Abraham K Joseph, Totir Liviu R, Fernando Rohan L
Iowa State University, Ames, IA 50011, USA.
Genet Sel Evol. 2007 Jan-Feb;39(1):27-38. doi: 10.1186/1297-9686-39-1-27. Epub 2007 Jan 11.
Markov chain Monte Carlo (MCMC) methods have been widely used to overcome computational problems in linkage and segregation analyses. Many variants of this approach exist and are practiced; among the most popular is the Gibbs sampler. The Gibbs sampler is simple to implement but has (in its simplest form) mixing and reducibility problems; furthermore in order to initiate a Gibbs sampling chain we need a starting genotypic or allelic configuration which is consistent with the marker data in the pedigree and which has suitable weight in the joint distribution. We outline a procedure for finding such a configuration in pedigrees which have too many loci to allow for exact peeling. We also explain how this technique could be used to implement a blocking Gibbs sampler.
马尔可夫链蒙特卡罗（MCMC）方法已被广泛用于克服连锁分析和分离分析中的计算问题。这种方法有许多变体且都在实际应用；其中最流行的是吉布斯采样器。吉布斯采样器易于实现，但（以其最简单的形式）存在混合和可约性问题；此外，为了启动一个吉布斯采样链，我们需要一个起始基因型或等位基因构型，它与家系中的标记数据一致，并且在联合分布中有合适的权重。我们概述了一种在具有过多位点以至于无法进行精确剥脱的家系中找到这种构型的程序。我们还解释了如何使用该技术来实现一个分块吉布斯采样器。
