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Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility.

ABSTRACT: To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry.

SUBMITTER: DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium

PROVIDER: S-EPMC3969612 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility.

Mahajan Anubha A Go Min Jin MJ Zhang Weihua W Below Jennifer E JE Gaulton Kyle J KJ Ferreira Teresa T Horikoshi Momoko M Johnson Andrew D AD Ng Maggie C Y MC Prokopenko Inga I Saleheen Danish D Wang Xu X Zeggini Eleftheria E Abecasis Goncalo R GR Adair Linda S LS Almgren Peter P Atalay Mustafa M Aung Tin T Baldassarre Damiano D Balkau Beverley B Bao Yuqian Y Barnett Anthony H AH Barroso Ines I Basit Abdul A Been Latonya F LF Beilby John J Bell Graeme I GI Benediktsson Rafn R Bergman Richard N RN Boehm Bernhard O BO Boerwinkle Eric E Bonnycastle Lori L LL Burtt Noël N Cai Qiuyin Q Campbell Harry H Carey Jason J Cauchi Stephane S Caulfield Mark M Chan Juliana C N JC Chang Li-Ching LC Chang Tien-Jyun TJ Chang Yi-Cheng YC Charpentier Guillaume G Chen Chien-Hsiun CH Chen Han H Chen Yuan-Tsong YT Chia Kee-Seng KS Chidambaram Manickam M Chines Peter S PS Cho Nam H NH Cho Young Min YM Chuang Lee-Ming LM Chuang Lee-Ming LM Collins Francis S FS Cornelis Marylin C MC Couper David J DJ Crenshaw Andrew T AT van Dam Rob M RM Danesh John J Das Debashish D de Faire Ulf U Dedoussis George G Deloukas Panos P Dimas Antigone S AS Dina Christian C Doney Alex S AS Donnelly Peter J PJ Dorkhan Mozhgan M van Duijn Cornelia C Dupuis Josée J Edkins Sarah S Elliott Paul P Emilsson Valur V Erbel Raimund R Eriksson Johan G JG Escobedo Jorge J Esko Tonu T Eury Elodie E Florez Jose C JC Fontanillas Pierre P Forouhi Nita G NG Forsen Tom T Fox Caroline C Fraser Ross M RM Frayling Timothy M TM Froguel Philippe P Frossard Philippe P Gao Yutang Y Gertow Karl K Gieger Christian C Gigante Bruna B Grallert Harald H Grant George B GB Grrop Leif C LC Groves Chrisropher J CJ Grundberg Elin E Guiducci Candace C Hamsten Anders A Han Bok-Ghee BG Hara Kazuo K Hassanali Neelam N Hattersley Andrew T AT Hayward Caroline C Hedman Asa K AK Herder Christian C Hofman Albert A Holmen Oddgeir L OL Hovingh Kees K Hreidarsson Astradur B AB Hu Cheng C Hu Frank B FB Hui Jennie J Humphries Steve E SE Hunt Sarah E SE Hunter David J DJ Hveem Kristian K Hydrie Zafar I ZI Ikegami Hiroshi H Illig Thomas T Ingelsson Erik E Islam Muhammed M Isomaa Bo B Jackson Anne U AU Jafar Tazeen T James Alan A Jia Weiping W Jöckel Karl-Heinz KH Jonsson Anna A Jowett Jeremy B M JB Kadowaki Takashi T Kang Hyun Min HM Kanoni Stavroula S Kao Wen Hong L WH Kathiresan Sekar S Kato Norihiro N Katulanda Prasad P Keinanen-Kiukaanniemi Kirkka M KM Kelly Ann M AM Khan Hassan H Khaw Kay-Tee KT Khor Chiea-Chuen CC Kim Hyung-Lae HL Kim Sangsoo S Kim Young Jin YJ Kinnunen Leena L Klopp Norman N Kong Augustine A Korpi-Hyövälti Eeva E Kowlessur Sudhir S Kraft Peter P Kravic Jasmina J Kristensen Malene M MM Krithika S S Kumar Ashish A Kumate Jesus J Kuusisto Johanna J Kwak Soo Heon