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

Abstract Details

Activity Number: 646
Type: Invited
Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #305928
Title: Assessing Gene-Set Association in Genomewide Association Studies
Author(s): Lin Chen and Li Hsu*+
Companies: Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center
Address: 1100 FAIRVIEW AVENUE N., M2-B500, Seattle, WA, ,
Keywords: penalized regression ; logistic ; group ridge penalty ; gwas ; gene-set analysis ; SNP association
Abstract:

GWAS have successfully identified susceptibility loci from marginal association analysis of SNPs. New insights into genetic variation underlying complex diseases can be gained by analyzing functionally related sets of genes simultaneously. Distinctive features of GWAS data present new challenges in the gene set analysis. These features include the large number of SNPs per gene, the modest and sparse SNP associations, and the additional information provided by LD patterns within genes. We propose a ``Gene-set Ridge regression in ASsociation Studies (GRASS)" algorithm. GRASS summarizes the genetic structure for each gene as eigenSNPs and uses a novel form of regularized regression technique (group ridge penalty) to select representative eigenSNPs for each gene and assess their joint association with disease risk. Both simulation and real datasets are used to illustrate the method.


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