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Activity Number: 183
Type: Contributed
Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract - #310017
Title: Elucidating Heritability via Kernel Machine Testing for Epistasis
Author(s): Jennifer Clark*+ and Michael Wu and Arnab Maity
Companies: UNC and The University of North Carolina and North Carolina State University
Keywords: Kernel Machines ; Epistasis
Abstract:

Advances in biotechnology have led to the development of GWAS identifying SNPs associated with complex diseases and traits. Understanding an individual's genetic disposition for these outcomes can provide information towards the development of individualized risk profiles and treatment while providing clues to biological mechanisms underlying complex traits. Although GWAS are responsible for identification of many SNPs for a range of outcomes, the variability explained by the main effects of these SNPs accounts for a fraction of the total variability that is attributable to genetics. Searching for this "missing heritability" is an emerging goal of modern genetics. There has been considerable interest in examining the potential for epistasis to explain heritability. The high-dimensionality of modern genetic data, the limited availability of samples, and poor understanding of how epistatic effects influence outcomes pose a grand challenge for statisticians. Multi-marker analysis has been found to be a powerful technique for mitigating some of these challenges. Consequently, we seek to develop a new strategy for identification of epistatic interactions at the multi-marker level.


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