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Activity Number: 286
Type: Contributed
Date/Time: Tuesday, July 31, 2007 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract - #310149
Title: Bayesian SUR Modeling of Multiple Ordinal Traits for Genome-Wide Epistatic QTL Mapping
Author(s): Samprit Banerjee*+ and Nengjun Yi
Companies: The University of Alabama at Birmingham and The University of Alabama at Birmingham
Address: Dept of Biostatistics, Section on Statistical Genetics, 1665 University Blvd, Birmingham, AL, 35294,
Keywords: Seemingly Unrelated Regression ; Bayesian Model Selection ; QTL mapping ; multiple traits ; epistasis

Unraveling the genetic etiology of complex traits by identifying complex epistatic quantitative trait loci (QTL) across the entire genome, still poses a bewildering challenge to contemporary statistical geneticists. The majority of the existing methods focus on a single trait, even though typically data on more than one phenotype are collected. In this paper, we present a multiple trait Bayesian composite model space approach to perform Seemingly Unrelated Regression (SUR) on complex traits in experimental crosses from two inbred lines. The joint analysis has several advantages over single trait analysis, including the expected improvement in statistical power to detect QTL and in precision of parameter estimation. It can also provide us with a greater insight in the nature of genetic correlations in certain regions of the genome by testing plieotropy and plieotropy vs. close linkage.

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Revised September, 2007