JSM 2013 Home
Online Program Home
My Program

Abstract Details

Activity Number: 589
Type: Topic Contributed
Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract - #310016
Title: Improving Genetic Risk Prediction by Leveraging Pleiotropy
Author(s): Cong Li*+ and Jia Kang and Can Yang and Hongyu Zhao
Companies: Yale University and Merck and Yale University and Yale University
Keywords: genetic risk prediction ; GWAS ; pleiotropy

Although hundreds of genome-wide association studies (GWAS) have been conducted on many hum complex traits in the last 8 years, there is only limited, if any, success in translating these GWAS data into useful genetic prediction models. This is largely due to the presence of numerous genetic variants carrying only small to moderate genetic effects that cannot be easily identified given the limited sample size. Recent studies have shown that different human traits may share common genetic basis. Therefore, an attractive approach to increase our prediction capability is to integrate data of genetically correlated phenotypes. Yet the utility of genetic correlation in genetic prediction is not clearly understood. In this paper, we analyzed a GWAS data of bipolar and related disorders (BARD) and schizophrenia (SC) with a bivariate linear mixed model based method and found that jointly predicting the two phenotypes increased the AUC by 3% and 0.7% for BARD and SC respectively. We also performed comprehensive simulation studies to investigate the utility of genetic correlation in genetic risk prediction. We show that when there exists substantial genetic correlation between two phenotypes

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

Back to the full JSM 2013 program

2013 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please 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.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.