This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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215
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Type:
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Invited
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Date/Time:
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Monday, August 2, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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ENAR
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Abstract - #305971 |
Title:
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Kernel-Machine Methods for Gene Mapping of Complex Traits
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Author(s):
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Michael Epstein*+
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Companies:
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Emory University
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Address:
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, , ,
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Keywords:
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association ;
gene ;
kernel
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Abstract:
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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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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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