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 understanding of quantitative inheritance in plants as well as in animals. Taking the newly available data into account, biometric models have been constructed for the mapping of quantitative trait loci (QTLs). In current approaches, 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, Biometrika 82, 711-732) 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.