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以考量潜在的多重连锁QTL效应。得益于测序技术的进步，当前QTL定位多使用高密度遗传标记图谱，这使得QTL模型中可能包含紧密连锁因而高度相关的遗传标记。尽管已有Lasso、经验贝叶斯Lasso（EBlasso）及弹性网（EN）等算法可用于此类QTL模型的推断，但学界仍亟需更具优势的方法，以在存在相关QTL的场景下检测更多的QTL。本研究开发了一种新颖的经验贝叶斯弹性网（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。模拟实验结果显示，相较于EN与EBlasso，EBEN具有更高的检测效能，且错误发现率几乎一致；尤为关键的是，EBEN能够识别出另外两种算法无法检出的相关QTL。在对真实数据集进行分析时，EBEN同样检测到了多于EN与EBlasso的遗传效应。EBEN为多QTL定位及其他相关应用场景下的高维稀疏模型推断提供了实用工具。实现EBEN算法的R软件包‘EBEN’已发布于R综合存档网络（Comprehensive R Archive Network, CRAN）。