported by grant from NSFC (81322010 and 81170735), National 863
Program (2012AA02A509), Shanghai Rising Star Program (12QH1401700), Shanghai Talent
Development Grant (2012041), Excellent Young Medical Expert of Shanghai
(XYQ2011041), and National Young Top Talent Support Program.
We would like to acknowledge support from the Hong Kong Foundation for Research
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and Development in Diabetes established under the auspices of the Chinese University of
Hong Kong, the Hong Kong Governments Research Grant Committee Central Allocation
Scheme (CUHK 1/04C) and Theme-based research scheme (T12-402/13-N), the Innovation
and Technology Fund (ITS/088/08 and ITS/487/09FP), a Chinese University Direct Grant,
and National Institutes of Health Grant NIH-RFA DK-085545-01 (from the National Institute
of Diabetes and Digestive and Kidney Diseases).
The Genetic Epidemiology Network of Salt Sensitivity (GenSalt) is supported by
research grants (U01HL072507, R01HL087263, and R01HL090682) from the National Heart,
Lung, and Blood Institute, NIH, Maryland.
AUTHOR CONTRIBUTIONS
The study was supervised by B.-J.K., Y.S.C., B.-G.H., K.L.M., J.M., L.Q., E.S.T., J.L., C.H.,
T.K., Q.C., S.J., W.Y.S., J.C.N.C., L.S.A., M.S., Y.Y.T., J.-Y.L., W.Z., N.K., X.O.S.,
R.C.W.M., W.J., D.G., J.H. and T.M. The experiments were conceived and designed by J.-
Y.H., Y.S.C., B.-J.K., B.-G,H., K.L.M., J.L., C.H., T.K., S.J., L.S.A., W.Z., X.O.S., S.M.,
D.G., J.E.H., J.H. and L.Q. The experiments were performed by Y.T., Q.C., R.Z., J.L., J.H.O.,
Y.-J.K., F.J., J.S., D.G., J.E.H., J.H. and S.M. Statistical analysis were performed by J.-Y.H.,
M.J.G., Y.S.C., X.S., J.L., Y.T., C.H., C.H.T.T., Y.W., F.T., R.T.H.O., Y.J.K., J.L., S.K., J.L.,
K.H., T.L.Y., Y.Y.T., Q.Z. and L.Q. The data were analyzed by J.-Y.H., M.J.G., Y.S.C., X.S.,
J.L., C.H., C.H.T.T., Y.W., S.J., R.T.H.O., T.Y., W.Y.S., J.L., N.R.L., S.K., K.T., J.L., K.H.,
J.C.N.C., L.S.A., J.-B.B, T.L.Y., Y.Y.T., R.C.W.M., S.M., Q.Z., L.Q. and K.L.M. Reagents,
materials and analysis tools were contributed by J.-Y.H., M.J.G., Y.S.C., J.L., Y.T., Q.C.,
R.T.H.O., T.O., W.Y.S., J.L., S.K., K.T., J.L., S.U., J.C.N.C., W.L., K.J.J., T.N., M.H.L.,
T.L.Y., Y.K., N.T., H.O., S.-H.L., M.-H.S., D.H.S., B.Y.C., J.S., Y.-T.G., Y.-B.X., W.Z., M.Y.,
X.O.S., R.C.W.M. and L.Q. The manuscript was written by J.-Y.H., Y.S.C., K.L.M., L.Q. and
E.S.T. All authors reviewed the manuscript. Dr. Yoon Shin Cho is the guarantor of this work
18
Diabetes Page 20 of 66 and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
COMPETING FINANCIAL INTERESTS
The authors declare no competing financial interests.
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Table 1. Study design and samples Stage Represetative Study Ethnic group Genotyping method Sample size KNIH KARE Korean Affymetrix 5.0 7,696 HEXA Korean Affymetrix 6.0 3,385 CAVAS Korean Illumina 1M 3,205 NUS SP2(1) Chinese Illumina 1M 933 SP2(2) Chinese Illumina 610K 1,044 SP2(3) Chinese Illumina 550K 305
Stage 1 VU/SCI SBCS Chinese Affymetrix 6.0 2,017 (discovery) SMHS Chinese Affymetrix 6.0/Illumina 660K 291 Tulane U GenSalt Han Chinese Affymetrix 6.0 1,832 NCGM CAGE Japanese Illumina 550K 756 UNC CLHNS Filipino Affymetrix 5.0 1,624 Harvard U CRC Chinese Illumina 610K 733 Yonsei U KCPS-II Korean Affymetrix 5.0 919 Stage 1 total 24,740 KNIH Health2 Korean TaqMan 5,277 RIKEN/UT BBJ Japanese Multiplex-PCR-invader assay 1,883 Stage 2 SJTU SJTUDS Han Chinese MassARRAY 3,412 (de novo replication) Ehime U JMGP Japanese TaqMan 10,299 CUHK CUHKS Han Chinese Sequenom MassARRAY 474 Stage 2 total 21,345 Overall AGEN AGEN-FPG East Asian 46,085
