Replicability and Prediction: Lessons and Challenges from GWAS
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Trends in Genetics |
| Vol/bind | 34 |
| Udgave nummer | 7 |
| Sider (fra-til) | 504-517 |
| Antal sider | 14 |
| ISSN | 0168-9525 |
| DOI | |
| Status | Udgivet - 2018 |
| Eksternt udgivet | Ja |
Bibliografisk note
Funding Information:
A.N. and J.A.R. were supported by the Ministerio de Ciencia e Innovación , Spain ( BFU2015-68649-P , 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 .
Funding Information:
A.N. and J.A.R. were supported by the Ministerio de Ciencia e Innovación, Spain (BFU2015-68649-P, 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.
Publisher Copyright:
© 2018 Elsevier Ltd
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