Bayesian Model Selection for Genome-Wide Epistatic Quantitative Trait Loci Analysis
Open Access
- 1 July 2005
- journal article
- research article
- Published by Oxford University Press (OUP) in Genetics
- Vol. 170 (3) , 1333-1344
- https://doi.org/10.1534/genetics.104.040386
Abstract
The problem of identifying complex epistatic quantitative trait loci (QTL) across the entire genome continues to be a formidable challenge for geneticists. The complexity of genome-wide epistatic analysis results mainly from the number of QTL being unknown and the number of possible epistatic effects being huge. In this article, we use a composite model space approach to develop a Bayesian model selection framework for identifying epistatic QTL for complex traits in experimental crosses from two inbred lines. By placing a liberal constraint on the upper bound of the number of detectable QTL we restrict attention to models of fixed dimension, greatly simplifying calculations. Indicators specify which main and epistatic effects of putative QTL are included. We detail how to use prior knowledge to bound the number of detectable QTL and to specify prior distributions for indicators of genetic effects. We develop a computationally efficient Markov chain Monte Carlo (MCMC) algorithm using the Gibbs sampler and Metropolis-Hastings algorithm to explore the posterior distribution. We illustrate the proposed method by detecting new epistatic QTL for obesity in a backcross of CAST/Ei mice onto M16i.Keywords
This publication has 56 references indexed in Scilit:
- Epistatic interaction between two nonstructural loci on chromosomes 7 and 3 influences hepatic lipase activity in BSB miceJournal of Lipid Research, 2004
- Epistasis: too often neglected in complex trait studies?Nature Reviews Genetics, 2004
- Modifying the Schwarz Bayesian Information Criterion to Locate Multiple Interacting Quantitative Trait LociGenetics, 2004
- A Model Selection Approach for the Identification of Quantitative Trait Loci in Experimental CrossesJournal of the Royal Statistical Society Series B: Statistical Methodology, 2002
- Model choice in gene mapping: what and whyTrends in Genetics, 2002
- Benchmark priors for Bayesian model averagingJournal of Econometrics, 2001
- The Variable Selection ProblemJournal of the American Statistical Association, 2000
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determinationBiometrika, 1995
- Bayes FactorsJournal of the American Statistical Association, 1995
- Variable Selection Via Gibbs SamplingJournal of the American Statistical Association, 1993