Gowane G R, Alex Rani, Worku Destaw, Chhotaray Supriya, Mukherjee Anupama, Vohra Vikas
Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, 132001, Haryana, India.
Department of Animal Science, College of Agriculture, Food and Climate Science, Injibara University, Injibara, Ethiopia.
Trop Anim Health Prod. 2025 Apr 1;57(3):149. doi: 10.1007/s11250-025-04407-6.
Genomic prediction is crucial in the developed dairy industry, but implementing it in resource-poor regions with numerically small breeds and with no historic pedigree information is challenging. This study explores possibilities for joint genomic prediction, using genomic best linear unbiased prediction (GBLUP) across four closely related breeds for sex-limited traits when recently collected genomic information and phenotypes are available. The data was simulated to cover low (0.1) and moderate (0.3) heritability scenarios. Principal Component Analysis (PCA) revealed genetic relatedness among breeds, with the first two components explaining 80% of variance. Combining breeds for genetic evaluation using only genomic information enhanced prediction accuracy and reduced bias in genomically estimated breeding values (GEBV) compared to single-breed models. Ancestry-specific allele frequencies and allelic effects had minimal impact due to genetic similarity between breeds. Multi-breed evaluation substantially improved accuracy. The multi-breed two-tailed selective genotyping model (MTB) had better accuracy of prediction than top-selected (MTOP) and randomly selected (MRND) models. However, looking into standard error for accuracy of prediction of GEBV and least bias of prediction, MRND model is recommended for multi-breed joint prediction evaluation in numerically small breeds. For 0.3 h scenario, MTOP gained 17.89% accuracy, MTB gained 20%, and MRND gained 24.39% over single breed models. Similar trends were seen in the low heritability (0.1) scenario. For small breeds without pedigree records data, adopting a multi-breed joint evaluation with random selective genotyping is recommended. This strategy has potential to integrate crucial breeds into genomic selection while conserving resources in genotyping and data recording in resource-poor regions.
基因组预测在发达的奶牛养殖业中至关重要，但在资源匮乏地区，对于数量较少且没有历史系谱信息的品种而言，实施基因组预测具有挑战性。本研究探讨了联合基因组预测的可能性，在有近期收集的基因组信息和表型数据的情况下，使用基因组最佳线性无偏预测（GBLUP）方法对四个密切相关品种的限性性状进行分析。数据模拟涵盖了低遗传力（0.1）和中等遗传力（0.3）的情况。主成分分析（PCA）揭示了品种间的遗传相关性，前两个成分解释了80%的方差。与单品种模型相比，仅使用基因组信息进行遗传评估时，合并品种可提高预测准确性并减少基因组估计育种值（GEBV）的偏差。由于品种间的遗传相似性，特定祖先的等位基因频率和等位基因效应影响极小。多品种评估显著提高了准确性。多品种双尾选择基因分型模型（MTB）的预测准确性优于顶级选择模型（MTOP）和随机选择模型（MRND）。然而，考虑到GEBV预测准确性的标准误差和最小预测偏差，对于数量较少的品种进行多品种联合预测评估，建议使用MRND模型。在遗传力为0.3的情况下，与单品种模型相比，MTOP的准确性提高了17.89%，MTB提高了20%，MRND提高了24.39%。在低遗传力（0.1）情况下也观察到了类似趋势。对于没有系谱记录数据的小品种而言，建议采用随机选择基因分型的多品种联合评估。这种策略有可能将关键品种纳入基因组选择，同时在资源匮乏地区的基因分型和数据记录中节省资源。
