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

Abstract

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/.

Improved methods for multi-trait fine mapping of pleiotropic risk loci

“…For multi-trait fine-mapping, we compared to GPA (Chung et al, 2014). To our knowledge, GPA is the only other method that performs multi-trait fine-mapping while leveraging functional annotation data.…”

“…To our knowledge, the only existing method that performs joint mapping for pleiotropy while incorporating functional annotation data is GPA (Chung et al, 2014). We show that our approach provides superior accuracy to GPA, likely due to the explicit modeling of LD in our framework.…”

The ubiquity of pleiotropy in human disease

“…Pleiotropy, defined here as one gene affecting more than one phenotype, has become increasingly important in interpreting both genotype-phenotype maps and underlying factors that affect comorbidity, and has been integrated into several methods for genome wide association studies (GWAS) (Chung et al 2014;Hill and Zhang 2012;Lee et al 2012;Li et al 2014a, b;Pendergrass et al 2013;Rzhetsky et al 2007;Sivakumaran et al 2011;Solovieff et al 2013;Stearns 2010;Wagner et al 2008;Wagner and Zhang 2011;Yang et al 2015). Studies of pleiotropy can impact our understanding of underlying disease processes, and implicitly, the prevention and treatment of disease as well as predicting adverse reactions to drugs.…”

“…Despite the recognized importance of pleiotropy in developing a comprehensive understanding of gene-disease relationships (Chung et al 2014;Hill and Zhang 2012;Lee et al 2012;Li et al 2014a, b;Pendergrass et al 2013;Rzhetsky Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00439-017-1854-z) contains supplementary material, which is available to authorized users. et al 2007;Sivakumaran et al 2011;Solovieff et al 2013;Stearns 2010;Wagner et al 2008;Wagner and Zhang 2011;Yang et al 2015), little has been done to investigate the relationship between pleiotropy and risk variation as measured by effect sizes.…”

A Penalized Regression Approach for Integrative Analysis in Gen ome- Wide Association Studies

“…However, due to limited sample size of a single GWAS and polygenicity of a complex trait, many existing methods do not have enough power to uncover the remaining risk genetic variants. Recently, increasing evidence suggests that complex traits may share common genetic bases, which is known as "pleiotropy" [15][16][17]. A systematic investigation of pleiotropy [18] suggests that 16.9% of genes and 4.6% of SNPs have been reported to statistical power in GWAS data analysis by integrating multiple from two aspects.…”