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Abstract Details

Activity Number: 309
Type: Invited
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: WNAR
Abstract - #303558
Title: Detecting Common and Rare Variants for General Traits: Gene-Trait Similarity Regression for Gene-Level Analysis
Author(s): Jung-Ying Tzeng*+ and Daowen Zhang and Wenbin Lu
Companies: North Carolina State University and North Carolina State University and North Carolina State University
Address: , Raleigh , NC, ,
Keywords: genetic association analysis
Abstract:

We introduce a gene-trait similarity model to perform gene-level or pathway-level of association analysis. The method uses genetic similarity to aggregate information from multiple polymorphic sites, with adaptive weights dependent on allele frequencies for accommodating rare and common variants. Collapsing information at the similarity level instead of the genotype level gains robustness across various genetic architectures of the causal variants, is applicable to any class of genetic variants without having to dichotomize the allele types, and captures nonlinear and interactive effects among markers. To assess gene-trait associations, we regress trait similarities for pairs of unrelated individuals on their genetic similarities, and assess association using a score test whose limiting distribution is derived. The proposed regression framework allows for covariates, has the capacity to model main, gene-gene, and gene-environment interaction effects, and can be applied to various trait types such as continuous, binary, and survival traits. We illustrate the utility of the model by simulation and real data application.


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