Yi Nengjun, George Varghese, Allison David B
Department of Biostatistics, University of Alabama, Birmingham 35294-0022, USA.
Genetics. 2003 Jul;164(3):1129-38. doi: 10.1093/genetics/164.3.1129.
In this article, we utilize stochastic search variable selection methodology to develop a Bayesian method for identifying multiple quantitative trait loci (QTL) for complex traits in experimental designs. The proposed procedure entails embedding multiple regression in a hierarchical normal mixture model, where latent indicators for all markers are used to identify the multiple markers. The markers with significant effects can be identified as those with higher posterior probability included in the model. A simple and easy-to-use Gibbs sampler is employed to generate samples from the joint posterior distribution of all unknowns including the latent indicators, genetic effects for all markers, and other model parameters. The proposed method was evaluated using simulated data and illustrated using a real data set. The results demonstrate that the proposed method works well under typical situations of most QTL studies in terms of number of markers and marker density.
在本文中，我们利用随机搜索变量选择方法来开发一种贝叶斯方法，用于在实验设计中识别复杂性状的多个数量性状基因座（QTL）。所提出的程序需要将多元回归嵌入到层次正态混合模型中，其中所有标记的潜在指标用于识别多个标记。具有显著效应的标记可以被识别为模型中后验概率较高的那些标记。采用一个简单易用的吉布斯采样器从所有未知量的联合后验分布中生成样本，这些未知量包括潜在指标、所有标记的遗传效应以及其他模型参数。使用模拟数据对所提出的方法进行了评估，并使用一个真实数据集进行了说明。结果表明，在所研究的大多数QTL研究的典型情况下，就标记数量和标记密度而言，所提出的方法效果良好。
