Yao Xiaoting, Li Jiaxin, Fu Jiaqi, Wang Xingquan, Ma Longgang, Nanaei Hojjat Asadollahpour, Shah Ali Mujtaba, Zhang Zhuangbiao, Bian Peipei, Zhou Shishuo, Wang Ao, Wang Xihong, Jiang Yu
Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China.
Animal Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz 7155863511, Iran.
Animals (Basel). 2025 Jan 17;15(2):261. doi: 10.3390/ani15020261.
Goats are essential to the dairy industry in Shaanxi, China, with udder traits playing a critical role in determining milk production and economic value for breeding programs. However, the direct measurement of these traits in dairy goats is challenging and resource-intensive. This study leveraged genotyping imputation to explore the genetic parameters and architecture of udder traits and assess the efficiency of genomic prediction methods. Using data from 635 Saanen dairy goats, genotyped for over 14,717,075 SNP markers and phenotyped for three udder traits, heritability was 0.16 for udder width, 0.32 for udder depth, and 0.13 for teat spacing, with genetic correlations of 0.79, 0.70, and 0.45 observed among the traits. Genome-wide association studies (GWAS) revealed four candidate genes with selection signatures linked to udder traits. Predictive models, including GBLUP, kernel ridge regression (KRR), and Adaboost.RT, were evaluated for genomic estimated breeding value (GEBV) prediction. Machine learning models (KRR and Adaboost.RT) outperformed GBLUP by 20% and 11% in predictive accuracy, showing superior stability and reliability. These results underscore the potential of machine learning approaches to enhance genomic prediction accuracy in dairy goats, providing valuable insights that could contribute to improvements in animal health, productivity, and economic outcomes within the dairy goat industry.
山羊对中国陕西的乳业至关重要，乳房性状在决定产奶量和育种计划的经济价值方面起着关键作用。然而，直接测量奶山羊的这些性状具有挑战性且资源密集。本研究利用基因分型填补技术来探索乳房性状的遗传参数和结构，并评估基因组预测方法的效率。使用来自635只萨能奶山羊的数据，对超过14,717,075个单核苷酸多态性（SNP）标记进行基因分型，并对三个乳房性状进行表型分析，乳房宽度的遗传力为0.16，乳房深度为0.32，乳头间距为0.13，各性状间的遗传相关性分别为0.79、0.70和0.45。全基因组关联研究（GWAS）揭示了四个与乳房性状相关的具有选择信号的候选基因。对包括GBLUP、核岭回归（KRR）和Adaboost.RT在内的预测模型进行了基因组估计育种值（GEBV）预测评估。机器学习模型（KRR和Adaboost.RT）在预测准确性方面比GBLUP分别高出20%和11%，显示出更高的稳定性和可靠性。这些结果强调了机器学习方法在提高奶山羊基因组预测准确性方面的潜力，提供了有价值的见解，有助于改善奶山羊产业的动物健康、生产力和经济效益。
