Department of Statistics and Applied Probability, National University of Singapore, 3 Science Drive 2, Singapore.
Theor Appl Genet. 2010 Jul;121(2):363-72. doi: 10.1007/s00122-010-1315-8. Epub 2010 Mar 25.
It is typical in QTL mapping experiments that the number of markers under investigation is large. This poses a challenge to commonly used regression models since the number of feature variables is usually much larger than the sample size, especially, when epistasis effects are to be considered. The greedy nature of the conventional stepwise procedures is well known and is even more conspicuous in such cases. In this article, we propose a two-phase procedure based on penalized likelihood techniques and extended Bayes information criterion (EBIC) for QTL mapping. The procedure consists of a screening phase and a selection phase. In the screening phase, the main and interaction features are alternatively screened by a penalized likelihood mechanism. In the selection phase, a low-dimensional approach using EBIC is applied to the features retained in the screening phase to identify QTL. The two-phase procedure has the asymptotic property that its positive detection rate (PDR) and false discovery rate (FDR) converge to 1 and 0, respectively, as sample size goes to infinity. The two-phase procedure is compared with both traditional and recently developed approaches by simulation studies. A real data analysis is presented to demonstrate the application of the two-phase procedure.
在 QTL 作图实验中，通常会研究大量的标记。这对常用的回归模型提出了挑战，因为特征变量的数量通常远远大于样本量，尤其是当要考虑上位效应时。传统逐步过程的贪婪性质是众所周知的，在这种情况下更为明显。在本文中，我们提出了一种基于惩罚似然技术和扩展贝叶斯信息准则（EBIC）的两阶段 QTL 作图方法。该方法由筛选阶段和选择阶段组成。在筛选阶段，通过惩罚似然机制交替筛选主效应和互作效应。在选择阶段，使用 EBIC 的降维方法应用于筛选阶段保留的特征，以识别 QTL。两阶段方法具有渐近性质，即随着样本量的增加，其阳性检出率（PDR）和假发现率（FDR）分别收敛到 1 和 0。通过模拟研究将两阶段方法与传统方法和最近开发的方法进行了比较。通过实际数据分析展示了两阶段方法的应用。
