3 p.m. - 4 p.m. Location: FO 2.702
Department of Statistics, Department of Biostatistics and Medical Informatics
University of Wisconsin Madison
Annotation Regression for Genome-Wide Association Studies
Although genome-wide association studies (GWAS) have been successful at identifying many disease-associated genetic variants, these studies are hampered by two obstacles. First, despite ever-increasing sample sizes, these studies are still underpowered for variants with weak effect sizes. Second, and more importantly, a large percentage of identified variants reside in non-coding regions, making them difficult to interpret. In this talk, I will propose a general regression framework utilizing functional annotation data in approaching the challenges. The annotation regression framework for GWAS (ARoG) is based on finite mixture of linear regression models where GWAS association measures are viewed as responses and functional annotations as predictors. This mixture framework addresses heterogeneity of effects of genetic variants by grouping them into clusters and high dimensionality of the functional annotations by enabling annotation selection within each cluster. The framework will be illustrated with computational experiments and analyses of schizophrenia data from Psychiatric Genomics Consortium. I will also discuss an extension of ARoG with multiple phenotypes (multiARoG), which jointly borrows information across phenotypes.
Sponsored by the Department of Mathematical Sciences