Analysis of multiple related phenotypes in genome-wide association studies

Most genome-wide association studies (GWAS) have been conducted by focusing on one phenotype of interest for identifying genetic variants associated with common complex phenotypes. However, despite many successful results from GWAS, only a small number of genetic variants tend to be identified and replicated given a very stringent genome-wide significance criterion, and explain only a small fraction of phenotype heritability. In order to improve power by using more information from data, we propose an alternative multivariate approach, which considers multiple related phenotypes simultaneously. We demonstrate through computer simulation that the multivariate approach can improve power for detecting disease-predisposing genetic variants and pleiotropic variants that have simultaneous effects on multiple related phenotypes. We apply the multivariate approach to a GWA dataset of 8,842 Korean individuals genotyped for 327,872 SNPs, and detect novel genetic variants associated with metabolic syndrome related phenotypes. Considering several related phenotype simultaneously, the multivariate approach provides not only more powerful results than the conventional univariate approach but also clue to identify pleiotropic genes that are important to the pathogenesis of many related complex phenotypes.