Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies

Abstract Ongoing increases in the size of human genotype and phenotype collections offer the promise of improved understanding of the genetics of complex diseases. In addition to the biological insights that can be gained from the nature of the variants that contribute to the genetic component of co...

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Autores principales:	Declan Bennett, Donal O’Shea, John Ferguson, Derek Morris, Cathal Seoighe
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/10af198e88bd4aad98597e673ebec426
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spelling	oai:doaj.org-article:10af198e88bd4aad98597e673ebec4262021-12-02T17:18:22ZControlling for background genetic effects using polygenic scores improves the power of genome-wide association studies10.1038/s41598-021-99031-32045-2322https://doaj.org/article/10af198e88bd4aad98597e673ebec4262021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99031-3https://doaj.org/toc/2045-2322Abstract Ongoing increases in the size of human genotype and phenotype collections offer the promise of improved understanding of the genetics of complex diseases. In addition to the biological insights that can be gained from the nature of the variants that contribute to the genetic component of complex trait variability, these data bring forward the prospect of predicting complex traits and the risk of complex genetic diseases from genotype data. Here we show that advances in phenotype prediction can be applied to improve the power of genome-wide association studies. We demonstrate a simple and efficient method to model genetic background effects using polygenic scores derived from SNPs that are not on the same chromosome as the target SNP. Using simulated and real data we found that this can result in a substantial increase in the number of variants passing genome-wide significance thresholds. This increase in power to detect trait-associated variants also translates into an increase in the accuracy with which the resulting polygenic score predicts the phenotype from genotype data. Our results suggest that advances in methods for phenotype prediction can be exploited to improve the control of background genetic effects, leading to more accurate GWAS results and further improvements in phenotype prediction.Declan BennettDonal O’SheaJohn FergusonDerek MorrisCathal SeoigheNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Declan Bennett Donal O’Shea John Ferguson Derek Morris Cathal Seoighe Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies
description	Abstract Ongoing increases in the size of human genotype and phenotype collections offer the promise of improved understanding of the genetics of complex diseases. In addition to the biological insights that can be gained from the nature of the variants that contribute to the genetic component of complex trait variability, these data bring forward the prospect of predicting complex traits and the risk of complex genetic diseases from genotype data. Here we show that advances in phenotype prediction can be applied to improve the power of genome-wide association studies. We demonstrate a simple and efficient method to model genetic background effects using polygenic scores derived from SNPs that are not on the same chromosome as the target SNP. Using simulated and real data we found that this can result in a substantial increase in the number of variants passing genome-wide significance thresholds. This increase in power to detect trait-associated variants also translates into an increase in the accuracy with which the resulting polygenic score predicts the phenotype from genotype data. Our results suggest that advances in methods for phenotype prediction can be exploited to improve the control of background genetic effects, leading to more accurate GWAS results and further improvements in phenotype prediction.
format	article
author	Declan Bennett Donal O’Shea John Ferguson Derek Morris Cathal Seoighe
author_facet	Declan Bennett Donal O’Shea John Ferguson Derek Morris Cathal Seoighe
author_sort	Declan Bennett
title	Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies
title_short	Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies
title_full	Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies
title_fullStr	Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies
title_full_unstemmed	Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies
title_sort	controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/10af198e88bd4aad98597e673ebec426
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Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies

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