Graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data.

ABSTRACT: Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand the functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Our simulation studies showed that incorporating functional annotation data using GGPA 2.0 not only improves the detection of disease-associated variants, but also provides a more accurate estimation of relationships among diseases. Ne

SUBMITTER: Deng Q 

PROVIDER: S-EPMC10370274 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature