Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians

Cho, Yoon Shin; Chen, Chien Hsiun; Hu, Cheng; Long, Jirong; Ong, Rick Twee Hee; Sim, Xueling; Takeuchi, Fumihiko; Wu, Ying; Go, Min Jin; Yamauchi, Toshimasa; Chang, Yi‐Cheng; Kwak, Soo Heon; Ma, Rong; Yamamoto, Ken; Adair, Linda S.; Aung, Tin; Cai, Qiuyin; Chang, Li; Chen, Yuan Tsong; Gao, Yu‐Tang; Hu, Frank B.; Kim, Hyung Lae; Kim, Sang Soo; Kim, Young Jin; Lee, Jeannette Jen Mai; Lee, Nanette R.; Li, Yun; Liu, Jing; Lü, Wei; Nakamura, Jiro; Nakashima, Eitaro; Ng, Daniel; Tay, Wan Ting; Tsai, Fuu Jen; Wong, Tien Yin; Yokota, Mitsuhiro; Wang, Zheng; Zhang, Rong; Wang, Congrong; So, Wing Yee; Ohnaka, Keizo; Ikegami, Hiroshi; Hara, Kazuo; Cho, Young Min; Cho, Nam H.; Chang, Tien-Jyun; Bao, Yuqian; Hedman, Åsa K.; Morris, Andrew P.; McCarthy, Mark I.; Takayanagi, Ryoichi; Park, Kyong Soo; Jia, Weiping; Chuang, Lee‐Ming; Chan, Juliana C.N.; Maeda, Shiro; Kadowaki, Takashi; Lee, Jong‐Young; Wu, Jer Yuarn; Teo, Yik Ying; Tai, E. Shyong; Shu, Xiao Ou; Mohlke, Karen L.; Kato, Norihiro; Han, Bok Ghee; Seielstad, Mark

doi:10.1038/ng.1019

Cited by 558 publications

(472 citation statements)

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“…This association was exclusive to East Asians, in whom the 192His allele is, in fact, common (MAF~10%) with a substantial effect size (allelic OR=1.79 [1.47-2.19]); 192His is virtually absent in other ancestries (MAF=0.014%). The rs2233580 association replicated in independent East Asian case-control data (n=3,301; p=5.9×10 −7 : Supplementary 9) and was distinct (r 2 <0.05) from previously-reported GWAS SNVs at the GCC1-PAX4 locus 6,8 . PAX4 encodes a transcription factor involved in islet differentiation and function 17 (Supplementary 10), and PAX4 variants have been implicated in early-onset monogenic diabetes 18 .…”

Section: Analysis Of Coding Variationsupporting

confidence: 55%

“…For five loci (SLC30A8, GCKR, PPARG, KCNJ11-ABCC8, PAM), the coding variants identified had previously been nominated as causal for their respective GWAS signals 2,7,13 . For the other seven loci, GWAS metaanalyses had previously highlighted a lead variant in non-coding sequence 2,5,6 . We (re)evaluated these relationships with conditional and credible set analyses, finding that, at most, the evidence supported a direct causal role for the coding variants concerned (Extended Data Table 5; Extended Data Figs.…”

Section: Analysis Of Coding Variationmentioning

confidence: 99%

“…Over the past decade, successive waves of T2D GWAS -featuring ever larger samples, progressively denser genotyping arrays supplemented by imputation against more complete reference panels, and richer ethnic diversity -have delivered >80 robust association signals [2][3][4][5][6][7][8] . However, in these studies, the alleles interrogated for association are predominantly common (minor allele frequency [MAF]>5%), and with limited exceptions 7,9 , the variants driving known association signals are also common, with individually-modest impacts on T2D risk [2][3][4][5][6][7][8]10 . Variation at known loci explains only a minority of observed T2D heritability 2,3,11 .…”

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confidence: 99%

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Genetic architecture of type 2 diabetes

Hara

Shojima

Hosoe

et al. 2014

Biochemical and Biophysical Research Communications

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The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lowerfrequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes.

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Section: Analysis Of Coding Variationsupporting

confidence: 55%

Section: Analysis Of Coding Variationmentioning

confidence: 99%

mentioning

confidence: 99%

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Genetic architecture of type 2 diabetes

Hara

Shojima

Hosoe

et al. 2014

Biochemical and Biophysical Research Communications

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“…S19). Of the established T2D and glycemic associated loci (2,3,(23)(24)(25)(26)(27)(28) whose gene expression proxies were associated with HbA1c levels, solute carrier family 30 (zinc transporter), member 8 (SLC30A8), glucose-6-phosphatase, catalytic, 2 (G6PC2), and proprotein convertase subtilisin/kexin type 1 (PCSK1) showed the highest expression using RNA-seq ( Fig. 1B and Database S1).…”

