The identification and characterisation of genomic changes (variants) that can lead to human diseases is one of the central aims of biomedical research. The generation of catalogues of genetic variants that have an impact on specific diseases is the basis of Personalised Medicine, where diagnoses and treatment protocols are selected according to each patient's profile. In this context, the study of complex diseases, such as Type 2 diabetes or cardiovascular alterations, is fundamental. However, these diseases result from the combination of multiple genetic and environmental factors, which makes the discovery of causal variants particularly challenging at a statistical and computational level. Genome-Wide Association Studies (GWAS), which are based on the statistical analysis of genetic variant frequencies across non-diseased and diseased individuals, have been successful in finding genetic variants that are associated to specific diseases or phenotypic traits. But GWAS methodology is limited when considering important genetic aspects of the disease and has not yet resulted in meaningful translation to clinical practice. This review presents an outlook on the study of the link between genetics and complex phenotypes. We first present an overview of the past and current statistical methods used in the field. Next, we discuss current practices and their main limitations. Finally, we describe the open challenges that remain and that might benefit greatly from further mathematical developments. ; L.A. was supported by grant BES-2017-081635. This publication is part of R&D and Innovation grant BES-2017-081635 funded by MCIN and by "FSE Investing in your future"I.M. was supported by grant FJCI-2017-31878. This publication is part of R&D and Innovation grant FJCI-2017-31878 funded by MCIN. C.S. received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433. ; Peer Reviewed ; Postprint (published version)
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)
