Han L, Xu S
Department of Botany and Plant Science, University of California, Riverside, CA 92521, USA.
Heredity (Edinb). 2008 Nov;101(5):453-64. doi: 10.1038/hdy.2008.78. Epub 2008 Aug 13.
An improved weighted least square (LS) method for quantitative trait loci (QTL) mapping using the estimating equation (EE) algorithm was developed recently. The method is more efficient than both the LS and the weighted LS methods and slightly less efficient than the mixture model maximum likelihood (ML) method. The iteration process of the EE algorithm is implicit. We developed a Fisher-scoring algorithm for the weighted LS method. The iteration process is explicit and easy to program. In addition, the method automatically provides an approximate variance-covariance matrix for the estimated QTL parameters as a by-product of the iteration process. As a consequence, a W-test statistic can be used for testing the significance of QTL. To compare the Fisher scoring algorithm with the expectation maximization (EM)-based ML method, we also developed a slightly simplified method to compute the variance-covariance matrix of the estimated parameters under the EM algorithm.
最近开发了一种改进的加权最小二乘法（LS），用于使用估计方程（EE）算法进行数量性状基因座（QTL）定位。该方法比LS法和加权LS法都更有效，比混合模型最大似然（ML）法效率略低。EE算法的迭代过程是隐式的。我们为加权LS法开发了一种Fisher评分算法。迭代过程是显式的，易于编程。此外，该方法自动提供估计的QTL参数的近似方差协方差矩阵，作为迭代过程的副产品。因此，可以使用W检验统计量来检验QTL的显著性。为了将Fisher评分算法与基于期望最大化（EM）的ML法进行比较，我们还开发了一种稍微简化的方法来计算EM算法下估计参数的方差协方差矩阵。
