Technical Abstract: Quantitative trait locus mapping often results in data on a number of traits that have well established causal relationships. Many multi-trait quantitative trait locus mapping methods that account for the correlation among the multiple traits have been developed to improve the statistical power and the precision of parameter estimation. However none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the quantitative trait locus may not be fully understood. In this paper, we developed a Bayesian multiple quantitative trait locus mapping method for causally related traits using a mixture structural equation model, which allows researchers to decompose quantitative trait locus effects into direct, indirect, and total effects. Parameters are estimated based on their marginal posterior distribution. The posterior distributions of parameters are estimated using Markov Chain Monte Carlo methods such as the Gibbs sampler and the Metropolis-Hasting algorithm. The number of quantitative trait loci affecting traits is determined by the Bayes factor. The performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait Bayesian analysis, our proposed method not only improved the statistical power of quantitative trait locus detection, accuracy, and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more biologically sensible model.