Joint analysis of multiple phenotypes in association studies using allele-based clustering approach for non-normal distributions.

ABSTRACT: In the study of complex diseases, several correlated phenotypes are usually measured. There is also increasing evidence showing that testing the association between a single-nucleotide polymorphism (SNP) and multiple-dependent phenotypes jointly is often more powerful than analyzing only one phenotype at a time. Therefore, developing statistical methods to test for genetic association with multiple phenotypes has become increasingly important. In this paper, we develop an Allele-based Clustering Approach (ACA) for the joint analysis of multiple non-normal phenotypes in association studies. In ACA, we consider the alleles at a SNP of interest as a dependent variable with two classes, and the correlated phenotypes as predictors to predict the alleles at the SNP of interest. We perform extens

SUBMITTER: Liang X 

PROVIDER: S-EPMC6188849 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature