Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies using multi-layer network.

ABSTRACT:

Motivation

Genome-wide association studies is an essential tool for analyzing associations between phenotypes and single nucleotide polymorphisms (SNPs). Most of binary phenotypes in large biobanks are extremely unbalanced, which leads to inflated type I error rates for many widely used association tests for joint analysis of multiple phenotypes. In this article, we first propose a novel method to construct a Multi-Layer Network (MLN) using individuals with at least one case status among all phenotypes. Then, we introduce a computationally efficient community detection method to group phenotypes into disjoint clusters based on the MLN. Finally, we propose a novel approach, MLN with Omnibus (MLN-O), to jointly analyse the association between phenotypes and a SNP. MLN-O uses the scor

SUBMITTER: Xie H 

PROVIDER: S-EPMC10697735 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature