Underestimated Effect Sizes in GWAS: Fundamental Limitations of Single SNP Analysis for Dichotomous Phenotypes

Stringer, Sven, Wray, Naomi R, Kahn, Rene S and Derks, Eske M (2011) Underestimated Effect Sizes in GWAS: Fundamental Limitations of Single SNP Analysis for Dichotomous Phenotypes. PLoS One, 6 11: e27964. doi:10.1371/journal.pone.0027964

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Author Stringer, Sven
Wray, Naomi R
Kahn, Rene S
Derks, Eske M
Title Underestimated Effect Sizes in GWAS: Fundamental Limitations of Single SNP Analysis for Dichotomous Phenotypes
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2011-11-28
Sub-type Article (original research)
DOI 10.1371/journal.pone.0027964
Open Access Status DOI
Volume 6
Issue 11
Start page e27964
Total pages 7
Place of publication San Francisco, CA, United States
Publisher Public Library of Science
Collection year 2012
Language eng
Abstract 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.
Keyword Genome-wide association
Individual genetic risk
Logistic-regression
Statistical-methods
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2012 Collection
 
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