This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 215
Type: Invited
Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract - #305971
Title: Kernel-Machine Methods for Gene Mapping of Complex Traits
Author(s): Michael Epstein*+
Companies: Emory University
Address: , , ,
Keywords: association ; gene ; kernel

Genetic association studies of complex traits often genotype a set of SNPs that summarize genetic variation across the genome and then tests for association between each SNP and trait using a univariate test. Such tests can lose power if the tested SNP is in incomplete linkage disequilibrium with the trait locus. Alternatively, one can test multiple SNPs across a specific region (e.g. gene) using multivariate methods but such tests have large degrees of freedom that can also compromise power. In this talk, I propose the use of kernel-machine methods for association mapping that uses information from multiple SNPs simultaneously in analysis but produces a test statistic with reduced degrees of freedom compared to multivariate approaches. Using both simulated and real data, we show kernel-machine methods provide a novel and powerful strategy for gene mapping.

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