We develop a Bayesian approach to infer the number of quantitative trait loci (QTL) affecting a phenotypic trait. A multi-locus model is fit to quantitative trait and molecular marker data, with the trait response modeled as a linear function of the additive and dominance effects of the unknown QTL genotypes. The number of QTL is unknown and must be estimated as well. Inference summaries for the number of QTL, their locations and corresponding effects are obtained from the corresponding marginal posterior densities obtained by integrating the likelihood, rather than by optimizing the joint likelihood surface. We use reversible jump Markov chain Monte Carlo (Green 1995) by treating the unknown QTL genotypes as augmented data and including these unknowns in the Markov chain cycle. Flowering time data from double haploid progeny of {\em Brassica napus} (Satagopan et al. 1996 Genetics) is examined to illustrate the proposed method.
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For archival purposes, the revjump code is available as revjump.tar.gz (gzip of TAR file). Now we recommend you use the R/qtlbim software.
A Bayesian approach to estimate the number of QTL affecting a trait and other genetic parameters is proposed. A multilocus model is fit to quantitative trait and molecular marker data instead of fitting one locus at a time. The phenotypic trait is modeled as a linear function of the additive and dominance effects of the unknown QTL genotypes. The number of QTL is considered as an unknown parameter. Inference summaries for the number of QTL, their locations and corresponding effects are obtained from the corresponding marginal posterior densities obtained by integrating the likelihood, rather than by optimizing the joint likelihood surface. This is done using reversible jump Markov chain Monte Carlo (Green 1995) by treating the unknown QTL genotypes as augmented data and then by including these unknowns in the Markov chain cycle along with the unknown parameters. Flowering time data from double haploid progeny of Brassica napus is examined to illustrate the proposed method.
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