title: Detecting interacting genetic loci with effects on quantitative traits where the nature and order of the interaction are unknown.
abstract: Standard techniques for single marker quantitative trait mapping perform poorly in detecting complex interacting genetic influences. When a genetic marker interacts with other genetic markers and/or environmental factors to influence a quantitative trait, a sample of individuals will show different effects according to their exposure to other interacting factors. This paper presents a Bayesian mixture model, which effectively models heterogeneous genetic effects apparent at a single marker. We compute approximate Bayes factors which provide an efficient strategy for screening genetic markers (genome-wide) for evidence of a heterogeneous effect on a quantitative trait. We present a simulation study which demonstrates that the approximation is good and provide a real data example which identifies a population-specific genetic effect on gene expression in the HapMap CEU and YRI populations. We advocate the use of the model as a strategy for identifying candidate interacting markers without any knowledge of the nature or order of the interaction. The source of heterogeneity can be modeled as an extension.
authors.affiliation: Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, United Kingdom. davies@stats.ox.ac.uk
authors.forename: Joanna L
authors.initials: JL
authors.lastname: Davies
authors.forename: Jotun
authors.initials: J
authors.lastname: Hein
authors.forename: Chris C
authors.initials: CC
authors.lastname: Holmes
mesh_terms: D000465:Algorithms; D000483:Alleles; D001499:Bayes Theorem; D003198:Computer Simulation; D004777:Environment; D056426:Genetic Loci; D005819:Genetic Markers; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D016017:Odds Ratio; D040641:Quantitative Trait Loci; D012984:Software
chemical_list: D005819:Genetic Markers