A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible correlations in variant effects.

ABSTRACT: For region-based sequencing data, power to detect genetic associations can be improved through analysis of multiple related phenotypes. With this motivation, we propose a novel test to detect association simultaneously between a set of rare variants, such as those obtained by sequencing in a small genomic region, and multiple continuous phenotypes. We allow arbitrary correlations among the phenotypes and build on a linear mixed model by assuming the effects of the variants follow a multivariate normal distribution with a zero mean and a specific covariance matrix structure. In order to account for the unknown correlation parameter in the covariance matrix of the variant effects, a data-adaptive variance component test based on score-type statistics is derived. As our approach can calculate

SUBMITTER: Sun J 

PROVIDER: S-EPMC4989219 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature