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Biometrics. 2018 Mar;74(1):165-175. doi: 10.1111/biom.12735. Epub 2017 Jun 26.

Multiple phenotype association tests using summary statistics in genome-wide association studies.

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1
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston 02115, U.S.A.

Abstract

We study in this article jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis.

KEYWORDS:

Correlated phenotypes; Fisher method; Linear mixed models; Pleiotropy; Summary statistics; Variance component test

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