Chen Zehua, Liu Jianbin
Department of Statistics & Applied Probability, National University of Singapore, Singapore.
Biometrics. 2009 Jun;65(2):470-7. doi: 10.1111/j.1541-0420.2008.01100.x. Epub 2008 Jul 18.
Quantitative trait loci mapping in experimental organisms is of great scientific and economic importance. There has been a rapid advancement in statistical methods for quantitative trait loci mapping. Various methods for normally distributed traits have been well established. Some of them have also been adapted for other types of traits such as binary, count, and categorical traits. In this article, we consider a unified mixture generalized linear model (GLIM) for multiple interval mapping in experimental crosses. The multiple interval mapping approach was proposed by Kao, Zeng, and Teasdale (1999, Genetics 152, 1203-1216) for normally distributed traits. However, its application to nonnormally distributed traits has been hindered largely by the lack of an efficient computation algorithm and an appropriate mapping procedure. In this article, an effective expectation-maximization algorithm for the computation of the mixture GLIM and an epistasis-effect-adjusted multiple interval mapping procedure is developed. A real data set, Radiata Pine data, is analyzed and the data structure is used in simulation studies to demonstrate the desirable features of the developed method.
在实验生物中进行数量性状基因座定位具有重大的科学和经济意义。数量性状基因座定位的统计方法取得了快速进展。针对正态分布性状的各种方法已经得到了很好的确立。其中一些方法也已适用于其他类型的性状，如二元性状、计数性状和分类性状。在本文中，我们考虑一种用于实验杂交中多重区间定位的统一混合广义线性模型（GLIM）。多重区间定位方法由Kao、Zeng和Teasdale（1999年，《遗传学》152卷，1203 - 1216页）针对正态分布性状提出。然而，由于缺乏有效的计算算法和合适的定位程序，其在非正态分布性状上的应用受到了很大阻碍。本文开发了一种用于混合GLIM计算的有效期望最大化算法以及一种上位效应调整后的多重区间定位程序。对一个真实数据集——辐射松数据进行了分析，并将数据结构用于模拟研究，以证明所开发方法的理想特性。
