Thompson R
Biometrics. 1976 Jun;32(2):283-304.
The design of experiments to estimate heritability when data are available on both parents and offspring and the offspring data have a hierarchical structure is considered. Univariate maximum likelihood (ML) estimation is discussed, and extensions to the multivariate case are outlined. The efficiency of ML estimation is evaluated in cases where simple regression estimators are available. Optimum designs for ML estimation are given when various strategies of selecting and mating are followed. The variance of the heritability estimate can be approximately halved relative to designs in which no selection of parents is done.
本文考虑了在父母和后代数据均可用且后代数据具有层次结构时，用于估计遗传力的实验设计。讨论了单变量最大似然（ML）估计，并概述了向多变量情况的扩展。在存在简单回归估计量的情况下，评估了ML估计的效率。给出了遵循各种选择和交配策略时ML估计的最优设计。相对于不进行亲本选择的设计，遗传力估计的方差可大约减半。
