GenPhySE, INRA, Université de Toulouse, INPT, ENVT, 31326 Castanet Tolosan, France
GABI, INRA, AgroParisTech, Université Paris Saclay, Département Sciences du Vivant, UMR 1313, 78352 Jouy-en-Josas, France.
Genetics. 2019 Aug;212(4):1075-1099. doi: 10.1534/genetics.119.302375. Epub 2019 Jun 17.
For years, animal selection in livestock species has been performed by selecting animals based on genetic inheritance. However, evolutionary studies have reported that nongenetic information that drives natural selection can also be inherited across generations (epigenetic, microbiota, environmental inheritance). In response to this finding, the concept of inclusive heritability, which combines all sources of information inherited across generations, was developed. To better predict the transmissible potential of each animal by taking into account these diverse sources of inheritance and improve selection in livestock species, we propose the "transmissibility model." Similarly to the animal model, this model uses pedigree and phenotypic information to estimate variance components and predict the transmissible potential of an individual, but differs by estimating the path coefficients of inherited information from parent to offspring instead of using a set value of 0.5 for both the sire and the dam (additive genetic relationship matrix). We demonstrated the structural identifiability of the transmissibility model, and performed a practical identifiability and power study of the model. We also performed simulations to compare the performances of the animal and transmissibility models for estimating the covariances between relatives and predicting the transmissible potential under different combinations of sources of inheritance. The transmissibility model provided similar results to the animal model when inheritance was of genetic origin only, but outperformed the animal model for estimating the covariances between relatives and predicting the transmissible potential when the proportion of inheritance of nongenetic origin was high or when the sire and dam path coefficients were very different.
多年来,家畜品种的动物选择一直是通过选择基于遗传继承的动物来进行的。然而,进化研究报告称,驱动自然选择的非遗传信息也可以在代际间遗传(表观遗传、微生物组、环境遗传)。针对这一发现,提出了包容性遗传率的概念,该概念将代际遗传的所有信息源结合在一起。为了更好地预测每只动物的可传递潜力,同时考虑到这些不同的遗传来源,并提高家畜品种的选择效果,我们提出了“可传递性模型”。与动物模型类似,该模型使用系谱和表型信息来估计方差分量并预测个体的可传递潜力,但不同之处在于估计从亲代到后代的遗传信息的路径系数,而不是对父本和母本使用 0.5 的固定值(加性遗传关系矩阵)。我们证明了可传递性模型的结构可识别性,并对该模型进行了实际的可识别性和功效研究。我们还进行了模拟,比较了动物模型和可传递性模型在估计亲属间协方差和预测不同遗传来源组合下的可传递潜力方面的性能。当遗传仅为遗传起源时,可传递性模型提供的结果与动物模型相似,但当非遗传起源的遗传比例较高或父本和母本路径系数差异很大时,它在估计亲属间协方差和预测可传递潜力方面优于动物模型。