Stage 1 includes 13 studies that provided full genome-wide association analysis results for fasting plasma glucose. Stage 2 includes 5 studies that provided de novo replication results of SNPs selected from stage 1. KNIH, Korea National Institute of Health; NUS, National University of Singapore; VU, Vanderbilt University; SCI, Shanghai Cancer Institute; Tulane U, Tulane University; NCGM, National Center for Global Health and Medicine; UNC, University of North Carolina; Harvard U, Harvard University; Yonsei U, Yonsei University; UT, The University of Tokyo; SJTU, Shanghai Jiao Tong University; Ehime U, Ehime University; CUHK, Chinese University of Hong Kong; KARE, Korea Association Resource Project; HEXA, Health Examinee Shared Control Study; CAVAS, Cardiovascular Disease Association Study; SP2, Singapore Prospective Study Program; SBCS, Shanghai Breast Cancer Study; SMHS, Shanghai Men's Health Study; GenSalt, Genetic Epidemiology Network of Salt-Sensitivity; CAGE, Cardiometabolic Genome Epidemiology Network; CLHNS, Cebu Longitudinal Health and Nutrition Survey; CRC, Cardiometabolic Risk in Chinese Study; KCPS-II, The Korean Cancer Prevention Study-II; Health2, Health2 Study; BBJ, BioBank Japan; SJTUDS, Shanghai Jiao Tong University Diabetes Study; JMGP, The Japanese Millennium Genome Project; CUHKS, Chinese University of Hong Kong Diabetes Study; AGEN, Asian Genetic Epidemiology Network Page 31 of 66 Diabetes
Table 2. Identified genetic loci associated with fasting plasma glucose at genome-wide significance in East Asian populations
Marker Effect Other Stage 1 (discovery) Stage 2 (de novo replication) Combined (Stage 1+2) Chr Candidate gene name allele allele β ± s.e.m P -value β ± s.e.m P -value β ± s.e.m P -value New loci identified in this study: rs733331 2 PDK1-RAPGEF4 A G 0.048 ± 0.009 7.18E-08 0.029 ± 0.007 3.85E-05 0.036 ± 0.006 6.98E-11 rs10815355 9 KANK1 T G 0.074 ± 0.012 3.73E-10 0.027 ± 0.009 3.59E-03 0.045 ± 0.007 1.26E-09 rs2018860 15 IGF1R A T 0.050 ± 0.009 1.72E-08 0.019 ± 0.007 7.21E-03 0.031 ± 0.006 2.99E-08
Previously reported loci:
rs780094 2 GCKR C T 0.052 ± 0.009 3.58E-09 - - 0.052 ± 0.009 3.58E-09 rs895636 2 SIX2-SIX3 T C 0.069 ± 0.010 2.53E-13 - - 0.069 ± 0.010 2.53E-13 rs13387347 2 G6PC2-ABCC11 C T 0.114 ± 0.009 2.35E-36 - - 0.114 ± 0.009 2.35E-36 rs9356744 6 CDKAL1 C T 0.057 ± 0.009 9.24E-10 - - 0.057 ± 0.009 9.24E-10 rs1974620 7 TMEM195 T C 0.063 ± 0.009 2.79E-11 - - 0.063 ± 0.009 2.79E-11 rs730497 7 GCK A G 0.121 ± 0.011 7.72E-27 - - 0.121 ± 0.011 7.72E-27 rs3802177 8 SLC30A8 G A 0.063 ± 0.009 5.23E-12 - - 0.063 ± 0.009 5.23E-12 rs4237150 9 GLIS3 C G 0.053 ± 0.009 4.31E-09 - - 0.053 ± 0.009 4.31E-09 rs10811661 9 CDKN2A/B T C 0.062 ± 0.009 8.66E-12 - - 0.062 ± 0.009 8.66E-12 rs3847554 11 MTNR1B T C 0.059 ± 0.009 2.20E-11 -- 0.059 ± 0.009 2.20E-11 rs6048216 20 FOXA2 T C 0.095 ± 0.013 1.91E-12 -- 0.095 ± 0.013 1.91E-12 s.e.m., standard error mean. Diabetes Page 32 of 66
G6PC2/ABCB11
GCK MTNR1B
P FOXA2 10 SLC30A8 SIX2-SIX3 CDKAL1 CDKN2A/B TMEM195 GLIS3 -log
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Chromosome
Figure 1. Genome-wide Manhattan plot of the meta-analysis for fasting plasma glucose in East Asian populations. Page 33 of 66 Diabetes
Figure 2. Regional association plots of three newly discovered FPG loci. (A) Diabetes Page 34 of 66
Figure 2. Regional association plots of three newly discovered FPG loci. (B) Page 35 of 66 Diabetes
Figure 2. Regional association plots of three newly discovered FPG loci. (C) Diabetes Page 36 of 66
STAGE 1 (GWAS meta-analysis of FPG)
Approximately 2.4 M directly genotyped or imputed SNPs were tested using the inverse variance meta-analysis in 24,740 individuals from 13 studies with East Asian ancestry
Previous FPG loci : 14 selected SNPs (with P < 5 x 10-7) : GCKR, SIX2-SIX3, G6PC2/ABCB11, 11 previous loci + 3 new loci CDKAL1, TMEM195, GCK, SLC30A8, GLIS3, CDKN2A/B, MTNR1B, FOXA2
STAGE 2 (De novo replication)
Three new SNPs were genotyped in 21,345 individuals from 5 studies with East Asian ancestry
Meta analyses of 3 SNPs (Stage1 + Stage2)
Supplementary Figure 1. Overall study work flow. Page 37 of 66 Diabetes
Supplementary Figure 2. Quantile-quantile plot for FPG. Diabetes Page 38 of 66
Supplementary Figure 3. Gene relationships among implicated loci (GRAIL)-VIZ plot of functional connections. Page 39 of 66 Diabetes
Supplementary Table 12. List of connection results from the GRAIL analysis for selected genes (
Supplementary Table 11. GRAIL P Gene number of genes tested within a locus. Analysis results present connections between previously known fasting glucose associated genes based on their prior literature.
