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Abstract Details
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Activity Number:
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309
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Type:
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Invited
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Date/Time:
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Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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WNAR
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| Abstract - #303656 |
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Title:
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Integrating Diverse Types of Genomics Data for Disease Risk Prediction
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Author(s):
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Hongyu Zhao*+ and Cong Li and Ning Sun
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Companies:
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Yale School of Public Health and Yale University and Yale University
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Address:
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Suite 503, New Haven, CT, 06510, United States
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Keywords:
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risk prediction ;
data integration ;
statistical genomics ;
genome wide association study ;
genomics ;
computational biology
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Abstract:
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Recent successes in genome wide association studies (GWAS) have identified hundreds of genetic variants associated with disease risk, but GWAS data alone only offer limited prediction power for assessing an individuals's risk. It is conceivable that incorporating additional types of data, e.g. expression QTL data, SNP annotation data, and pathway information, may improve prediction accuracy. In this presentation, we will first examine the informativeness of other types of genomics data on disease risk association and then propose a statistical framework to integrate these data for more informed risk prediction. The usefulness of our approach will be demonstrated through its application to a large cohort of Crohn's disease patients.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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