GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation
Year: 2014
Type: article
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/.
Source: PLoS Genetics
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Funders: U.S. Department of Veterans Affairs, Office of Research and Development, National Institutes of Health, National Institute on Alcohol Abuse and Alcoholism, National Institute on Drug Abuse, National Institute of General Medical Sciences, National Center for Research Resources, National Center for Advancing Translational Sciences
Awards: G1041632141, G1064084138, G1250591687, G152221931, G1826970460, G205508870, G2266666766, G2273635344, G2835737691, G3478055865, Deep sequencing studies for cannabis and stimulant dependence, Genetics of Alcohol Dependence in African-Americans, Statistical Methods to Map Genes for Complex Traits, Genetics of Opioid Dependence, Genetics of Alcohol Dependence in American Populations, Genetics of Cocaine Dependence, Yale University Clinical and Translational Science Award Program, Yale University Clinical and Translational Science Award Program, Genetics of Cocaine Dependence, G9000006508, G9000097481, G9000145302, G9000240960
OpenAlex ID: W2054344444