Type 2 diabetes mellitus (T2D) is a highly prevalent common metabolic disease, which exemplifies many of the challenges and approaches for other complex diseases.
Early genome-wide association studies (GWAS) were successful for T2D, aided in large part by collaboration and data sharing by the genetics research community.
The substantial portion of T2D heritability unexplained by GWAS triggered a debate on whether rare or common variants were dominant characteristics of T2D genetic architecture. More recent sequencing studies and GWAS have produced empirical results consistent with common variant models.
A substantial number of experimental approaches have been used to investigate disease mechanisms and biology for T2D, including deep phenotyping of variant carriers, higher-resolution 'fine mapping' studies, integration with epigenomic and transcriptomic data sets and functional studies in model systems.
Larger and larger genetic studies will be needed to identify or characterize additional T2D associations in the population. At the same time, open access to these data will be needed for a broad experimental community to produce biological insights that might be missed by the analyses of GWAS consortia.
An integrated T2D knowledge base and portal, recently embraced by the T2D genetics community, is one possible mechanism to maximize the global use of genetic data sets to be produced in coming years.
As with other complex diseases, unbiased association studies followed by physiological and experimental characterization have for years formed a paradigm for identifying genes or processes of relevance to type 2 diabetes mellitus (T2D). Recent large-scale common and rare variant genome-wide association studies (GWAS) suggest that substantially larger association studies are needed to identify most T2D loci in the population. To hasten clinical translation of genetic discoveries, new paradigms are also required to aid specialized investigation of nascent hypotheses. We argue for an integrated T2D knowledgebase, designed for a worldwide community to access aggregated large-scale genetic data sets, as one paradigm to catalyse convergence of these efforts.
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Hemminki, K., Li, X., Sundquist, K. & Sundquist, J. Familial risks for type 2 diabetes in Sweden. Diabetes Care 33, 293–297 (2010).
Almgren, P. et al. Heritability and familiality of type 2 diabetes and related quantitative traits in the Botnia Study. Diabetologia 54, 2811–2819 (2011).
Kahn, S. E., Cooper, M. E. & Del Prato, S. Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future. Lancet 383, 1068–1083 (2014).
Fowler, M. J. Microvascular and macrovascular complications of diabetes. Clin. Diabetes 26, 77–82 (2008).
Tancredi, M. et al. Excess mortality among persons with type 2 diabetes. N. Engl. J. Med. 373, 1720–1732 (2015).
Altshuler, D. et al. The common PPARγ Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat. Genet. 26, 76–80 (2000).
Gloyn, A. L. et al. Large-scale association studies of variants in genes encoding the pancreatic β-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes 52, 568–572 (2003).
Grant, S. F. et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat. Genet. 38, 320–323 (2006).
Guan, W., Pluzhnikov, A., Cox, N. J. & Boehnke, M. Meta-analysis of 23 type 2 diabetes linkage studies from the international type 2 diabetes linkage analysis consortium. Hum. Hered. 66, 35–49 (2008).
Altshuler, D., Daly, M. J. & Lander, E. S. Genetic mapping in human disease. Science 322, 881–888 (2008).
McCarthy, M. I. Genomics, type 2 diabetes, and obesity. N. Engl. J. Med. 363, 2339–2350 (2010).
McClellan, J. & King, M. C. Genetic heterogeneity in human disease. Cell 141, 210–217 (2010).
Goldstein, D. B. Common genetic variation and human traits. N. Engl. J. Med. 360, 1696–1698 (2009).
Risch, N. & Merikangas, K. The future of genetic studies of complex human diseases. Science 273, 1516–1517 (1996).
Reich, D. E. & Lander, E. S. On the allelic spectrum of human disease. Trends Genet. 17, 502–510 (2001).
International HapMap Consortium. A haplotype map of the human genome. Nature 437, 1299–1320 (2005).
Diabetes Genetics Initiative. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316, 1331–1336 (2007).
Scott, L. J. et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316, 1341–1345 (2007).
Sladek, R. et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881–885 (2007).
Steinthorsdottir, V. et al. A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat. Genet. 39, 770–775 (2007).
Zeggini, E. et al. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 316, 1336–1341 (2007).
Zeggini, E. et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat. Genet. 40, 638–645 (2008). This is the first example of collaboration and data sharing to identify new association signals for T2D.
de Bakker, P. I. et al. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum. Mol. Genet. 17, R122–R128 (2008).
