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Replicability and Prediction: Lessons and Challenges from GWAS

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TRENDS IN GENETICS
卷 34, 期 7, 页码 504-517

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ELSEVIER SCIENCE LONDON
DOI: 10.1016/j.tig.2018.03.005

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资金

  1. Ministerio de Ciencia e Innovacion, Spain [BFU2015-68649-P]
  2. Direccio General de Recerca, Generalitat de Catalunya [2014SGR1311, 2014SGR866]
  3. Spanish National Institute of Bioinformatics [PT13/0001/0026]
  4. REEM of the Instituto de Salud Carlos III through the Maria de Maeztu Programme for Units of Excellence in RD [RD16/0015/0017, MDM-2014-0370]
  5. EU's Horizon 2020 research and innovation program [634143]
  6. US NIH [1-P01-GM099568, 2-R01-DK087694, 3]
  7. H2020 Societal Challenges Programme [634143] Funding Source: H2020 Societal Challenges Programme

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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.

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