Integrative Models of GWAS SNPs, Genomic, and Epigenomic Data

• Activity Number: 105
• Type: Invited
• Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
• Sponsor: IMS
• Author(s): Sunduz Keles* and Sunyoung Shin and Chandler Zuo
• Companies: University of Wisconsin and University of Wisconsin and University of Wisconsin
• Keywords: GWAS ; Epigenome ; Data integration ; Mixture of Regressions ; MAD-Bayes

Abstract

Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) that are associated with many diseases. Since a majority of these SNPs have been mapped to intergenic or intronic regions and are functionally unclassified, GWAS-identified genetic associations are largely functionally unexplored. In parallel to these efforts, consortia projects such as ENCODE and REMC generated a wealth of (epi)genomic data. We will present supervised and unsupervised methods and efficient algorithms for integrating these data. Our methods build on clustering models and novel mixtures of regressions and aid in exploring the functional impact of GWAS SNPs on phenotypes through other (epi)genomic data.