Cuyabano Beatriz Castro Dias, Rovere Gabriel, Lim Dajeong, Kim Tae Hun, Lee Hak Kyo, Lee Seung Hwan, Gondro Cedric
Department of Animal Science, Michigan State University, 474 S Shaw Ln, East Lansing, MI 48824, USA.
French National Institute for Agriculture, Food, and Environnement (INRAE), Génétique Animale et Biologie Intégrative (GABI), (Current Institution), 78350 Jouy-en-Josas, France.
Animals (Basel). 2021 Jul 9;11(7):2050. doi: 10.3390/ani11072050.
It is widely known that the environment influences phenotypic expression and that its effects must be accounted for in genetic evaluation programs. The most used method to account for environmental effects is to add herd and contemporary group to the model. Although generally informative, the herd effect treats different farms as independent units. However, if two farms are located physically close to each other, they potentially share correlated environmental factors. We introduce a method to model herd effects that uses the physical distances between farms based on the Global Positioning System (GPS) coordinates as a proxy for the correlation matrix of these effects that aims to account for similarities and differences between farms due to environmental factors. A population of Hanwoo Korean cattle was used to evaluate the impact of modelling herd effects as correlated, in comparison to assuming the farms as completely independent units, on the variance components and genomic prediction. The main result was an increase in the reliabilities of the predicted genomic breeding values compared to reliabilities obtained with traditional models (across four traits evaluated, reliabilities of prediction presented increases that ranged from 0.05 ± 0.01 to 0.33 ± 0.03), suggesting that these models may overestimate heritabilities. Although little to no significant gain was obtained in phenotypic prediction, the increased reliability of the predicted genomic breeding values is of practical relevance for genetic evaluation programs.
众所周知，环境会影响表型表达，并且在遗传评估计划中必须考虑其影响。考虑环境影响最常用的方法是在模型中加入畜群和同期组。尽管畜群效应通常具有参考价值，但它将不同农场视为独立单位。然而，如果两个农场地理位置彼此靠近，它们可能共享相关的环境因素。我们引入了一种对畜群效应进行建模的方法，该方法使用基于全球定位系统（GPS）坐标的农场之间的物理距离作为这些效应相关矩阵的代理，旨在考虑由于环境因素导致的农场之间的异同。使用一群韩牛来评估将畜群效应建模为相关效应（与假设农场为完全独立单位相比）对方差分量和基因组预测的影响。主要结果是，与传统模型获得的可靠性相比，预测的基因组育种值的可靠性有所提高（在所评估的四个性状中，预测可靠性的提高范围为0.05±0.01至0.33±0.03），这表明这些模型可能高估了遗传力。尽管在表型预测方面几乎没有或没有获得显著收益，但预测的基因组育种值可靠性的提高对遗传评估计划具有实际意义。
