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Activity Number: 695
Type: Contributed
Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #315363
Title: Annotation Regression of Genome-Wide Association Studies (ARoG) with an Application to Psychiatric Genomics Consortium Data
Author(s): Sunyoung Shin* and Sunduz Keles
Companies: University of Wisconsin and University of Wisconsin
Keywords: ENCODE ; Genome-wide association studies ; Lasso ; Mixture regression models
Abstract:

Genome-wide association studies (GWAS) have been successful at identification of genetic variants that are significantly associated with specific diseases or phenotypes. Our current on-going efforts aim to characterize roles of these variants in deriving the phenotype by integrating genomic and functional data. We have developed a novel statistical framework, named ARoG for Annotation Regression of GWAS, which integrates ENCODE genomic and epigenomic data into GWAS. This integrative framework aims to both (i) boost signals for variants with weak effects and (ii) elucidate epigenomic information that explains the variant association. Application of ARoG to Psychiatric Genomics Consortium (PGC) data illustrates that ARoG is able to group variants based on the effect of their annotation pattern heterogeneity on PGC phenotypes and identify novel variants associated with the phenotypes.


Authors who are presenting talks have a * after their name.

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