Xu Shizhong, Hu Zhiqiu
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.
Theor Appl Genet. 2010 Jun;121(1):47-63. doi: 10.1007/s00122-010-1290-0. Epub 2010 Feb 24.
We developed a generalized linear model of QTL mapping for discrete traits in line crossing experiments. Parameter estimation was achieved using two different algorithms, a mixture model-based EM (expectation-maximization) algorithm and a GEE (generalized estimating equation) algorithm under a heterogeneous residual variance model. The methods were developed using ordinal data, binary data, binomial data and Poisson data as examples. Applications of the methods to simulated as well as real data are presented. The two different algorithms were compared in the data analyses. In most situations, the two algorithms were indistinguishable, but when large QTL are located in large marker intervals, the mixture model-based EM algorithm can fail to converge to the correct solutions. Both algorithms were coded in C++ and interfaced with SAS as a user-defined SAS procedure called PROC QTL.
我们针对品系杂交实验中的离散性状开发了一种广义线性模型的QTL定位方法。在异质残差方差模型下，使用两种不同算法进行参数估计，一种是基于混合模型的期望最大化（EM）算法，另一种是广义估计方程（GEE）算法。以有序数据、二元数据、二项式数据和泊松数据为例开发了这些方法。展示了这些方法在模拟数据和实际数据中的应用。在数据分析中对这两种不同算法进行了比较。在大多数情况下，这两种算法难以区分，但当大的QTL位于大的标记区间时，基于混合模型的EM算法可能无法收敛到正确的解。两种算法均用C++编码，并作为名为PROC QTL的用户定义SAS过程与SAS接口。
