Instituto Nacional de Investigación Agropecuaria (INIA), Montevideo, Uruguay.
Cátedra de Mejora y Conservación de Recursos Genéticos e Instituto de Investigación sobre Producción Agropecuaria, Ambiente y Salud, Facultad de Ciencias Agrarias, UNLZ, Buenos Aires, Argentina.
J Anim Breed Genet. 2020 Jul;137(4):356-364. doi: 10.1111/jbg.12470. Epub 2020 Feb 20.
Model-based accuracy, defined as the theoretical correlation between true and estimated breeding value, can be obtained for each individual as a function of its prediction error variance (PEV) and inbreeding coefficient F, in BLUP, GBLUP and SSGBLUP genetic evaluations. However, for computational convenience, inbreeding is often ignored in two places. First, in the computation of reliability = 1-PEV/(1 + F). Second, in the set-up, using Henderson's rules, of the inverse of the pedigree-based relationship matrix A. Both approximations have an effect in the computation of model-based accuracy and result in wrong values. In this work, first we present a reminder of the theory and extend it to SSGBLUP. Second, we quantify the error of ignoring inbreeding with real data in three scenarios: BLUP evaluation and SSGBLUP in Uruguayan dairy cattle, and BLUP evaluations in a line of rabbit closed for >40 generations with steady increase of inbreeding up to an average of 0.30. We show that ignoring inbreeding in the set-up of the A-inverse is equivalent to assume that non-inbred animals are actually inbred. This results in an increase of apparent PEV that is negligible for dairy cattle but considerable for rabbit. Ignoring inbreeding in reliability = 1-PEV/(1 + F) leads to underestimation of reliability for BLUP evaluations, and this underestimation is very large for rabbit. For SSGBLUP in dairy cattle, it leads to both underestimation and overestimation of reliability, both for genotyped and non-genotyped animals. We strongly recommend to include inbreeding both in the set-up of A-inverse and in the computation of reliability from PEVs.
基于模型的准确性，定义为真实和估计育种值之间的理论相关性，可以作为每个个体的预测误差方差 (PEV) 和近交系数 F 的函数在 BLUP、GBLUP 和 SSGBLUP 遗传评估中获得。然而，为了计算方便，近交通常在两个地方被忽略。首先，在计算可靠性=1-PEV/(1+F)时。其次，在使用亨德森规则设置基于系谱的关系矩阵 A 的逆时。这两个近似都会对基于模型的准确性计算产生影响，并导致错误的值。在这项工作中，我们首先回顾了理论并将其扩展到 SSGBLUP。其次，我们使用实际数据在三个场景中量化了忽略近交的误差：乌拉圭奶牛的 BLUP 评估和 SSGBLUP，以及 40 多代近交稳定增加到平均 0.30 的兔子封闭系的 BLUP 评估。我们表明，在 A 逆的设置中忽略近交相当于假设非近交动物实际上是近交的。这会导致表观 PEV 的增加，对于奶牛来说可以忽略不计，但对于兔子来说则相当可观。在 BLUP 评估中忽略可靠性=1-PEV/(1+F)会导致可靠性的低估，对于兔子来说，这种低估非常大。对于奶牛的 SSGBLUP，它会导致基因型和非基因型动物的可靠性都被低估和高估。我们强烈建议在 A 逆的设置中包含近交，并从 PEV 计算可靠性。
