Zhou Le, Zhu Lin, Chang Chencheng, Ma Fengying, Liu Zaixia, Gu Mingjuan, Na Risu, Zhang Wenguang
College of Animal Science and Technology, Inner Mongolia Agricultural University, Hohhot 010018, China.
Key Laboratory of Animal Genetics, Breeding and Reproduction of the Inner Mongolia Autonomous Region, College of Animal Science and Technology, Inner Mongolia Agricultural University, Hohhot 010018, China.
Animals (Basel). 2025 Apr 12;15(8):1118. doi: 10.3390/ani15081118.
Genomic selection (GS) is a technique that integrates genomic data, pedigree information, and individual phenotypes to enhance genetic improvements of economically important traits in livestock. While it has shown significant effects in dairy cattle, its efficacy in beef cattle is lower due to breed diversity and differences in reproductive structures. Therefore, this study evaluated the impact of heritability levels, marker densities, and assessment methods (such as pedigree-based BLUP, genomic BLUP, and weighted genomic BLUP) on genomic prediction accuracy across multiple beef cattle breeds through simulations. Three beef cattle populations were simulated with heritability levels set at 0.3, 0.5, and 0.7 and marker densities set at 50 k and 770 k. The results showed that the predictive accuracy of PBLUP and GBLUP increased with higher heritability and larger reference populations. Increasing the marker density also improved the accuracy of genomic predictions; even a low marker density (50 k SNP) can significantly enhance the accuracy of genetic evaluation, although the size of the reference population needs to be optimized according to population structure, heritability, and the genetic architecture of the trait. Overall, integrating pedigree, genomic, and weighted SNP information can significantly improve the precision of GEBV prediction and reduce bias. In particular, the wGBLUP method demonstrated an improvement in the prediction accuracy of low-heritability traits in small but high-density marker populations.
基因组选择（GS）是一种整合基因组数据、系谱信息和个体表型以提高家畜经济重要性状遗传改良的技术。虽然它在奶牛中已显示出显著效果，但由于品种多样性和生殖结构差异，其在肉牛中的功效较低。因此，本研究通过模拟评估了遗传力水平、标记密度和评估方法（如基于系谱的最佳线性无偏预测（BLUP）、基因组BLUP和加权基因组BLUP）对多个肉牛品种基因组预测准确性的影响。模拟了三个肉牛群体，遗传力水平设定为0.3、0.5和0.7，标记密度设定为50k和770k。结果表明，PBLUP和GBLUP的预测准确性随着遗传力的提高和参考群体的增大而增加。增加标记密度也提高了基因组预测的准确性；即使是低标记密度（50k SNP）也能显著提高遗传评估的准确性，尽管参考群体的大小需要根据群体结构、遗传力和性状的遗传结构进行优化。总体而言，整合系谱、基因组和加权SNP信息可以显著提高基因组估计育种值（GEBV）预测的精度并减少偏差。特别是，wGBLUP方法在小但高密度标记群体中显示出对低遗传力性状预测准确性的提高。
