Multiple phenotype association tests using summary statistics in genome†wide association studies

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.