Abstract Details
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.
Back to the full JSM 2015 program
|
For program information, contact the JSM Registration Department or phone (888) 231-3473.
For Professional Development information, contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
2015 JSM Online Program Home
ASA Meetings Department
732 North Washington Street, Alexandria, VA 22314
(703) 684-1221 • meetings@amstat.org
Copyright © American Statistical Association.