BAYESIAN ANALYSIS FOR GENES WITH PLEIOTROPIC EFFECTS

一般情况下需要链接和分离分析程序 可以处理多个基因的上位性效应的家系，以及多个 标记基因座，是公认的。实现这一目标的方法 这个项目有三个独特的方面： 1)包含对几个定性或定量的多效性效应 特征。 2)对连锁和分离分析的贝叶斯观点，即 可以说，介于模型依赖方法和非参数方法之间。 3)马尔可夫链蒙特卡罗的一种保守方法，主要使用它 在可以找到其速率的下界的上下文中 融合。

The need for linkage and segregation analysis programs for general pedigrees that can handle epistatic effects of several genes, and multiple marker loci, is generally recognized. The approach to this goal taken in this project has three distinctive aspects: 1) Inclusion of pleiotropic effects on several qualitative or quantitative traits. 2) A Bayesian perspective on linkage and segregation analysis that is, so to speak, half-way between model-dependent and non-parametric methods. 3) A conservative approach to Markov chain Monte Carlo, using it primarily in contexts where it is possible to find lower bounds on its rate of convergence.