SH Laakso Markku M Lagou Vasiliki V Lakka Timo A TA Langenberg Claudia C Langford Cordelia C Lawrence Robert R Leander Karin K Lee Jen-Mai JM Lee Nanette R NR Li Man M Li Xinzhong X Li Yun Y Liang Junbin J Liju Samuel S Lim Wei-Yen WY Lind Lars L Lindgren Cecilia M CM Lindholm Eero E Liu Ching-Ti CT Liu Jian Jun JJ Lobbens Stéphane S Long Jirong J Loos Ruth J F RJ Lu Wei W Luan Jian'an J Lyssenko Valeriya V Ma Ronald C W RC Maeda Shiro S Mägi Reedik R Männisto Satu S Matthews David R DR Meigs James B JB Melander Olle O Metspalu Andres A Meyer Julia J Mirza Ghazala G Mihailov Evelin E Moebus Susanne S Mohan Viswanathan V Mohlke Karen L KL Morris Andrew D AD Mühleisen Thomas W TW Müller-Nurasyid Martina M Musk Bill B Nakamura Jiro J Nakashima Eitaro E Navarro Pau P Ng Peng-Keat PK Nica Alexandra C AC Nilsson Peter M PM Njølstad Inger I Nöthen Markus M MM Ohnaka Keizo K Ong Twee Hee TH Owen Katharine R KR Palmer Colin N A CN Pankow James S JS Park Kyong Soo KS Parkin Melissa M Pechlivanis Sonali S Pedersen Nancy L NL Peltonen Leena L Perry John R B JR Peters Annette A Pinidiyapathirage Janini M JM Platou Carl G CG Potter Simon S Price Jackie F JF Qi Lu L Radha Venkatesan V Rallidis Loukianos L Rasheed Asif A Rathman Wolfgang W Rauramaa Rainer R Raychaudhuri Soumya S Rayner N William NW Rees Simon D SD Rehnberg Emil E Ripatti Samuli S Robertson Neil N Roden Michael M Rossin Elizabeth J EJ Rudan Igor I Rybin Denis D Saaristo Timo E TE Salomaa Veikko V Saltevo Juha J Samuel Maria M Sanghera Dharambir K DK Saramies Jouko J Scott James J Scott Laura J LJ Scott Robert A RA Segrè Ayellet V AV Sehmi Joban J Sennblad Bengt B Shah Nabi N Shah Sonia S Shera A Samad AS Shu Xiao Ou XO Shuldiner Alan R AR Sigurđsson Gunnar G Sijbrands Eric E Silveira Angela A Sim Xueling X Sivapalaratnam Suthesh S Small Kerrin S KS So Wing Yee WY Stančáková Alena A Stefansson Kari K Steinbach Gerald G Steinthorsdottir Valgerdur V Stirrups Kathleen K Strawbridge Rona J RJ Stringham Heather M HM Sun Qi Q Suo Chen C Syvänen Ann-Christine AC Takayanagi Ryoichi R Takeuchi Fumihiko F Tay Wan Ting WT Teslovich Tanya M TM Thorand Barbara B Thorleifsson Gudmar G Thorsteinsdottir Unnur U Tikkanen Emmi E Trakalo Joseph J Tremoli Elena E Trip Mieke D MD Tsai Fuu Jen FJ Tuomi Tiinamaija T Tuomilehto Jaakko J Uitterlinden Andre G AG Valladares-Salgado Adan A Vedantam Sailaja S Veglia Fabrizio F Voight Benjamin F BF Wang Congrong C Wareham Nicholas J NJ Wennauer Roman R Wickremasinghe Ananda R AR Wilsgaard Tom T Wilson James F JF Wiltshire Steven S Winckler Wendy W Wong Tien Yin TY Wood Andrew R AR Wu Jer-Yuarn JY Wu Ying Y Yamamoto Ken K Yamauchi Toshimasa T Yang Mingyu M Yengo Loic L Yokota Mitsuhiro M Young Robin R Zabaneh Delilah D Zhang Fan F Zhang Rong R Zheng Wei W Zimmet Paul Z PZ Altshuler David D Bowden Donald W DW Cho Yoon Shin YS Cox Nancy J NJ Cruz Miguel M Hanis Craig L CL Kooner Jaspal J Lee Jong-Young JY Seielstad Mark M Teo Yik Ying YY Boehnke Michael M Parra Esteban J EJ Chambers Jonh C JC Tai E Shyong ES McCarthy Mark I MI Morris Andrew P AP

