Sorensen D, Fernando R, Gianola D
Department of Animal Science, Iowa State University, Ames, IA 50011-3150, USA.
Genet Res. 2001 Feb;77(1):83-94. doi: 10.1017/s0016672300004845.
A method is proposed to infer genetic parameters within a cohort, using data from all individuals in an experiment. An application is the study of changes in additive genetic variance over generations, employing data from all generations. Inferences about the genetic variance in a given generation are based on its marginal posterior distribution, estimated via Markov chain Monte Carlo methods. As defined, the additive genetic variance within the group is directly related to the amount of selection response to be expected if parents are chosen within the group. Results from a simulated selection experiment are used to illustrate properties of the method. Four sets of data are analysed: directional selection with and without environmental trend, and random selection, with and without environmental trend. In all cases, posterior credibility intervals of size 95% assign relatively high density to values of the additive genetic variance and heritability in the neighbourhood of the true values. Properties and generalizations of the method are discussed.
提出了一种利用实验中所有个体的数据来推断队列内遗传参数的方法。一个应用是研究多代间加性遗传方差的变化，使用所有世代的数据。关于给定世代遗传方差的推断基于其边际后验分布，通过马尔可夫链蒙特卡罗方法进行估计。如所定义的，组内的加性遗传方差与如果在组内选择亲本时预期的选择反应量直接相关。模拟选择实验的结果用于说明该方法的性质。分析了四组数据：有和没有环境趋势的定向选择，以及有和没有环境趋势的随机选择。在所有情况下，95%大小的后验可信区间对真实值附近的加性遗传方差和遗传力值赋予了相对较高的密度。讨论了该方法的性质和推广。
