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This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

Open Access Proceedings

Power of association tests in the presence of multiple causal variants

Yanming Di*, Gu Mi, Luna Sun, Rongrong Dong, Hong Zhu and Lili Peng

Author Affiliations

Department of Statistics, Oregon State University, 44 Kidder Hall, Corvallis, OR 97331, USA

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BMC Proceedings 2011, 5(Suppl 9):S63  doi:10.1186/1753-6561-5-S9-S63

Published: 29 November 2011

Abstract

We show that the statistical power of a single single-nucleotide polymorphism (SNP) score test for genetic association reflects the cumulative effect of all causal SNPs that are correlated with the test SNP. Statistical significance of a score test can sometimes be explained by the collective effect of weak correlations between the test SNP and multiple causal SNPs. In a finite population, weak but significant correlations between the test SNP and the causal SNPs can arise by chance alone. As a consequence, when a single-SNP score test shows significance, the causal SNPs contributing to the power of the test are not necessarily located near the test SNP, nor do they have to be in linkage disequilibrium with the test SNP. These findings are confirmed with the Genetic Analysis Workshop 17 mini-exome data. The findings of this study highlight the often overlooked importance of long-range and weak linkage disequilibrium in genetic association studies.