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).
Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).
Voight, B. F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579–589 (2010). This is an early illustration of the paradigm now established for GWAS analyses.
Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42, 105–116 (2010).
Zhou, K. et al. Common variants near ATM are associated with glycaemic response to metformin in type 2 diabetes. Nat. Genet. 43, 117–120 (2011).
Saxena, R. et al. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat. Genet. 42, 142–148 (2010).
Strawbridge, R. J. et al. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes 60, 2624–2634 (2011).
Cho, Y. S. et al. Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in East Asians. Nat. Genet. 44, 67–72 (2012).
Kooner, J. S. et al. Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nat. Genet. 43, 984–989 (2011).
Yamauchi, T. et al. A genome-wide association study in the Japanese population identifies susceptibility loci for type 2 diabetes at UBE2E2 and C2CD4A–C2CD4B. Nat. Genet. 42, 864–868 (2010).
Voight, B. F. et al. The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 8, e1002793 (2012).
Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).
Scott, R. A. et al. Large-scale association analyses identify new loci influencing glycaemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 991–1005 (2012).
Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009). A thorough summary of the debate around different models of genetic architecture is presented here, following the results of early GWAS.
Hirschhorn, J. N. Genomewide association studies — illuminating biologic pathways. N. Engl. J. Med. 360, 1699–1701 (2009).
Dickson, S. P., Wang, K., Krantz, I., Hakonarson, H. & Goldstein, D. B. Rare variants create synthetic genome-wide associations. PLoS Biol. 8, e1000294 (2010).
Bodmer, W. & Bonilla, C. Common and rare variants in multifactorial susceptibility to common diseases. Nat. Genet. 40, 695–701 (2008).
Pritchard, J. K. Are rare variants responsible for susceptibility to complex diseases? Am. J. Hum. Genet. 69, 124–137 (2001).
Goldstein, D. B. The importance of synthetic associations will only be resolved empirically. PLoS Biol. 9, e1001008 (2011).
Anderson, C. A., Soranzo, N., Zeggini, E. & Barrett, J. C. Synthetic associations are unlikely to account for many common disease genome-wide association signals. PLoS Biol. 9, e1000580 (2011).
Wray, N. R., Purcell, S. M. & Visscher, P. M. Synthetic associations created by rare variants do not explain most GWAS results. PLoS Biol. 9, e1000579 (2011).
Waters, K. M. et al. Consistent association of type 2 diabetes risk variants found in Europeans in diverse racial and ethnic groups. PLoS Genet. 6, e1001078 (2010).
Stahl, E. A. et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat. Genet. 44, 483–489 (2012).
Agarwala, V., Flannick, J., Sunyaev, S., Go, T. D. C. & Altshuler, D. Evaluating empirical bounds on complex disease genetic architecture. Nat. Genet. 45, 1418–1427 (2013). This is a comprehensive study of the support from empirical data sets for different models of T2D genetic architecture.
Huyghe, J. R. et al. Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nat. Genet. 45, 197–201 (2013).
Albrechtsen, A. et al. Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes. Diabetologia 56, 298–310 (2013).
Steinthorsdottir, V. et al. Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat. Genet. 46, 294–298 (2014).
Estrada, K. et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA 311, 2305–2314 (2014).
Mahajan, A. et al. Identification and functional characterization of G6PC2 coding variants influencing glycaemic traits define an effector transcript at the G6PC2-ABCB11 locus. PLoS Genet. 11, e1004876 (2015).
Wessel, J. et al. Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat. Commun. 6, 5897 (2015).
Manning, A. K. et al. A low frequency AKT2 coding variant enriched in the Finnish population is associated with fasting insulin levels. (Abstract #56) The 64th Annual Meeting of The American Society of Human Genetics, San Diego, Californiahttp://www.ashg.org/2014meeting/pdf/2014_ASHG_Meeting_Platform_Abstracts.pdf (18–22 Oct 2014).
Bonnefond, A. et al. Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes. Nat. Genet. 44, 297–301 (2012). This is an early example of the ability of functional assays to filter benign from deleterious alleles and improve the power of aggregate association tests.
Majithia, A. R. et al. Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proc. Natl Acad. Sci. USA 111, 13127–13132 (2014).
Flannick, J. et al. Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nat. Genet. 46, 357–363 (2014). This is one of the first studies to identify protective rare variants associated with T2D, demonstrating the need for large-scale data aggregation.
Rutter, G. A. Think zinc: new roles for zinc in the control of insulin secretion. Islets 2, 49–50 (2010).
Nicolson, T. J. et al. Insulin storage and glucose homeostasis in mice null for the granule zinc transporter ZnT8 and studies of the type 2 diabetes-associated variants. Diabetes 58, 2070–2083 (2009).
Lohmueller, K. E. et al. Whole-exome sequencing of 2,000 Danish individuals and the role of rare coding variants in type 2 diabetes. Am. J. Hum. Genet. 93, 1072–1086 (2013).
Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature http://dx.doi.org/10.1038/nature18642 (2016). A comprehensive characterization is presented here of the genetic architecture of T2D using various sequencing approaches.
Moutsianas, L. et al. The power of gene-based rare variant methods to detect disease-associated variation and test hypotheses about complex disease. PLoS Genet. 11, e1005165 (2015). This simulation study shows the need for potentially numerous aggregate association analyses to identify disease genes with different allelic spectra.
Zuk, O. et al. Searching for missing heritability: designing rare variant association studies. Proc. Natl Acad. Sci. USA 111, E455–E464 (2014). Approaches for rare variant association studies are thoroughly explained and examined.
Mahajan, A. et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat. Genet. 46, 234–244 (2014).
Scott, R. A. et al. Genome-wide association study imputed to 1000 Genomes reveals 18 novel associations with type 2 diabetes. (Abstract 53). The 64th Annual Meeting of The American Society of Human Genetics, San Diego, Californiahttp://www.ashg.org/2014meeting/pdf/2014_ASHG_Meeting_Platform_Abstracts.pdf (18–22 Oct 2014).
Sigma Type 2 Diabetes Consortium. Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico. Nature 506, 97–101 (2014).
Ng, M. C. Y. et al. Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes. PLoS Genet. 10, e1004517 (2014).
Moltke, I. et al. A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes. Nature 512, 190–193 (2014). The largest effect association observed for T2D to date is described here, demonstrating the power of studying population isolates as well as insights that can be learned from variant carrier phenotyping.
Mahajan, A. et al. Large-scale exome chip association analysis identifies novel type 2 diabetes susceptibility loci and highlights candidate effector genes. (Abstract #299). The 65th Annual Meeting of The American Society of Human Genetics, Baltimore, Marylandhttp://www.ashg.org/2015meeting/pdf/57715_Platform.pdf (6–10 Oct 2015).
Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Iyengar, S. K. et al. Genome-wide association and trans-ethnic meta-analysis for advanced diabetic kidney disease: Family Investigation of Nephropathy and Diabetes (FIND). PLoS Genet. 11, e1005352 (2015).
Prokopenko, I. et al. A central role for GRB10 in regulation of islet function in man. PLoS Genet. 10, e1004235 (2014).
Horikoshi, M. et al. Discovery and fine-mapping of glycaemic and obesity-related trait loci using hhigh-density imputation. PLoS Genet. 11, e1005230 (2015).
McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. bioRxiv http://dx.doi.org/10.1101/035170 (2015).
Dimas, A. S. et al. Impact of type 2 diabetes susceptibility variants on quantitative glycaemic traits reveals mechanistic heterogeneity. Diabetes 63, 2158–2171 (2014).
Ingelsson, E. et al. Detailed physiologic characterization reveals diverse mechanisms for novel genetic loci regulating glucose and insulin metabolism in humans. Diabetes 59, 1266–1275 (2010).
Scott, R. A. et al. Common genetic variants highlight the role of insulin resistance and body fat distribution in type 2 diabetes, independent of obesity. Diabetes 63, 4378–4387 (2014).
Rosengren, A. H. et al. Reduced insulin exocytosis in human pancreatic β-cells with gene variants linked to type 2. Diabetes 61, 1726–1733 (2012).
Lawlor, D. A., Harbord, R. M., Sterne, J. A., Timpson, N. & Davey Smith, G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat. Med. 27, 1133–1163 (2008).
Fall, T. et al. The role of adiposity in cardiometabolic traits: a Mendelian randomization analysis. PLoS Med. 10, e1001474 (2013).
