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Comparison of Two Statistical Methods for Detecting Quantitative Trait Genes

机译：两种检测数量性状基因的统计方法的比较

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摘要

Methods based on classical linear regression models and maximum likelihood principles have been well studied in the detection of quantitative trait genes or loci (QTL). Recently,Bayesian models have gained some popularity among theoreticians, which result in publication of many papers. Empirical Bayesian (E-Bayes) method is one of the latest Bayesian models. In this paper, we compare by extensive simulations the E-Bayes method with inclusive composite interval mapping (ICIM), which was proposed to improve the algorithm of traditional composite interval mapping. The results indicated that E-Bayes have no significant advantages,compared with ICIM,although E-Bayes saved a lot of computation time compared with the classic Bayesian model.
机译：基于经典线性回归模型和最大似然原理的方法已经在定量性状基因或基因座（QTL）的检测中得到了很好的研究。最近，贝叶斯模型在理论家中受到了欢迎，这导致了许多论文的发表。经验贝叶斯（E-Bayes）方法是最新的贝叶斯模型之一。在本文中，我们通过广泛的仿真将E-Bayes方法与包含性复合间隔映射（ICIM）进行了比较，该方法旨在改进传统的复合间隔映射算法。结果表明，尽管与传统的贝叶斯模型相比，E-Bayes节省了大量的计算时间，但与ICIM相比，E-Bayes并没有明显的优势。

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