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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.

Original publication




Journal article


Genetic epidemiology

Publication Date





299 - 308


Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, United Kingdom.


Humans, Genetic Markers, Models, Statistical, Bayes Theorem, Odds Ratio, Environment, Genotype, Alleles, Quantitative Trait Loci, Algorithms, Models, Genetic, Computer Simulation, Software, Genetic Loci