An optimal kernel-based multivariate U-statistic to test for associations with multiple phenotypes.

ABSTRACT: Set-based analysis that jointly considers multiple predictors in a group has been broadly conducted for association tests. However, their power can be sensitive to the distribution of phenotypes, and the underlying relationships between predictors and outcomes. Moreover, most of the set-based methods are designed for single-trait analysis, making it hard to explore the pleiotropic effect and borrow information when multiple phenotypes are available. Here, we propose a kernel-based multivariate U-statistics (KMU) that is robust and powerful in testing the association between a set of predictors and multiple outcomes. We employed a rank-based kernel function for the outcomes, which makes our method robust to various outcome distributions. Rather than selecting a single kernel, our test stati

SUBMITTER: Wen Y 

PROVIDER: S-EPMC9291634 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature