Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada, N1G-2W1.
Department of Animal and Dairy Science, University of Georgia, Athens 30602.
J Dairy Sci. 2018 Sep;101(9):8076-8086. doi: 10.3168/jds.2017-14193. Epub 2018 Jun 21.
The success and sustainability of a breeding program incorporating genomic information is largely dependent on the accuracy of predictions. For low heritability traits, large training populations are required to achieve high accuracies of genomic estimated breeding values (GEBV). By including genotyped and nongenotyped animals simultaneously in the evaluation, the single-step genomic BLUP (ssGBLUP) approach has the potential to deliver more accurate and less biased genomic evaluations. The aim of this study was to compare the accuracy and bias of genomic predictions for various traits in Canadian Holstein cattle using ssGBLUP and multi-step genomic BLUP (msGBLUP) under different strategies, such as (1) adding genomic information of cows in the analysis, (2) testing different adjustments of the genomic relationship matrix, and (3) using a blending approach to obtain GEBV from msGBLUP. The following genomic predictions were evaluated regarding accuracy and bias: (1) GEBV estimated by ssGBLUP; (2) direct genomic value estimated by msGBLUP with polygenic effects of 5 and 20%; and (3) GEBV calculated by a blending approach of direct genomic value with estimated breeding values using polygenic effects of 5 and 20%. The effect of adding genomic information of cows in the evaluation was also assessed for each approach. When genomic information was included in the analyses, the average improvement in observed reliability of predictions was observed to be 7 and 13 percentage points for reproductive and workability traits, respectively, compared with traditional BLUP. Absolute deviation from 1 of the regression coefficient of the linear regression of de-regressed estimated breeding values on genomic predictions went from 0.19 when using traditional BLUP to 0.22 when using the msGBLUP method, and to 0.14 when using the ssGBLUP method. The use of polygenic weight of 20% in the msGBLUP slightly improved the reliability of predictions, while reducing the bias. A similar trend was observed when a blending approach was used. Adding genomic information of cows increased reliabilities, while decreasing bias of genomic predictions when using the ssGBLUP method. Differences between using a training population with cows and bulls or with only bulls for the msGBLUP method were small, likely due to the small number of cows included in the analysis. Predictions for lowly heritable traits benefit greatly from genomic information, especially when all phenotypes, pedigrees, and genotypes are used in a single-step approach.
繁殖计划的成功和可持续性在很大程度上取决于预测的准确性。对于低遗传力性状，需要大量的训练群体才能实现基因组估计育种值（GEBV）的高准确性。通过同时将基因型和非基因型动物纳入评估，单步基因组 BLUP（ssGBLUP）方法有可能提供更准确和偏差更小的基因组评估。本研究的目的是比较在加拿大荷斯坦牛中使用 ssGBLUP 和多步基因组 BLUP（msGBLUP）在不同策略下对各种性状的基因组预测的准确性和偏差，例如：（1）在分析中添加母牛的基因组信息；（2）测试基因组关系矩阵的不同调整；（3）使用混合方法从 msGBLUP 获得 GEBV。对以下基因组预测进行了准确性和偏差评估：（1）ssGBLUP 估计的 GEBV；（2）msGBLUP 估计的直接基因组值，多基因效应为 5%和 20%；（3）使用多基因效应为 5%和 20%的直接基因组值与估计育种值的混合方法计算的 GEBV。还评估了在每种方法中添加母牛基因组信息的效果。当将母牛的基因组信息纳入分析时，与传统 BLUP 相比，繁殖和工作能力性状的预测观察可靠性平均提高了 7%和 13%。从使用传统 BLUP 时的线性回归中去回归估计育种值与基因组预测的线性回归的回归系数的 1 的绝对偏差从 0.19 变为使用 msGBLUP 方法时的 0.22，而使用 ssGBLUP 方法时则变为 0.14。在 msGBLUP 中使用 20%的多基因权重略微提高了预测的可靠性，同时降低了偏差。当使用混合方法时，观察到类似的趋势。当使用 ssGBLUP 方法时，添加母牛的基因组信息会提高基因组预测的可靠性，同时降低偏差。在 msGBLUP 方法中使用包含母牛和公牛的训练群体或仅使用公牛的训练群体之间的差异很小，这可能是由于分析中包含的母牛数量较少。低遗传力性状的预测非常受益于基因组信息，特别是当所有表型、系谱和基因型都在单步方法中使用时。
