GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation

Results from Genome-Wide Association Studies (GWAS) have shown that complex diseases are often affected by many genetic variants with small or moderate effects. Identifications of these risk variants remain a very challenging problem. There is a need to develop more powerful statistical methods to leverage available information to improve upon traditional approaches that focus on a single GWAS dataset without incorporating additional data. In this paper, we propose a novel statistical approach, GPA (Genetic analysis incorporating Pleiotropy and Annotation), to increase statistical power to identify risk variants through joint analysis of multiple GWAS data sets and annotation information because: (1) accumulating evidence suggests that different complex diseases share common risk bases, i.e., pleiotropy; and (2) functionally annotated variants have been consistently demonstrated to be enriched among GWAS hits. GPA can integrate multiple GWAS datasets and functional annotations to seek association signals, and it can also perform hypothesis testing to test the presence of pleiotropy and enrichment of functional annotation. Statistical inference of the model parameters and SNP ranking is achieved through an EM algorithm that can handle genome-wide markers efficiently. When we applied GPA to jointly analyze five psychiatric disorders with annotation information, not only did GPA identify many weak signals missed by the traditional single phenotype analysis, but it also revealed relationships in the genetic architecture of these disorders. Using our hypothesis testing framework, statistically significant pleiotropic effects were detected among these psychiatric disorders, and the markers annotated in the central nervous system genes and eQTLs from the Genotype-Tissue Expression (GTEx) database were significantly enriched. We also applied GPA to a bladder cancer GWAS data set with the ENCODE DNase-seq data from 125 cell lines. GPA was able to detect cell lines that are biologically more relevant to bladder cancer. The R implementation of GPA is currently available at http://dongjunchung.github.io/GPA/.Author Summary: In the past 10 years, many genome wide association studies (GWAS) have been conducted to identify the genetic bases of complex human traits. As of January, 2014, more than 12,000 single-nucleotide polymorphisms (SNPs) have been reported to be significantly associated with at least one complex trait/disease. On one hand, about 85% of identified risk variants are located in non-coding regions, which motivates a systematic understanding of the function of non-coding variants in regulatory elements in the human genome. On the other hand, complex diseases are often affected by many genetic variants with small or moderate effects. To address these issues, we propose a statistical approach, GPA, to integrating information from multiple GWAS datasets and functional annotation. Notably, our approach only requires marker-wise p-values as input, making it especially useful when only summary statistics, instead of the full genotype and phenotype data, are available. We applied GPA to analyze GWAS datasets of five psychiatric disorders and bladder cancer, where the central nervous system genes, eQTLs from the Genotype-Tissue Expression (GTEx), and the ENCODE DNase-seq data from 125 cell lines were used as functional annotation. The analysis results suggest that GPA is an effective method for integrative data analysis in the post-GWAS era.