Bayesian Analysis of Quantitative Trait Locus Data Using Reversible Jump Markov Chain Monte Carlo

this paper, we utilize reversible jump Markov chain Monte Carlo (MCMC) methodology (Green 1995) in order to compute the posterior quantities required for fully Bayesian inference. It yields posterior densities not only for the parameters, given the number of QTL, but also for the number of QTL itself. As an example, the algorithm is applied to simulated data, according to a standard design in plant breeding. KEYWORDS: Quantitative trait locus; breeding scheme; Bayesian inference; reversible jump Markov chain Monte Carlo. 2 1 INTRODUCTION Many important features in plants and animals, such as yield or quality measures, are complex, quantitatively inherited traits. Since the first report on the use of phenotypic markers for understanding quantitative inheritance (Sax (1923)), the field of marker systems developed rapidly, especially after the advent of molecular markers (see Tanksley (1993) for a review). Simultaneously, biometrical models have been developed to localiz