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Go, Hwang, Park, Kim, Oh, Kim, Han, and Kim: Genome-Wide Association Study Identifies Two Novel Loci with Sex-Specific Effects for Type 2 Diabetes Mellitus and Glycemic Traits in a Korean Population
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Diabetes & Metabolism Journal 2014; 38(5): 375-387.

Published online: 17 October 2014

DOI: https://doi.org/10.4093/dmj.2014.38.5.375

Genome-Wide Association Study Identifies Two Novel Loci with Sex-Specific Effects for Type 2 Diabetes Mellitus and Glycemic Traits in a Korean Population

Min Jin Go, Joo-Yeon Hwang, Tae-Joon Park, Young Jin Kim, Ji Hee Oh, Yeon-Jung Kim, Bok-Ghee Han, Bong-Jo Kim

Division of Structural and Functional Genomics, Center for Genome Science, Korea National Institute of Health, Cheongwon, Korea.

Corresponding author: Bong-Jo Kim. Division of Structural and Functional Genomics, Center for Genome Science, National Institute of Health, Korea Centers for Disease Control and Prevention, 187 Osongsaengmyeong2-ro, Yeonjae-ri, Osong-eup, Cheongwon 363-951, Korea. kbj6181@cdc.go.kr

Received 20 November 2013       Accepted 31 December 2013

Copyright © 2014 Korean Diabetes Association

(open-access):

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Until recently, genome-wide association study (GWAS)-based findings have provided a substantial genetic contribution to type 2 diabetes mellitus (T2DM) or related glycemic traits. However, identification of allelic heterogeneity and population-specific genetic variants under consideration of potential confounding factors will be very valuable for clinical applicability. To identify novel susceptibility loci for T2DM and glycemic traits, we performed a two-stage genetic association study in a Korean population.

Methods

We performed a logistic analysis for T2DM, and the first discovery GWAS was analyzed for 1,042 cases and 2,943 controls recruited from a population-based cohort (KARE, n=8,842). The second stage, de novo replication analysis, was performed in 1,216 cases and 1,352 controls selected from an independent population-based cohort (Health 2, n=8,500). A multiple linear regression analysis for glycemic traits was further performed in a total of 14,232 nondiabetic individuals consisting of 7,696 GWAS and 6,536 replication study participants. A meta-analysis was performed on the combined results using effect size and standard errors estimated for stage 1 and 2, respectively.

Results

A combined meta-analysis for T2DM identified two new (rs11065756 and rs2074356) loci reaching genome-wide significance in CCDC63 and C12orf51 on the 12q24 region. In addition, these variants were significantly associated with fasting plasma glucose and homeostasis model assessment of β-cell function. Interestingly, two independent single nucleotide polymorphisms were associated with sex-specific stratification in this study.

Conclusion

Our study showed a strong association between T2DM and glycemic traits. We further observed that two novel loci with multiple diverse effects were highly specific to males. Taken together, these findings may provide additional insights into the clinical assessment or subclassification of disease risk in a Korean population.

Keywords: Genome-wide association study, Glycemic trait, Sex-specific, Type 2 diabetes

INTRODUCTION

Type 2 diabetes mellitus (T2DM) is a major public health problem due to its rapidly rising incidence and prevalence worldwide [1]. T2DM, which is characterized by insulin resistance and hyperglycemia, is known to be associated with a marked increase in the risk for cardiovascular and metabolic diseases, such as obesity, hypertension, and dyslipidemia [2,3]. T2DM-related quantitative traits, such as fasting plasma glucose (FPG), fasting plasma insulin (FPI), homeostasis model assessment of β-cell function (HOMA-B), and homeostasis model assessment of insulin resistance (HOMA-IR), are diagnostic/prognostic indices [4].
The etiology of T2DM is affected by a multi-factorial interplay between genetic and environmental factors. Recent studies have demonstrated that genetic factors play an important role in fasting glucose variation, β-cell function, and insulin sensitivity as well as T2DM [5,6,7,8]. Genome-wide association study (GWAS)-based findings have provided compelling evidence of T2DM loci, which show an association with glycemic traits as an independent factor [9,10,11,12].
Although over 75 loci for T2DM have been intensively identified through genome-wide meta-analyses of multi-ancestry [13,14,15,16], progress towards understanding the genetic basis for allelic heterogeneity or population-specific genetic effects has not been fully explored in East-Asian populations [17,18,19]. To gain insight into the genetic heterogeneity of T2DM, we identified ethnic-specific novel variants influencing T2DM and extended their association with glycemic traits in a Korean population.

METHODS

Study subjects

Stage 1 subjects for the GWA screen were recruited from the Korea Association Resource (KARE) [20]. In this study, 1,042 subjects were included as T2DM cases according to the following criteria: (1) treatment of T2DM; (2) FPG ≥7 mmol/L or plasma glucose 2-hour after ingestion of 75 g oral glucose ≥11.1 mmol/L; and (3) age of disease onset ≥40 years. The inclusion criteria of nondiabetic control subjects (n=2,943) were as follows: (1) no history of diabetes; and (2) FPG <5.6 mmol/L and plasma glucose 2-hour after ingestion of 75 g oral glucose <7.8 mmol/L at both baseline and follow-up studies. For GWA analysis of T2DM related quantitative traits (such as FPG, FPI, HOMA-B, and HOMA-IR values), 7,696 nondiabetic subjects were selected from KARE study participants.
Stage 2 subjects for the replication study were selected from another population-based cohort, Health2 cohort, which consisted of a total of 8,500 participants aged 40 to 69 years from five regional cities in Korea [20]. The stage 2 criteria for grouping T2DM cases (n=1,216) and nondiabetic controls (n=1,352) were the same as those for stage 1, except stage 2 control subjects were >60 years old and had no first-degree relatives with diabetes. For association analysis between T2DM related quantitative traits and single nucleotide polymorphisms (SNPs) that passed the stage 1 threshold, 6,536 nondiabetic subjects were selected from Health2 study participants.
All studies were approved by the ethics review committees of the respective institutes and all participants provided their written informed consent.

Genotyping

KARE study subjects were genotyped using the Affymetrix Genome-Wide Human SNP array 5.0 (Affymetrix Inc., Santa Clara, CA, USA) [20]. Only unrelated samples with a missing genotype call rate below 4% and a heterozygosity of less than 30% were included for subsequent GWA analyses for T2DM and its related traits. SNPs with a high missing gene call rate (>5%), low minor allele frequency (MAF; <0.01) and significant deviation from Hardy-Weinberg equilibrium (P<1×10-6) were eliminated before association analyses. Detailed criteria for sample and SNP filtering from KARE genome-wide scan are available by Cho et al. [20].
Among the 21 SNPs taken forward to the stage 2 replication study (Appendix 1), four SNPs (rs4376068, rs11086668, rs-6439472, and rs2074356) were genotyped by an allelic discrimination assay using the TaqMan reaction (Applied Biosystems, Foster City, CA, USA), while 17 SNPs were genotyped by the GoldenGate assay in a set of 384 multiplexed SNPs (Illumina Inc., San Diego, CA, USA) from approximately 7,861 Health2 study subjects. For three SNPs (rs360481, rs6439472, and rs4777379), we were not able to design a functional assay on either platform. Duplicate genotyping for approximately 1% to 2.5% of samples was performed as a quality control. All 21 genotyped SNPs satisfied a concordance rate in duplicates of over 99% and a genotype success rate over 98%.

SNP imputation

In each GWAS data, imputation analysis was performed using IMPUTE against all of the HapMap Asian (Japanese in Tokyo, Japan+Han Chinese in Beijing, China) population (release 22/NCBI build 36 and dbSNP build 126 for total 4,573,409 SNPs [21]. Of these SNPs, we removed SNPs with a posterior probability score <0.90, low genotype information content (info <0.5), HWE (P<1×10-7), MAF<0.01, and SNP missing rate >0.1.

Association analyses

An association analysis was performed using PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/) and SAS programs version 9.1 (SAS institute Inc., Cary, NC, USA). Associations between SNPs and T2DM were tested using logistic regression analysis with an additive model (1-degree of freedom) after adjusting for age, sex, BMI, and recruitment area. T2DM related quantitative traits, such as FPG, FPI, HOMA-B, and HOMA-IR, were tested for association using the linear regression analysis with an additive model (1-degree of freedom) after adjusting for age, sex, BMI, and recruitment area. HOMA values were estimated by the HOMA Calculator v2.2.2 program (http://www.dtu.ox.ac.uk/index.php?maindoc=/homa/index.php) using FPG and insulin concentrations. Among the T2DM related traits, FPI, HOMA-B, and HOMA-IR showing a skewed distribution of measurements were transformed by the natural log before the association analysis. Stage 1 and 2 association results were combined using an inverse-variance meta-analysis method assuming fixed effects with Cochran's Q test, which was used to assess between-study heterogeneity [22]. To detect the significant evidence of the sex-specific association of SNPs with T2DM and its related traits, the interaction between SNPs and sex was tested by a likelihood ratio test for a linear regression model.

RESULTS

Stage 1 KARE GWA for T2DM was analyzed using the trend test while controlling for age, sex, BMI, and recruitment area as covariates. The quantile-quantile plot of the observed P values derived from the trend test showed a significant deviation from the null distribution only in the tail, which suggested an association of these SNPs with T2DM (Fig. 1A). The estimated genomic control inflation factor (λ) was 1.008, which indicated limited evidence of population stratification in the KARE study samples. Stage 1 association results revealed 24 independent SNPs (pair-wise linkage disequilibrium [LD] statistic r2 <0.2 within a genomic region 500-Kb window) that passed our arbitrary stage 1 threshold for replication (GWAS P value of <10-4 and MAF of ≥0.01 in T2DM cases and controls) (Fig. 1B). We were able to genotype 21 of 24 selected SNPs in our stage 2 samples of 1,216 patients and 1,352 controls recruited to the Health2 study cohort from five different regions of the country; we analyzed their association with T2DM and performed a meta-analysis of the stage 1 and stage 2 results (Appendix 1).
Nine independent SNPs were replicated from the stage 2 association analysis, four of which showed strong evidence of an association with T2DM and achieved genome-wide significance (P<5×10-8) (Table 1). Rs7754840 on chromosome 6p22.3 localizes to CDKAL1 (odds ratio [OR], 1.30; 95% confidence interval [CI], 1.19 to 1.41; P=8.16×10-10), and rs10811661 on chromosome 9p21.3 localizes near CDKN2A/B (OR, 1.29; 95% CI, 1.18 to 1.40; P=6.14×10-9) (Table 1). Both SNPs have been previously reported for their association with T2DM [23,24,25]. Two novel T2DM-associated loci were found in the chromosome 12q24 region, one on 12q24.11 (rs11065756; OR, 1.40; 95% CI, 1.25 to 1.56; P=9.43×10-9) in intron 9 of CCDC63 (coiled-coil domain containing 63) and the other on 12q24.13 (rs2074356; OR, 1.43; 95% CI, 1.27 to 1.62; P=1.12×10-8) in intron 30 ofC12orf51 (Fig. 2A) with different patterns of LD (Fig. 2B). To dissect locus heterogeneity by an interrelationship between these two loci in the 12q24 region (~1.7 Mb), we performed conditional association analyses in which each SNP (rs11065756 and rs2074356) was tested for an association with T2DM after adjusting for the genotype of the other SNP as a covariate. The strength of the association for each SNP was not significantly decreased after adjustment (data not shown), which demonstrated that the T2DM association signals detected by both loci were independent for one variant phenotype.
The T2DM-associated SNPs were further tested for their association with T2DM-related quantitative traits, such as FPG, FPI, HOMA-B, and HOMA-IR, in a total of 14,232 nondiabetic individuals consisting of 7,696 GWA and 6,536 subsequent replication study samples. Along with two SNPs previously associated with T2DM, rs7754840 in CDKAL1 (P=1.73×10-8, β=0.0324±0.0058 mmol/L) and rs10811661 near CDKN2A/B (P=8.28×10-7, β=0.0286±0.0058 mmol/L), these two new loci, rs11065756 in CCDC63 (P=2.41×10-6, β=0.0360±0.0076 mmol/L) and rs2074356 in C12orf51 (P=1.21×10-13, β=0.0606±0.0082 mmol/L) showed compelling evidence for an association with FPG (Table 2). Consistent evidence for an association with T2DM at these loci was further detected for reduced β-cell function as assessed using HOMA-B (rs7754840 in CDKAL1, P=3.29×10-4, β=-0.0160±0.0044; rs10811661 near CDKN2A/B, P=1.34 ×10-3, β=-0.0142±0.0044; rs11065756 in CCDC63, P=3.56 ×10-4, β=-0.0207±0.0058; rs2074356 in C12orf51, P=1.27 ×10-9, β=-0.0376±0.0062). In contrast, these variants responsible for elevated levels of FPG and reduced HOMA-B did not show an effect on FPI or insulin sensitivity as estimated using HOMA-IR (Table 2).
To gain insight into the sex-specific heritability of the loci identified in our study on T2DM and its related traits, we performed sex-specific analyses and looked for evidence of an interaction of these loci with sex by performing a likelihood ratio test within a linear regression model. Male-specific association was observed for only two 12q24 loci for T2DM, FPG and HOMA-B. This sex-specific stratification was further demonstrated by a significant SNP-sex interaction. However, previous SNPs (rs7754840 in CDKAL1 and rs10811661 in CDKN2A/B) did not generate significant evidence for sex-specificity (Table 3).

DISCUSSION

Considering the effect of common diseases of major public health, the genetic architecture of T2DM has been intensively studied using GWA analyses for many years [10,13,25,26,27,28,29]. Most previous T2DM-associated GWAS-findings were identified from European ancestries, but those from non-European groups were not fully understood on the basis of the genetic effects of ethnic predisposition on T2DM. We performed GWAS and follow-up replication analysis in a Korean population. Our meta-analysis newly identified two genome-wide significant associations as novel predisposing factors for T2DM and glycemic traits, such as FPG and HOMA-B.
To address population-specific genetic heterogeneity [30], we tested lookup validation for the newly identified SNPs (rs11065756 in CCDC63 and rs2074356 in C12orf51) in 1,924 cases and 2,938 controls from the Wellcome Trust Case Control Consortium (WTCCC). A significant association between T2DM and rs11065756 in CCDC63 (P=0.37) was not detected from WTCCC samples. Importantly, the minor A allele frequency of SNP rs11065756 in WTCCC samples was 0.07, whereas that in KARE study samples was 0.19. For rs2074356 in C12orf51, an association analysis could not be performed in WTCCC samples because this SNP was monomorphic in Europeans (minor T allele frequency is 0.00). In KARE study samples, the minor T allele frequency of this SNP was 0.16. Taken together, the MAF differences in these two SNPs between Asian (Korean) and European populations may reflect different association results for T2DM in the two populations. This comparison demonstrates that the difference in allele frequency is the one of the major factors indicating population-specificity of disease-causing alleles. Previous Eurocentric GWAS findings, which were not replicated in our study, showed a considerable difference in MAF between Asian and European populations [18].
A 12q24 region has been linked to T2DM and obesity related traits [31,32,33,34] in numerous studies. Recent GWAS also demonstrated genetic associations of 12q24 loci with type 1 diabetes mellitus (T1DM), which indicate SH2B3 (SH2B adaptor protein 3), TRAFD1 (TRAF-type zinc finger domain containing 1), and PTPN11 (protein tyrosine phosphatase, nonreceptor type 11) as T1DM candidate genes [35,36,37]. In addition, multiple diverse effects have been reported for its association with 1-hour plasma glucose [38], waist hip ratio [20], metabolic traits [39], congenital heart defect [40], drinking behavior [41], and blood pressure [42,43] from current GWASs, which suggest its pleiotropic effects [44].
Furthermore, interactions between genetic variants and sex-specificity have been reported in the chromosome 12q24 region for T2DM [45] and obesity-related traits [32,34,46]. In this study, we observed male-specific associations in susceptibility to T2DM and T2DM-related traits, such as FPG and HOMA-B (Table 3). Male-specific genetic effects were reported to have strong associations with liver aminotransferase levels [47], alcohol consumption [48] and hypertension [49]. This sex-specific genetic architecture was further demonstrated by sex-biased genetic effects on gene regulation [50].
In conclusion, our meta-analysis demonstrated multiple signals and significant sex differences for T2DM and T2DM-related traits as well as ethnic-specific characteristics in a Korean population. This information will be analyzed to gain further insight into gene-gene and gene-environment interaction. We propose that GWAS-based findings in a systematic consideration of potential confounding factors, such as genetic heterogeneity, ethnic predisposition, gender specificity, and pleiotropic effects, may provide predictive or prognostic genetic information to a wider clinical application.

Figures and Tables

Fig. 1
Genome-wide association of single nucleotide polymorphisms (SNPs) with type 2 diabetes mellitus (T2DM) in Korea Association Resource (KARE) study samples. (A) Quantile-quantile plot for test statistics. The observed P values were plotted as a function of the expected P values of the null distribution for T2DM. The shaded region represents the 95% concentration band. (B) Scatter plots of P values derived from genome-wide scan results for T2DM. Single-marker tests of association with T2DM were scrutinized by the 1 degree of freedom trend test. The trend test P value of each SNP is plotted (Y axis) as -log10 (P) according to its chromosomal location (X axis). SNPs from the KARE genome-wide association study with P value <10-4 are shown in red.
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Fig. 2
Signal region on chromosome 12q24 covering type 2 diabetes mellitus (T2DM)-associated loci. (A) Signal plot of -log10 (P values) using the trend test for T2DM association in a genomic region (in Mb). Black and gray dots indicate genotyped single nucleotide polymorphisms (SNPs) in Korea Association Resource genome-wide association study and imputed SNPs, respectively. Red diamonds indicate the strongest association signals detected in the genome-wide scan. Genomic positions are based on National Center for Biotechnology Information (NCBI) genome build 36 and dbSNP build 128. In the bottom of the signal plot, the locations of known genes are indicated with red boxes and green lines, which indicate exons and introns, respectively. Genetic information was obtained from NCBI build 36. (B) Plot of linkage disequilibrium (r2) for all SNPs across the region from Japanese in Tokyo, Japan and Han Chinese in Beijing, China founders in HapMap (release 22). This plot was generated using the Haploview 4.1 program.
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Table 1
Genetic loci associated with type 2 diabetes mellitus after adjusting for age, sex, body mass index, and recruitment area
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SNP, single nucleotide polymorphism; CHR, chromosome; KARE, Korea Association Resource; GWAS, genome-wide association study; RAF, risk allele frequency; OR, odds ratio; CI, confidence interval.

Table 2
Corroborative association of strong type 2 diabetes mellitus-associated single nucleotide polymorphisms with glycemic traits
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RAF, risk allele frequency; HOMA-B, homeostasis model assessment of β-cell function; HOMA-IR, homeostasis model assessment of insulin resistance.

Table 3
Results of a sex-specific effect for type 2 diabetes mellitus (T2DM) and T2DM-related traits
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CI, confidence interval; BMI, body mass index; FPG, fasting plasma glucose; HOMA-B, homeostasis model assessment of β-cell function.

ACKNOWLEDGMENTS

This work was supported by a grant obtained from the Korea Center for Disease Control and Prevention (4845-301, 4851-302, 4851-307) and an intramural grant obtained from the Korea National Institute of Health (2012-N73002-00).

Notes

No potential conflict of interest relevant to this article was reported.

Appendix

Appendix 1

Twenty-four type 2 diabetes mellitus-associated single nucleotide polymorphisms taken to the stage 2 replication study

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References

1. Zimmet P, Alberti KG, Shaw J. Global and societal implications of the diabetes epidemic. Nature. 2001; 414:782–787.
2. Stumvoll M, Goldstein BJ, van Haeften TW. Type 2 diabetes: principles of pathogenesis and therapy. Lancet. 2005; 365:1333–1346.
3. Tkac I. Metabolic syndrome in relationship to type 2 diabetes and atherosclerosis. Diabetes Res Clin Pract. 2005; 68:Suppl1. S2–S9.
4. Jenkins AB, Samaras K, Carey DG, Kelly P, Campbell LV. Improved indices of insulin resistance and insulin secretion for use in genetic and population studies of type 2 diabetes mellitus. Twin Res. 2000; 3:148–151.
5. Tirosh A, Shai I, Tekes-Manova D, Israeli E, Pereg D, Shochat T, Kochba I, Rudich A. Israeli Diabetes Research Group. Normal fasting plasma glucose levels and type 2 diabetes in young men. N Engl J Med. 2005; 353:1454–1462.
6. Bouatia-Naji N, Rocheleau G, Van Lommel L, Lemaire K, Schuit F, Cavalcanti-Proenca C, Marchand M, Hartikainen AL, Sovio U, De Graeve F, Rung J, Vaxillaire M, Tichet J, Marre M, Balkau B, Weill J, Elliott P, Jarvelin MR, Meyre D, Polychronakos C, Dina C, Sladek R, Froguel P. A polymorphism within the G6PC2 gene is associated with fasting plasma glucose levels. Science. 2008; 320:1085–1088.
7. Vaxillaire M, Veslot J, Dina C, Proenca C, Cauchi S, Charpentier G, Tichet J, Fumeron F, Marre M, Meyre D, Balkau B, Froguel P. DESIR Study Group. Impact of common type 2 diabetes risk polymorphisms in the DESIR prospective study. Diabetes. 2008; 57:244–254.
8. Chen WM, Erdos MR, Jackson AU, Saxena R, Sanna S, Silver KD, Timpson NJ, Hansen T, Orru M, Grazia Piras M, Bonnycastle LL, Willer CJ, Lyssenko V, Shen H, Kuusisto J, Ebrahim S, Sestu N, Duren WL, Spada MC, Stringham HM, Scott LJ, Olla N, Swift AJ, Najjar S, Mitchell BD, Lawlor DA, Smith GD, Ben-Shlomo Y, Andersen G, Borch-Johnsen K, Jorgensen T, Saramies J, Valle TT, Buchanan TA, Shuldiner AR, Lakatta E, Bergman RN, Uda M, Tuomilehto J, Pedersen O, Cao A, Groop L, Mohlke KL, Laakso M, Schlessinger D, Collins FS, Altshuler D, Abecasis GR, Boehnke M, Scuteri A, Watanabe RM. Variations in the G6PC2/ABCB11 genomic region are associated with fasting glucose levels. J Clin Invest. 2008; 118:2620–2628.
9. Bouatia-Naji N, Bonnefond A, Cavalcanti-Proenca C, Sparso T, Holmkvist J, Marchand M, Delplanque J, Lobbens S, Rocheleau G, Durand E, De Graeve F, Chevre JC, Borch-Johnsen K, Hartikainen AL, Ruokonen A, Tichet J, Marre M, Weill J, Heude B, Tauber M, Lemaire K, Schuit F, Elliott P, Jorgensen T, Charpentier G, Hadjadj S, Cauchi S, Vaxillaire M, Sladek R, Visvikis-Siest S, Balkau B, Levy-Marchal C, Pattou F, Meyre D, Blakemore AI, Jarvelin MR, Walley AJ, Hansen T, Dina C, Pedersen O, Froguel P. A variant near MTNR1B is associated with increased fasting plasma glucose levels and type 2 diabetes risk. Nat Genet. 2009; 41:89–94.
10. Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, Wheeler E, Glazer NL, Bouatia-Naji N, Gloyn AL, Lindgren CM, Magi R, Morris AP, Randall J, Johnson T, Elliott P, Rybin D, Thorleifsson G, Steinthorsdottir V, Henneman P, Grallert H, Dehghan A, Hottenga JJ, Franklin CS, Navarro P, Song K, Goel A, Perry JR, Egan JM, Lajunen T, Grarup N, Sparso T, Doney A, Voight BF, Stringham HM, Li M, Kanoni S, Shrader P, Cavalcanti-Proenca C, Kumari M, Qi L, Timpson NJ, Gieger C, Zabena C, Rocheleau G, Ingelsson E, An P, O'Connell J, Luan J, Elliott A, McCarroll SA, Payne F, Roccasecca RM, Pattou F, Sethupathy P, Ardlie K, Ariyurek Y, Balkau B, Barter P, Beilby JP, Ben-Shlomo Y, Benediktsson R, Bennett AJ, Bergmann S, Bochud M, Boerwinkle E, Bonnefond A, Bonnycastle LL, Borch-Johnsen K, Bottcher Y, Brunner E, Bumpstead SJ, Charpentier G, Chen YD, Chines P, Clarke R, Coin LJ, Cooper MN, Cornelis M, Crawford G, Crisponi L, Day IN, de Geus EJ, Delplanque J, Dina C, Erdos MR, Fedson AC, Fischer-Rosinsky A, Forouhi NG, Fox CS, Frants R, Franzosi MG, Galan P, Goodarzi MO, Graessler J, Groves CJ, Grundy S, Gwilliam R, Gyllensten U, Hadjadj S, Hallmans G, Hammond N, Han X, Hartikainen AL, Hassanali N, Hayward C, Heath SC, Hercberg S, Herder C, Hicks AA, Hillman DR, Hingorani AD, Hofman A, Hui J, Hung J, Isomaa B, Johnson PR, Jorgensen T, Jula A, Kaakinen M, Kaprio J, Kesaniemi YA, Kivimaki M, Knight B, Koskinen S, Kovacs P, Kyvik KO, Lathrop GM, Lawlor DA, Le Bacquer O, Lecoeur C, Li Y, Lyssenko V, Mahley R, Mangino M, Manning AK, Martinez-Larrad MT, McAteer JB, McCulloch LJ, McPherson R, Meisinger C, Melzer D, Meyre D, Mitchell BD, Morken MA, Mukherjee S, Naitza S, Narisu N, Neville MJ, Oostra BA, Orru M, Pakyz R, Palmer CN, Paolisso G, Pattaro C, Pearson D, Peden JF, Pedersen NL, Perola M, Pfeiffer AF, Pichler I, Polasek O, Posthuma D, Potter SC, Pouta A, Province MA, Psaty BM, Rathmann W, Rayner NW, Rice K, Ripatti S, Rivadeneira F, Roden M, Rolandsson O, Sandbaek A, Sandhu M, Sanna S, Sayer AA, Scheet P, Scott LJ, Seedorf U, Sharp SJ, Shields B, Sigurethsson G, Sijbrands EJ, Silveira A, Simpson L, Singleton A, Smith NL, Sovio U, Swift A, Syddall H, Syvanen AC, Tanaka T, Thorand B, Tichet J, Tonjes A, Tuomi T, Uitterlinden AG, van Dijk KW, van Hoek M, Varma D, Visvikis-Siest S, Vitart V, Vogelzangs N, Waeber G, Wagner PJ, Walley A, Walters GB, Ward KL, Watkins H, Weedon MN, Wild SH, Willemsen G, Witteman JC, Yarnell JW, Zeggini E, Zelenika D, Zethelius B, Zhai G, Zhao JH, Zillikens MC. DIAGRAM Consortium. GIANT Consortium. Global BPgen Consortium. Borecki IB, Loos RJ, Meneton P, Magnusson PK, Nathan DM, Williams GH, Hattersley AT, Silander K, Salomaa V, Smith GD, Bornstein SR, Schwarz P, Spranger J, Karpe F, Shuldiner AR, Cooper C, Dedoussis GV, Serrano-Rios M, Morris AD, Lind L, Palmer LJ, Hu FB, Franks PW, Ebrahim S, Marmot M, Kao WH, Pankow JS, Sampson MJ, Kuusisto J, Laakso M, Hansen T, Pedersen O, Pramstaller PP, Wichmann HE, Illig T, Rudan I, Wright AF, Stumvoll M, Campbell H, Wilson JF. Anders Hamsten on behalf of Procardis Consortium. MAGIC investigators. Bergman RN, Buchanan TA, Collins FS, Mohlke KL, Tuomilehto J, Valle TT, Altshuler D, Rotter JI, Siscovick DS, Penninx BW, Boomsma DI, Deloukas P, Spector TD, Frayling TM, Ferrucci L, Kong A, Thorsteinsdottir U, Stefansson K, van Duijn CM, Aulchenko YS, Cao A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Waterworth DM, Vollenweider P, Peltonen L, Mooser V, Abecasis GR, Wareham NJ, Sladek R, Froguel P, Watanabe RM, Meigs JB, Groop L, Boehnke M, McCarthy MI, Florez JC, Barroso I. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010; 42:105–116.
11. Lyssenko V, Nagorny CL, Erdos MR, Wierup N, Jonsson A, Spegel P, Bugliani M, Saxena R, Fex M, Pulizzi N, Isomaa B, Tuomi T, Nilsson P, Kuusisto J, Tuomilehto J, Boehnke M, Altshuler D, Sundler F, Eriksson JG, Jackson AU, Laakso M, Marchetti P, Watanabe RM, Mulder H, Groop L. Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion. Nat Genet. 2009; 41:82–88.
12. Prokopenko I, Langenberg C, Florez JC, Saxena R, Soranzo N, Thorleifsson G, Loos RJ, Manning AK, Jackson AU, Aulchenko Y, Potter SC, Erdos MR, Sanna S, Hottenga JJ, Wheeler E, Kaakinen M, Lyssenko V, Chen WM, Ahmadi K, Beckmann JS, Bergman RN, Bochud M, Bonnycastle LL, Buchanan TA, Cao A, Cervino A, Coin L, Collins FS, Crisponi L, de Geus EJ, Dehghan A, Deloukas P, Doney AS, Elliott P, Freimer N, Gateva V, Herder C, Hofman A, Hughes TE, Hunt S, Illig T, Inouye M, Isomaa B, Johnson T, Kong A, Krestyaninova M, Kuusisto J, Laakso M, Lim N, Lindblad U, Lindgren CM, McCann OT, Mohlke KL, Morris AD, Naitza S, Orru M, Palmer CN, Pouta A, Randall J, Rathmann W, Saramies J, Scheet P, Scott LJ, Scuteri A, Sharp S, Sijbrands E, Smit JH, Song K, Steinthorsdottir V, Stringham HM, Tuomi T, Tuomilehto J, Uitterlinden AG, Voight BF, Waterworth D, Wichmann HE, Willemsen G, Witteman JC, Yuan X, Zhao JH, Zeggini E, Schlessinger D, Sandhu M, Boomsma DI, Uda M, Spector TD, Penninx BW, Altshuler D, Vollenweider P, Jarvelin MR, Lakatta E, Waeber G, Fox CS, Peltonen L, Groop LC, Mooser V, Cupples LA, Thorsteinsdottir U, Boehnke M, Barroso I, Van Duijn C, Dupuis J, Watanabe RM, Stefansson K, McCarthy MI, Wareham NJ, Meigs JB, Abecasis GR. Variants in MTNR1B influence fasting glucose levels. Nat Genet. 2009; 41:77–81.
13. Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, Welch RP, Zeggini E, Huth C, Aulchenko YS, Thorleifsson G, McCulloch LJ, Ferreira T, Grallert H, Amin N, Wu G, Willer CJ, Raychaudhuri S, McCarroll SA, Langenberg C, Hofmann OM, Dupuis J, Qi L, Segre AV, van Hoek M, Navarro P, Ardlie K, Balkau B, Benediktsson R, Bennett AJ, Blagieva R, Boerwinkle E, Bonnycastle LL, Bengtsson Bostrom K, Bravenboer B, Bumpstead S, Burtt NP, Charpentier G, Chines PS, Cornelis M, Couper DJ, Crawford G, Doney AS, Elliott KS, Elliott AL, Erdos MR, Fox CS, Franklin CS, Ganser M, Gieger C, Grarup N, Green T, Griffin S, Groves CJ, Guiducci C, Hadjadj S, Hassanali N, Herder C, Isomaa B, Jackson AU, Johnson PR, Jorgensen T, Kao WH, Klopp N, Kong A, Kraft P, Kuusisto J, Lauritzen T, Li M, Lieverse A, Lindgren CM, Lyssenko V, Marre M, Meitinger T, Midthjell K, Morken MA, Narisu N, Nilsson P, Owen KR, Payne F, Perry JR, Petersen AK, Platou C, Proenca C, Prokopenko I, Rathmann W, Rayner NW, Robertson NR, Rocheleau G, Roden M, Sampson MJ, Saxena R, Shields BM, Shrader P, Sigurdsson G, Sparso T, Strassburger K, Stringham HM, Sun Q, Swift AJ, Thorand B, Tichet J, Tuomi T, van Dam RM, van Haeften TW, van Herpt T, van Vliet-Ostaptchouk JV, Walters GB, Weedon MN, Wijmenga C, Witteman J, Bergman RN, Cauchi S, Collins FS, Gloyn AL, Gyllensten U, Hansen T, Hide WA, Hitman GA, Hofman A, Hunter DJ, Hveem K, Laakso M, Mohlke KL, Morris AD, Palmer CN, Pramstaller PP, Rudan I, Sijbrands E, Stein LD, Tuomilehto J, Uitterlinden A, Walker M, Wareham NJ, Watanabe RM, Abecasis GR, Boehm BO, Campbell H, Daly MJ, Hattersley AT, Hu FB, Meigs JB, Pankow JS, Pedersen O, Wichmann HE, Barroso I, Florez JC, Frayling TM, Groop L, Sladek R, Thorsteinsdottir U, Wilson JF, Illig T, Froguel P, van Duijn CM, Stefansson K, Altshuler D, Boehnke M, McCarthy MI. MAGIC investigators. GIANT Consortium. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet. 2010; 42:579–589.
14. Sanghera DK, Blackett PR. Type 2 diabetes genetics: beyond GWAS. J Diabetes Metab. 2012; 3:pii6948.
15. Morris AP, Voight BF, Teslovich TM, Ferreira T, Segre AV, Steinthorsdottir V, Strawbridge RJ, Khan H, Grallert H, Mahajan A, Prokopenko I, Kang HM, Dina C, Esko T, Fraser RM, Kanoni S, Kumar A, Lagou V, Langenberg C, Luan J, Lindgren CM, Muller-Nurasyid M, Pechlivanis S, Rayner NW, Scott LJ, Wiltshire S, Yengo L, Kinnunen L, Rossin EJ, Raychaudhuri S, Johnson AD, Dimas AS, Loos RJ, Vedantam S, Chen H, Florez JC, Fox C, Liu CT, Rybin D, Couper DJ, Kao WH, Li M, Cornelis MC, Kraft P, Sun Q, van Dam RM, Stringham HM, Chines PS, Fischer K, Fontanillas P, Holmen OL, Hunt SE, Jackson AU, Kong A, Lawrence R, Meyer J, Perry JR, Platou CG, Potter S, Rehnberg E, Robertson N, Sivapalaratnam S, Stancakova A, Stirrups K, Thorleifsson G, Tikkanen E, Wood AR, Almgren P, Atalay M, Benediktsson R, Bonnycastle LL, Burtt N, Carey J, Charpentier G, Crenshaw AT, Doney AS, Dorkhan M, Edkins S, Emilsson V, Eury E, Forsen T, Gertow K, Gigante B, Grant GB, Groves CJ, Guiducci C, Herder C, Hreidarsson AB, Hui J, James A, Jonsson A, Rathmann W, Klopp N, Kravic J, Krjutskov K, Langford C, Leander K, Lindholm E, Lobbens S, Mannisto S, Mirza G, Muhleisen TW, Musk B, Parkin M, Rallidis L, Saramies J, Sennblad B, Shah S, Sigurethsson G, Silveira A, Steinbach G, Thorand B, Trakalo J, Veglia F, Wennauer R, Winckler W, Zabaneh D, Campbell H, van Duijn C, Uitterlinden AG, Hofman A, Sijbrands E, Abecasis GR, Owen KR, Zeggini E, Trip MD, Forouhi NG, Syvanen AC, Eriksson JG, Peltonen L, Nothen MM, Balkau B, Palmer CN, Lyssenko V, Tuomi T, Isomaa B, Hunter DJ, Qi L. Wellcome Trust Case Control Consortium. Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) Investigators. Genetic Investigation of ANthropometric Traits (GIANT) Consortium. Asian Genetic Epidemiology Network-Type 2 Diabetes (AGEN-T2D) Consortium. South Asian Type 2 Diabetes (SAT2D) Consortium. Shuldiner AR, Roden M, Barroso I, Wilsgaard T, Beilby J, Hovingh K, Price JF, Wilson JF, Rauramaa R, Lakka TA, Lind L, Dedoussis G, Njolstad I, Pedersen NL, Khaw KT, Wareham NJ, Keinanen-Kiukaanniemi SM, Saaristo TE, Korpi-Hyovalti E, Saltevo J, Laakso M, Kuusisto J, Metspalu A, Collins FS, Mohlke KL, Bergman RN, Tuomilehto J, Boehm BO, Gieger C, Hveem K, Cauchi S, Froguel P, Baldassarre D, Tremoli E, Humphries SE, Saleheen D, Danesh J, Ingelsson E, Ripatti S, Salomaa V, Erbel R, Jockel KH, Moebus S, Peters A, Illig T, de Faire U, Hamsten A, Morris AD, Donnelly PJ, Frayling TM, Hattersley AT, Boerwinkle E, Melander O, Kathiresan S, Nilsson PM, Deloukas P, Thorsteinsdottir U, Groop LC, Stefansson K, Hu F, Pankow JS, Dupuis J, Meigs JB, Altshuler D, Boehnke M, McCarthy MI. DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012; 44:981–990.
16. McCarthy MI, Zeggini E. Genome-wide association studies in type 2 diabetes. Curr Diab Rep. 2009; 9:164–171.
17. Cho YM, Kim TH, Lim S, Choi SH, Shin HD, Lee HK, Park KS, Jang HC. Type 2 diabetes-associated genetic variants discovered in the recent genome-wide association studies are related to gestational diabetes mellitus in the Korean population. Diabetologia. 2009; 52:253–261.
18. Cho YS, Chen CH, Hu C, Long J, Ong RT, Sim X, Takeuchi F, Wu Y, Go MJ, Yamauchi T, Chang YC, Kwak SH, Ma RC, Yamamoto K, Adair LS, Aung T, Cai Q, Chang LC, Chen YT, Gao Y, Hu FB, Kim HL, Kim S, Kim YJ, Lee JJ, Lee NR, Li Y, Liu JJ, Lu W, Nakamura J, Nakashima E, Ng DP, Tay WT, Tsai FJ, Wong TY, Yokota M, Zheng W, Zhang R, Wang C, So WY, Ohnaka K, Ikegami H, Hara K, Cho YM, Cho NH, Chang TJ, Bao Y, Hedman AK, Morris AP, McCarthy MI. DIAGRAM Consortium. MuTHER Consortium. Takayanagi R, Park KS, Jia W, Chuang LM, Chan JC, Maeda S, Kadowaki T, Lee JY, Wu JY, Teo YY, Tai ES, Shu XO, Mohlke KL, Kato N, Han BG, Seielstad M. Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat Genet. 2011; 44:67–72.
19. Hara K, Fujita H, Johnson TA, Yamauchi T, Yasuda K, Horikoshi M, Peng C, Hu C, Ma RC, Imamura M, Iwata M, Tsunoda T, Morizono T, Shojima N, So WY, Leung TF, Kwan P, Zhang R, Wang J, Yu W, Maegawa H, Hirose H. DIAGRAM consortium. Kaku K, Ito C, Watada H, Tanaka Y, Tobe K, Kashiwagi A, Kawamori R, Jia W, Chan JC, Teo YY, Shyong TE, Kamatani N, Kubo M, Maeda S, Kadowaki T. Genome-wide association study identifies three novel loci for type 2 diabetes. Hum Mol Genet. 2014; 23:239–246.
20. Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban HJ, Yoon D, Lee MH, Kim DJ, Park M, Cha SH, Kim JW, Han BG, Min H, Ahn Y, Park MS, Han HR, Jang HY, Cho EY, Lee JE, Cho NH, Shin C, Park T, Park JW, Lee JK, Cardon L, Clarke G, McCarthy MI, Lee JY, Lee JK, Oh B, Kim HL. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet. 2009; 41:527–534.
21. Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet. 2007; 39:906–913.
22. Ioannidis JP, Patsopoulos NA, Evangelou E. Heterogeneity in meta-analyses of genome-wide association investigations. PLoS One. 2007; 2:e841.
23. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes of BioMedical Research. Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H, Roix JJ, Kathiresan S, Hirschhorn JN, Daly MJ, Hughes TE, Groop L, Altshuler D, Almgren P, Florez JC, Meyer J, Ardlie K, Bengtsson Bostrom K, Isomaa B, Lettre G, Lindblad U, Lyon HN, Melander O, Newton-Cheh C, Nilsson P, Orho-Melander M, Rastam L, Speliotes EK, Taskinen MR, Tuomi T, Guiducci C, Berglund A, Carlson J, Gianniny L, Hackett R, Hall L, Holmkvist J, Laurila E, Sjogren M, Sterner M, Surti A, Svensson M, Svensson M, Tewhey R, Blumenstiel B, Parkin M, Defelice M, Barry R, Brodeur W, Camarata J, Chia N, Fava M, Gibbons J, Handsaker B, Healy C, Nguyen K, Gates C, Sougnez C, Gage D, Nizzari M, Gabriel SB, Chirn GW, Ma Q, Parikh H, Richardson D, Ricke D, Purcell S. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007; 316:1331–1336.
24. Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, Timpson NJ, Perry JR, Rayner NW, Freathy RM, Barrett JC, Shields B, Morris AP, Ellard S, Groves CJ, Harries LW, Marchini JL, Owen KR, Knight B, Cardon LR, Walker M, Hitman GA, Morris AD, Doney AS, McCarthy MI, Hattersley AT. Wellcome Trust Case Control Consortium (WTCCC). Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science. 2007; 316:1336–1341.
25. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos MR, Stringham HM, Chines PS, Jackson AU, Prokunina-Olsson L, Ding CJ, Swift AJ, Narisu N, Hu T, Pruim R, Xiao R, Li XY, Conneely KN, Riebow NL, Sprau AG, Tong M, White PP, Hetrick KN, Barnhart MW, Bark CW, Goldstein JL, Watkins L, Xiang F, Saramies J, Buchanan TA, Watanabe RM, Valle TT, Kinnunen L, Abecasis GR, Pugh EW, Doheny KF, Bergman RN, Tuomilehto J, Collins FS, Boehnke M. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007; 316:1341–1345.
26. Prokopenko I, McCarthy MI, Lindgren CM. Type 2 diabetes: new genes, new understanding. Trends Genet. 2008; 24:613–621.
27. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P, Vincent D, Belisle A, Hadjadj S, Balkau B, Heude B, Charpentier G, Hudson TJ, Montpetit A, Pshezhetsky AV, Prentki M, Posner BI, Balding DJ, Meyre D, Polychronakos C, Froguel P. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007; 445:881–885.
28. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, de Bakker PI, Abecasis GR, Almgren P, Andersen G, Ardlie K, Bostrom KB, Bergman RN, Bonnycastle LL, Borch-Johnsen K, Burtt NP, Chen H, Chines PS, Daly MJ, Deodhar P, Ding CJ, Doney AS, Duren WL, Elliott KS, Erdos MR, Frayling TM, Freathy RM, Gianniny L, Grallert H, Grarup N, Groves CJ, Guiducci C, Hansen T, Herder C, Hitman GA, Hughes TE, Isomaa B, Jackson AU, Jorgensen T, Kong A, Kubalanza K, Kuruvilla FG, Kuusisto J, Langenberg C, Lango H, Lauritzen T, Li Y, Lindgren CM, Lyssenko V, Marvelle AF, Meisinger C, Midthjell K, Mohlke KL, Morken MA, Morris AD, Narisu N, Nilsson P, Owen KR, Palmer CN, Payne F, Perry JR, Pettersen E, Platou C, Prokopenko I, Qi L, Qin L, Rayner NW, Rees M, Roix JJ, Sandbaek A, Shields B, Sjogren M, Steinthorsdottir V, Stringham HM, Swift AJ, Thorleifsson G, Thorsteinsdottir U, Timpson NJ, Tuomi T, Tuomilehto J, Walker M, Watanabe RM, Weedon MN, Willer CJ, Illig T, Hveem K, Hu FB, Laakso M, Stefansson K, Pedersen O, Wareham NJ, Barroso I, Hattersley AT, Collins FS, Groop L, McCarthy MI, Boehnke M, Altshuler D. Wellcome Trust Case Control Consortium. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008; 40:638–645.
29. Qi Q, Li H, Loos RJ, Liu C, Wu Y, Hu FB, Wu H, Lu L, Yu Z, Lin X. Common variants in KCNQ1 are associated with type 2 diabetes and impaired fasting glucose in a Chinese Han population. Hum Mol Genet. 2009; 18:3508–3515.
30. Barroso I, Luan J, Wheeler E, Whittaker P, Wasson J, Zeggini E, Weedon MN, Hunt S, Venkatesh R, Frayling TM, Delgado M, Neuman RJ, Zhao J, Sherva R, Glaser B, Walker M, Hitman G, McCarthy MI, Hattersley AT, Permutt MA, Wareham NJ, Deloukas P. Population-specific risk of type 2 diabetes conferred by HNF4A P2 promoter variants: a lesson for replication studies. Diabetes. 2008; 57:3161–3165.
31. Wilson SG, Adam G, Langdown M, Reneland R, Braun A, Andrew T, Surdulescu GL, Norberg M, Dudbridge F, Reed PW, Sambrook PN, Kleyn PW, Spector TD. Linkage and potential association of obesity-related phenotypes with two genes on chromosome 12q24 in a female dizygous twin cohort. Eur J Hum Genet. 2006; 14:340–348.
32. Lewis CE, North KE, Arnett D, Borecki IB, Coon H, Ellison RC, Hunt SC, Oberman A, Rich SS, Province MA, Miller MB. Sex-specific findings from a genome-wide linkage analysis of human fatness in non-Hispanic whites and African Americans: the HyperGEN study. Int J Obes (Lond). 2005; 29:639–649.
33. Norris JM, Langefeld CD, Scherzinger AL, Rich SS, Bookman E, Beck SR, Saad MF, Haffner SM, Bergman RN, Bowden DW, Wagenknecht LE. Quantitative trait loci for abdominal fat and BMI in Hispanic-Americans and African-Americans: the IRAS Family study. Int J Obes (Lond). 2005; 29:67–77.
34. Wu J, Pankow JS, Tracy RP, North KE, Myers RH, Feitosa ME, Province MA, Borecki IB. A QTL on 12q influencing an inflammation marker and obesity in white women: the NHLBI Family Heart Study. Obesity (Silver Spring). 2009; 17:525–531.
35. Todd JA, Walker NM, Cooper JD, Smyth DJ, Downes K, Plagnol V, Bailey R, Nejentsev S, Field SF, Payne F, Lowe CE, Szeszko JS, Hafler JP, Zeitels L, Yang JH, Vella A, Nutland S, Stevens HE, Schuilenburg H, Coleman G, Maisuria M, Meadows W, Smink LJ, Healy B, Burren OS, Lam AA, Ovington NR, Allen J, Adlem E, Leung HT, Wallace C, Howson JM, Guja C, Ionescu-Tirgoviste C, Simmonds MJ, Heward JM, Gough SC, Dunger DB, Wicker LS, Clayton DG. Genetics of Type 1 Diabetes in Finland. Wellcome Trust Case Control Consortium. Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes. Nat Genet. 2007; 39:857–864.
36. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007; 447:661–678.
37. Barrett JC, Clayton DG, Concannon P, Akolkar B, Cooper JD, Erlich HA, Julier C, Morahan G, Nerup J, Nierras C, Plagnol V, Pociot F, Schuilenburg H, Smyth DJ, Stevens H, Todd JA, Walker NM, Rich SS. Type 1 Diabetes Genetics Consortium. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat Genet. 2009; 41:703–707.
38. Go MJ, Hwang JY, Kim YJ, Hee Oh J, Kim YJ, Heon Kwak S, Soo Park K, Lee J, Kim BJ, Han BG, Cho MC, Cho YS, Lee JY. New susceptibility loci in MYL2, C12orf51 and OAS1 associated with 1-h plasma glucose as predisposing risk factors for type 2 diabetes in the Korean population. J Hum Genet. 2013; 58:362–365.
39. Kim YJ, Go MJ, Hu C, Hong CB, Kim YK, Lee JY, Hwang JY, Oh JH, Kim DJ, Kim NH, Kim S, Hong EJ, Kim JH, Min H, Kim Y, Zhang R, Jia W, Okada Y, Takahashi A, Kubo M, Tanaka T, Kamatani N, Matsuda K. MAGIC consortium. Park T, Oh B, Kimm K, Kang D, Shin C, Cho NH, Kim HL, Han BG, Lee JY, Cho YS. Large-scale genome-wide association studies in East Asians identify new genetic loci influencing metabolic traits. Nat Genet. 2011; 43:990–995.
40. Cordell HJ, Topf A, Mamasoula C, Postma AV, Bentham J, Zelenika D, Heath S, Blue G, Cosgrove C, Granados Riveron J, Darlay R, Soemedi R, Wilson IJ, Ayers KL, Rahman TJ, Hall D, Mulder BJ, Zwinderman AH, van Engelen K, Brook JD, Setchfield K, Bu'Lock FA, Thornborough C, O'Sullivan J, Stuart AG, Parsons J, Bhattacharya S, Winlaw D, Mital S, Gewillig M, Breckpot J, Devriendt K, Moorman AF, Rauch A, Lathrop GM, Keavney BD, Goodship JA. Genome-wide association study identifies loci on 12q24 and 13q32 associated with tetralogy of Fallot. Hum Mol Genet. 2013; 22:1473–1481.
41. Yang X, Lu X, Wang L, Chen S, Li J, Cao J, Chen J, Hao Y, Li Y, Zhao L, Li H, Liu D, Wang L, Lu F, Shen C, Yu L, Wu X, Zhao Q, Ji X, Guo D, Peng X, Huang J, Gu D. Common variants at 12q24 are associated with drinking behavior in Han Chinese. Am J Clin Nutr. 2013; 97:545–551.
42. Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Dehghan A, Glazer NL, Morrison AC, Johnson AD, Aspelund T, Aulchenko Y, Lumley T, Kottgen A, Vasan RS, Rivadeneira F, Eiriksdottir G, Guo X, Arking DE, Mitchell GF, Mattace-Raso FU, Smith AV, Taylor K, Scharpf RB, Hwang SJ, Sijbrands EJ, Bis J, Harris TB, Ganesh SK, O'Donnell CJ, Hofman A, Rotter JI, Coresh J, Benjamin EJ, Uitterlinden AG, Heiss G, Fox CS, Witteman JC, Boerwinkle E, Wang TJ, Gudnason V, Larson MG, Chakravarti A, Psaty BM, van Duijn CM. Genome-wide association study of blood pressure and hypertension. Nat Genet. 2009; 41:677–687.
43. Kato N, Takeuchi F, Tabara Y, Kelly TN, Go MJ, Sim X, Tay WT, Chen CH, Zhang Y, Yamamoto K, Katsuya T, Yokota M, Kim YJ, Ong RT, Nabika T, Gu D, Chang LC, Kokubo Y, Huang W, Ohnaka K, Yamori Y, Nakashima E, Jaquish CE, Lee JY, Seielstad M, Isono M, Hixson JE, Chen YT, Miki T, Zhou X, Sugiyama T, Jeon JP, Liu JJ, Takayanagi R, Kim SS, Aung T, Sung YJ, Zhang X, Wong TY, Han BG, Kobayashi S, Ogihara T, Zhu D, Iwai N, Wu JY, Teo YY, Tai ES, Cho YS, He J. Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians. Nat Genet. 2011; 43:531–538.
44. Solovieff N, Cotsapas C, Lee PH, Purcell SM, Smoller JW. Pleiotropy in complex traits: challenges and strategies. Nat Rev Genet. 2013; 14:483–495.
45. McCarthy JJ, Somji A, Weiss LA, Steffy B, Vega R, Barrett-Connor E, Talavera G, Glynne R. Polymorphisms of the scavenger receptor class B member 1 are associated with insulin resistance with evidence of gene by sex interaction. J Clin Endocrinol Metab. 2009; 94:1789–1796.
46. Ericson U, Rukh G, Stojkovic I, Sonestedt E, Gullberg B, Wirfalt E, Wallstrom P, Orho-Melander M. Sex-specific interactions between the IRS1 polymorphism and intakes of carbohydrates and fat on incident type 2 diabetes. Am J Clin Nutr. 2013; 97:208–216.
47. Li Q, Qu HQ, Rentfro AR, Grove ML, Mirza S, Lu Y, Hanis CL, Fallon MB, Boerwinkle E, Fisher-Hoch SP, McCormick JB. PNPLA3 polymorphisms and liver aminotransferase levels in a Mexican American population. Clin Invest Med. 2012; 35:E237–E245.
48. Baik I, Cho NH, Kim SH, Han BG, Shin C. Genome-wide association studies identify genetic loci related to alcohol consumption in Korean men. Am J Clin Nutr. 2011; 93:809–816.
49. Heo SG, Hwang JY, Uhmn S, Go MJ, Oh B, Lee JY, Park JW. Male-specific genetic effect on hypertension and metabolic disorders. Hum Genet. 2014; 133:311–319.
50. Dimas AS, Nica AC, Montgomery SB, Stranger BE, Raj T, Buil A, Giger T, Lappalainen T, Gutierrez-Arcelus M. MuTHER Consortium. McCarthy MI, Dermitzakis ET. Sex-biased genetic effects on gene regulation in humans. Genome Res. 2012; 22:2368–2375.
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