GENE GRAIL P Gene-value
GIPR 3.02E-05
SLC2A2 4.04E-04
SLC30A8 1.46E-03
MTNR1B 4.80E-03
IGF2BP2 4.93E-03
IGF1R 7.21E-03
GLIS3 0.020424543
CRY2 0.032827548
MST1 0.034270905
CHRM4 0.035928386
GPR4 0.036750714
EDG2 0.041266393 Diabetes Page 40 of 66
Supplementary Table 12. List of connection results from the GRAIL analysis for selected genes (P Gene < 0.05) in
Gene-values are the results for the test of the gene itself, and thus are not adjusted for the number of genes tested within a locus. Analysis results present connections between previously known fasting glucose associated genes based on their prior literature.
SELECTED SIMILAR GENES (Rank in parantheses) SLC2A2(10), IGF1R(37), FOXA2(127), SLC30A8(196), NR1H3(227), PCSK1(355), PDK1(417), TCF7L2(570), MTNR1B(768), ADCY5(856), F2(1026), PLCG1(1048), PTPRJ(1068), GPX1(1180), CHRM4(1337), EDG2(1402), GPR4(1545), APEH(1568), RHOA(1684), MADD(1685), FADS2(1918), ACP2(1940), LRP4(1978) GIPR(34), FOXA2(44), SLC30A8(130), IGF1R(139), TCF7L2(246), PDK1(283), NR1H3(285), GLIS3(953), GPX1(1120), F2(1176), PTPRJ(1339), PCSK1(1354), AMT(1475), APEH(1605), FADS2(1740), MADD(1840) ZHX3(45), SLC2A2(79), RREB1(110), GLIS3(182), IGF1R(336), FOXA2(337), ARFGAP2(415), BSN(798), GIPR(820), PACSIN3(1239), NICN1(1344) CRY2(11), ADCY5(194), IGF1R(225), NR1H3(263), GPR4(287), CHRM4(355), EDG2(576), F2(612), FOXA2(667), PLCG1(880), PTPRJ(994), MADD(1127), GIPR(1247), APEH(1257), LRP4(1287), RHOA(1572), SLC2A2(1596) IGF1R(10), RBMS2(85), C14orf68(122), PACSIN3(158), FOXA2(208), MADD(575), ZHX3(1109), RREB1(1180), KIAA0652(1237), TCF7L2(1606), PLCG1(1859), RTN2(1996) IGF2BP2(52), PLCG1(103), FOXA2(175), PTPRJ(196), PDK1(418), MADD(519), NR1H3(526), SLC2A2(576), RHOA(589), PACSIN3(628), F2(740), GIPR(883), MST1(909), TCF7L2(1037), TOP1(1072), ARHGAP1(1743), LRP4(1816) FOXA2(103), RREB1(107), ZHX3(200), SLC30A8(403), RTN2(809), USH2A(1018), SLC2A2(1288), ARFGAP2(1642), TCF7L2(1646), PACSIN3(1716), RBMS2(1952), PHF21A(1968) MTNR1B(51), FOXA2(139), NUDCD3(620), ADCY5(677), NR1H3(891), DDB2(1039), PLCG1(1553), RHOA(1582), RTN2(1645), ZHX3(1789), AMT(1887), PTPRJ(1932), MADD(1957) FOXA2(17), APEH(86), PLCG1(229), PTPRJ(232), TCTA(237), IGF1R(247), F2(473), MADD(642), NR1H3(743), ZHX3(994), RHOA(1208), PACSIN3(1536), RBMS2(1701), AMT(1756), KIAA0652(1962) NR1H3(231), ADCY5(236), IGF1R(327), EDG2(443), FOXA2(470), PLCG1(504), MTNR1B(531), RHOA(682), MDK(732), PTPRJ(1077), LRP4(1105), GPR4(1108), MADD(1132), F2(1247), GPX1(1482) EDG2(73), RHOA(201), PLCG1(266), MADD(317), IGF1R(359), NR1H3(473), F2(475), PTPRJ(564), MTNR1B(606), FOXA2(686), ARHGAP1(897), PACSIN3(926), CHRM4(1517), PDK1(1529), APEH(1605), MST1(1743), LRP4(1943) RHOA(56), GPR4(64), PLCG1(161), FOXA2(268), NR1H3(338), IGF1R(378), PTPRJ(400), MADD(525), ADCY5(770), CHRM4(974), ARHGAP1(1130), LRP4(1137), PACSIN3(1441), F2(1475), ACP2(1692), MTNR1B(1891), APEH(1962) Page 41 of 66 Diabetes
SUPPLEMENTARY NOTE
1. DESCRIPTION OF PARTICIPATING STUDIES
1.1. Stage 1: GWAS meta analysis
The Korea Association Resource Study (KARE)
The KARE project has been described previously (1). This project was initiated in 2007 to
undertake a large scale GWAS. A total of 10,038 participants aged between 40 and 69 years
were recruited through two population based prospective cohort studies conducted in the
Ansung (n = 5,018) and Ansan (n = 5,020) areas of South Korea. Both cohorts were designe
d to allow longitudinal prospective studies and adopted the same investigational strate
gy. More than 260 traits have been extensively examined through epidemiological surv
eys, physical examinations, and laboratory tests. For this study, we excluded known
diabetes, individuals treated with anti diabetic medicine and individuals with FPG more than
7 mmol/L. Consequently, 7,696 nondiabetic subjects were only tested for FPG.
Genotyping and imputation A total of 10,004 KARE study samples were genotyped using
the Affymetrix Genome Wide Human SNP array 5.0. Genotypes were called using the
Bayesian Robust Linear Modeling using the Mahalanobis Distance (BRLMM) algorithm.
