Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The trans-ancestral genomic architecture of glycemic traits

Subjects

Abstract

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10−8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Summary of all 242 loci identified in this study.
Fig. 2: Trait variance explained by associated loci.
Fig. 3: Transferability of PGSs across ancestries.
Fig. 4: Trans-ancestry fine-mapping.
Fig. 5: Epigenomic landscape of trait-associated variants.
Fig. 6: Tissues and cell types that are significantly enriched in genes in loci associated with glycemic traits.
Fig. 7: Gene-set enrichment analyses.

Data availability

Ancestry-specific and overall meta-analysis summary level results are available through the MAGIC website (https://www.magicinvestigators.org/). Summary statistics are also available through the GWAS catalog (https://www.ebi.ac.uk/gwas/) with the following accession codes: GCST90002225, GCST90002226, GCST90002227, GCST90002228, GCST90002229, GCST90002230, GCST90002231, GCST90002232, GCST90002233, GCST90002234, GCST90002235, GCST90002236, GCST90002237, GCST90002238, GCST90002239, GCST90002240, GCST90002241, GCST90002242, GCST90002243, GCST90002244, GCST90002245, GCST90002246, GCST90002247 and GCST90002248.

Code availability

Source code implementing the methods described in the paper are publicly available at https://doi.org/10.5281/zenodo.4607311.

References

  1. Use of Glycated Haemoglobin (HbA1c) in the Diagnosis of Diabetes Mellitus: Abbreviated Report of a WHO Consultation Report No. WHO/NMH/CHP/CPM/11.1 (World Health Organization, 2011).

  2. Goodarzi, M. O. et al. Fasting insulin reflects heterogeneous physiological processes: role of insulin clearance. Am. J. Physiol. Endocrinol. Metab. 301, E402–E408 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Dimas, A. S. et al. Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity. Diabetes 63, 2158–2171 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Udler, M. S. et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLoS Med. 15, e1002654 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Udler, M. S., McCarthy, M. I., Florez, J. C. & Mahajan, A. Genetic risk scores for diabetes diagnosis and precision medicine. Endocr. Rev. 40, 1500–1520 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  6. The Emerging Risk Factors Collaboration Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet 375, 2215–2222 (2010).

    Article  PubMed Central  CAS  Google Scholar 

  7. Wheeler, E. et al. Impact of common genetic determinants of hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: a transethnic genome-wide meta-analysis. PLoS Med. 14, e1002383 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  8. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Manning, A. K. et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Walford, G. A. et al. Genome-wide association study of the modified Stumvoll insulin sensitivity index identifies BCL2 and FAM19A2 as novel insulin sensitivity loci. Diabetes 65, 3200–3211 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Horikoshi, M. et al. Discovery and fine-mapping of glycaemic and obesity-related trait loci using high-density imputation. PLoS Genet. 11, e1005230 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Mahajan, A. et al. Identification and functional characterization of G6PC2 coding variants influencing glycemic traits define an effector transcript at the G6PC2-ABCB11 locus. PLoS Genet. 11, e1004876 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Hwang, J. Y. et al. Genome-wide association meta-analysis identifies novel variants associated with fasting plasma glucose in East Asians. Diabetes 64, 291–298 (2015).

    Article  CAS  PubMed  Google Scholar 

  14. Chen, P. et al. Multiple nonglycemic genomic loci are newly associated with blood level of glycated hemoglobin in East Asians. Diabetes 63, 2551–2562 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Scott, R. A. et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 991–1005 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Spanakis, E. K. & Golden, S. H. Race/ethnic difference in diabetes and diabetic complications. Curr. Diabetes Rep. 13, 814–823 (2013).

    Article  Google Scholar 

  17. Tillin, T. et al. Insulin resistance and truncal obesity as important determinants of the greater incidence of diabetes in Indian Asians and African Caribbeans compared with Europeans: the Southall And Brent REvisited (SABRE) cohort. Diabetes Care 36, 383–393 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Whincup, P. H. et al. Early emergence of ethnic differences in type 2 diabetes precursors in the UK: the Child Heart and Health Study in England (CHASE Study). PLoS Med. 7, e1000263 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. The 1000 Genomes Project Consortium A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  CAS  Google Scholar 

  20. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 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).

  24. Mahajan, A. et al. Trans-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Preprint at medRxiv https://doi.org/10.1101/2020.09.22.20198937 (2020).

  25. Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Spracklen, C. N. et al. Identification of type 2 diabetes loci in 433,540 East Asian individuals. Nature 582, 240–245 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Vujkovic, M. et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat. Genet. 52, 680–691 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Luo, Y. et al. Transcription factor Ets1 regulates expression of thioredoxin-interacting protein and inhibits insulin secretion in pancreatic beta-cells. PLoS ONE 9, e99049 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Braccini, L. et al. PI3K-C2γ is a Rab5 effector selectively controlling endosomal Akt2 activation downstream of insulin signalling. Nat. Commun. 6, 7400 (2015).

    Article  CAS  PubMed  Google Scholar 

  30. Aschard, H., Vilhjálmsson, B. J., Joshi, A. D., Price, A. L. & Kraft, P. Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. Am. J. Hum. Genet. 96, 329–339 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Nolte, I. M. et al. Missing heritability: is the gap closing? An analysis of 32 complex traits in the Lifelines Cohort Study. Eur. J. Hum. Genet. 25, 877–885 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Ge, T., Chen, C. Y., Ni, Y., Feng, Y. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Dastani, Z. et al. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals. PLoS Genet. 8, e1002607 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Spracklen, C. N. et al. Identification and functional analysis of glycemic trait loci in the China Health and Nutrition Survey. PLoS Genet. 14, e1007275 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc. Natl Acad. Sci. USA 114, 2301–2306 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Kichaev, G. et al. Leveraging polygenic functional enrichment to improve GWAS power. Am. J. Hum. Genet. 104, 65–75 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. Shriner, D. & Rotimi, C. N. Whole-genome-sequence-based haplotypes reveal single origin of the sickle allele during the Holocene wet phase. Am. J. Hum. Genet. 102, 547–556 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kramer, H. J. et al. African ancestry-specific alleles and kidney disease risk in Hispanics/Latinos. J. Am. Soc. Nephrol. 28, 915–922 (2017).

    Article  CAS  PubMed  Google Scholar 

  42. Ravenhall, M. et al. Novel genetic polymorphisms associated with severe malaria and under selective pressure in North-eastern Tanzania. PLoS Genet. 14, e1007172 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Hodonsky, C. J. et al. Genome-wide association study of red blood cell traits in Hispanics/Latinos: The Hispanic Community Health Study/Study of Latinos. PLoS Genet. 13, e1006760 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Gurdasani, D. et al. Uganda genome resource enables insights into population history and genomic discovery in Africa. Cell 179, 984–1002 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Rees, M. G. et al. Cellular characterisation of the GCKR P446L variant associated with type 2 diabetes risk. Diabetologia 55, 114–122 (2012).

    Article  CAS  PubMed  Google Scholar 

  46. Bonomo, J. A. et al. The ras responsive transcription factor RREB1 is a novel candidate gene for type 2 diabetes associated end-stage kidney disease. Hum. Mol. Genet. 23, 6441–6447 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 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).

    Article  CAS  PubMed  Google Scholar 

  48. Scott, R. A. et al. A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease. Sci. Transl. Med. 8, 341ra76 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Nai, A. et al. TMPRSS6 rs855791 modulates hepcidin transcription in vitro and serum hepcidin levels in normal individuals. Blood 118, 4459–4462 (2011).

    Article  CAS  PubMed  Google Scholar 

  50. Ng, N. H. J. et al. Tissue-specific alteration of metabolic pathways influences glycemic regulation. Preprint at bioRxiv https://doi.org/10.1101/790618 (2019).

  51. Soranzo, N. et al. Common variants at 10 genomic loci influence hemoglobin A1C levels via glycemic and nonglycemic pathways. Diabetes 59, 3229–3239 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Sarnowski, C. et al. Impact of rare and common genetic variants on diabetes diagnosis by hemoglobin A1c in multi-ancestry cohorts: the trans-omics for precision medicine program. Am. J. Hum. Genet. 105, 706–718 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Nagel, M. et al. Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. Nat. Genet. 50, 920–927 (2018).

    Article  CAS  PubMed  Google Scholar 

  55. Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50, 912–919 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Schmidt, E. M. et al. GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31, 2601–2606 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Iotchkova, V. et al. GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat. Genet. 51, 343–353 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 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 glycemic traits to their downstream effectors. PLoS Genet. 11, e1005694 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Civelek, M. et al. Genetic regulation of adipose gene expression and cardio-metabolic traits. Am. J. Hum. Genet. 100, 428–443 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Scott, L. J. et al. The genetic regulatory signature of type 2 diabetes in human skeletal muscle. Nat. Commun. 7, 11764 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Ben Harouch, S., Klar, A. & Falik Zaccai, T. C. in GeneReviews (eds Adam, M. P. et al.) (Univ. of Washington, 1993).

  64. Agus, D. B. et al. Vitamin C crosses the blood-brain barrier in the oxidized form through the glucose transporters. J. Clin. Invest. 100, 2842–2848 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Wolking, S. et al. Focal epilepsy in glucose transporter type 1 (Glut1) defects: case reports and a review of literature. J. Neurol. 261, 1881–1886 (2014).

    Article  PubMed  Google Scholar 

  66. Guallar, D. et al. RNA-dependent chromatin targeting of TET2 for endogenous retrovirus control in pluripotent stem cells. Nat. Genet. 50, 443–451 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Bian, F. et al. TET2 facilitates PPARγ agonist-mediated gene regulation and insulin sensitization in adipocytes. Metabolism 89, 39–47 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Yoo, Y. et al. TET-mediated hydroxymethylcytosine at the Pparγ locus is required for initiation of adipogenic differentiation. Int. J. Obes. 41, 652–659 (2017).

    Article  CAS  Google Scholar 

  69. Lees, J. A. et al. Lipid transport by TMEM24 at ER-plasma membrane contacts regulates pulsatile insulin secretion. Science 355, eaah6171 (2017).

  70. Pottekat, A. et al. Insulin biosynthetic interaction network component, TMEM24, facilitates insulin reserve pool release. Cell Rep. 4, 921–930 (2013).

    Article  CAS  PubMed  Google Scholar 

  71. Androulakis, I. I. et al. Patients with apparently nonfunctioning adrenal incidentalomas may be at increased cardiovascular risk due to excessive cortisol secretion. J. Clin. Endocrinol. Metab. 99, 2754–2762 (2014).

    Article  CAS  PubMed  Google Scholar 

  72. Altieri, B. et al. Adrenocortical tumors and insulin resistance: what is the first step? Int. J. Cancer 138, 2785–2794 (2016).

    Article  CAS  PubMed  Google Scholar 

  73. Johansson, M. et al. The influence of obesity-related factors in the etiology of renal cell carcinoma-A Mendelian randomization study. PLoS Med. 16, e1002724 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Diamanti-Kandarakis, E. & Dunaif, A. Insulin resistance and the polycystic ovary syndrome revisited: an update on mechanisms and implications. Endocr. Rev. 33, 981–1030 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. The 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. 44, 981–990 (2012).

    Article  CAS  Google Scholar 

  76. Leong, A. et al. Mendelian randomization analysis of hemoglobin A1C as a risk factor for coronary artery disease. Diabetes Care 42, 1202–1208 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Duncan, L. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10, 3328 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Mostafavi, H. et al. Variable prediction accuracy of polygenic scores within an ancestry group. eLife 9, e48376 (2020).

  79. Choi, S. W., Mak, T. S. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analyses. Nat. Protoc. 15, 2759–2772 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. D’Orazio, P. et al. Approved IFCC recommendation on reporting results for blood glucose (abbreviated). Clin. Chem. 51, 1573–1576 (2005).

    Article  PubMed  CAS  Google Scholar 

  81. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. The 1000 Genomes Project Consortium An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    Article  PubMed Central  CAS  Google Scholar 

  83. Li, Y., Willer, C. J., Ding, J., Scheet, P. & Abecasis, G. R. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816–834 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Pei, Y. F., Zhang, L., Li, J. & Deng, H. W. Analyses and comparison of imputation-based association methods. PLoS ONE 5, e10827 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).

    Article  CAS  PubMed  Google Scholar 

  87. Morris, A. P. Transethnic meta-analysis of genomewide association studies. Genet. Epidemiol. 35, 809–822 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Canela-Xandri, O., Rawlik, K. & Tenesa, A. An atlas of genetic associations in UK Biobank. Nat. Genet. 50, 1593–1599 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Benyamin, B. et al. Novel loci affecting iron homeostasis and their effects in individuals at risk for hemochromatosis. Nat. Commun. 5, 4926 (2014).

    Article  CAS  PubMed  Google Scholar 

  93. Binesh, N. & Rezghi, M. Fuzzy clustering in community detection based on nonnegative matrix factoriztion with two novel evaluation criteria. Appl. Soft Comput. 69, 689–703 (2018).

    Article  Google Scholar 

  94. Scott, R. A. et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66, 2888–2902 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Mikkelsen, T. S. et al. Comparative epigenomic analysis of murine and human adipogenesis. Cell 143, 156–169 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. GTEx Consortium Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Article  PubMed Central  Google Scholar 

  99. Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2017).

    Article  CAS  PubMed  Google Scholar 

  100. Westra, H. J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Joehanes, R. et al. Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies. Genome Biol. 18, 16 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank all investigators, staff members and study participants for their contribution to all participating studies. The funders had no role in study design, data collection, analysis, decision to publish or preparation of the manuscript. The authors received no specific funding for this work. A full list of funding as well as individual and study acknowledgments appears in the Supplementary Note.

Author information

Authors and Affiliations

Authors