Beidaihe Central Experiment Station, Chinese Academy of Fishery Sciences, Qinhuangdao 066100, People's Republic of China.
Brief Bioinform. 2014 Jan;15(1):20-9. doi: 10.1093/bib/bbs062. Epub 2012 Sep 28.
The iteratively reweighted least square (IRLS) method is mostly identical to maximum likelihood (ML) method in terms of parameter estimation and power of quantitative trait locus (QTL) detection. But the IRLS is greatly superior to ML in terms of computing speed and the robustness of parameter estimation. In conjunction with the priors of parameters, ML can analyze multiple QTL model based on Bayesian theory, whereas under a single QTL model, IRLS has very limited statistical power to detect multiple QTLs. In this study, we proposed the iteratively reweighted least absolute shrinkage and selection operator (IRLASSO) for extending IRLS to simultaneously map multiple QTLs. The LASSO with coordinate descent step is employed to efficiently estimate non-zero genetic effect of each locus scanned over entire genome. Simulations demonstrate that IRLASSO has a higher precision of parameter estimation and power to detect QTL than IRLS, and is able to estimate residual variance more accurately than the unweighted LASSO based on LS. Especially, IRLASSO is very fast, usually taking less than five iterations to converge. The barley dataset from the North American Barley Genome Mapping Project is reanalyzed by our proposed method.
迭代重加权最小二乘法（IRLS）在参数估计和数量性状基因座（QTL）检测能力方面与最大似然（ML）方法基本相同。但在计算速度和参数估计稳健性方面，IRLS 大大优于 ML。结合参数的先验信息，ML 可以基于贝叶斯理论分析多个 QTL 模型，而在单 QTL 模型下，IRLS 检测多个 QTL 的统计能力非常有限。在这项研究中，我们提出了迭代重加权最小绝对收缩和选择算子（IRLASSO），将 IRLS 扩展到同时映射多个 QTL。使用坐标下降步的 LASSO 可以有效地估计整个基因组上扫描的每个位点的非零遗传效应。模拟表明，IRLASSO 比 IRLS 具有更高的参数估计精度和 QTL 检测能力，并且能够比基于 LS 的无权重 LASSO 更准确地估计残差方差。特别是，IRLASSO 非常快，通常只需不到五次迭代即可收敛。通过我们提出的方法对北美大麦基因组图谱计划中的大麦数据集进行了重新分析。
