Replicability and prediction: lessons and challenges from GWAS

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• dc.contributor.author Marigorta, Urko M.
• dc.contributor.author Rodríguez, Juan Antonio
• dc.contributor.author Gibson, Greg
• dc.contributor.author Navarro i Cuartiellas, Arcadi, 1969-
• dc.date.accessioned 2019-07-24T08:32:14Z
• dc.date.available 2019-07-24T08:32:14Z
• dc.date.issued 2018
• dc.description.abstract Since the publication of the Wellcome Trust Case Control Consortium (WTCCC) landmark study a decade ago, genome-wide association studies (GWAS) have led to the discovery of thousands of risk variants involved in disease etiology. This success story has two angles that are often overlooked. First, GWAS findings are highly replicable. This is an unprecedented phenomenon in complex trait genetics, and indeed in many areas of science, which in past decades have been plagued by false positives. At a time of increasing concerns about the lack of reproducibility, we examine the biological and methodological reasons that account for the replicability of GWAS and identify the challenges ahead. In contrast to the exemplary success of disease gene discovery, at present GWAS findings are not useful for predicting phenotypes. We close with an overview of the prospects for individualized prediction of disease risk and its foreseeable impact in clinical practice.
• dc.description.sponsorship A.N. and J.A.R. were supported by the Ministerio de Ciencia e Innovación, Spain (BFU2015-68649P, MINECO/FEDER,UE), the Direcció General de Recerca, Generalitat de Catalunya (2014SGR1311 and 2014SGR866), the Spanish National Institute of Bioinformatics (PT13/0001/0026), and the REEM (RD16/0015/0017) of the Instituto de Salud Carlos III, grant MDM-2014-0370 through the“María de Maeztu ”Programme for Units of Excellence in R&D to UPF’s Department of Experimental and Health Sciences). The authors have also received funding from the EU’s Horizon 2020 research and innovation program 2014-2020 under Grant Agreement No. 634143 (MedBioinformatics). U.M.M. and G.G. were funded by US NIH grants 1-P01 GM099568 (Project 3) and 2-R01-DK087694
• dc.format.mimetype application/pdf
• dc.identifier.citation Marigorta UM, Rodríguez JA, Gibson G, Navarro A. Replicability and prediction: lessons and challenges from GWAS. Trends Genet. 2018;34(7): 504-17. DOI: 10.1016/j.tig.2018.03.005
• dc.identifier.doi http://dx.doi.org/10.1016/j.tig.2018.03.005
• dc.identifier.issn 0168-9525
• dc.identifier.uri http://hdl.handle.net/10230/42159
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Trends in Genetics. 2018;34(7): 504-17
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/BFU2015-68649-P
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/634143
• dc.rights © Elsevier http://dx.doi.org/10.1016/j.tig.2018.03.005
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.other Estudi d'associació del genoma complet
• dc.subject.other Genètica
• dc.title Replicability and prediction: lessons and challenges from GWAS
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion