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首页> 外文期刊>Journal of genetics >An improved procedure of mapping a quantitative trait locus via the EM algorithm using posterior probabilities
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An improved procedure of mapping a quantitative trait locus via the EM algorithm using posterior probabilities

机译：使用后验概率通过EM算法绘制定量性状基因座的改进程序

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Mapping a locus controlling a quantitative genetic trait (e.g. blood pressure) to a specific genomic region is of considerable contemporary interest. Data on the quantitative trait under consideration and several codominant genetic markers with known genomic locations are collected from members of families and statistically analysed to estimate the recombination fraction, e???, between the putative quantitative trait locus and a genetic marker. One of the major complications in estimating e??? for a quantitative trait in humans is the lack of haplotype information on members of families. We have devised a computationally simple two-stage method of estimation of e??? in the absence of haplotypic information using the expectation-maximization (EM) algorithm. In the first stage, parameters of the quantitative trait locus (QTL) are estimated on the basis of data of a sample of unrelated individuals and a Bayes's rule is used to classify each parent into a QTL genotypic class. In the second stage, we have proposed an EM algorithm for obtaining the maximum-likelihood estimate of e??? based on data of informative families (which are identified upon inferring parental QTL genotypes performed in the first stage). The purpose of this paper is to investigate whether, instead of using genotypically `classified' data of parents, the use of posterior probabilities of QT genotypes of parents at the second stage yields better estimators. We show, using simulated data, that the proposed procedure using posterior probabilities is statistically more efficient than our earlier classification procedure, although it is computationally heavier.
机译：将控制定量遗传特征（例如血压）的基因座映射到特定基因组区域具有相当大的当代意义。从家庭成员中收集有关所考虑的数量性状和几种具有已知基因组位置的遗传标记的数据，并进行统计分析以估计推定的数量性状基因座和遗传标记之间的重组率。估计e的主要并发症之一？？？人类定量特征的原因是缺乏家庭成员的单体型信息。我们设计了一种估计e的计算简单的两阶段方法。在没有单倍型信息的情况下，使用期望最大化（EM）算法。在第一阶段，基于不相关个体样本的数据估算定量性状基因座（QTL）的参数，并使用贝叶斯规则将每个父母分类为QTL基因型类别。在第二阶段，我们提出了一种EM算法来获得e的最大似然估计。基于信息性家族的数据（这些信息可通过推断在第一阶段进行的父母QTL基因型确定）。本文的目的是调查在第二阶段使用父母QT基因型的后验概率而不是使用基因型的父母“分类”数据是否能产生更好的估计。我们使用模拟数据显示，尽管计算量较大，但使用后验概率的拟议程序在统计上比我们之前的分类程序更有效。

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