An efficient Bayesian model selection approach for interacting quantitative trait loci models with many effects. Academic Article uri icon

Overview

abstract

  • We extend our Bayesian model selection framework for mapping epistatic QTL in experimental crosses to include environmental effects and gene-environment interactions. We propose a new, fast Markov chain Monte Carlo algorithm to explore the posterior distribution of unknowns. In addition, we take advantage of any prior knowledge about genetic architecture to increase posterior probability on more probable models. These enhancements have significant computational advantages in models with many effects. We illustrate the proposed method by detecting new epistatic and gene-sex interactions for obesity-related traits in two real data sets of mice. Our method has been implemented in the freely available package R/qtlbim (http://www.qtlbim.org) to facilitate the general usage of the Bayesian methodology for genomewide interacting QTL analysis.

publication date

  • May 4, 2007

Research

keywords

  • Bayes Theorem
  • Models, Genetic
  • Quantitative Trait Loci

Identity

PubMed Central ID

  • PMC1931520

Scopus Document Identifier

  • 34547095545

Digital Object Identifier (DOI)

  • 10.1534/genetics.107.071365

PubMed ID

  • 17483424

Additional Document Info

volume

  • 176

issue

  • 3