JSM 2005 - Toronto

Abstract #304696

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Activity Number: 33
Type: Contributed
Date/Time: Sunday, August 7, 2005 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #304696
Title: Genome Scans with Gene-Covariate Interaction
Author(s): Jie Peng*+ and Hsiu-Khuern Tang and David Siegmund
Companies: University of California, Davis and Hewlett-Packard Company and Stanford University
Address: Department of Statistics, Davis, CA, 95616, United States
Keywords: gene mapping ; linkage analysis ; gene-environment/covariate interaction ; score statistics ; quantitative traits
Abstract:

Genetic control of a complex trait is widely thought to involve a number of loci, which may interact with one another and/or with environmental covariates. Standard genome scanning methods usually ignore these possibilities, presumably because they involve larger, more complex models and/or because of difficulties in formulating a suitable model. In this paper, genetic models for gene-covariate interaction are described. Methods of linkage analysis that utilize special features of these models and the corresponding score statistics are derived. Their power is compared with that of simple genome scans that ignore these features, and substantial gains in power are observed when the gene-covariate interaction is strong. Quantitative trait mapping and affected sibpair mapping are discussed. For the latter case, a simpler statistic is proposed that has similar performance to the score statistic, but does not require the estimation of nuisance parameters. Because the nuisance parameters are not estimable solely from affected sibpair data, this statistic is much easier to apply in practice. Approximations for the P-value and power are derived under the framework of local alternatives.

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