Abbasi, A. et al. Bilirubin as a potential causal factor in type 2 diabetes risk: a Mendelian randomization study. Diabetes 64, 1459–1469 (2015).
De Silva, N. M. et al. Mendelian randomization studies do not support a role for raised circulating triglyceride levels influencing type 2 diabetes, glucose levels, or insulin resistance. Diabetes 60, 1008–1018 (2011).
Haase, C. L., Tybjærg-Hansen, A., Nordestgaard, B. G. & Frikke-Schmidt, R. H.D. L. Cholesterol and risk of type 2 diabetes: a Mendelian randomization study. Diabetes 64, 3328–3333 (2015).
Yaghootkar, H. et al. Mendelian randomization studies do not support a causal role for reduced circulating adiponectin levels in insulin resistance and type 2 diabetes. Diabetes 62, 3589–3598 (2013).
Sluijs, I. et al. A mendelian randomization study of circulating uric acid and type 2 diabetes. Diabetes 64, 3028–3036 (2015).
Pal, A. et al. PTEN mutations as a cause of constitutive insulin sensitivity and obesity. N. Engl. J. Med. 367, 1002–1011 (2012). This paper provides a nice illustration of the power of deep phenotyping studies to gain molecular, cellular and physiological insights into disease pathways.
Wang, L. et al. PTEN deletion in pancreatic α-cells protects against high-fat diet–induced hyperglucagonemia and insulin resistance. Diabetes 64, 147–157 (2015).
Perry, J. R. B. et al. Stratifying type 2 diabetes cases by BMI identifies genetic risk variants in LAMA1 and enrichment for risk variants in lean compared to obese cases. PLoS Genetics 8, e1002741 (2012). This is an example in which phenotypic sample stratification identified a novel GWAS locus.
Manning, A. K. et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycaemic traits and insulin resistance. Nat. Genet. 44, 659–669 (2012).
Li, L. et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci. Transl. Med. 7, 311ra174 (2015).
Bonnefond, A. et al. Association between large detectable clonal mosaicism and type 2 diabetes with vascular complications. Nat. Genet. 45, 1040–1043 (2013).
Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).
Walford, G. A. et al. Metabolite traits and genetic risk provide complementary information for the prediction of future type 2 diabetes. Diabetes Care 37, 2508–2514 (2014).
Wurtz, P. et al. Circulating metabolite predictors of glycemia in middle-aged men and women. Diabetes Care 35, 1749–1756 (2012).
Chambers, J. C. et al. Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study. Lancet Diabetes Endocrinol. 3, 526–534 (2015).
Talmud, P. J. et al. Sixty-five common genetic variants and prediction of type 2 diabetes. Diabetes 64, 1830–1840 (2015).
Vassy, J. L. et al. Polygenic type 2 diabetes prediction at the limit of common variant detection. Diabetes 63, 2172–2182 (2014).
Flannick, J. et al. Assessing the phenotypic effects in the general population of rare variants in genes for a dominant Mendelian form of diabetes. Nat. Genet. 45, 1380–1385 (2013).
Patel, K., Weedon, M. N., Ellard, S., Oram, R. A. & Hattersley, A. T. Type 1 diabetes genetic risk score — a novel tool to differentiate monogenic diabetes from T1D. (Abstract 1746-P) Diabetes 64 (Suppl. 1), A453 (2015).
Oram, R. A. et al. A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults. Diabetes Care 39, 337–344 (2016).
Gaulton, K. J. et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, 1415–1425 (2015). This is a nice example of the common workflow to localize common variant associations to causal variants and molecular disease mechanisms.
Shea, J. et al. Comparing strategies to fine-map the association of common SNPs at chromosome 9p21 with type 2 diabetes and myocardial infarction. Nat. Genet. 43, 801–805 (2011).
Yaghootkar, H. et al. Association analysis of 29,956 individuals confirms that a low-frequency variant at CCND2 halves the risk of type 2 diabetes by enhancing insulin secretion. Diabetes 64, 2279–2285 (2015).
Maller, J. B. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012). An early fine mapping study is presented here that established the now commonly used 'credible set' methodology.
Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 12, 581–594 (2013).
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012). This is the first prominent and clear demonstration that GWAS variants cluster within regulatory regions of the human genome.
Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2012).
Parker, S. C. J. et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants. Proc. Natl Acad. Sci. USA 110, 17921–17926 (2013).
Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat. Genet. 46, 136–143 (2014).
Soccio, Raymond, E. et al. Genetic variation determines PPARγ function and anti-diabetic drug response in vivo. Cell 162, 33–44 (2015).
Gaulton, K. J. et al. A map of open chromatin in human pancreatic islets. Nat. Genet. 42, 255–259 (2010). This is one of the earliest studies that used epigenomic annotations to prioritize potentially causal variants at T2D GWAS loci.
Stitzel, M. L. et al. Global epigenomic analysis of primary human pancreatic islets provides insights into type 2 diabetes susceptibility loci. Cell Metab. 12, 443–455 (2010).
Morán, I. et al. Human β cell transcriptome analysis uncovers lncRNAs that are tissue-specific, dynamically regulated, and abnormally expressed in type 2 diabetes. Cell Metab. 16, 435–448 (2012).
van de Bunt, M. et al. The miRNA profile of human pancreatic islets and Beta-Cells and relationship to type 2 diabetes pathogenesis. PLoS ONE 8, e55272 (2013).
Fadista, J. et al. Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism. Proc. Natl Acad. Sci. USA 111, 13924–13929 (2014).
van de Bunt, M. et al. Transcript expression data from human islets links regulatory signals from genome-wide association studies for type 2 diabetes and glycaemic traits to their downstream effectors. PLoS Genet. 11, e1005694 (2015). This paper shows how eQTL analysis can be used to hypothesize effector transcripts at GWAS loci.
Nica, A. C. et al. Cell-type, allelic, and genetic signatures in the human pancreatic beta cell transcriptome. Genome Res. 23, 1554–1562 (2013).
Dayeh, T. et al. Genome-wide DNA methylation analysis of human pancreatic islets from type 2 diabetic and non-diabetic donors identifies candidate genes that influence insulin secretion. PLoS Genet. 10, e1004160 (2014).
Volkmar, M. et al. DNA methylation profiling identifies epigenetic dysregulation in pancreatic islets from type 2 diabetic patients. EMBO J. 31, 1405–1426 (2012).
Nilsson, E. et al. Altered DNA methylation and differential expression of genes influencing metabolism and inflammation in adipose tissue from subjects with type 2 diabetes. Diabetes 63, 2962–2976 (2014).
Yuan, W. et al. An integrated epigenomic analysis for type 2 diabetes susceptibility loci in monozygotic twins. Nat. Commun. 5, 5719 (2014).
Hodson, D. J. et al. ADCY5 couples glucose to insulin secretion in human islets. Diabetes 63, 3009–3021 (2014).
Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015). This is a comprehensive investigation of the disease mechanisms responsible for a GWAS association, using nearly all relevant modern experimental and computation techniques.
Locke, J. M., Hysenaj, G., Wood, A. R., Weedon, M. N. & Harries, L. W. Targeted allelic expression profiling in human islets identifies cis-regulatory effects for multiple variants identified by type 2 diabetes genome-wide association studies. Diabetes 64, 1484–1491 (2015).
Kulzer, Jennifer, R. et al. A common functional regulatory variant at a type 2 diabetes locus upregulates ARAP1 expression in the pancreatic beta cell. Am. J. Hum. Genet. 94, 186–197 (2014).
Ragvin, A. et al. Long-range gene regulation links genomic type 2 diabetes and obesity risk regions to HHEX, SOX4, and IRX3. Proc. Natl Acad. Sci. USA 107, 775–780 (2010).
Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014).
Elbein, Steven, C. et al. Genetic risk factors for type 2 diabetes: a trans-regulatory genetic architecture? Am. J. Hum. Genet. 91, 466–477 (2012).
Small, K. S. et al. Identification of an imprinted master trans regulator at the KLF14 locus related to multiple metabolic phenotypes. Nat. Genet. 43, 561–564 (2011). This is a nice example of the power of trans -eQTL analysis to identify disease mechanisms responsible for a GWAS association.
Florez, J. C. et al. TCF7L2 polymorphisms and progression to diabetes in the Diabetes Prevention Program. N. Engl. J. Med. 355, 241–250 (2006).
