A hierarchical clustering method for dimension reduction in joint analysis of multiple phenotypes.

ABSTRACT: Genome-wide association studies (GWAS) have become a very effective research tool to identify genetic variants of underlying various complex diseases. In spite of the success of GWAS in identifying thousands of reproducible associations between genetic variants and complex disease, in general, the association between genetic variants and a single phenotype is usually weak. It is increasingly recognized that joint analysis of multiple phenotypes can be potentially more powerful than the univariate analysis, and can shed new light on underlying biological mechanisms of complex diseases. In this paper, we develop a novel variable reduction method using hierarchical clustering method (HCM) for joint analysis of multiple phenotypes in association studies. The proposed method involves two steps.

SUBMITTER: Liang X 

PROVIDER: S-EPMC5980772 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature