Department of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA.
Instituto Nacional de Investigación Agropecuaria (INIA), 11500, Montevideo, Uruguay.
Genet Sel Evol. 2022 Sep 27;54(1):66. doi: 10.1186/s12711-022-00752-4.
Although single-step GBLUP (ssGBLUP) is an animal model, SNP effects can be backsolved from genomic estimated breeding values (GEBV). Predicted SNP effects allow to compute indirect prediction (IP) per individual as the sum of the SNP effects multiplied by its gene content, which is helpful when the number of genotyped animals is large, for genotyped animals not in the official evaluations, and when interim evaluations are needed. Typically, IP are obtained for new batches of genotyped individuals, all of them young and without phenotypes. Individual (theoretical) accuracies for IP are rarely reported, but they are nevertheless of interest. Our first objective was to present equations to compute individual accuracy of IP, based on prediction error covariance (PEC) of SNP effects, and in turn, are obtained from PEC of GEBV in ssGBLUP. The second objective was to test the algorithm for proven and young (APY) in PEC computations. With large datasets, it is impossible to handle the full PEC matrix, thus the third objective was to examine the minimum number of genotyped animals needed in PEC computations to achieve IP accuracies that are equivalent to GEBV accuracies.
Correlations between GEBV and IP for the validation animals using SNP effects from ssGBLUP evaluations were ≥ 0.99. When all available genotyped animals were used for PEC computations, correlations between GEBV and IP accuracy were ≥ 0.99. In addition, IP accuracies were compatible with GEBV accuracies either with direct inversion of the genomic relationship matrix (G) or using the algorithm for proven and young (APY) to obtain the inverse of G. As the number of genotyped animals included in the PEC computations decreased from around 55,000 to 15,000, correlations were still ≥ 0.96, but IP accuracies were biased downwards.
Theoretical accuracy of indirect prediction can be successfully obtained by computing SNP PEC out of GEBV PEC from ssGBLUP equations using direct or APY G inverse. It is possible to reduce the number of genotyped animals in PEC computations, but accuracies may be underestimated. Further research is needed to approximate SNP PEC from ssGBLUP to limit the computational requirements with many genotyped animals.
虽然单步 GBLUP（ssGBLUP）是一种动物模型，但 SNP 效应可以从基因组估计育种值（GEBV）中反推出来。预测 SNP 效应可以为每个个体计算间接预测（IP），方法是将 SNP 效应乘以其基因含量，当基因型动物数量较大、未在官方评估中基因型动物和需要中期评估时，这很有帮助。通常，为新一批基因型个体获得 IP，它们都是年轻的，没有表型。个体（理论）准确性的 IP 很少被报道，但它们仍然是有趣的。我们的第一个目标是根据 SNP 效应的预测误差协方差（PEC），提出计算 IP 个体准确性的方程，反过来，这些方程又可以从 ssGBLUP 中的 GEBV PEC 中获得。第二个目标是在 PEC 计算中测试已证明和年轻（APY）算法。对于大型数据集，不可能处理完整的 PEC 矩阵，因此第三个目标是检查 PEC 计算中所需的最小基因型动物数量，以达到与 GEBV 准确性相当的 IP 准确性。
使用 ssGBLUP 评估中的 SNP 效应对验证动物进行 GEBV 和 IP 之间的相关性为 ≥ 0.99。当所有可用的基因型动物都用于 PEC 计算时，GEBV 和 IP 准确性之间的相关性为 ≥ 0.99。此外，IP 准确性与 GEBV 准确性兼容，无论是直接反转基因组关系矩阵（G）还是使用已证明和年轻（APY）算法获得 G 的逆。随着 PEC 计算中包括的基因型动物数量从大约 55000 减少到 15000，相关性仍然为 ≥ 0.96，但 IP 准确性存在向下偏差。
可以通过从 ssGBLUP 方程中的 GEBV PEC 计算 SNP PEC，使用直接或 APY G 逆成功获得间接预测的理论准确性。可以减少 PEC 计算中的基因型动物数量，但准确性可能会被低估。需要进一步研究，以便从 ssGBLUP 近似 SNP PEC，从而减少大量基因型动物的计算要求。
