Sahana Varadanayakanahalli N, Gowane Gopal Ramdasji, Nayee Nilesh, Khan Kashif Dawood, Yadav Ashish, Paul Rinki, Vohra Vikas, Alex Rani
Division of Animal Genetics and Breeding, ICAR-National Dairy Research Institute, Karnal, 132001, Haryana, India.
National Dairy Development Board, Anand, 388001, Gujarat, India.
Sci Rep. 2025 Aug 26;15(1):31421. doi: 10.1038/s41598-025-15733-y.
Genomic selection has proven effective for accelerating genetic improvement in livestock, but its application in developing countries faces challenges, particularly for numerically small breeds, wherein, establishing large, single-breed reference population is difficult. To address this limitation, current study aimed to investigate the effectiveness of multi-breed reference populations to improve the genomic prediction accuracy in numerically small breeds with limited phenotypic and genomic resources. Genotypic and phenotypic data from 1,298 Gir, 1,291 Sahiwal, and 500 Kankrej indigenous cattle were used to enhance the genomic prediction accuracy in Kankrej, utilizing a multi-breed reference population. Principal Component Analysis (PCA)-K-means-based clustering showed overlap between Gir and Kankrej, indicating genetic similarity between these two breeds. Linkage disequilibrium (LD) decay patterns corroborated these results, showing a similar trend in the LD decay plot in the Gir-Kankrej, suggesting shared haplotype blocks that can be utilized in combined analyses. Heritability estimates for 305-day first lactation milk yield (305-DMY) were 0.30 ± 0.07 for Gir, 0.27 ± 0.07 for Sahiwal, and 0.17 ± 0.01 for Kankrej cattle. Genomic estimated breeding values (GEBV) with single-breed reference population were predicted with accuracies of 0.65 for Gir, 0.60 for Sahiwal, and 0.49 for Kankrej. Multi-breed reference populations were created for all possible breed combinations. Three genomic evaluation strategies, viz., shared Genomic Relationship Matrix (GRM), non-shared GRM, and metafounder-corrected shared GRM, were compared. We observed a significant gain in accuracy for the numerically small breed, Kankrej, using a multi-breed approach as compared to a single-breed approach. Using a multi-breed evaluation with shared and non-shared GRM and the metafounder approach led to accuracy improvements of 23.6%, 24.6%, and 16.9%, respectively, while using the Gir-Kankrej multi-breed reference population. Validation using the linear regression (LR) method showed that the best way to predict GEBV for the Kankrej breed was through multi-breed evaluation with the 'shared GRM' approach. Our findings indicate that in the absence of large breed specific reference, multibreed genomic evaluation offers a viable strategy for enhancing genomic prediction accuracy for numerically small breeds in India.
基因组选择已被证明在加速家畜遗传改良方面是有效的，但其在发展中国家的应用面临挑战，特别是对于数量较少的品种，在这些品种中建立大型单一品种参考群体很困难。为解决这一限制，当前研究旨在调查多品种参考群体在具有有限表型和基因组资源的数量较少品种中提高基因组预测准确性的有效性。利用1298头吉尔牛、1291头萨希瓦尔牛和500头坎克雷吉本地牛的基因型和表型数据，通过多品种参考群体提高坎克雷吉牛的基因组预测准确性。基于主成分分析（PCA）-K均值的聚类显示吉尔牛和坎克雷吉牛之间存在重叠，表明这两个品种之间存在遗传相似性。连锁不平衡（LD）衰减模式证实了这些结果，在吉尔牛-坎克雷吉牛的LD衰减图中显示出类似趋势，表明存在可用于联合分析的共享单倍型块。吉尔牛305天第一泌乳期产奶量（305-DMY）的遗传力估计值为0.30±0.07，萨希瓦尔牛为0.27±0.07，坎克雷吉牛为0.17±0.01。使用单一品种参考群体预测基因组估计育种值（GEBV），吉尔牛的预测准确率为0.65，萨希瓦尔牛为0.60，坎克雷吉牛为0.49。为所有可能的品种组合创建了多品种参考群体。比较了三种基因组评估策略，即共享基因组关系矩阵（GRM）、非共享GRM和元奠基者校正共享GRM。我们观察到，与单一品种方法相比，使用多品种方法时数量较少的品种坎克雷吉牛在准确性上有显著提高。使用共享和非共享GRM以及元奠基者方法进行多品种评估，在使用吉尔牛-坎克雷吉牛多品种参考群体时，准确性分别提高了23.6%、24.6%和16.9%。使用线性回归（LR）方法进行验证表明，预测坎克雷吉牛GEBV的最佳方法是通过“共享GRM”方法进行多品种评估。我们的研究结果表明，在缺乏大型特定品种参考群体的情况下，多品种基因组评估为提高印度数量较少品种的基因组预测准确性提供了一种可行策略。
