A hierarchical statistical model for estimating population properties of quantitative genes

BACKGROUND: Earlier methods for detecting major genes responsible for a quantitative trait rely critically upon a well-structured pedigree in which the segregation pattern of genes exactly follow Mendelian inheritance laws. However, for many outcrossing species, such pedigrees are not available and genes also display population properties. RESULTS: In this paper, a hierarchical statistical model is proposed to monitor the existence of a major gene based on its segregation and transmission across two successive generations. The model is implemented with an EM algorithm to provide maximum likelihood estimates for genetic parameters of the major locus. This new method is successfully applied to identify an additive gene having a large effect on stem height growth of aspen trees. The estimates of population genetic parameters for this major gene can be generalized to the original breeding population from which the parents were sampled. A simulation study is presented to evaluate finite sample properties of the model. CONCLUSIONS: A hierarchical model was derived for detecting major genes affecting a quantitative trait based on progeny tests of outcrossing species. The new model takes into account the population genetic properties of genes and is expected to enhance the accuracy, precision and power of gene detection.

背景：以往用于检测数量性状主效基因的方法，核心依赖于结构严谨的系谱群体，其中基因的分离模式严格遵循孟德尔遗传定律。但对于许多异交物种而言，这类完善的系谱往往难以获取，且基因同时表现出群体遗传学特性。
结果：本文提出一种分层统计模型，基于主效基因在两个连续世代中的分离与传递规律，对其存在性进行检测。该模型借助EM（期望最大化，Expectation-Maximization）算法实现，可为主效位点的遗传参数提供极大似然估计。本方法已成功应用于鉴定一个对山杨茎高生长具有显著效应的加性基因。该主效基因的群体遗传参数估计值可推广至亲本采样所在的原始育种群体。此外，本文还通过模拟试验评估了该模型的有限样本性能。
结论：本研究针对异交物种的后裔测验场景，构建了用于检测影响数量性状主效基因的分层模型。该新模型充分考虑了基因的群体遗传学特性，有望提升基因检测的准确性、精确性与检验效能。