基于贝叶斯统计的谷物胚乳性状QTL多区间作图方法

贝叶斯统计学已被广泛地应用在现代科学的各个研究领域。本研究将贝叶斯统计方法和谷物三倍体胚乳性状数量遗传模型相结合，以F₂群体中各植株的分子标记基因型以及植株上若干粒自交种子胚乳性状的单粒观测值为数据模式，提出了胚乳数量性状基因座(QTL)多区间作图的贝叶斯方法。该方法首先构建胚乳性状的多区间多QTL遗传模型，然后通过基于Gibbs抽样和Metropolis-Hastings算法实现的马尔可夫链蒙特卡罗(MCMC)方法同时获得多个QTL效应和位置的估计。方法的有效性通过一条长染色体的模拟实验进行了验证，结果表明，本文提出的贝叶斯多区间方法能够准确地估计胚乳性状QTL的位置和效应，并可有效区分两种显性效应。

Bayesian Statistics-Based Multiple Interval Mapping of QTL Controlling Endosperm Traits in Cereals

The endosperm of plants is a major source of food, feed and industrial raw materials. The genetic analysis of endosperm traits poses numerous challenges due to its complex genetic composition and unique physical and developmental properties. Modern molecular techniques and statistical methods have greatly improved the mapping of quantitative trait loci (QTL) underlying endosperm traits. In recent years, Bayesian statistics-based analyzing methods have been developed for mapping QTL underlying diploid quantitative traits, but these methods have not been effective to the mapping of triploid endosperm characters. On the basis f Bayesian statistics and quantitative genetic model of triploid endosperm traits, a Bayesian multiple interval method for mapping QTL underlying endosperm traits was proposed. This method used the DNA molecular marker genotypes of each plant in F₂ segregation population and the single endosperm observation of a few endosperms of each plant as data set to analyze endosperm QTL. After constructing the multiple-QTL model, the Bayesian estimates of multiple QTL position and effects were obtained through MCMC algorithm implementing via Gibbs and Metropolis-Hastings sampling. The validation of the statistical procedure was verified through chromosome level simulation studies. The results showed that the proposed Bayesian method can estimate the multiple QTL positions and effects as well as distinguish the two dominance effects.