Data from: Empirical Bayesian elastic net for multiple quantitative trait locus mapping

In multiple quantitative trait locus (QTL) mapping, a high-dimensional sparse regression model is usually employed to account for possible multiple linked QTLs. The QTL model may include closely linked and thus highly correlated genetic markers, especially when high-density marker maps are used in QTL mapping because of the advancement in sequencing technology. Although existing algorithms, such as Lasso, empirical Bayesian Lasso (EBlasso) and elastic net (EN) are available to infer such QTL models, more powerful methods are highly desirable to detect more QTLs in the presence of correlated QTLs. We developed a novel empirical Bayesian EN (EBEN) algorithm for multiple QTL mapping that inherits the efficiency of our previously developed EBlasso algorithm. Simulation results demonstrated that EBEN provided higher power of detection and almost the same false discovery rate compared with EN and EBlasso. Particularly, EBEN can identify correlated QTLs that the other two algorithms may fail to identify. When analyzing a real dataset, EBEN detected more effects than EN and EBlasso. EBEN provides a useful tool for inferring high-dimensional sparse model in multiple QTL mapping and other applications. An R software package ‘EBEN’ implementing the EBEN algorithm is available on the Comprehensive R Archive Network (CRAN).

在多数量性状位点（quantitative trait locus，QTL）定位研究中，通常采用高维稀疏回归模型来分析潜在的多个连锁QTL。由于测序技术的进步，在QTL定位中使用高密度标记图谱时，QTL模型可能包含紧密连锁且高度相关的遗传标记。尽管现有算法（如套索回归（Lasso）、经验贝叶斯套索回归（empirical Bayesian Lasso，EBlasso）和弹性网（elastic net，EN））可用于推断此类QTL模型，但在存在相关QTL的场景下，仍亟需更具优势的方法以检出更多QTL。本研究开发了一种全新的经验贝叶斯弹性网（empirical Bayesian EN，EBEN）算法用于多QTL定位，该算法继承了我们此前开发的EBlasso算法的高效性。仿真结果表明，相较于EN和EBlasso，EBEN拥有更高的检测效力，且假发现率（false discovery rate）与二者基本一致。尤为关键的是，EBEN能够识别另外两种算法无法检出的相关QTL。在分析真实数据集时，EBEN检出的效应量多于EN和EBlasso。EBEN为多QTL定位及其他应用场景下的高维稀疏模型推断提供了实用工具。一款实现EBEN算法的R软件包‘EBEN’可在综合R档案网络（Comprehensive R Archive Network，CRAN）上获取。