We introduce a Bayesian framework for simultaneous feature selection and outlier detection in sparse high-dimensional regression models, with a focus on quantitative trait locus (QTL) mapping in experimental crosses. More specifically, we incorporate the robust mean shift outlier handling mechanism into the multiple QTL mapping regression model and apply LASSO regularization concurrently to the genetic effects and the mean-shift terms through the flexible extended Bayesian LASSO (EBL) prior structure, thereby combining QTL mapping and outlier detection into a single sparse model representation problem. The EBL priors on the mean-shift terms prevent outlying phenotypic values from distorting the genotype-phenotype association and allow their detection as cases with outstanding mean shift values following the LASSO shrinkage. Simulation results demonstrate the effectiveness of our new methodology at mapping QTLs in the presence of outlying phenotypic values and simultaneously identifying the potential outliers, while maintaining a comparable performance to the standard EBL on outlier-free data.

中文翻译：

---

鲁棒的定量性状基因座定位和离群值检测的贝叶斯框架

我们介绍了一个用于稀疏高维回归模型中同时特征选择和离群值检测的贝叶斯框架，重点是实验杂交中的数量性状基因座（QTL）映射。更具体地说，我们将稳健的均值漂移离群值处理机制纳入了多个QTL映射回归模型中，并通过灵活的扩展贝叶斯LASSO（EBL）先验结构将LASSO正则化同时应用于遗传效应和均值漂移项，从而结合了QTL映射和离群值检测成为一个稀疏模型表示问题。均值漂移项的EBL先验可防止偏远的表型值扭曲基因型与表型的关联，并允许将其检测为LASSO收缩后具有出色均值漂移值的情况。