GPA: A statistical approach to prioritizing GWAS results by integrating pleiotropy information and annotation data

TL;DR

GPA is a novel statistical method that integrates multiple GWAS datasets and functional annotations to improve detection of genetic variants associated with complex diseases, revealing new signals and biological insights.

Contribution

The paper introduces GPA, a new statistical approach for integrative analysis of GWAS and annotation data, enhancing detection of pleiotropic effects and functional enrichment.

Findings

GPA identified weak signals missed by single GWAS analyses.

GPA revealed genetic architectures of psychiatric disorders.

GPA detected relevant cell lines in bladder cancer GWAS.

Abstract

Genome-wide association studies (GWAS) suggests that a complex disease is typically affected by many genetic variants with small or moderate effects. Identification of these risk variants remains to be a very challenging problem. Traditional approaches focusing on a single GWAS dataset alone ignore relevant information that could potentially improve our ability to detect these variants. We proposed a novel statistical approach, named GPA, to performing integrative analysis of multiple GWAS datasets and functional annotations. Hypothesis testing procedures were developed to facilitate statistical inference of pleiotropy and enrichment of functional annotation. We applied our approach to perform systematic analysis of five psychiatric disorders. Not only did GPA identify many weak signals missed by the original single phenotype analysis, but also revealed interesting genetic architectures of these disorders. We also applied GPA to the bladder cancer GWAS data with the ENCODE DNase-seq data from 125 cell lines and showed that GPA can detect cell lines that are more biologically relevant to the phenotype of interest.