Xu S
Department of Botany and Plant Sciences, University of California, Riverside 92521, USA.
Heredity (Edinb). 1998 Mar;80 ( Pt 3):364-73. doi: 10.1046/j.1365-2540.1998.00307.x.
The simple regression method of mapping quantitative trait loci (QTL) is further investigated in comparison with the mixture model maximum likelihood method under high heritabilities, dominant and missing markers. No significant difference between the two methods is detected in terms of errors of parameter estimation and statistical powers, with the exception that the estimation of residual variance provided by the regression method is confounded with part of the QTL variance. The test statistic profiles show some difference between the two methods, but the difference is only detectable at the micro level. An alternative method, referred to as iteratively reweighted least squares, is proposed, which can correct the deficiency of parameter confounding in the regression method yet retains the properties of simplicity and rapidity of the ordinary regression method. Like the existing regression method, the weighted least squares method can be useful in QTL mapping in conjunction with the permutation tests and construction of confidence intervals by bootstrapping.
在高遗传力、显性和缺失标记的情况下，将映射数量性状基因座（QTL）的简单回归方法与混合模型最大似然方法进行了进一步比较。在参数估计误差和统计功效方面，未检测到两种方法之间存在显著差异，但回归方法提供的残差方差估计与部分QTL方差混淆。检验统计量概况显示两种方法之间存在一些差异，但这种差异仅在微观层面可检测到。提出了一种替代方法，称为迭代加权最小二乘法，它可以纠正回归方法中参数混淆的缺陷，同时保留普通回归方法简单快速的特性。与现有的回归方法一样，加权最小二乘法结合排列检验和通过自助法构建置信区间，可用于QTL映射。
