INRA, UMR 1313 Génétique Animale et Biologie Intégrative, F-78350 Jouy-en-Josas, France.
Genet Sel Evol. 2011 Aug 18;43(1):30. doi: 10.1186/1297-9686-43-30.
In future Best Linear Unbiased Prediction (BLUP) evaluations of dairy cattle, genomic selection of young sires will cause evaluation biases and loss of accuracy once the selected ones get progeny.
To avoid such bias in the estimation of breeding values, we propose to include information on all genotyped bulls, including the culled ones, in BLUP evaluations. Estimated breeding values based on genomic information were converted into genomic pseudo-performances and then analyzed simultaneously with actual performances. Using simulations based on actual data from the French Holstein population, bias and accuracy of BLUP evaluations were computed for young sires undergoing progeny testing or genomic pre-selection. For bulls pre-selected based on their genomic profile, three different types of information can be included in the BLUP evaluations: (1) data from pre-selected genotyped candidate bulls with actual performances on their daughters, (2) data from bulls with both actual and genomic pseudo-performances, or (3) data from all the genotyped candidates with genomic pseudo-performances. The effects of different levels of heritability, genomic pre-selection intensity and accuracy of genomic evaluation were considered.
Including information from all the genotyped candidates, i.e. genomic pseudo-performances for both selected and culled candidates, removed bias from genetic evaluation and increased accuracy. This approach was effective regardless of the magnitude of the initial bias and as long as the accuracy of the genomic evaluations was sufficiently high.
The proposed method can be easily and quickly implemented in BLUP evaluations at the national level, although some improvement is necessary to more accurately propagate genomic information from genotyped to non-genotyped animals. In addition, it is a convenient method to combine direct genomic, phenotypic and pedigree-based information in a multiple-step procedure.
在未来的奶牛最佳线性无偏预测(BLUP)评估中,一旦经过基因组选择的年轻公牛拥有后代,就会导致评估偏差和准确性降低。
为了避免在估计育种值时出现这种偏差,我们建议在 BLUP 评估中包含所有已基因分型公牛的信息,包括淘汰的公牛。基于基因组信息的估计育种值被转换为基因组伪性能,然后与实际性能同时进行分析。利用基于法国荷斯坦牛群体实际数据的模拟,计算了经历后裔测定或基因组预选的年轻公牛的 BLUP 评估的偏差和准确性。对于基于基因组谱预选的公牛,可以在 BLUP 评估中包含三种不同类型的信息:(1)具有实际女儿性能的预选基因分型候选公牛的数据,(2)具有实际和基因组伪性能的公牛的数据,或(3)具有基因组伪性能的所有基因分型候选公牛的数据。考虑了不同遗传力水平、基因组预选强度和基因组评估准确性的影响。
包含所有基因分型候选公牛的信息,即选择和淘汰候选公牛的基因组伪性能,消除了遗传评估中的偏差并提高了准确性。这种方法在初始偏差的幅度大小无关紧要,只要基因组评估的准确性足够高,就非常有效。
该方法可以在国家一级的 BLUP 评估中轻松快速地实施,尽管需要进行一些改进以更准确地从基因分型动物传播基因组信息。此外,它是一种方便的方法,可以在多步骤过程中结合直接的基因组、表型和系谱信息。