Fogarty, M. P., Panhuis, T. M., Vadlamudi, S., Buchkovich, M. L. & Mohlke, K. L. Allele-specific transcriptional activity at type 2 diabetes — associated single nucleotide polymorphisms in regions of pancreatic islet open chromatin at the JAZF1 locus. Diabetes 62, 1756–1762 (2013).
Fogarty, M. P., Cannon, M. E., Vadlamudi, S., Gaulton, K. J. & Mohlke, K. L. Identification of a regulatory variant that binds, FOXA1 and FOXA2 at the CDC123/CAMK1D type 2 diabetes, GWAS locus. PLoS Genet. 10, e1004633 (2014).
Travers, M. E. et al. Insights into the molecular mechanism for type 2 diabetes susceptibility at the KCNQ1 locus from temporal changes in imprinting status in human islets. Diabetes 62, 987–992 (2013).
Claussnitzer, M. et al. Leveraging cross-species transcription factor binding site patterns: from diabetes risk loci to disease mechanisms. Cell 156, 343–358 (2014).
Wei, F.-Y. et al. Deficit of tRNALys modification by Cdkal1 causes the development of type 2 diabetes in mice. J. Clin. Invest. 121, 3598–3608 (2011).
McCulloch, L. J. et al. GLUT2 (SLC2A2) is not the principal glucose transporter in human pancreatic beta cells: implications for understanding genetic association signals at this locus. Mol. Genet. Metab. 104, 648–653 (2011).
Beer, N. L. et al. The P446L variant in GCKR associated with fasting plasma glucose and triglyceride levels exerts its effect through increased glucokinase activity in liver. Hum. Mol. Genet. 18, 4081–4088 (2009).
Smith, S. B. et al. Rfx6 directs islet formation and insulin production in mice and humans. Nature 463, 775–780 (2010).
Zhu, H. et al. The Lin28/let-7 axis regulates glucose metabolism. Cell 147, 81–94 (2011).
Dai, N. et al. IGF2BP2/IMP2-Deficient mice resist obesity through enhanced translation of Ucp1 mRNA and other mRNAs encoding mitochondrial proteins. Cell Metab. 21, 609–621 (2015).
Mercader, J. M. et al. Identification of novel type 2 diabetes candidate genes involved in the crosstalk between the mitochondrial and the insulin signaling systems. PLoS Genet. 8, e1003046 (2012).
Taneera, J. et al. Expression profiling of cell cycle genes in human pancreatic islets with and without type 2 diabetes. Mol. Cell. Endocrinol. 375, 35–42 (2013).
Burns, S. M. et al. High-throughput luminescent reporter of insulin secretion for discovering regulators of pancreatic beta-cell function. Cell Metab. 21, 126–137 (2015).
The American Diabetes Association. Genetics portal for type 2 diabetes debuts. Diabetes Dispatch http://www.diabetesdispatchextra.org/genetics-portal-for-type-2-diabetes-debuts (2015).
Landrum, M. J. et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 44, D862–D868 (2016).
Stenson, P. D. et al. Human Gene Mutation Database (HGMD): 2003 update. Hum. Mutat. 21, 577–581 (2003).
Brookes, A. J. & Robinson, P. N. Human genotype-phenotype databases: aims, challenges and opportunities. Nat. Rev. Genet. 16, 702–715 (2015).
1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
Rosenbloom, K. R. et al. The UCSC Genome Browser database: 2015 update. Nucleic Acids Res. 43, D670–D681 (2015).
Ward, L. D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012).
Exome Aggregation Consortium. Analysis of protein-coding genetic variation in 60,706 humans. bioRxiv http://dx.doi.org/10.1101/030338 (2015).
Blankenberg, D. et al. in Current Protocols in Molecular Biology (eds Ausubel, F. M. et al.) (Wiley, 2010).
Reardon, S. Pharma firms join NIH on drug development. Nature http://dx.doi.org/10.1038/nature.2014.14672 (2014).
Barrett, J. C., Dunham, I. & Birney, E. Using human genetics to make new medicines. Nat. Rev. Genet. 16, 561–562 (2015).
Goldman, M. The innovative medicines initiative: a European response to the innovation challenge. Clin. Pharmacol. Ther. 91, 418–425 (2012).
Kimball, R., Ross, M., Thornthwaite, W., Mundy, J. & Becker, K. The Data Warehouse Lifecycle Toolkit 2nd edn (Wiley, 2008).
