WorkshopsA gene-trait similarity regression method for common and rare variants with general trait values
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In this talk, we introduce a gene-trait similarity model to aggregate information from SNPs that are in the same gene/pathway to study the genetics of drug response. 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 avoids canceling signals with opposite etiological effects, is applicable to any class of genetic variants without having to dichotomize the allele types, and can capture non-additive effects among markers by using certain similarity metrics. 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. We show how this framework can be applied to a various of trait types such as continuous, binary, and survival traits.
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Jung-Ying Tzeng
2011-06-22
10:05:00 - 11:00:00
308 , Mathematics Research Center Building (ori. New Math. Bldg.)
In this talk, we introduce a gene-trait similarity model to aggregate information from SNPs that are in the same gene/pathway to study the genetics of drug response. 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 avoids canceling signals with opposite etiological effects, is applicable to any class of genetic variants without having to dichotomize the allele types, and can capture non-additive effects among markers by using certain similarity metrics. 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. We show how this framework can be applied to a various of trait types such as continuous, binary, and survival traits.