Markov chain Monte Carlo segregation and linkage analysis for oligogenic models
Markov chain Monte Carlo segregation and linkage analysis for oligogenic models
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DOI：
10.1086/515506
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发表时间：
1997-09-01
影响因子：
9.8
通讯作者：
Heath, SC
中科院分区：
文献类型：
--
作者：
Heath, SC
本文介绍了一种基于系谱数据的分离和连锁分析新方法。利用可逆跳跃马尔可夫链蒙特卡罗方法实现了马尔可夫链在不同数量性状位点模型对应的参数子空间间跳跃的采样方案。QTL数量、位置和效应的联合估计是可能的，避免了连锁分析中由于QTL数量的错误指定而产生的问题。用第九届遗传分析研讨会模拟的数据集说明了该方法；该数据集具有几个寡生性状，是通过使用1497个成员的谱系产生的。该方法具有较好的混合特性，能够正确地从试验数据集中恢复模拟模型。该方法似乎具有强大的连锁分析和回答有关复杂性状遗传控制的更一般问题的巨大潜力。
A new method for segregation and linkage analysis, with pedigree data, is described. Reversible jump Markov chain Monte Carlo methods are used to implement a sampling scheme in which the Markov chain can jump between parameter subspaces corresponding to models with different numbers of quantitative-trait loci (QTL's). Joint estimation of QTL number, position, and effects is possible, avoiding the problems that can arise from misspecification of the number of QTL's in a linkage analysis. The method is illustrated by use of a data set simulated for the 9th Genetic Analysis Workshop; this data set had several oligogenic traits, generated by use of a 1,497-member pedigree. The mixing characteristics of the method appear to be good, and the method correctly recovers the simulated model from the test data set. The approach appears to have great potential both for robust linkage analysis and for the answering of more general questions regarding the genetic control of complex traits.