Espeland, M. A. et al. Consent for genetics studies among clinical trial participants: findings from Action for Health in Diabetes (Look AHEAD). Clin. Trials 3, 443–456 (2006).
Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).
Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).
Pal, A. et al. Loss-of-function mutations in the cell-cycle control gene CDKN2A impact on glucose homeostasis in humans. Diabetes 65, 527–533 (2015).
Torekov, S. S. et al. KCNQ1 long QT syndrome patients have hyperinsulinemia and symptomatic hypoglycemia. Diabetes 63, 1315–1325 (2014).
The authors thank B. Alexander for help with figure design and creation, as well as helpful discussions.
The authors declare no competing financial interests.
The proportion of phenotypic variance in a population owing to genetic differences, as opposed to environmental differences.
- Genome-wide association studies
(GWAS). An approach for genetic mapping that compares frequencies of variants across the genome between disease cases and matched controls. This is a paradigm for identifying genes or biological processes that are relevant to a phenotype by identifying correlations between polymorphic genetic markers and the presence of the phenotype.
- Causal variants
Specific mutations underlying the molecular cascade that produces a phenotypic trait; by design, in most genetic mapping studies, the associated genetic marker is merely correlated with the underlying causal variant.
- Effector transcripts
The specific RNA transcript (for example, mRNA transcribed from a gene) for which the function or expression is altered by the causal variant, leading to a phenotypic difference.
- Genetic architecture
The number, frequencies and effects on disease of genetic variants in a population.
- Common disease common variant hypothesis
(CDCV hypothesis). The hypothesis that, owing to historical human population growth, some disease loci for common diseases may harbour alleles common in the population.
- Linkage disequilibrium
Correlations among nearby variants, owing to historical patterns of demography and recombination, exploited by genome-wide association studies to map common variant associations.
Traits pertaining to the physiology of blood glucose regulation, usually involving measures of glucose, insulin or other related hormones.
- Rare variant models
A model of genetic architecture in which rare variants (for example, those with a frequency < 1%) explain most of the heritability.
- Synthetic associations
A hypothesis based on simulations that multiple causal rare variants of strong effects might cause a common variant statistical association.
- Common variant models
Models of genetic architecture in which common variants (for example, those with a frequency > 1%) explain most of the heritability.
A technique to infer the unknown genotype of a variant in an individual based on correlations with nearby genotyped variants.
- Allelic series
A number of alleles of a gene or locus with a range of phenotypic and/or molecular effects that are of use to infer a genetic–phenotypic dose–response curve.
An idealized model in which a phenotype is caused by a large number of variants, each with small and normally distributed phenotypic effects.
The study of the expression levels of all transcripts in a cell.
The study of all epigenetic modifications of a cell, including DNA methylation and histone modifications, which are largely responsible for the genes expressed in a specific tissue at a given developmental stage or metabolic state.
- Homeostasis model assessments
A method based on fasting measures of glucose and insulin levels that is used to estimate β-cell function or insulin resistance.
- Mendelian randomization
A technique that uses genetic variation to infer causal relationships between correlated phenotypes.
- Fine mapping
An approach to localize common variant association signals to potentially causal variants, using exhaustive candidate enumeration and genotyping in large case–control samples.
- Protein-truncating variants
Variants, such as nonsense, frameshift, readthrough or splice site mutations, that lead to incomplete protein sequences and possibly non-functional proteins.
- Expression quantitative trait loci
(eQTLs). Associations between a genetic marker and expression levels of a transcript.
Expression quantitative trait loci (eQTLs) on the same chromosome and typically near the location of the gene that encodes the associated transcript.
Expression quantitative trait loci (eQTLs) in a different chromosome from the gene encoding the associated transcript.
- CRISPR–Cas9 editing
A technique for precise and efficient editing of genetic information within a cell.
The study of all protein–protein interactions in a system.
- Business intelligence
A term, commonly used in business, that denotes a set of techniques for transforming raw data into meaningful insights.
- Big data
A term for data sets that are so large or complex that new paradigms are needed to extract meaningful insights from them.
- Data warehouses
A system for carrying out integrated analyses across multiple initially disparate data sources.
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Flannick, J., Florez, J. Type 2 diabetes: genetic data sharing to advance complex disease research. Nat Rev Genet 17, 535–549 (2016). https://doi.org/10.1038/nrg.2016.56
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