Abstract
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 the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the 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 the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Accession codes
Data deposits
Whole-genome sequence data from the GoT2D project are available by application to the European Genome-Phenome Archive (https://www.ebi.ac.uk/ega/home) under accession number EGAS00001001459 and from dbGAP (http://www.ncbi.nlm.nih.gov/gap) under accession number phs000840.v1.p1. Whole-exome sequence data from the T2D-GENES project are available from the European Genome-Phenome Archive (https://www.ebi.ac.uk/ega/home) under accession number EGAS00001001460 and from dbGAP (http://www.ncbi.nlm.nih.gov/gap) under accession numbers phs000847.v1.p1, phs001093.v1.p1, phs001095.v1.p1, phs001096.v1.p1, phs001097.v1.p1, phs001098.v1.p1, phs001099.v1.p1, phs001100.v1.p1 and phs001102.v1.p1. Summary-level data from the exome array component of this project (and from the exome and genome sequences) can be freely accessed at the Accelerating Medicines Partnership T2D portal (http://www.type2diabetesgenetics.org), and similar data from the GoT2D-imputed data at http://www.diagram-consortium.org.
References
Willemsen, G. et al. The concordance and heritability of type 2 diabetes in 34,166 twin pairs from international twin registers: the discordant twin (DISCOTWIN) consortium. Twin Res. Hum. Genet. 18, 762–771 (2015)
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)
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)
Voight, B. F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579–589 (2010)
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)
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 (2011)
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)
Ma, R. C. et al. Genome-wide association study in a Chinese population identifies a susceptibility locus for type 2 diabetes at 7q32 near PAX4 . Diabetologia 56, 1291–1305 (2013)
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)
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)
Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (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)
Albrechtsen, A. et al. Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes. Diabetologia 56, 298–310 (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)
Lee, S., Teslovich, T. M., Boehnke, M. & Lin, X. General framework for meta-analysis of rare variants in sequencing association studies. Am. J. Hum. Genet. 93, 42–53 (2013)
Kang, H. M. et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010)
Collombat, P. et al. Opposing actions of Arx and Pax4 in endocrine pancreas development. Genes Dev. 17, 2591–2603 (2003)
Kooptiwut, S. et al. Defective PAX4 R192H transcriptional repressor activities associated with maturity onset diabetes of the young and early onset-age of type 2 diabetes. J. Diabetes Complications 26, 343–347 (2012)
Langenberg, C. et al. Design and cohort description of the InterAct Project: an examination of the interaction of genetic and lifestyle factors on the incidence of type 2 diabetes in the EPIC Study. Diabetologia 54, 2272–2282 (2011)
Oppelt, A. et al. Production of phosphatidylinositol 5-phosphate via PIKfyve and MTMR3 regulates cell migration. EMBO Rep. 14, 57–64 (2013)
Kozlitina, J. et al. Exome-wide association study identifies a TM6SF2 variant that confers susceptibility to nonalcoholic fatty liver disease. Nat. Genet. 46, 352–356 (2014)
Mahdessian, H. et al. TM6SF2 is a regulator of liver fat metabolism influencing triglyceride secretion and hepatic lipid droplet content. Proc. Natl Acad. Sci. USA 111, 8913–8918 (2014)
Thiagalingam, A., Lengauer, C., Baylin, S. B. & Nelkin, B. D. RREB1, a ras responsive element binding protein, maps to human chromosome 6p25. Genomics 45, 630–632 (1997)
Murphy, R., Ellard, S. & Hattersley, A. T. Clinical implications of a molecular genetic classification of monogenic β-cell diabetes. Nat. Clin. Pract. Endocrinol. Metab. 4, 200–213 (2008)
Dickson, S. P., Wang, K., Krantz, I., Hakonarson, H. & Goldstein, D. B. Rare variants create synthetic genome-wide associations. PLoS Biol. 8, e1000294 (2010)
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)
Sim, X. et al. Transferability of type 2 diabetes implicated loci in multi-ethnic cohorts from Southeast Asia. PLoS Genet. 7, e1001363 (2011)
Goldstein, D. B. The importance of synthetic associations will only be resolved empirically. PLoS Biol. 9, e1001008 (2011)
Wakefield, J. A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am. J. Hum. Genet. 81, 208–227 (2007)
Maller, J. B. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012)
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012)
Mikkelsen, T. S. et al. Comparative epigenomic analysis of murine and human adipogenesis. Cell 143, 156–169 (2010)
Parker, S. C. 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)
Gaulton, K. J. et al. A map of open chromatin in human pancreatic islets. Nat. Genet. 42, 255–259 (2010)
Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012)
Pickrell, J. K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014)
Falconer, D. S. The inheritance of liability to certain diseases, estimated from the incidence among relatives. Ann. Hum. Genet. 29, 51–76 (1965)