Nature genetics 20140209 3

To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals o ...[more]

PMID: 24509480

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Project description:While genome-wide associations studies (GWAS) have successfully elucidated the genetic architecture of complex human traits and diseases, understanding mechanisms that lead from genetic variation to pathophysiology remains an important challenge. Methods are needed to systematically bridge this crucial gap to facilitate experimental testing of hypotheses and translation to clinical utility. Here, we leveraged cross-phenotype associations to identify traits with shared genetic architecture, using linkage disequilibrium (LD) information to accurately capture shared SNPs by proxy, and calculate significance of enrichment. This shared genetic architecture was examined across differing biological scales through incorporating data from catalogs of clinical, cellular, and molecular GWAS. We have created an interactive web database (interactive Cross-Phenotype Analysis of GWAS database (iCPAGdb); http://cpag.oit.duke.edu ) to facilitate exploration and allow rapid analysis of user-uploaded GWAS summary statistics. This database revealed well-known relationships among phenotypes, as well as the generation of novel hypotheses to explain the pathophysiology of common diseases. Application of iCPAGdb to a recent GWAS of severe COVID-19 demonstrated unexpected overlap of GWAS signals between COVID-19 and human diseases, including with idiopathic pulmonary fibrosis driven by the DPP9 locus. Transcriptomics from peripheral blood of COVID-19 patients demonstrated that DPP9 was induced in SARS-CoV-2 compared to healthy controls or those with bacterial infection. Further investigation of cross-phenotype SNPs with severe COVID-19 demonstrated colocalization of the GWAS signal of the ABO locus with plasma protein levels of a reported receptor of SARS-CoV-2, CD209 (DC-SIGN), pointing to a possible mechanism whereby glycosylation of CD209 by ABO may regulate COVID-19 disease severity. Thus, connecting genetically related traits across phenotypic scales links human diseases to molecular and cellular measurements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches.

Project description:BackgroundWhile genome-wide associations studies (GWAS) have successfully elucidated the genetic architecture of complex human traits and diseases, understanding mechanisms that lead from genetic variation to pathophysiology remains an important challenge. Methods are needed to systematically bridge this crucial gap to facilitate experimental testing of hypotheses and translation to clinical utility.ResultsHere, we leveraged cross-phenotype associations to identify traits with shared genetic architecture, using linkage disequilibrium (LD) information to accurately capture shared SNPs by proxy, and calculate significance of enrichment. This shared genetic architecture was examined across differing biological scales through incorporating data from catalogs of clinical, cellular, and molecular GWAS. We have created an interactive web database (interactive Cross-Phenotype Analysis of GWAS database (iCPAGdb)) to facilitate exploration and allow rapid analysis of user-uploaded GWAS summary statistics. This database revealed well-known relationships among phenotypes, as well as the generation of novel hypotheses to explain the pathophysiology of common diseases. Application of iCPAGdb to a recent GWAS of severe COVID-19 demonstrated unexpected overlap of GWAS signals between COVID-19 and human diseases, including with idiopathic pulmonary fibrosis driven by the DPP9 locus. Transcriptomics from peripheral blood of COVID-19 patients demonstrated that DPP9 was induced in SARS-CoV-2 compared to healthy controls or those with bacterial infection. Further investigation of cross-phenotype SNPs associated with both severe COVID-19 and other human traits demonstrated colocalization of the GWAS signal at the ABO locus with plasma protein levels of a reported receptor of SARS-CoV-2, CD209 (DC-SIGN). This finding points to a possible mechanism whereby glycosylation of CD209 by ABO may regulate COVID-19 disease severity.ConclusionsThus, connecting genetically related traits across phenotypic scales links human diseases to molecular and cellular measurements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches. The iCPAGdb web portal is accessible at http://cpag.oit.duke.edu and the software code at https://github.com/tbalmat/iCPAGdb .

Dataset Information

Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility.

Publications

Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility.

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