Bayesian mapping of quantitative trait loci under the identity-by-descent-based variance component model

Variance component analysis of quantitative trait loci (QTL) is an importan t strategy of genetic mapping for complex traits in humans. The method is r obust because it can handle an arbitrary number of alleles with arbitrary m odes of gene actions. The variance component method is usually implemented using the proportion of alleles with identity-by-descent (IBD) shared by re latives. As a result, information about marker linkage phases in the parent s is not required. The method has been studied extensively under either the maximum-likelihood framework or the sib-pair regression paradigm. However, virtually all investigations are limited to normally distributed traits un der a single QTL model. In this study, we develop a Bayes method to map mul tiple QTL. We also extend the Bayesian mapping procedure to identify QTL re sponsible for the variation of complex binary diseases in humans under a th reshold model. The method can also treat the number of QTL as a parameter a nd infer its posterior distribution. We use the reversible jump Markov chai n Monte Carlo method to infer the posterior distributions of parameters of interest. The Bayesian mapping procedure ends with an estimation of the joi nt posterior distribution of the number of QTL and the locations and varian ces of the identified QTL. Utilities of the method are demonstrated using a simulated population consisting of multiple full-sib families.