Bayesian analysis of quantitative trait locus data using reversible jump Markov chain Monte Carlo

The advent of molecular markers has created a great potential for the under standing of quantitative inheritance in plants as well as in animals. Takin g the newly available data into account, biometric models have been constru cted for the mapping of quantitative trait loci (QTLs). In current approach es, the lack of knowledge on the number and location of the most important QTLs contributing to a trait is a major problem. In this paper, we utilize reversible jump Markov chain Monte Carlo methodology (Green, 1995, Biometri ka 82, 711-732) in order to compute the posterior quantities required for f ully Bayesian inference. It yields posterior densities not only for the par ameters, 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 stan dard design in plant breeding.