Satagopan J M, Yandell B S, Newton M A, Osborn T C
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York 10021-6094, USA.
Genetics. 1996 Oct;144(2):805-16. doi: 10.1093/genetics/144.2.805.
Markov chain Monte Carlo (MCMC) techniques are applied to simultaneously identify multiple quantitative trait loci (QTL) and the magnitude of their effects. Using a Bayesian approach a multi-locus model is fit to quantitative trait and molecular marker data, instead of fitting one locus at a time. The phenotypic trait is modeled as a linear function of the additive and dominance effects of the unknown QTL genotypes. Inference summaries for the locations of the QTL and their effects are derived from the corresponding marginal posterior densities obtained by integrating the likelihood, rather than by optimizing the joint likelihood surface. This is done using MCMC by treating the unknown QTL, genotypes, and any missing marker genotypes, as augmented data and then by including these unknowns in the Markov chain cycle alone with the unknown parameters. Parameter estimates are obtained as means of the corresponding marginal posterior densities. High posterior density regions of the marginal densities are obtained as confidence regions. We examine flowering time data from double haploid progeny of Brassica napus to illustrate the proposed method.
马尔可夫链蒙特卡罗（MCMC）技术被应用于同时识别多个数量性状基因座（QTL）及其效应大小。采用贝叶斯方法，将多基因座模型应用于数量性状和分子标记数据，而不是一次拟合一个基因座。表型性状被建模为未知QTL基因型的加性和显性效应的线性函数。QTL位置及其效应的推断总结是从通过对似然进行积分得到的相应边际后验密度中得出的，而不是通过优化联合似然表面。这通过MCMC来完成，即将未知的QTL、基因型以及任何缺失的标记基因型视为扩充数据，然后将这些未知数与未知参数一起纳入马尔可夫链循环中。参数估计值作为相应边际后验密度的均值获得。边际密度的高后验密度区域作为置信区域获得。我们研究了甘蓝型油菜双单倍体后代的开花时间数据，以说明所提出的方法。
