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. Statistical inference of the model parameters and SNP ranking is achieved through an EM algorithm that can handle genome-wide markers efficiently. 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. 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. 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. Identifications of these risk variants remain a very challenging problem.
Results from Genome-Wide Association Studies (GWAS) have shown that complex diseases are often affected by many genetic variants with small or moderate effects. 5 Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America Department of Genetics, Yale School of Medicine, West Haven, Connecticut, United States of America VA Cooperative Studies Program Coordinating Center, West Haven, Connecticut, United States of America.
4 Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America VA CT Healthcare Center, West Haven, Connecticut, United States of America Department of Genetics, Yale School of Medicine, West Haven, Connecticut, United States of America Department of Neurobiology, Yale School of Medicine, New Haven, Connecticut, United States of America.3 Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.2 Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America Department of Mathematics, Hong Kong Baptist University, Hong Kong, China.1 Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America.