Section: Significancementioning

confidence: 99%

Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism

Fadista

Vikman

Laakso

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Genetic variation can modulate gene expression, and thereby phenotypic variation and susceptibility to complex diseases such as type 2 diabetes (T2D). Here we harnessed the potential of DNA and RNA sequencing in human pancreatic islets from 89 deceased donors to identify genes of potential importance in the pathogenesis of T2D. We present a catalog of genetic variants regulating gene expression (eQTL) and exon use (sQTL), including many long noncoding RNAs, which are enriched in known T2D-associated loci. Of 35 eQTL genes, whose expression differed between normoglycemic and hyperglycemic individuals, siRNA of tetraspanin 33 (TSPAN33), 5′-nucleotidase, ecto (NT5E), transmembrane emp24 protein transport domain containing 6 (TMED6), and p21 protein activated kinase 7 (PAK7) in INS1 cells resulted in reduced glucose-stimulated insulin secretion. In addition, we provide a genome-wide catalog of allelic expression imbalance, which is also enriched in known T2D-associated loci. Notably, allelic imbalance in paternally expressed gene 3 (PEG3) was associated with its promoter methylation and T2D status. Finally, RNA editing events were less common in islets than previously suggested in other tissues. Taken together, this study provides new insights into the complexity of gene regulation in human pancreatic islets and better understanding of how genetic variation can influence glucose metabolism.T ype 2 diabetes (T2D) is an increasing global health problem (1). Although genome-wide association studies (GWAS) have yielded more than 70 loci associated with T2D or related traits (2, 3), they have not provided the expected breakthrough in our understanding of the pathogenesis of the disease. They have nonetheless pointed at a central role of the pancreatic islets and β-cell dysfunction in the development of the disease (4, 5). It therefore seems pertinent to focus on human pancreatic islets to obtain insights into the molecular mechanisms causing the disease (6, 7). Given that most SNPs associated with T2D lie in noncoding regions, the majority of causal variants are likely to regulate gene expression rather than protein function per se. Therefore, combination of DNA and RNA sequencing in the same individuals may help to disentangle the role these SNPs play in the pathogenesis of the disease (8). Although the human pancreatic islet transcriptome has been previously described (6, 9-18), using microarrays or RNA sequencing of a limited number of nondiabetic individuals, this has not allowed a more global analysis of the complexity of the islet transcriptome in T2D. Here we combined genotypic imputation, expression microarrays, and exome and RNA sequencing (ExomeSeq and RNA-Seq) in a large number of human pancreatic islets from deceased donors with and without T2D. This study identified a number of novel genes, including long intergenic noncoding RNAs (lincRNAs), whose expression and/or splicing influences insulin secretion and is associated with glycemia. In addition, we provide a catalog of RNA editing and allele-specific expr...

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“…Single nucleotide polymorphisms (SNPs) in more than 60 regions throughout the genome (so-called susceptibility loci containing multiple genes) were found to be associated with the risk of type 2 diabetes [39,[41][42][43][44]. Most of these SNPs are common, with minor allele frequencies of 10-90%.…”

Section: Genetic Variantsmentioning

confidence: 99%

The potential of novel biomarkers to improve risk prediction of type 2 diabetes

et al. 2013

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The incidence of type 2 diabetes can be reduced substantially by implementing preventive measures in highrisk individuals, but this requires prior knowledge of disease risk in the individual. Various diabetes risk models have been designed, and these have all included a similar combination of factors, such as age, sex, obesity, hypertension, lifestyle factors, family history of diabetes and metabolic traits. The accuracy of prediction models is often assessed by the area under the receiver operating characteristic curve (AROC) as a measure of discrimination, but AROCs should be complemented by measures of calibration and reclassification to estimate the incremental value of novel biomarkers. This review discusses the potential of novel biomarkers to improve model accuracy. The range of molecules that serve as potential predictors of type 2 diabetes includes genetic variants, RNA transcripts, peptides and proteins, lipids and small metabolites. Some of these biomarkers lead to a statistically significant increase of model accuracy, but their incremental value currently seems too small for routine clinical use. However, only a fraction of potentially relevant biomarkers have been assessed with regard to their predictive value. Moreover, serial measurements of biomarkers may help determine individual risk. In conclusion, current risk models provide valuable tools of risk estimation, but perform suboptimally in the prediction of individual diabetes risk. Novel biomarkers still fail to have a clinically applicable impact. However, more efficient use of biomarker data and technological advances in their measurement in clinical settings may allow the development of more accurate predictive models in the future.

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Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians

Cited by 558 publications

References 36 publications

Genetic architecture of type 2 diabetes

Genetic architecture of type 2 diabetes

Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism

The potential of novel biomarkers to improve risk prediction of type 2 diabetes

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