采用双层层次广义线性模型估计宏观和微观环境敏感性的遗传方差。
Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models.
机构信息
Wageningen UR Livestock Research, Animal Breeding and Genomics Centre, PO Box 65, 8200 AB, Lelystad, The Netherlands.
出版信息
Genet Sel Evol. 2013 Jul 4;45(1):23. doi: 10.1186/1297-9686-45-23.
BACKGROUND
Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike's information criterion using h-likelihood to select the best fitting model.
METHODS
We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike's information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters.
RESULTS
Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike's information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed.
CONCLUSION
The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.
背景
环境敏感性的遗传变异表明，动物在对环境因素的反应上存在遗传差异。环境因素要么是可识别的（例如温度），称为宏环境因素，要么是未知的，称为微环境因素。本研究的目的是开发一种统计方法，同时估计宏环境和微环境敏感性的遗传参数，研究遗传参数估计的偏差和精度，并使用 h 似然来开发和评估 Akaike 信息准则，以选择最佳拟合模型。
方法
我们假设宏环境和微环境敏感性的遗传变异表现为线性反应规范斜率的遗传方差和环境方差。使用 ASReml 中的双层广义线性模型，将估计宏环境敏感性遗传方差的反应规范模型与用于估计微环境敏感性遗传方差的剩余方差结构模型相结合。使用近似 h 似然构建了作为模型选择准则的 Akaike 信息准则。模拟了具有大量半同胞后代群体的父本群体，以研究估计遗传参数的偏差和精度。
结果
需要设计 100 个父本，每个父本至少有 100 个后代，才能使估计方差的标准差低于真实值的 50%。随着后代数量的增加，跨重复的估计标准差大大降低，特别是对于宏环境和微环境敏感性的遗传方差。遗传相关估计的跨重复标准差相当大（在 0.1 到 0.4 之间），尤其是当父本后代较少时。实际上，对于任何参数的估计都没有观察到偏差。当每个父本的后代数量为 100 时，使用 Akaike 信息准则，在 100 次重复中的至少 90%的情况下，真实的遗传模型被选为最佳统计模型。将该模型应用于奶牛泌乳奶产量表明，微环境和宏环境敏感性的遗传方差是存在的。
结论
本文提出的算法和模型选择准则有助于更好地理解宏环境和微环境敏感性的遗传控制。设计或数据集应至少有 100 个父本，每个父本至少有 100 个后代。
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