BAYESIAN ANALYSIS FOR GENES WITH PLEIOTROPIC EFFECTS

需要连锁和分离分析程序，为一般 可以处理几个基因的上位效应的谱系，以及多个 标记基因座，这是公认的。为实现这一目标， 该项目有三个独特的方面： 1)纳入对几种定性或定量的多效性效应 性状 2)一个贝叶斯观点的联系和隔离分析，所以， 可以说，介于模型依赖和非参数方法之间。 3)马尔可夫链蒙特卡罗的保守方法，主要使用它 在有可能找到其速率的下限的情况下， 收敛

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.