@article {12,
	title = {Multiple phenotype association tests using summary statistics in genome-wide association studies.},
	journal = {Biometrics},
	volume = {74},
	year = {2018},
	month = {2018 Mar},
	pages = {165-175},
	abstract = {<p>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.</p>},
	keywords = {Analysis of Variance, Computer Simulation, Genome-Wide Association Study, Humans, Linear Models, Lipids, Models, Genetic, Phenotype},
	issn = {1541-0420},
	doi = {10.1111/biom.12735},
	author = {Liu, Zhonghua and Lin, Xihong}
}