Agarwala, V., Flannick, J. & Sunyaev, S., GoT2D Consortium & Altshuler, D. Evaluating empirical bounds on complex disease genetic architecture. Nat. Genet. 45, 1418–1427 (2013)
McClellan, J. & King, M. C. Genetic heterogeneity in human disease. Cell 141, 210–217 (2010)
Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010)
Flannick, J. et al. Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nat. Genet. 46, 357–363 (2014)
Bonnefond, A. et al. Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes. Nat. Genet. 44, 297–301 (2012)
Sigma Type 2 Diabetes Consortium et al. Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico. Nature 506, 97–101 (2014)
Moltke, I. et al. A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes. Nature 512, 190–193 (2014)
Sigma Type 2 Diabetes Consortium et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA 311, 2305–2314 (2014)
Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80–84 (2014)
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)
Guey, L. T. et al. Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants. Genet. Epidemiol. 35, 236–246 (2011)
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009)
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011)
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010)
Jun, G. et al. Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data. Am. J. Hum. Genet. 91, 839–848 (2012)
Abecasis, G. R. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012)
Handsaker, R. E., Korn, J. M., Nemesh, J. & McCarroll, S. A. Discovery and genotyping of genome structural polymorphism by sequencing on a population scale. Nat. Genet. 43, 269–276 (2011)
Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007)
Li, Y., Sidore, C., Kang, H. M., Boehnke, M. & Abecasis, G. R. Low-coverage sequencing: implications for design of complex trait association studies. Genome Res. 21, 940–951 (2011)
Price, A. L. et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 83, 132–135, author reply 135–139 (2008)
Weale, M. E. Quality control for genome-wide association studies. Methods Mol. Biol. 628, 341–372 (2010)
Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007)
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006)
Fuchsberger, C., Abecasis, G. R. & Hinds, D. A. minimac2: faster genotype imputation. Bioinformatics 31, 782–784 (2015)
Firth, D. Bias reduction of maximum-likelihood-estimates. Biometrika 80, 27–38 (1993)
Ma, C., Blackwell, T., Boehnke, M. & Scott, L. J. Recommended joint and meta-analysis strategies for case-control association testing of single low-count variants. Genet. Epidemiol. 37, 539–550 (2013)
Morris, A. P. Transethnic meta-analysis of genomewide association studies. Genet. Epidemiol. 35, 809–822 (2011)
Seldin, M. F., Pasaniuc, B. & Price, A. L. New approaches to disease mapping in admixed populations. Nat. Rev. Genet. 12, 523–528 (2011)
Price, A. L. et al. Sensitive detection of chromosomal segments of distinct ancestry in admixed populations. PLoS Genet. 5, e1000519 (2009)
Churchhouse, C. & Marchini, J. Multiway admixture deconvolution using phased or unphased ancestral panels. Genet. Epidemiol. 37, 1–12 (2013)
Purcell, S. M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014)
Lee, S., Wu, M. C. & Lin, X. Optimal tests for rare variant effects in sequencing association studies. Biostatistics 13, 762–775 (2012)
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. 39, 906–913 (2007)
Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999)
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010)
Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362–9367 (2009)
Korn, J. M. et al. Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Nat. Genet. 40, 1253–1260 (2008)
Rice, W. R. A consensus combined P-value test and the family-wide significance of component tests. Biometrics 46, 303–308 (1990)
Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012)
Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011)
Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012)
Ernst, J. & Kellis, M. Discovery and characterization of chromatin states for systematic annotation of the human genome. Nat. Biotechnol. 28, 817–825 (2010)
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005)
Lage, K. et al. A human phenome–interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol. 25, 309–316 (2007)
Nepusz, T., Yu, H. & Paccanaro, A. Detecting overlapping protein complexes in protein–protein interaction networks. Nat. Methods 9, 471–472 (2012)
Jia, P., Zheng, S., Long, J., Zheng, W. & Zhao, Z. dmGWAS: dense module searching for genome-wide association studies in protein–protein interaction networks. Bioinformatics 27, 95–102 (2011)
Lambert, B. W., Terwilliger, J. D. & Weiss, K. M. ForSim: a tool for exploring the genetic architecture of complex traits with controlled truth. Bioinformatics 24, 1821–1822 (2008)
Eyre-Walker, A. Evolution in health and medicine Sackler colloquium: Genetic architecture of a complex trait and its implications for fitness and genome-wide association studies. Proc. Natl Acad. Sci. USA 107 (Suppl 1), 1752–1756 (2010)
Lyssenko, V. et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N. Engl. J. Med. 359, 2220–2232 (2008)
Acknowledgements
Grant support and acknowledgments are listed in the Supplementary Information.
Author information
Authors and Affiliations
Contributions
Author contributions are described in the Supplementary Information.
Corresponding authors
Ethics declarations
Competing interests
R.A.D. has been a member of advisory boards for Astra Zeneca, Novo Nordisk, Janssen, Lexicon and Boehringer-Ingelheim; received research support from Bristol Myers Squibb, Boehringer- Ingelheim, Takeda and Astra Zeneca; and is a member of speakers’ bureaus for Novo-Nordisk and Astra Zeneca. J.C.F. has received consulting honoraria from Pfizer and PanGenX. M.I.M. has received consulting and advisory board honoraria from Pfizer, Lilly, and NovoNordisk. G.M. and P.D. are co-founders of Genomics PLC, which provides genome analytics. D.A. is an employee of and holds equity in Vertex Pharmaceuticals.
Extended data figures and tables
Extended Data Figure 1 Summary of samples and quality control procedures.
This figure summarizes data generation for whole-genome sequencing (GoT2D), exome sequencing (GoT2D and T2D-GENES), exome array genotyping (DIAGRAM), and GWAS imputation (DIAGRAM).
Extended Data Figure 2 Power for single and aggregate variant association.
a–g, Power to detect single-variant association (α = 5 × 10−8) at varying minor allele frequencies (x-axis) and allelic ORs (y-axis) for seven effective sample size (Neff) scenarios relevant to the genomes (a–c) and exomes (d–g) components of this project. a, Variant observed in 2,657 samples (the effective size of the GoT2D integrated panel). b, Variant observed in 28,350 samples (the effective size of the imputed data set). c, Variant observed in the GoT2D integrated panel and the imputed data set (effective sample size 31,007). d, Ancestry-specific variant in 2,000 samples (the size of each of the non-European exome sequence data sets). e, European-specific variant in 5,000 samples (the combined size of the European exome sequence data sets). f, Variant observed with shared frequency across all ancestry groups in 12,940 samples (the size of the combined exome sequence data set). g, Variant observed in the combined exome array and sequencing data set (effective sample size 82,758). h, i, Power for gene-based test of association (SKAT-O) according to liability variance explained. In h, 50% of the variants contribute to disease risk and the remaining 50% have no effect on disease risk; in i, 100% of the variants contribute to disease risk. For each, sample sizes considered are 2,000 (ancestry-specific effects; green) and 12,940 (ancestry-shared effects; blue). Power is shown for two levels of significance (α = 2.5 × 10−6 and α = 0.001). From these simulation studies, it is clear that under the optimistic model, where effects are shared across all ethnicities (blue line) and all variants contribute, power is >60% for 1% variance explained and α = 2.5 × 10−6. However, power declines rapidly if either criterion is relaxed.
Extended Data Figure 3 Single variant analyses.
a–c, Manhattan plot of single-variant analyses generated from exome sequence data in 6,504 cases and 6,436 controls of African American, East Asian, European, Hispanic, and South Asian ancestry (a); exome array genotypes in 28,305 cases and 51,549 controls of European ancestry (b); and combined meta-analysis of exome array and exome sequence samples (c). Coding variants are categorized according to their relationships to the previously reported lead variant from GWAS region. Loci achieving genome-wide significance only in the combined analysis are highlighted in bold. The HNF1A variant reaching genome-wide significance in the combined analysis is a synonymous variant (Thr515Thr). The dashed horizontal line in each panel designates the threshold for genome-wide significance (P < 5 × 10−8).
Extended Data Figure 4 Classification of coding variants according to their relationship to reported lead variants for each GWAS region.
The ideogram shows the location of 25 coding variant associations at 16 loci described in the text. The number in each circle corresponds to the number of associated variants at each locus. Variants are grouped into five categories based on inferred relationship with the GWAS lead variant. For some of these categories, the figure includes representative regional association plots based on exome array meta-analysis data from 28,305 cases and 51,549 controls. The locus displayed for each category is designated in bold. The first plot in each panel shows the unconditional association results; the middle plot the association results after conditioning on the non-coding GWAS SNP; and the last plot the results after conditioning on the most significantly associated coding variant. Each point represents an SNP in the exome array meta-analysis, plotted with its P value (on a –log10 scale) as a function of the genomic position (hg19). In each panel, the lead coding variant is represented by the purple symbol. The colour-coding of all other SNPs indicates LD with the lead SNP (estimated by European r2 from 1000G March 2012 reference panel: red r2 ≥ 0.8; gold 0.6 ≤ r2 < 0.8; green 0.4 ≤ r2 < 0.6; cyan 0.2 ≤ r2 < 0.4; blue r2 < 0.2; grey r2 unknown). Gene annotations are taken from the University of California Santa Cruz genome browser. GWS: genome-wide significance. *Seven variants, three at ASCC2, and one each at THADA, TSPAN8, FES and HNF4A did not achieve genome-wide significance themselves, but are included because they fall into genes and/or regions with other significant association signals (see text).
Extended Data Figure 5 Exclusion of synthetic associations and construction of credible causal variant sets at T2D GWAS loci.
Ten T2D GWAS loci were selected for synthetic association testing (P < 0.001; see Methods). a, The effect size observed at the GWAS index SNV (sequence data) before (navy blue) and after (light blue, grey) conditioning on candidate rare and low-frequency (MAF <5%) variants which could produce synthetic association. b, Example of synthetic association exclusion at the TCF7L2 locus. Error bars represent 95% confidence intervals for the index SNP odds ratio as rare variants are greedily added to the model. c, The size of credible sets at T2D GWAS loci when constructed from the GoT2D data, compared to the sizes when restricted to variants in the 1000G or HapMap data.
Extended Data Figure 6 Genome enrichment analysis in GoT2D whole genome sequence data.
n = 2,657. a, Functional annotation categories were defined using transcription, chromatin state and transcription factor binding data from GENCODE, ENCODE and other studies. b, T2D association statistics for variants at each T2D locus were jointly modelled with functional annotation using fgwas. In the resulting model we identified enrichment of coding exons (CDS), transcription factor binding sites (TFBS), mature adipose active enhancers and promoters (hASC-t4 EnhA, TssA), pancreatic islet active and weak enhancers (HI EnhA, EnhWk), pre-adipose active and weak enhancers (hASC-t1 EnhA, EnhWk), embryonic stem cell active promoters (H1-hESC TssA) and 5′UTRs. Dots represent enrichment estimates and horizontal lines the 95% confidence intervals. c, At the CCND2 locus, three variants not present in HapMap2 have a combined 90% posterior probability of being causal (rs4238013, rs3217801, rs73040004). One of these variants, rs3217801, is a 2-bp indel that overlaps an islet enhancer element.
Extended Data Figure 7 Low frequency variants in exome array data.
Results from meta-analysis of 43,045 low-frequency and common coding variants on the exome array (assayed in 79,854 European subjects). a, Observed allelic ORs as a property of allele MAF. Variants missing in more than eight cohorts or polymorphic in only one cohort were excluded. Coloured lines represent contours for liability variance explained. Regions shaded grey denote ranges of OR and MAF consistent with 80% power (in this case, at α = 5 × 10−7) to detect single-variant associations in this data set (given the observed range of missing data). Variants with a black collar are those highlighted by a bounding analysis as having a probability >0.8 of having liability-scale variance (LVE) > 0.1%. b, Distribution of each variant in the MAF/OR space was computed by assuming T2D prevalence of 8% and a beta and normal distribution for MAF and OR, respectively. Probability is obtained by integrating the joint MAF–OR distributions over ranges of LVE. c, Single variant association, liability and bounding results for the known T2D GWAS variants on the exome array (see Methods).
Supplementary information
Supplementary Information
This file contains Supplementary Tables and Figures 1– 32 (see separate excel file for Supplementary Table 20) and Author contribution and acknowledgement lists. (PDF 23107 kb)
Supplementary Table 20
This file contains an Overview of 634 genes at 81 GWAS-identified T2D loci. (XLSX 77 kb)
Rights and permissions
About this article
Cite this article
Fuchsberger, C., Flannick, J., Teslovich, T. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016). https://doi.org/10.1038/nature18642
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nature18642
This article is cited by
-
Next-Gen GWAS: full 2D epistatic interaction maps retrieve part of missing heritability and improve phenotypic prediction
Genome Biology (2024)
-
An early look at birth cohort genetics in China
Nature (2024)
-
RFX6 haploinsufficiency predisposes to diabetes through impaired beta cell function
Diabetologia (2024)
-
Lessons and Applications of Omics Research in Diabetes Epidemiology
Current Diabetes Reports (2024)
-
Integrated epigenome, whole genome sequence and metabolome analyses identify novel multi-omics pathways in type 2 diabetes: a Middle Eastern study
BMC Medicine (2023)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.