A BAYESIAN-APPROACH TO DETECT QUANTITATIVE TRAIT LOCI USING MARKOV-CHAIN MONTE-CARLO

Markov chain Monte Carlo (MCMC) techniques are applied to simultaneous ly identify multiple quantitative trait loci (QTL) and the magnitude o f their effects. Using a Bayesian approach a multi-locus model is fit to quantitative trait and molecular marker data, instead of fitting on e locus at a time. The phenotypic trait is modeled as a linear functio n of the additive and dominance effects of the unknown QTL genotypes. Inference summaries for the locations of the QTL and their effects are derived from the corresponding marginal posterior densities obtained by integrating the Likelihood, rather than by optimizing the joint lik elihood surface. This is done using MCMC by treating the unknown QTL g enotypes, and any missing marker genotypes, as augmented data and then by including these unknowns in the Markov chain cycle along with the unknown parameters. Parameter estimates are obtained as means of the c orresponding marginal posterior densities. High posterior density regi ons of the marginal densities are obtained as confidence regions. We e xamine flowering time data from double haploid progeny of Brassica nap us to illustrate the proposed method.