%0 Journal Article %J Biometrics %D 2018 %T Multiple phenotype association tests using summary statistics in genome-wide association studies. %A Liu, Zhonghua %A Lin, Xihong %K Analysis of Variance %K Computer Simulation %K Genome-Wide Association Study %K Humans %K Linear Models %K Lipids %K Models, Genetic %K Phenotype %X

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.

%B Biometrics %V 74 %P 165-175 %8 2018 Mar %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/28653391?dopt=Abstract %R 10.1111/biom.12735