Multivariate analysis of genome-wide data to identify potential pleiotropic genes for five major psychiatric disorders using MetaCCA

•Sample size is increased by leveraging five large GWAS summary statistics.•Statistical power is increased compared to the conventional standard GWAS.•Simultaneously multivariate analyses related traits lead to richer findings. : Genome-wide association studies have been extensively applied in identifying SNP associated with major psychiatric disorders. However, the SNPs identified by the prevailing univariate approach only explain a small percentage of the genetic variance of traits, and the extensive data have shown the major psychiatric disorders have common biological mechanisms and the overlapping pathophysiological pathways. : We applied the genetic pleiotropy-informed metaCCA method on summary statistics data from the Psychiatric Genomics Consortium Cross-Disorder Group to examine the overlapping genetic relations between the five major psychiatric disorders. Furthermore, to refine all genes, we performed gene-based association analyses for the five disorders respectively using VEGAS2. Gene enrichment analysis was applied to explore the potential functional significance of the identified genes. : After metaCCA analysis, 1147 SNPs reached the Bonferroni corrected threshold (p