Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations.

Autor:	Coram MA; Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA., Fang H; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA., Candille SI; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA., Assimes TL; Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA., Tang H; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: huatang@stanford.edu.
Jazyk:	angličtina
Zdroj:	American journal of human genetics [Am J Hum Genet] 2017 Aug 03; Vol. 101 (2), pp. 218-226. Date of Electronic Publication: 2017 Jul 27.
DOI:	10.1016/j.ajhg.2017.06.015
Abstrakt:	An essential component of precision medicine is the ability to predict an individual's risk of disease based on genetic and non-genetic factors. For complex traits and diseases, assessing the risk due to genetic factors is challenging because it requires knowledge of both the identity of variants that influence the trait and their corresponding allelic effects. Although the set of risk variants and their allelic effects may vary between populations, a large proportion of these variants were identified based on studies in populations of European descent. Heterogeneity in genetic architecture underlying complex traits and diseases, while broadly acknowledged, remains poorly characterized. Ignoring such heterogeneity likely reduces predictive accuracy for minority individuals. In this study, we propose an approach, called XP-BLUP, which ameliorates this ethnic disparity by combining trans-ethnic and ethnic-specific information. We build a polygenic model for complex traits that distinguishes candidate trait-relevant variants from the rest of the genome. The set of candidate variants are selected based on studies in any human population, yet the allelic effects are evaluated in a population-specific fashion. Simulation studies and real data analyses demonstrate that XP-BLUP adaptively utilizes trans-ethnic information and can substantially improve predictive accuracy in minority populations. At the same time, our study highlights the importance of the continued expansion of minority cohorts. (Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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