Samples that exhibited the following properties were excluded: low genotyping calls (< 96%),
excessive heterozygosity, sex inconsistency, discordant ethnic membership, or cryptic
relatedness. Markers with high missing gene call rates (> 5%), low MAF (< 0.01), or
significant deviation from Hardy Weinberg equilibrium (P < 1 × 10 6) were excluded.
Imputation analysis was performed using IMPUTE against all of the HapMap Asian (JPT +
CHB) population (release 22/NCBI, build 36, and dbSNP build 126) as a reference panel. We
used posterior probability to call the genotype from imputation data and then performed
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association analyses on imputed data. Of these, SNPs in with a posterior probability score < 0.90, high genotype information content (info < 0.5), HWE (P < 1 × 10 7), and MAF
< 0.01 were omitted.
Health Examinee shared control study (HEXA)
The HEXA cohort is one of the Korean Genome and Epidemiology Study (KoGES) population based cohorts which were initiated in 2001 aiming to identify risk factors of life style related complex diseases such as type 2 diabetes, hypertension, and dyslipidemia.
Approximately 3,700 of 1,200,000 individuals aged 40 69 years from the HEXA cohort were randomly selected as a shared control group for the Korean cancer and coronary artery disease (CAD) GWAS (2). Genotyping was conducted with the Affymetrix Genome Wide
Human SNP array 6.0 in 2008.
Genotyping and imputation A total of 4,302 HEXA study samples were genotyped using the Affymetrix Genome Wide Human SNP array 6.0. Genotypes were called using the
Birdseed algorithm. Samples that exhibited the following properties were excluded: low genotyping calls (< 96%), excessive heterozygosity, sex inconsistency, discordant ethnic membership, or cryptic relatedness. Markers with high missing gene call rates (> 5%), low
MAF (< 0.01), or significant deviation from Hardy Weinberg equilibrium (P < 1 × 10 6) were excluded. Imputation analysis was performed using IMPUTE against all of the HapMap
Asian (JPT + CHB) population (release 22/NCBI, build 36, and dbSNP build 126) as a reference panel. We used posterior probability to call the genotype from imputation data and then performed association analyses on imputed data. Of these, SNPs with a posterior probability score < 0.90, high genotype information content (info < 0.5), HWE (P < 1 × 10 7), and MAF < 0.01 were omitted.
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Cardiovascular disease Association Study (CAVAS) cohort
Study participants were selected from an ongoing population based cohort, the Korean
Genome and Epidemiology Study (KoGES). Participants were recruited from among
residents aged 40 69 years of three rural cities Yangpyeong in Gyeonggi do province,
Goryeong in Gyeongsangbuk do province, and Namwon in Jeollabuk do province, Korea. A
total of 8,702 men and women were recruited from 2004 through 2008 for the baseline study.
Of them, 4,052 healthy participants with no history of hypertension, type 2 diabetes,
hyperlipidemia, heart disease, blood vessel disease of the brain, or cancer were selected for
SNP genotyping.
Genotyping and imputation A total of 4,034 CAVAS study samples were genotyped using
Illumina HumanOmni1 Quad v1.0. Genotypes were called using the BeadStudio for CAVAS.
Samples that exhibited the following properties were excluded: low genotyping calls (< 98%),
excessive heterozygosity, sex inconsistency, discordant ethnic membership, or cryptic
relatedness. Markers with high missing gene call rates (> 5%), low MAF (< 0.01), or
significant deviation from Hardy Weinberg equilibrium (P < 1 × 10 6) were excluded.
Imputation analysis was performed using IMPUTE against all of the HapMap Asian (JPT +
CHB) population (release 22/NCBI, build 36, and dbSNP build 126) as a reference panel. We
used posterior probability to call the genotype from imputation data and then performed
association analyses on imputed data. Of these, SNPs with a posterior probability
score < 0.90, high genotype information content (info < 0.5), HWE (P < 1 × 10 7), and MAF
< 0.01 were omitted.
Singapore Prospective study Program (SP2)
The Singapore Prospective Study Program (SP2) is a cross sectional study of 6,968 adult
Singaporean Chinese, Malay and Asian Indian men and women, aged 24 95
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years. Individuals who participated in previous cross sectional studies, the Thyroid and
Heart Study 1982–1984 (3), National Health Survey 1992 (4), National University of
Singapore Heart Study 1993–1995 (5), or National Health Survey 1998 (6), were invited to participate. 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). Individuals who were successfully re contacted and gave informed consent answered a questionnaire and attended a clinic examination. Height (m) and weight (kg) were measured similarly in all datasets using standard protocols and were used to derive BMI as weight over height squared
(kg/m2). Institutional review board approval was provided by the National Healthcare Group domain specific review board.
Genotyping and imputation Genotyping assays for the SDCS and SP2 were conducted together as these studies were originally part of a Singaporean Chinese case control study of type 2 diabetes. A total of 3,066 Chinese adults from the SP2 were genotyped using 1M duo v3 (n = 1,016), Human Hap 610 Quad (n = 1,467), and Hap 550 (n = 583) and 2,210 Chinese adult diabetes cases from the SDCS were genotyped using 1M duo v3 (n = 1,015) and Human
Hap 610 Quad (n = 1,195) Beadchips® (http://www.illumina.com/). SiMES Malay adults (n
= 3,072) were genotyped using Human Hap 610 Quad arrays.
Duplicate samples were plated for the SDCS and SP2 studies. A total of 8 duplicate samples were available for the SDCS study, while a total of 198 duplicates were available for the SP2 study. The average SNP concordance rate between chips for the post quality control
(QC) duplicated samples was computed based on 531,805 post QC common SNPs between
1M duo v3 and 610 Quad chips and 496,653 post QC common SNPs between the 1M duo v3 and 550 chips. The mean concordance was > 95%, and 5 discrepant SNPs were removed
(rs10953303, rs11075260, rs1447826, rs274646, and rs430794). For each array in each
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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 cluster files (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.
Samples were removed based on the following conditions: sample call rates of less
than 95%, excessive heterozygosity, cryptic relatedness, discordant ethnic membership, or
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, were used
to assess the overall distribution of missingness and heterozygosity across all the samples.
Identity by state measures were performed by pair wise comparison of samples to detect
cryptic relatedness such as monozygotic twins, full sibling pairs, and parent offspring pairs.
One sample from each relationship was excluded from further analysis and where duplicate
samples had been genotyped in different SNP arrays, samples from the denser array was
retained. Population structure ascertainment to prevent confounding of study results was
performed by using principal component analysis (PCA) with 4 panels from the International
HapMap Project (http://hapmap.ncbi.nlm.nih.gov) and the Singapore Genome Variation
Project (http://www.nus cme.org.sg/SGVP) with a thinned set of SNPs to reduce linkage
disequilibrium (LD). Individuals who showed ethnic membership discordant from their self
reported ethnicity were excluded from the analysis. For SiMES Malays who showed a
continuous cloud suggesting some degree of admixture, the first two principal components
were used for correction of population structure in association testing in SiMES. A total of
2,431 SP2 samples, 1,992 SDCS samples, and 2,522 SiMES samples with BMI data were
available after sample QC procedures.
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We excluded sex and mitochondrial SNPs, together with gross Hardy Weinberg equilibrium (HWE) outliers (P value < 1 × 10 4). SNPs that were monomorphic or rare
(minor allele frequency (MAF) < 1%) and SNPs with low call rates (< 95%) were also excluded. Where more than one chip was used for genotyping, Mantel extension tests were carried out to detect differences in allele frequencies of SNPs between the chips; 62 such
SNPs were detected in the SP2 and 69 such SNPs were detected in the SDCS and removed from the analyses.
Imputation procedures were performed using IMPUTE v0.5.0. (7) and genotype calls were based on HapMap Phase 1 and 2 East Asian samples (CHB and JPT) of NCBI build 36 for all Chinese samples (SP2, SDCS). For the Malay samples, all HapMap reference panels
(CEU, YRI and JPT + CHB) on NCBI build 36 were used for imputation to better capture local patterns of haplotype variation (8). Actual genotyped calls were placed back into the files and only imputed SNPs, and posterior probability ≥ 0.90 and call rate ≥ 95% were used.
Shanghai Breast Cancer Study (SBCS)
The SBCS, described in detail elsewhere (9; 10), included two recruitment phases. During the initial phase (SBCS I), 1,459 breast cancer patients and 1,556 controls were recruited between 1996 and 1998 through a rapid case ascertainment system and the population based
Shanghai Cancer Registry. Blood samples were obtained from 1,193 (82%) cases and 1,310
(84%) controls. The second phase of participant recruitment (SBCS II) was conducted between 2002 and 2005 using a protocol similar to the one used during the initial phase. A total of 1,989 incident cases and 1,918 community controls were recruited. The majority of cases (n = 1,932, 97.1%) and controls (n = 1,857, 96.8%) provided a blood sample or an exfoliated buccal cell sample. The age range of participants was 20 70 years with an average age of 50 years.
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Shanghai Men’s Health Study (SMHS)
The SMHS, described in detail elsewhere (11), is a population based cohort study of
61,504 Chinese men who were aged between 40 and 74 years, were free of cancer at
enrollment, and lived in urban Shanghai, China. Recruitment for the SMHS was initiated in
April 2002 and completed in June 2006. A total of 83,058 eligible male residents of eight
communities in urban Shanghai were invited to participate by trained interviewers through in
person contact; 61,504 enrolled in the study with a response rate of 74.0%. Reasons for non
participation were refusal (21.1%), out of area during enrollment (3.1%), and other
miscellaneous reasons including poor health or hearing problems (1.8%).
All participants of these studies were recruited in Shanghai during the same time
period using similar study protocols. Structured questionnaires including the same core
questions, were used to collected information on socio demographic factors, reproductive
history, lifestyle factors, and dietary habits. Anthropometrics, including weight, height, and
waist and hip circumferences were taken by trained interviewers. Blood samples were
collected using EDTA containing BD Vacutainer tubes from study participants at the time of
their in person interview. The lipid profiles were measured using an ACE Clinical Chemistry
System. Fasting status was defined as an interval between the last meal and blood draw of 8
hours or longer. A total of 2,017 participants from the SBCS and 291 participants from the
SMHS were included in this study.
Genotyping and imputation Genomic DNA was extracted from buffy coats by using a
Qiagen DNA purification kit (Valencia, CA) or Puregene DNA purification kit (Minneapolis,
MN) according to the manufacturers’ instructions and then used for genotyping assays. The
GWAS genotyping was performed using the Affymetrix Genome Wide Human SNP Array
6.0 (Affy 6.0) platform or Illumina 660, following manufacturers’ protocols. After sample
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quality control, we exclude SNPs with MAF < 0.01, call rate < 95%, and concordance rate <
95% among duplicated QC samples. Genotypes were imputed using the program MACH
(http://www.sph.umich.edu/csg/abecasis/MACH/download/), which determines the probable distribution of missing genotypes conditional on a set of known haplotypes, while simultaneously estimating the fine scale recombination map. Phased autosome SNP data from HapMap Phase II Asians (release 22) were used as the reference. To test for associations between the imputed SNP data with BMI, linear regression (additive model) was used, in which SNPs were represented by the expected allele count, an approach that takes into account the degree of uncertainty of genotype imputation
(http://www.sph.umich.edu/csg/abecasis/MACH/download/).
The Genetic Epidemiology Network of Salt-Sensitivity (GenSalt) study
GenSalt study participants were recruited from six sites in rural areas of northern China from
October 2003 to July 2005 (12). The selection of these study sites was based on the homogeneity of the study population with regard to ethnicity and environmental exposures, including lifestyle, nutritional factors, and habitual dietary intake. The residents in these regions are of the Han ethnicity, the ethnic majority in China. A community based blood pressure screening was conducted among persons aged 18 60 years in the study villages to identify potential probands and their families for the study. Those with a mean systolic blood pressure between 130 160 mmHg and/or diastolic blood pressure between 85 100 mmHg and no use of antihypertensive medications and their spouses, siblings, and offspring were recruited as volunteers for a dietary intervention study. In general, individuals who had stage
2 hypertension, secondary hypertension, use of antihypertensive medications, history of clinical cardiovascular disease, diabetes, chronic kidney disease, pregnancy, or heavy alcohol use were excluded from the study. A total of 1,906 individuals (1,010 men and 896 women)
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met the eligibility criteria for the dietary intervention study. Of these individuals, 1,843
(96.7%) completed the entire 21 day dietary intervention and were included in the GWAS.
The completeness of the study questionnaire data, blood pressure and anthropometric data,
and blood and urine sample collection is near 100%. The institutional review boards at all
participating institutes approved the study, and written, informed consent was obtained from
each participant.
A standard questionnaire was administered by trained staff at the baseline
examination to collect information on demographic characteristics, personal and family
medical history, and lifestyle risk factors (including cigarette smoking, alcohol consumption,
and physical activity). Three blood pressure measurements were obtained each morning
during the 3 day baseline examination by trained and certified observers using a random–zero
sphygmomanometer according to a standard protocol. Blood pressure was measured with
the participant in a seated position after 5 minutes of rest. In addition, participants were
advised to avoid consumption of alcohol, coffee or tea, or cigarettes and exercise for at least
30 minutes prior to their blood pressure measurements. Body weight, height, and waist
circumference were measured twice with the participant in light indoor clothing without
shoes. Waist circumference was measured one cm above the participant’s navel during
minimal respiration. Overnight (≥ 8 hours) fasting blood specimens were obtained for
measurement of glucose and lipids. Plasma glucose was measured using a modified
hexokinase enzymatic method (Hitachi automatic clinical analyser, model 7060, Japan).
Lymphocytic DNA samples were obtained from GenSalt family members (probands, parents,
spouses, siblings, and offspring). Genome wide SNPs were genotyped using Affymetrix®
Genome Wide Human Array 6.0 at the Affymetrix genotyping facility. After removing sex
linked SNPs, mitochondrial SNPs, and ‘unassigned’ SNPs that had no annotated
chromosomal location, 871,166 SNPs were chosen for examination. Strict procedures for
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extensive QC were used to check the data for any obvious errors, remove uninformative data, and find and remove all Mendelian errors in three stages. In stage 1, we removed participants with gender discrepancies between reported sex and that estimated by PLINK and those who had potential pedigree errors, as determined by GRR (13). In stage 2, we removed monomorphic SNPs, Affymetrix ‘housekeeping’ SNPs, SNPs with missing rates of >25% or a MAF of <1%. In the final stage, Mendelian errors were found and removed using PLINK
(14) and PedCheck (15). After the QC process, 820,015 autosomal SNPs from 1,881 participants remained. An additional 1,792,556 SNPs were imputed from a HapMap reference panel using data on 90 individuals from the JPT and CHB populations. The QC processes removed imputed SNPs with R2 < 0.3, MAF < 1%, Hardy Weinberg P value < 10 6, or
Mendelian errors. Finally, 2,216,774 autosomal SNPs were used for GWAS analyses.
Cardio-metabolic Genome Epidemiology (CAGE)
The Cardio metabolic Genome Epidemiology (CAGE) Network is an ongoing collaborative effort to investigate genetic and environmental factors and their interactions affecting cardiometabolic traits/disorders among Asians, including the Japanese (16 18). CAGE participants were recruited in a population based or hospital based setting, depending on the design of the member studies. Participation rates varied among the member studies
(approximately from 25% in the community based survey to 80% in the work place based survey). In the present meta analysis, 756 population based Japanese samples were used for preliminary screening of fasting glucose association after excluding individuals with diabetes.
These participants were enrolled at two separate sites, the Osaka and Shimane districts.
Participants in the Osaka district (n = 390) are part of the Japanese general population cohort samples (19), who have an annual check up. Participants in the Shimane district (n = 366) are people who visited the Shimane Institute of Health Science for a health screening
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examination between July 2003 and March 2007. In both populations, we included the
participants’ data in the test of fasting glucose association, when their blood samples were
collected after at least 6 h of fasting.
Genotyping and imputation Genotyping was performed with Infinium HumanHap550
BeadArray (Illumina, San Diego, CA, USA), which interrogated 550K SNPs, according to
the manufacturer’s protocol. Genotype calling was performed using BeadStudio software
(Illumina) and genotype calls with a GenCall Score < 0.53 were dropped from the analysis.
QC of SNPs and samples was performed as previously described (18). Briefly, data cleaning
and analysis were performed using PLINK software (version 1.06). Among the assayed SNPs,
we excluded SNPs for the following reasons: 1) a genotype call rate < 0.95; 2) a significant
(P < 10 6) deviation from HWE; or 3) MAF, < 0.01. The remaining 451,382 SNPs were
analyzed in the genome scan. The average call rate for the 451,382 QC’d SNPs was 99.7% in
756 samples tested for an association with fasting glucose level. Imputation of genotypes to
the HapMap Phase 2 (JPT + CHB) set was carried out using BEAGLE software (version
3.0.4).
Cebu Longitudinal Health and Nutrition Survey (CLHNS)
The Cebu Longitudinal Health and Nutrition Survey (CLHNS) is an ongoing community
based birth cohort study that began in 1983 (20). The baseline survey randomly recruited
3,327 pregnant women from the Metropolitan Cebu area, the Philippines in 1983 84 (3,080
singleton live births), and since followed them and their offspring to the present. Trained field
staff conducted in home interviews and collected anthropometric measurements at each visit.
Blood samples for biomarker measurement and DNA extraction were obtained in 2005. For
this study of 1,779 CLHNS mothers, weight, height, and the calculated BMI were ascertained
in the 2005 survey.
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Genotyping methods and quality control SNP genotyping, quality control and genotype imputation have been previously described (21). Briefly, SNP genotyping was performed with the Affymetrix Genome Wide Human SNP Array 5.0, using the standard protocol recommended by the manufacturer. Genotype calling was performed using Birdseed (version
2). The sample call rate was 99.6%. For marker quality control, SNPs with poor mapping, call rate < 90%, and/or deviation from Hardy Weinberg equilibrium (P < 10 6) were removed prior to imputation. We applied a hidden Markov model algorithm implemented in MACH to impute genotypes in CLHNS mother samples based on HapMap (Release 22) JPT + CHB samples. A total of 2,206,824 SNPs with MAF ≥ 0.01 and imputation quality Rsq > 0.3 were tested for associations using the software mach2qtl.
Cardiometabolic Risk in Chinese study (CRC)
The Cardiometabolic Risk in Chinese (CRC) Study is a community based health examination survey of 6,431 individuals (aged 18 93 years; 53.7% men) who were randomly selected from residents living in the urban area of Xuzhou, China in 2009. Written consent was obtained from each participant. The study was reviewed and approved by the ethics committee of the Central Hospital of Xuzhou, Affiliated Hospital of Medical School of
Southeast University, Nanjing, China (22; 23).
Genotyping and imputation A total of 811 study samples were included in a GWAS that was carried out on Illumina Human660 Quad Bead Chips at the Chinese National Human
Genome Center in Shanghai, China. Genotype clustering was conducted with Illumina
BeadStudio 3.3 software. Genotype imputation was performed by each participating study using either MACH. Height was measured to the nearest 0.5 cm without shoes.
The Korean Cancer Prevention Study-II (KCPS-II)
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The KCPS II has been described previously (24). The KCPS II included 266,258 individuals,
aged 20 77 years, who visited 16 health promotion centers across South Korea from April
2004 to December 2008. Participants were interviewed at baseline to obtain exposure data.
Cancer diagnoses were identified through 2008 using data from the national cancer registry
and hospitalization records. Mortality outcomes were ascertained through 2009 by reviewing
death certificates. A computerized search of death certificate data from the National
Statistical Office in Korea was performed using the unique identification number assigned at
birth. For the study, we selected 325 CRC patients who provided a blood sample. Cancer free
cohort members (n = 977) were randomly selected as controls. Therefore, a total of 1,302
individuals were genotyped.
Genotyping methods and quality control Cohort samples were genotyped on the
Affymetrix Genome wide Human SNP Array 5.0 at DNA Link. For the data obtained from
this chip, the following internal quality control (QC) measures were used: the QC call rate
(dynamic model algorithm) always exceeded 86%, and the heterozygosity of X chromosome
markers was used to identify the gender of each individual. Genotype calling was performed
with the Birdseed (v2) algorithm. A total of 1,004 individuals were genotyped via this
platform in the first discovery phase. However, 10 of 1,004 individuals were removed
because of low genotyping call rates (< 95%). PLINK (v1.07) was used to estimate identity
by state (IBS) over all SNPs, and four individuals were shown to be biological relatives, so
one member of each pair was excluded. Eleven individuals were also excluded as a result of
gender mismatches. Therefore, 979 individuals were available for this genome wide analysis.
A default set of 400,794 SNPs were used for further analysis, as recommended by Affymetrix.
For quality assurance screening, we flagged SNPs with genotype call rates < 95%, minor
allele frequencies (MAF) < 0.01, and SNPs showing deviation from Hardy Weinberg
equilibrium (HWE) at P < 0.0001. The final set of acceptable markers included 317,859
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Diabetes Page 54 of 66
autosomal SNPs.
1.2. Stage 2: de novo replication
Health2 cohort
Samples were selected from another community based cohort provided by the Health2 study.
We examined 7,861 individuals selected from the 8,500 participants. The participants were aged 40 69 years. The study objective and the strategy for clinical measurements of the
Health2 cohort were similar to those of the discovery stage participants (1; 2).
Genotyping Genotyping for the Health 2 data carried out by TaqMan (Applied Biosystems
Co., Ltd., Foster City, CA).
BioBank Japan
DNA samples were obtained from peripheral blood of individuals enrolled from an annual health check up conducted at the Hiroshima Atomic Bomb Casualty Council, Health
Management Center, National Center for Global Health and Medicine, Keio University or
Hiranuma Clinic.
Genotyping Genotyping was performed using multiplex polymerase chain reaction (PCR) invader assays. Genotyping success rates were > 95 %, and concordance rates examined in selected 188 duplicate samples were 100 %.
The Japanese Millenium Genome Project (JMGP)
JMPG comprises 7 independent study cohorts for studies of cardiovascular diseases and related risk factors. The Ohasama study of Tohoku University, the Shigarakiand Takashima studies of Shiga University of Medical Science, and the Nomura study and Toon health study
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Page 55 of 66 Diabetes
of Ehime University are general population based genetic epidemiological studies of subjects
recruited via a medical check up process for community residents. The Yokohama
(Yokohama City University) and Matsuyama (Ehime University) cohorts are derived from
employees of large manufacturing industries located in Kanagawa and Matsuyama City,
Ehime Prefecture (western part of Japan), respectively. In all cohorts, clinical parameters
were obtained from personal health records during the annual or biennial medical check up
process.
Genotyping Samples were genotyped using the TaqMan assay (Applied Biosystems by Life
Technologies, Carlsbad, CA). A total 10,299 participant whom fasting plasma glucose data
and genotyping results were available were included in the replication analysis.
Shanghai Jiao Tong University Diabetes Study (SJTUDS)
The subjects were selected from Shanghai Diabetes Study I and II, which were community
based random sample epidemiological studies of diabetes and related metabolic disorders. All
subjects (n = 3,412) were Chinese Hans with normal glucose tolerance as assessed by a
standard 75 g oral glucose tolerant tests (OGTTs), and negative family history of diabetes.
Phenotypes for anthropometric and biochemical traits related to glucose metabolism were
measured. OGTTs were performed in the morning after an overnight fast. Blood samples
were obtained at the fasting and 2 h during OGTTs. Plasma glucose and serum insulin were
measured. Basal insulin sensitivity and beta cell function were calculated from fasting plasma
glucose and insulin using HOMA.
Genotyping The SNPs were genotyped using primer extension of multiplex products with
detection by matrix assisted laser desorption ionization–time of flight mass spectroscopy
using a Mass ARRAY Compact Analyzer (Sequenom, San Diego, CA, USA). After quality
control, 3,300 subjects were included into the final analysis.
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Diabetes Page 56 of 66
Chinese University of Hong Kong Diabetes Study (CUHKS )
The study design, ascertainment, inclusion criteria and phenotyping of the study subjects have been described previously (25; 26). All subjects were of southern Han Chinese ancestry residing in Hong Kong. This study cohort consists of 474 adults (44.9% male, mean age 41.8
± 8.9 years, mean BMI 23.1 ± 3.4 years) with FPG < 6.1 mmol/l recruited from hospital staff and volunteers from a community based health screening program. This study was approved by the Clinical Research Ethics Committee of the Chinese University of Hong Kong. All participants gave written informed consent as appropriate.
Genotyping Genotyping was performed using primer extension of multiplex products with detection by MALDI TOF mass spectroscopy on a Sequenom MassARRAY platform (San
Diego, CA, USA).
2. SUPPLEMENTARY FIGURES AND TABLE LEGENDS
Supplementary Figure 1. Overall study work flow.
Supplementary Figure 2. Quantile-quantile plot for FPG. The observed P values (y axis) were compared with the expected P values under the null distribution (x axis) for FPG. The expected null distribution is plotted along the black diagonal. The entire distribution of observed P values is plotted in red. The distribution after excluding the previously reported
FPG loci is plotted in blue. The distribution after excluding all known and new loci is plotted in green.
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Supplementary Figure 3. Gene relationships among implicated loci (GRAIL)-VIZ plot
of functional connections.
Supplementary Table 1. Descriptive characteristics of cohort studies
Supplementary Table 2. Information for genotyping, Imputation and association
analysis
Supplementary Table 3. Association results of stage 1 meta-analysis for established FPG
loci
Supplementary Table 4. Evidence of association of 3 new loci with fasting plasma
glucose in Europeans from the MAGIC consortium
Supplementary Table 5. Associations for two KANK1 variants with glucose and
proinsulin levels in 8,633 METSIM samples
Supplementary Table 6. Association of SNPs rs10815355 and rs3824420 with insulin-
related traits such as fasting insulin and homeostatic model for assessment of beta-cell
function (HOMA-B) in KARE study
Supplementary Table 7. Comparison of SNP minor allele frequency and linkage
disequilibrium among ethnic groups
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Diabetes Page 58 of 66
Supplementary Table 8. Conditional analyses for SNPs, rs10815355 and rs3824420.
Association analysis of one SNP for fasting glucose was adjusted with the other SNP.
Supplementary Table 9. Conditional analyses for fasting glucose SNPs. The association of SNPs with fasting glucose was adjusted with insulin related traits.
Supplementary Table 10. Genetic association of 3 new FPG loci with type 2 diabetes.
Results of type 2 diabetes association were obtained from AGEN T2D consortium.
Supplementary Table 11. List of regions contained in the 43 loci identified by genome- wide association meta-analysis (using published PubMed abstracts prior to December,
2006) with the gene identified by GRAIL analysis. GRAIL PRegion value column presents the best significant PGene value adjusted for the multiple comparisons within a locus with at least one gene. The right panel presents the keywords over described in PubMed abstracts featuring the most connected genes functionally.
Supplementary Table 12. List of connection results from the GRAIL analysis for selected genes (PGene < 0.05) in Supplementary Table 11. GRAIL PGene values are the results for the test of the gene itself, and thus are not adjusted for the number of genes tested within a locus. Analysis results present connections between previously known fasting glucose associated genes based on their prior literature.
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