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Genome-wide association studies (GWASs) identified hundreds of signals associated with type 2 diabetes (T2D). To gain insight into their underlying molecular mechanisms, we have created the translational human pancreatic islet genotype tissue-expression resource (TIGER), aggregating >500 human islet genomic datasets from five cohorts in the Horizon 2020 consortium T2DSystems. We impute genotypes using four reference panels and meta-analyze cohorts to improve the coverage of expression quantitative trait loci (eQTL) and develop a method to combine allele-specific expression across samples (cASE). We identify >1 million islet eQTLs, 53 of which colocalize with T2D signals. Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identify eight cASE colocalizations, among which we found a T2D-associated SLC30A8 variant. We make all data available through the TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translates into therapeutic insight and precision medicine for T2D. ; This work has been supported by the European Union's Horizon 2020 research and innovation program T2Dsystems under grant agreement no. 667191 . L.A. was supported by grant BES-2017-081635 of the Severo Ochoa Program, awarded by the Spanish government . I.M. was supported by the FJCI-2017-31878 Juan de la Cierva grant, awarded by the Spanish government . Work in the Cnop and Eizirik labs was further supported by the Fonds National de la Recherche Scientifique (FNRS), the Brussels Region Innoviris project DiaType , and the Walloon Region SPW-EER Win2Wal project BetaSource, Belgium . D.L.E. is supported by a grant from the Welbio–FNRS , Belgium. P.M., L.G., D.L.E., and M.C. are supported by the Innovative Medicines Initiative 2 Joint Undertaking Rhapsody , under grant agreement no. 115881 , which is supported by the European Union's Horizon 2020 research and innovation programme, EFPIA and the Swiss State Secretariat for Education' Research and Innovation (SERI) under contract number 16.0097 . J.M.M. is supported by American Diabetes Association Innovative and Clinical Translational Award 1-19-ICTS-068 . J.C. is supported by an Expanding Excellence in England Award from Research England . H.M., J.L.S.E., and L.E. are supported by the Swedish Strategic Research Foundation ( IRC15-0067 ). A.L.G. is a Wellcome Trust Senior Fellow in Basic Biomedical Science. This work was funded in Oxford and Stanford by the Wellcome Trust ( 095101 , 200837 , 106130 , and 203141 [all to A.L.G.]) and the NIH ( U01-DK105535 and U01-DK085545 [A.L.G.]). The research was funded by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) (A.L.G.). I.M.-E. was supported by the EFDS/Novo Nordisk Rising Star Programme . Work in the Ferrer lab was supported by the Imperial College London Research Computing Service , the NIHR Imperial BRC , and the Centre for Genomic Regulation (CRG) genomics facility , and grants from Ministerio de Ciencia e Innovación ( BFU2014-54284-R and RTI2018-095666-B-I00 ), the Medical Research Council ( MR/L02036X/1 ), the Wellcome Trust Senior Investigator Award ( WT101033 ), and the European Research Council Advanced Grant ( 789055 ). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. The technical support group from the Barcelona Supercomputing Center is gratefully acknowledged. Finally, we thank the entire Computational Genomics group at the BSC for their helpful discussions and valuable comments on the manuscript. We also acknowledge Cristian Opi and Laia Codó from the Barcelona Supercomputing Center for excellent website design and allocation of technical support and Isabelle Millard and Anyishaï Musuaya from the ULB Center for Diabetes Research for excellent technical and experimental support. ; Peer Reviewed ; "Article signat per 30 autors/es: Lorena Alonso, Anthony Piron, Ignasi Morán, Marta Guindo-Martínez, Sílvia Bonàs-Guarch, Goutham Atla, Irene Miguel-Escalada, Romina Royo, Montserrat Puiggròs, Xavier Garcia-Hurtado, Mara Suleiman, Lorella Marselli, Jonathan L.S. Esguerra, Jean-Valéry Turatsinze, Jason M. Torres, Vibe Nylander, Ji Chen, Lena Eliasson, Matthieu Defrance, Ramon Amela, MAGIC24, Hindrik Mulder, Anna L. Gloyn, Leif Groop, Piero Marchetti, Decio L. Eizirik, Jorge Ferrer, Josep M. Mercader, Miriam Cnop, David Torrents" ; Postprint (published version)

The reanalysis of existing GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics of complex diseases. By reanalyzing publicly available type 2 diabetes (T2D) genome-wide association studies (GWAS) data for 70,127 subjects, we identify seven novel associated regions, five driven by common variants (LYPLAL1, NEUROG3, CAMKK2, ABO, and GIP genes), one by a low-frequency (EHMT2), and one driven by a rare variant in chromosome Xq23, rs146662057, associated with a twofold increased risk for T2D in males. rs146662057 is located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a modulator of insulin sensitivity, and exhibits allelic specific activity in muscle cells. Beyond providing insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel genetic resources and analytical approaches. ; This work has been sponsored by the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government. This work was supported by an EFSD/Lilly research fellowship. Josep M. Mercader was supported by Sara Borrell Fellowship from the Instituto Carlos III and Beatriu de Pinós fellowship from the Agency for Management of University and Research Grants (AGAUR). Sílvia Bonàs was FI-DGR Fellowship from FI-DGR 2013 from Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR, Generalitat de Catalunya). This study makes use of data generated by the WTCCC. A full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113. This study also makes use of data generated by the UK10K Consortium, derived from samples from UK10K COHORT IMPUTATION (EGAS00001000713). A full list of the investigators who contributed to the generation of the data is available in www.UK10K.org. Funding for UK10K was provided by the Wellcome Trust under award WT091310. We acknowledge PRACE for awarding us to access MareNostrum supercomputer, based in Spain at Barcelona. The technical support group, particularly Pablo Ródenas and Jorge Rodríguez, from the Barcelona Supercomputing Center is gratefully acknowledged. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 667191. Mercè Planas-Fèlix is funded by the Obra Social Fundación la Caixa fellowship under the Severo Ochoa 2013 program. Work from Irene Miguel-Escalada, Ignasi Moran, Goutham Atla, and Jorge Ferrer was supported by the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre, the Wellcome Trust (WT101033), Ministerio de Economía y Competitividad (BFU2014-54284-R) and Horizon 2020 (667191). Irene Miguel-Escalada has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska–Curie grant agreement No 658145. We acknowledge Prof. Giulio Cossu (Institute of Inflammation and Repair, University of Manchester) for providing the muscle myoblast cell line. We also acknowledge the InterAct and SIGMA Type 2 Diabetes Consortia for access to the data to replicate the rs146662075 variant. A full list of the investigators of the SIGMA Type 2 Diabetes and the InterAct consortia is provided in Supplementary Notes 3 and 4. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent research center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk). This research has been conducted using the UK Biobank Resource (application number 16803). We also acknowledge Bianca C. Porneala, MS for his technical assistance in the collection and curation of the genotype and phenotype data from Partners Biobank. We also thank Marcin von Grotthuss for their support for uploading the summary statistics data to the Type 2 Diabetes Genetic Portal (AMP-T2D portal). Finally, we thank all the Computational Genomics group at the BSC for their helpful discussions and valuable comments on the manuscript. ; Peer Reviewed ; Postprint (published version)

The reanalysis of existing GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics of complex diseases. By reanalyzing publicly available type 2 diabetes (T2D) genome-wide association studies (GWAS) data for 70,127 subjects, we identify seven novel associated regions, five driven by common variants (LYPLAL1, NEUROG3, CAMKK2, ABO, and GIP genes), one by a low-frequency (EHMT2), and one driven by a rare variant in chromosome Xq23, rs146662057, associated with a twofold increased risk for T2D in males. rs146662057 is located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a modulator of insulin sensitivity, and exhibits allelic specific activity in muscle cells. Beyond providing insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel genetic resources and analytical approaches. ; This work has been sponsored by the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government. This work was supported by an EFSD/Lilly research fellowship. Josep M. Mercader was supported by Sara Borrell Fellowship from the Instituto Carlos III and Beatriu de Pinós fellowship from the Agency for Management of University and Research Grants (AGAUR). Sílvia Bonàs was FI-DGR Fellowship from FI-DGR 2013 from Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR, Generalitat de Catalunya). This study makes use of data generated by the WTCCC. A full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113. This study also makes use of data generated by the UK10K Consortium, derived from samples from UK10K COHORT IMPUTATION (EGAS00001000713). A full list of the investigators who contributed to the generation of the data is available in www.UK10K.org. Funding for UK10K was provided by the Wellcome Trust under award WT091310. We acknowledge PRACE for awarding us to access MareNostrum supercomputer, based in Spain at Barcelona. The technical support group, particularly Pablo Ródenas and Jorge Rodríguez, from the Barcelona Supercomputing Center is gratefully acknowledged. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 667191. Mercè Planas-Fèlix is funded by the Obra Social Fundación la Caixa fellowship under the Severo Ochoa 2013 program. Work from Irene Miguel-Escalada, Ignasi Moran, Goutham Atla, and Jorge Ferrer was supported by the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre, the Wellcome Trust (WT101033), Ministerio de Economía y Competitividad (BFU2014-54284-R) and Horizon 2020 (667191). Irene Miguel-Escalada has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska–Curie grant agreement No 658145. We acknowledge Prof. Giulio Cossu (Institute of Inflammation and Repair, University of Manchester) for providing the muscle myoblast cell line. We also acknowledge the InterAct and SIGMA Type 2 Diabetes Consortia for access to the data to replicate the rs146662075 variant. A full list of the investigators of the SIGMA Type 2 Diabetes and the InterAct consortia is provided in Supplementary Notes 3 and 4. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent research center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk). This research has been conducted using the UK Biobank Resource (application number 16803). We also acknowledge Bianca C. Porneala, MS for his technical assistance in the collection and curation of the genotype and phenotype data from Partners Biobank. We also thank Marcin von Grotthuss for their support for uploading the summary statistics data to the Type 2 Diabetes Genetic Portal (AMP-T2D portal). Finally, we thank all the Computational Genomics group at the BSC for their helpful discussions and valuable comments on the manuscript. ; Peer Reviewed ; Postprint (published version)