Bayesian shrinkage analysis estimates all QTLs effects simultaneously, which shrinks the effect of "insignificant" QTLs close to zero so that it does not need special model selection. Bayesian shrinkage estimation usually has an excellent performance on multiple QTLs mapping, but it could not give a probabilistic explanation of how often a QTLs is included in the model, also called posterior inclusion probability, which is important to assess the importance of a QTL. In this research, two methods, FitMix and SimMix, are proposed to approximate the posterior probabilities. Under the assumption of mixture distribution of the estimated QTL effect, FitMix and SimMix mathematically and intuitively fit mixture distribution, respectively. The simulation results showed that both methods gave very reasonable estimates for posterior probabilities. We also applied the two methods to map QTLs for the North American Barley Genome Mapping Project data.

中文翻译：

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贝叶斯收缩分析中QTL的后包含概率的推断。

贝叶斯收缩分析同时估计所有QTL的影响，这将“无意义” QTL的影响缩小到接近零，因此不需要特殊的模型选择。贝叶斯收缩估计通常在多个QTL映射上具有出色的性能，但是无法给出模型中包含QTL频率的概率解释，也称为后验包含概率，这对于评估QTL的重要性很重要。在这项研究中，提出了两种方法，FitMix和SimMix，以近似后验概率。在估计QTL效果的混合物分布的假设下，FitMix和SimMix分别在数学和直观上拟合混合物分布。仿真结果表明，两种方法都给出了后验概率的非常合理的估计。