Yang Ruifei, Prakapenka Dzianis, Liang Zuoxiang, Da Yang
Department of Animal Science, University of Minnesota, Saint Paul, MN 55108, USA.
College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
Int J Mol Sci. 2025 Jun 13;26(12):5687. doi: 10.3390/ijms26125687.
The contributions of additive, dominance, haplotype, and epistasis effects up to the third order to the accuracy of predicting daughter pregnancy rate (DPR) phenotypic values and to the phenotypic variance in U.S. Holstein cows were investigated using five samples with 25,827-133,934 cows and 74,855-75,209 SNPs. Heritability estimates showed that only additive × additive (A × A) epistasis effects had nonzero heritability and all other second- and third-order epistasis effects had zero heritability, and hence A × A was the only epistasis effects included in the prediction models. Based on the results of the largest sample with 133,934 cows, genomic heritability estimate was 0.044-0.054 for additive heritability, 0.005 for dominance heritability, 0.011-0.022 for haplotype heritability, and 0.052-0.062 for A × A heritability. The combination of additive (A) and A × A effects was the best prediction model based on the prediction accuracy. This best model improved the prediction accuracy over the A-only model by 4.88%, and had total heritability of 0.099 as the summation of the additive and A × A heritability estimates. Dominance and haplotype effects had minor contributions (0.97-2.44%) to prediction accuracy in models without A × A effects but had no contribution to prediction accuracy when A × A was in the prediction model. The partition of A × A effects into inter- and intra-chromosome A × A effects showed that inter-chromosome A × A were mainly responsible for the A × A contributions to prediction accuracy and phenotypic variance. Sample size had a major impact on prediction accuracy and the sample of 90,000 cows or 81,000 cows per training population had peak prediction accuracies that were 32.70-35.85% higher than in the sample with 25,827 cows. The largest sample with 133,934 cows had the smallest variations in prediction accuracy and slightly lower average prediction accuracy than in the sample with 90,000 cows.
利用五个包含25,827 - 133,934头奶牛和74,855 - 75,209个单核苷酸多态性（SNP）的样本，研究了加性、显性、单倍型以及直至三阶的上位性效应，对美国荷斯坦奶牛女儿怀孕率（DPR）表型值预测准确性和表型方差的贡献。遗传力估计表明，只有加性×加性（A×A）上位性效应具有非零遗传力，而所有其他二阶和三阶上位性效应的遗传力均为零，因此A×A是预测模型中唯一包含的上位性效应。基于拥有133,934头奶牛的最大样本结果，加性遗传力的基因组遗传力估计值为0.044 - 0.054，显性遗传力为0.005，单倍型遗传力为0.011 - 0.022，A×A遗传力为0.052 - 0.062。基于预测准确性，加性（A）和A×A效应的组合是最佳预测模型。这个最佳模型比仅使用A的模型将预测准确性提高了4.88%，作为加性和A×A遗传力估计值之和，其总遗传力为0.099。在没有A×A效应的模型中，显性和单倍型效应对预测准确性的贡献较小（0.97 - 2.44%），但当A×A包含在预测模型中时，它们对预测准确性没有贡献。将A×A效应分为染色体间和染色体内的A×A效应表明，染色体间的A×A效应主要负责A×A对预测准确性和表型方差的贡献。样本大小对预测准确性有重大影响，每个训练群体90,000头奶牛或81,000头奶牛的样本具有最高的预测准确性，比拥有25,827头奶牛的样本高出32.70 - 35.85%。拥有133,9
