Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA.
Smithfield Premium Genetics, Rose Hill, NC, USA.
J Anim Sci. 2021 Apr 1;99(4). doi: 10.1093/jas/skab085.
Genomic information has a limited dimensionality (number of independent chromosome segments [Me]) related to the effective population size. Under the additive model, the persistence of genomic accuracies over generations should be high when the nongenomic information (pedigree and phenotypes) is equivalent to Me animals with high accuracy. The objective of this study was to evaluate the decay in accuracy over time and to compare the magnitude of decay with varying quantities of data and with traits of low and moderate heritability. The dataset included 161,897 phenotypic records for a growth trait (GT) and 27,669 phenotypic records for a fitness trait (FT) related to prolificacy in a population with dimensionality around 5,000. The pedigree included 404,979 animals from 2008 to 2020, of which 55,118 were genotyped. Two single-trait models were used with all ancestral data and sliding subsets of 3-, 2-, and 1-generation intervals. Single-step genomic best linear unbiased prediction (ssGBLUP) was used to compute genomic estimated breeding values (GEBV). Estimated accuracies were calculated by the linear regression (LR) method. The validation population consisted of single generations succeeding the training population and continued forward for all generations available. The average accuracy for the first generation after training with all ancestral data was 0.69 and 0.46 for GT and FT, respectively. The average decay in accuracy from the first generation after training to generation 9 was -0.13 and -0.19 for GT and FT, respectively. The persistence of accuracy improves with more data. Old data have a limited impact on the predictions for young animals for a trait with a large amount of information but a bigger impact for a trait with less information.
基因组信息的维度有限（与有效种群大小相关的独立染色体片段数量 [Me]）。在加性模型下，当非基因组信息（系谱和表型）与具有高精度的 Me 动物等同时，基因组准确性在世代间的持续时间应该很高。本研究的目的是评估随着时间的推移准确性的衰减，并比较衰减的幅度与不同数量的数据以及低和中度遗传力的性状。数据集包括一个群体中与繁殖力相关的生长性状（GT）的 161,897 个表型记录和一个适应性状（FT）的 27,669 个表型记录，该群体的维度约为 5000。系谱包括 2008 年至 2020 年的 404,979 只动物，其中 55,118 只进行了基因分型。使用所有祖先数据和 3、2 和 1 个世代间隔的滑动子集，使用两个单性状模型。单步基因组最佳线性无偏预测（ssGBLUP）用于计算基因组估计育种值（GEBV）。估计准确性通过线性回归（LR）方法计算。验证群体由继训练群体之后的单个世代组成，并继续进行所有可用的世代。使用所有祖先数据进行训练后的第一代的平均准确性分别为 0.69 和 0.46，分别用于 GT 和 FT。从训练后的第一代到第 9 代的平均准确性衰减分别为 -0.13 和 -0.19，分别用于 GT 和 FT。准确性的持久性随着数据的增加而提高。对于信息量较大的性状，旧数据对年轻动物的预测影响有限，但对于信息量较小的性状，旧数据的影响更大。
