Underestimated effect sizes in GWAS: fundamental limitations of single SNP analysis for dichotomous phenotypes.

Complex diseases are often highly heritable. However, for many complex traits only a small proportion of the heritability can be explained by observed genetic variants in traditional genome-wide association (GWA) studies. Moreover, for some of those traits few significant SNPs have been identified....

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Autores principales:	Sven Stringer, Naomi R Wray, René S Kahn, Eske M Derks
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/05d4391e243f4e0299ae9ca865085982
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spelling	oai:doaj.org-article:05d4391e243f4e0299ae9ca8650859822021-11-18T07:33:31ZUnderestimated effect sizes in GWAS: fundamental limitations of single SNP analysis for dichotomous phenotypes.1932-620310.1371/journal.pone.0027964https://doaj.org/article/05d4391e243f4e0299ae9ca8650859822011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22140493/?tool=EBIhttps://doaj.org/toc/1932-6203Complex diseases are often highly heritable. However, for many complex traits only a small proportion of the heritability can be explained by observed genetic variants in traditional genome-wide association (GWA) studies. Moreover, for some of those traits few significant SNPs have been identified. Single SNP association methods test for association at a single SNP, ignoring the effect of other SNPs. We show using a simple multi-locus odds model of complex disease that moderate to large effect sizes of causal variants may be estimated as relatively small effect sizes in single SNP association testing. This underestimation effect is most severe for diseases influenced by numerous risk variants. We relate the underestimation effect to the concept of non-collapsibility found in the statistics literature. As described, continuous phenotypes generated with linear genetic models are not affected by this underestimation effect. Since many GWA studies apply single SNP analysis to dichotomous phenotypes, previously reported results potentially underestimate true effect sizes, thereby impeding identification of true effect SNPs. Therefore, when a multi-locus model of disease risk is assumed, a multi SNP analysis may be more appropriate.Sven StringerNaomi R WrayRené S KahnEske M DerksPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 11, p e27964 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sven Stringer Naomi R Wray René S Kahn Eske M Derks Underestimated effect sizes in GWAS: fundamental limitations of single SNP analysis for dichotomous phenotypes.
description	Complex diseases are often highly heritable. However, for many complex traits only a small proportion of the heritability can be explained by observed genetic variants in traditional genome-wide association (GWA) studies. Moreover, for some of those traits few significant SNPs have been identified. Single SNP association methods test for association at a single SNP, ignoring the effect of other SNPs. We show using a simple multi-locus odds model of complex disease that moderate to large effect sizes of causal variants may be estimated as relatively small effect sizes in single SNP association testing. This underestimation effect is most severe for diseases influenced by numerous risk variants. We relate the underestimation effect to the concept of non-collapsibility found in the statistics literature. As described, continuous phenotypes generated with linear genetic models are not affected by this underestimation effect. Since many GWA studies apply single SNP analysis to dichotomous phenotypes, previously reported results potentially underestimate true effect sizes, thereby impeding identification of true effect SNPs. Therefore, when a multi-locus model of disease risk is assumed, a multi SNP analysis may be more appropriate.
format	article
author	Sven Stringer Naomi R Wray René S Kahn Eske M Derks
author_facet	Sven Stringer Naomi R Wray René S Kahn Eske M Derks
author_sort	Sven Stringer
title	Underestimated effect sizes in GWAS: fundamental limitations of single SNP analysis for dichotomous phenotypes.
title_short	Underestimated effect sizes in GWAS: fundamental limitations of single SNP analysis for dichotomous phenotypes.
title_full	Underestimated effect sizes in GWAS: fundamental limitations of single SNP analysis for dichotomous phenotypes.
title_fullStr	Underestimated effect sizes in GWAS: fundamental limitations of single SNP analysis for dichotomous phenotypes.
title_full_unstemmed	Underestimated effect sizes in GWAS: fundamental limitations of single SNP analysis for dichotomous phenotypes.
title_sort	underestimated effect sizes in gwas: fundamental limitations of single snp analysis for dichotomous phenotypes.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/05d4391e243f4e0299ae9ca865085982
work_keys_str_mv	AT svenstringer underestimatedeffectsizesingwasfundamentallimitationsofsinglesnpanalysisfordichotomousphenotypes AT naomirwray underestimatedeffectsizesingwasfundamentallimitationsofsinglesnpanalysisfordichotomousphenotypes AT reneskahn underestimatedeffectsizesingwasfundamentallimitationsofsinglesnpanalysisfordichotomousphenotypes AT eskemderks underestimatedeffectsizesingwasfundamentallimitationsofsinglesnpanalysisfordichotomousphenotypes
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Underestimated effect sizes in GWAS: fundamental limitations of single SNP analysis for dichotomous phenotypes.

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