Shi Rui, Brito Luiz F, Li Shanshan, Han Liyun, Guo Gang, Wen Wan, Yan Qingxia, Chen Shaohu, Wang Yachun
State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, MARA, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; Animal Breeding and Genomics Group, Wageningen University & Research, 6700 AH Wageningen, the Netherlands.
Department of Animal Sciences, Purdue University, West Lafayette, IN 47907.
J Dairy Sci. 2025 Jan;108(1):707-725. doi: 10.3168/jds.2024-25121. Epub 2024 Oct 29.
Accurate genomic predictions of breeding values for traits included in the selection indexes are paramount for optimizing genetic progress in populations under selection. The size of the reference populations is a major factor influencing the accuracy of genomic predictions, which is even more important for lowly heritable traits, such as fertility and reproduction indicators. Combining data from different geographical regions or countries can be beneficial for genomic prediction of these lowly heritable traits. Therefore, the objectives of this study were to (1) evaluate the benefits of performing across-regional genomic evaluations for reproduction traits in Chinese Holstein cattle and (2) assess the feasibility of validating genomic predictions across environments based on reaction norm models (RNM) and the linear regression (LR) method after accounting for genotype-by-environment interactions. Phenotypic records from 194,574 cows collected across 47 farms located in 2 regions of China were used for this study. The reference and validation populations were defined based on birth year for applying the LR validation method. The traits evaluated included: interval from first to last insemination (IFL), conception rate at the first insemination (CR_f), and number of inseminations (NS) recorded in heifers and first-parity cows. The results indicated that combining data from different regions resulted in greater genomic prediction accuracies compared with using data from single regions, with increases ranging from 2.74% to 93.81%. This improvement was particularly notable for the region with the least amount of available data, where the increases ranged from 26.49% to 93.81%. Furthermore, the predictive abilities could be validated for all studied traits based on the LR method across different environments when fitting RNM. The prediction accuracies and bias of genomic breeding values based on RNM were better than regular single-trait animal models in extreme climatic conditions for IFL and NS, whereas limited increases in predictive abilities were observed for CR_f. Across-regional genomic prediction by RNM can account for genotype-by-environment interactions, potentially increase the accuracy of genomic prediction, and predict the performances of individuals in the environments with limited phenotypic data available.
对选择指数中包含的性状进行准确的育种值基因组预测，对于优化选择群体的遗传进展至关重要。参考群体的大小是影响基因组预测准确性的一个主要因素，对于低遗传力性状（如繁殖力和繁殖指标）而言更为重要。合并来自不同地理区域或国家的数据，对于这些低遗传力性状的基因组预测可能有益。因此，本研究的目的是：（1）评估在中国荷斯坦奶牛中对繁殖性状进行跨区域基因组评估的益处；（2）在考虑基因型与环境互作后，基于反应规范模型（RNM）和线性回归（LR）方法评估跨环境验证基因组预测的可行性。本研究使用了来自中国2个地区47个农场的194,574头奶牛的表型记录。基于出生年份定义参考群体和验证群体，以应用LR验证方法。评估的性状包括：首次配种至最后一次配种的间隔时间（IFL）、首次配种时的受胎率（CR_f）以及小母牛和头胎母牛记录的配种次数（NS）。结果表明，与使用单个区域的数据相比，合并不同区域的数据可提高基因组预测准确性，提高幅度在2.74%至93.81%之间。对于可用数据量最少的区域，这种提高尤为显著，提高幅度在26.49%至93.81%之间。此外，在拟合RNM时，基于LR方法可以在不同环境下对所有研究性状的预测能力进行验证。在极端气候条件下，对于IFL和NS，基于RNM的基因组育种值预测准确性和偏差优于常规单性状动物模型，而对于CR_f，预测能力的提高有限。通过RNM进行跨区域基因组预测可以考虑基因型与环境互作，有可能提高基因组预测的准确性，并预测在可用表型数据有限的环境中个体的表现。
