Leveraging pleiotropic association using sparse group variable selection in genomics data.

ABSTRACT:

Background

Genome-wide association studies (GWAS) have identified genetic variants associated with multiple complex diseases. We can leverage this phenomenon, known as pleiotropy, to integrate multiple data sources in a joint analysis. Often integrating additional information such as gene pathway knowledge can improve statistical efficiency and biological interpretation. In this article, we propose statistical methods which incorporate both gene pathway and pleiotropy knowledge to increase statistical power and identify important risk variants affecting multiple traits.

Methods

We propose novel feature selection methods for the group variable selection in multi-task regression problem. We develop penalised likelihood methods exploiting different penalties to induce structure

SUBMITTER: Sutton M 

PROVIDER: S-EPMC8742466 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature