Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

Kyle J. Gaulton | Yoonjung Yoonie Joo | Cassandra N. Spracklen | Y. J. Kim | Jacob M. Keaton | Jason M. Torres | Ellen M. Schmidt | M. Fornage | M. Nalls | C. Gieger | F. Kronenberg | O. Franco | A. Reiner | A. Peters | J. Pankow | I. Ntalla | J. Cook | R. Mägi | M. McCarthy | E. Zeggini | A. Morris | S. Redline | G. Abecasis | U. Thorsteinsdóttir | K. Stefánsson | C. Rotimi | A. Adeyemo | O. Pedersen | N. Grarup | T. Jørgensen | I. Brandslund | C. Lindgren | L. Groop | M. Laakso | Mark A Pereira | K. Strauch | M. Boehnke | S. Yusuf | Y. Kamatani | Y. Okada | T. Frayling | A. Hattersley | N. W. Rayner | D. Shriner | T. Kawaguchi | S. Myers | C. Kooperberg | T. Wong | A. Zonderman | M. Evans | E. Tai | A. Dehghan | A. Köttgen | G. Thorleifsson | V. Steinthorsdottir | W. So | R. Ma | S. Kardia | T. Meitinger | Yongmei Liu | B. Psaty | C. Haiman | M. Province | N. Sattar | M. Kals | A. Metspalu | A. Doumatey | A. Motala | F. Pirie | K. Fischer | E. Ingelsson | Michael H. Preuss | K. Mohlke | T. Buchanan | J. Tuomilehto | V. Lyssenko | J. Florez | K. Taylor | J. Rotter | P. Froguel | Latchezar Dimitrov | L. Lind | E. Parra | C. Aguilar-Salinas | J. Jonas | Wei-Min Chen | A. Kho | B. Cade | J. Long | X. Shu | Jian-Min Yuan | W. Koh | W. Zheng | J. Brody | D. Gorkin | J. Meigs | D. Becker | L. Bielak | P. Peyser | N. Wareham | Jianjun Liu | J. Dupuis | A. Stilp | T. Sofer | M. Horikoshi | A. Mahajan | H. Grallert | M. Udler | W. Hsueh | D. Bharadwaj | H. Gerstein | Xiuqing Guo | C. Sabanayagam | F. Jasmine | M. Kibriya | H. Ahsan | C. Schurmann | M. Roden | C. Herder | K. North | M. Ikram | L. Orozco | D. Saleheen | James G. Wilson | J. Mercader | I. Miguel-Escalada | A. Gloyn | J. Kooner | E. Bottinger | S. Rich | N. Robertson | Daniel S. Evans | Jennifer A. Smith | M. Ingelsson | Ching-Yu Cheng | X. Sim | R. Noordam | S. Tajuddin | Jin-Fang Chai | A. Kasturiratne | J. Luan | R. Scott | A. Stančáková | F. Takeuchi | L. Yanek | Weihua Zhang | M. Canouil | H. J. de Silva | Q. Duan | T. Katsuya | C. Khor | J. Kuusisto | C. Langenberg | M. Loh | T. Louie | L. Raffel | D. Bowden | J. Chambers | B. Freedman | N. Kato | A. Wickremasinghe | D. Mook-Kanamori | J. Bork-Jensen | R. Loos | C. Brummett | R. Varma | L. Yengo | Ching‐Ti Liu | R. V. van Dam | B. Thorand | C. Palmer | M. Goodarzi | Y. Hung | L. Adair | A. Bertoni | Zhengming Chen | Yu Guo | Liming Li | A. Takahashi | F. Matsuda | Sonia S Anand | J. Jukema | P. Gordon-Larsen | C. Fuchsberger | G. Paré | A. Linneberg | J. Engert | S. Preissl | F. Tsai | G. Nadkarni | M. Cushman | M. Hayes | A. Locke | S. Trompet | V. Giedraitis | I. Ford | M. Graff | S. Rüeger | M. Ng | M. Jørgensen | H. Kitajima | J. Kriebel | L. Lange | R. Li-Gao | Kevin Sandow | D. Witte | C. Lecoeur | N. Lee | S. Hackinger | I. Pan | R. Mckean-Cowdin | M. Gross | K. V. van Dijk | K. Kohara | Y. Tabara | J. Below | Bong-Jo Kim | Juyoung Lee | S. Kwak | G. Chandak | C. Hanis | Wei Huang | Duk-Hwan Kim | T. Kadowaki | B. Prins | K. Eckardt | T. Yamauchi | E. Kabagambe | B. Porneala | M. Nakatochi | Yuan-Tsong Chen | B. Tomlinson | M. Yokota | L. Chuang | Yaxing Wang | A. Xiang | T. Tusié-Luna | M. Akiyama | Guanjie Chen | U. Nayak | L. Rasmussen-Torvik | M. Sale | Chien-Hsiun Chen | Ken Yamamoto | Li-Ching Chang | M. Isono | Jer-Yuarn Wu | A. Valladares-Salgado | Y. Joo | D. Taliun | Finngen | M. Wuttke | M. Chee | M. Vujkovic | L. Raffield | S. Maeda | P. An | N. Maruthur | T. Hansen | S. Schönherr | L. Tong | A. Leong | M. Sander | Y. Hai | R. Jensen | Vasiliki Mamakou | G. Dedoussis | J. Torres | C. Sarnowski | J. Nano | L. Petty | K. Läll | S. Ligthart | Joshua Chiou | Myung-Shik Lee | E. Whitsel | F. Kandeel | A. Howard | Ken Suzuki | M. Cruz | W. Sheu | E. Ipp | P. Genter | J. Floyd | Xi Luo | A. Luk | Sohee Han | Canqing Yu | Z. Bian | I. Millwood | R. Walters | J. Lv | K. Park | Simin Liu | A. Huerta-Chagoya | C. González-Villalpando | N. Wacher-Rodarte | Ji Chen | Jung-Jin Lee | M. Y. Hwang | M. González-Villalpando | F. Collins | L. Speidel | Kuang Lin | S. Ichihara | C. Yajnik | K. R. Mani | A. Nicolas | C. Spracklen | Jinxiu Shi | S. Du | David U. Gorkin | L. Emery | Sanghoon Moon | F. Abaitua | J. V. van Klinken | M. Igase | Jennifer A. Brody | Md. Tariqul Islam | C. Hwu | Rebecca Rohde | C. Tam | Kyungheon Yoon | Jingyi Tan | Mengna Huang | F. Bragg | V. J. Lim | Hyeok Sun Choi | Hye-Mi Jang | Dong Mun Shin | Liang Zhang | V. Kaur | D. Nousome | Yun Li | A. Lamri | J. Yao | Gauri Prasad | J. Chan | Shyh-Huei Chen | Jorge Ferrer | K. Gaulton | G. Yu | Meng Sun | Meraj Ahmad | Jerry L. Nadler | Bianca C. Porneala | M. Shahriar | S. Patel | Ellie Wheeler | Y. Chen | L. Raffield | S. Das | Guozhi Jiang | Y. Cho | J. Keaton | A. Morris | Pietro della Briotta Parolo | K. Roll | Uma Nayak | Kristi Läll | C. González‐Villalpando | S. Moon | M. Preuss | Victor J. Y. Lim | Weihua Zhang | Hidetoshi Kitajima | R. Scott | Darryl Nousome | Teresa Tusié-Luna | Ellen M. Schmidt | B. Psaty | A. Morris | Symen Ligthart | R. Loos | R. Scott | M. McCarthy | T. Wong | K. W. van Dijk | J. Ferrer | R. Scott | T. Wong | A. Peters | A. R. Wickremasinghe | K. Taylor | Daniel S. Evans | M. McCarthy | T. Wong | Daniel Taliun | C. Palmer | Rebecca R. Rohde | Jennifer A. Smith | K. Taylor | A. Peters | T. Wong | Alicia Huerta-Chagoya | M. Vujković | A. Morris

[1]  J. Novembre,et al.  Getting genetic ancestry right for science and society , 2021, Science.

[2]  Jacob C. Ulirsch,et al.  Insights from complex trait fine-mapping across diverse populations , 2021, medRxiv.

[3]  Kyle J. Gaulton,et al.  Single cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk , 2021, Nature Genetics.

[4]  Brian E. Cade,et al.  Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program , 2019, Nature.

[5]  Stephen C. J. Parker,et al.  Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D , 2020, Nature Communications.

[6]  V. M. V. Program.  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 , 2020 .

[7]  Alexander E. Lopez,et al.  Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ethnic meta-analysis , 2020, Nature Genetics.

[8]  Audrey Y. Chu,et al.  Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers , 2020, Nature Medicine.

[9]  William J. Astle,et al.  Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations , 2020, Cell.

[10]  Nathan A. Bihlmeyer,et al.  Tissue-Specific Alteration of Metabolic Pathways Influences Glycemic Regulation , 2019, bioRxiv.

[11]  Mark I. McCarthy,et al.  Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals , 2019, bioRxiv.

[12]  A. Gloyn.  Exocrine or endocrine? A circulating pancreatic elastase that regulates glucose homeostasis , 2019, Nature Metabolism.

[13]  Kyle J. Gaulton,et al.  Single cell chromatin accessibility reveals pancreatic islet cell type- and state-specific regulatory programs of diabetes risk , 2019, bioRxiv.

[14]  David Torrents,et al.  Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes , 2019, Nature Genetics.

[15]  Cassandra N. Spracklen,et al.  Identification of type 2 diabetes loci in 433,540 East Asian individuals , 2019, bioRxiv.

[16]  E. Zeggini,et al.  Genomics of disease risk in globally diverse populations , 2019, Nature Reviews Genetics.

[17]  S. Mane,et al.  CELA2A mutations predispose to early-onset atherosclerosis and metabolic syndrome and affect plasma insulin and platelet activation , 2019, Nature Genetics.

[18]  Alicia R. Martin,et al.  Clinical use of current polygenic risk scores may exacerbate health disparities , 2019, Nature Genetics.

[19]  S. Grant,et al.  Genome-scale Capture C promoter interactions implicate effector genes at GWAS loci for bone mineral density , 2019, Nature Communications.

[20]  S. Myers,et al.  A method for genome-wide genealogy estimation for thousands of samples , 2019, Nature Genetics.

[21]  M. Kanai,et al.  Identification of 28 new susceptibility loci for type 2 diabetes in the Japanese population , 2019, Nature Genetics.

[22]  Y. J. Kim,et al.  The Korea Biobank Array: Design and Identification of Coding Variants Associated with Blood Biochemical Traits , 2019, Scientific Reports.

[23]  Mary E. Haas,et al.  DNA Sequence Variation in ACVR1C Encoding the Activin Receptor-Like Kinase 7 Influences Body Fat Distribution and Protects Against Type 2 Diabetes , 2018, Diabetes.

[24]  Anthony J. Payne,et al.  Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps , 2018, Nature Genetics.

[25]  P. Donnelly,et al.  The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.

[26]  Y. J. Kim,et al.  Nonsynonymous Variants in PAX4 and GLP1R Are Associated With Type 2 Diabetes in an East Asian Population , 2018, Diabetes.

[27]  Stephen Burgess,et al.  Genomic atlas of the human plasma proteome , 2018, Nature.

[28]  David Z. Pan,et al.  Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS , 2018, Nature Communications.

[29]  P. Visscher,et al.  Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry , 2018, bioRxiv.

[30]  P. Tsai,et al.  Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition , 2018, Nature Genetics.

[31]  Frank B. Hu,et al.  Global aetiology and epidemiology of type 2 diabetes mellitus and its complications , 2018, Nature Reviews Endocrinology.

[32]  Nicola J. Rinaldi,et al.  Genetic effects on gene expression across human tissues , 2017, Nature.

[33]  Hannes P. Eggertsson,et al.  Whole genome characterization of sequence diversity of 15,220 Icelanders , 2017, Scientific Data.

[34]  Kyle J. Gaulton,et al.  Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 Diabetes susceptibility loci , 2017, bioRxiv.

[35]  M. Kanai,et al.  Genome-wide association study identifies 112 new loci for body mass index in the Japanese population , 2017, Nature Genetics.

[36]  R. Mägi,et al.  Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution , 2017, Human molecular genetics.

[37]  Stephen C. J. Parker,et al.  A Type 2 Diabetes–Associated Functional Regulatory Variant in a Pancreatic Islet Enhancer at the ADCY5 Locus , 2017, Diabetes.

[38]  Nicholette D. Palmer,et al.  Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms , 2017, Cell.

[39]  Giovanni Malerba,et al.  Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes , 2017, Nature Genetics.

[40]  Tanya M. Teslovich,et al.  An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans , 2017, Diabetes.

[41]  Christopher R. Gignoux,et al.  Human demographic history impacts genetic risk prediction across diverse populations , 2016, bioRxiv.

[42]  R. Mägi,et al.  Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel , 2017, European Journal of Human Genetics.

[43]  Neil M Davies,et al.  Software Application Profile: PHESANT: a tool for performing automated phenome scans in UK Biobank , 2017, International journal of epidemiology.

[44]  Laura J. Scott,et al.  Genetic regulatory signatures underlying islet gene expression and type 2 diabetes , 2017, Proceedings of the National Academy of Sciences.

[45]  Philippe Froguel,et al.  Decreased STARD10 Expression Is Associated with Defective Insulin Secretion in Humans and Mice , 2017, American journal of human genetics.

[46]  Andrew P Morris,et al.  Guidance for the utility of linear models in meta-analysis of genetic association studies of binary phenotypes , 2016, European Journal of Human Genetics.

[47]  B. Anderson,et al.  Youth-Onset Type 2 Diabetes Consensus Report: Current Status, Challenges, and Priorities , 2016, Diabetes Care.

[48]  Andrew P Morris,et al.  Multi-ethnic genome-wide association study identifies novel locus for type 2 diabetes susceptibility , 2016, European Journal of Human Genetics.

[49]  Samuel E. Jones,et al.  Genetic Evidence for a Link Between Favorable Adiposity and Lower Risk of Type 2 Diabetes, Hypertension, and Heart Disease , 2016, Diabetes.

[50]  M. M. Rahman,et al.  Worldwide trends in diabetes since 1980 : pooled analysis of 751 population-based measurement studies with over 4 . 4 million participants , 2016 .

[51]  Sungkyoung Choi,et al.  Transancestral fine-mapping of four type 2 diabetes susceptibility loci highlights potential causal regulatory mechanisms , 2016, Human molecular genetics.

[52]  Ashutosh Kumar Singh,et al.  Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015 , 2016, Lancet.

[53]  Mark I. McCarthy,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 , 2015, PLoS genetics.

[54]  Gabor T. Marth,et al.  A global reference for human genetic variation , 2015, Nature.

[55]  Wei Zheng,et al.  Addressing Population‐Specific Multiple Testing Burdens in Genetic Association Studies , 2015, Annals of human genetics.

[56]  J. Al-Aama,et al.  A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes , 2014, Nature.

[57]  Tanya M. Teslovich,et al.  Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility , 2014, Nature Genetics.

[58]  Luca Pagani,et al.  Revisiting the thrifty gene hypothesis via 65 loci associated with susceptibility to type 2 diabetes. , 2014, American journal of human genetics.

[59]  Mark I. McCarthy,et al.  Pancreatic islet enhancer clusters enriched in type 2 diabetes risk–associated variants , 2013, Nature Genetics.

[60]  Tanya M. Teslovich,et al.  Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico , 2013, Nature.

[61]  Joseph K. Pickrell.  Joint analysis of functional genomic data and genome-wide association studies of 18 human traits , 2013, bioRxiv.

[62]  C. Wallace,et al.  Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics , 2013, PLoS genetics.

[63]  Chandra Y Osborn,et al.  Racial and ethnic disparities in diabetes complications in the northeastern United States: the role of socioeconomic status. , 2013, Journal of the National Medical Association.

[64]  Jake K. Byrnes,et al.  Bayesian refinement of association signals for 14 loci in 3 common diseases , 2012, Nature Genetics.

[65]  Kenny Q. Ye,et al.  An integrated map of genetic variation from 1,092 human genomes , 2012, Nature.

[66]  Data production leads,et al.  An integrated encyclopedia of DNA elements in the human genome , 2012 .

[67]  Bronwen L. Aken,et al.  GENCODE: The reference human genome annotation for The ENCODE Project , 2012, Genome research.

[68]  Tanya M. Teslovich,et al.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes , 2012, Nature Genetics.

[69]  ENCODEConsortium,et al.  An Integrated Encyclopedia of DNA Elements in the Human Genome , 2012, Nature.

[70]  T. Jin,et al.  Nodal induces apoptosis through activation of the ALK7 signaling pathway in pancreatic INS-1 β-cells. , 2012, American journal of physiology. Endocrinology and metabolism.

[71]  Alexander A. Morgan,et al.  Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases , 2012, PLoS genetics.

[72]  P. Visscher,et al.  Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits , 2012, Nature Genetics.

[73]  Karen L. Mohlke,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 , 2012, PLoS genetics.

[74]  Avner Schlessinger,et al.  ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI , 2012 .

[75]  R. Scharfmann,et al.  A genetically engineered human pancreatic β cell line exhibiting glucose-inducible insulin secretion. , 2011, The Journal of clinical investigation.

[76]  Eleazar Eskin,et al.  Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. , 2011, American journal of human genetics.

[77]  Yun Li,et al.  METAL: fast and efficient meta-analysis of genomewide association scans , 2010, Bioinform..

[78]  Ayellet V. Segrè,et al.  Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis , 2010, Nature Genetics.

[79]  P. Donnelly,et al.  Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip , 2009, PLoS genetics.

[80]  John Adams.  Assessing , 2020, Transport Planning.

[81]  B. Browning,et al.  Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. , 2007, American journal of human genetics.

[82]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[83]  Jon Wakefield,et al.  A Bayesian measure of the probability of false discovery in genetic epidemiology studies. , 2007, American journal of human genetics.

[84]  Tony O’Hagan.  Bayes factors , 2006 .

[85]  S. Gabriel,et al.  Assessing the impact of population stratification on genetic association studies , 2004, Nature Genetics.

[86]  K. Roeder,et al.  Genomic Control for Association Studies , 1999, Biometrics.

[87]  Thrifty Genotype Rendered Detrimental by Progress? , 1989, The Lancet.

[88]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[89]  J. Neel.  Diabetes mellitus: a "thrifty" genotype rendered detrimental by "progress"? , 1962, American journal of human genetics.