Gutierrez-Reinoso Miguel A, Aponte Pedro M, Garcia-Herreros Manuel
Facultad de Ciencias Agropecuarias y Recursos Naturales, Carrera de Medicina Veterinaria, Universidad Técnica de Cotopaxi (UTC), Latacunga 05-0150, Ecuador.
Laboratorio de Biotecnología Animal, Departamento de Ciencia Animal, Facultad de Ciencias Veterinarias, Universidad de Concepción (UdeC), Chillán 3780000, Chile.
Animals (Basel). 2021 Feb 25;11(3):599. doi: 10.3390/ani11030599.
Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future.
基因组学包含一系列当前有价值的技术,这些技术在奶牛商业育种计划中作为选择工具得以应用。基于基因组育种值(GEBVs)对生产和繁殖性状进行的密集后代测试,对于提高奶牛生产力至关重要。了解关键基因和单倍型,包括其调控机制,作为生产力性状的标记,可能会改进当前和未来奶牛选择的策略。全基因组关联研究(GWAS),如数量性状基因座(QTL)、单核苷酸多态性(SNP)或单步基因组最佳线性无偏预测(ssGBLUP)方法,已被纳入全球奶牛计划中,用于估计标记辅助选择产生的效应。基于基因组预测准确性的遗传进展增加,也有助于理解奶牛后代的遗传效应。然而,近交系内的杂交严重增加了纯合性,并积累了近亲繁殖的负面影响,如繁殖性能下降。因此,基于奶牛生产系统经验 - 传统模型的不准确偏差估计面临着更高的风险,即仅基于低遗传力表型性状来选择高遗传价值候选个体时产生的错误可能导致次优结果。这延长了世代间隔并增加了成本,因为遗传增益显著减少。基因组预测的显著进展提高了对优良候选个体的准确选择。本综述的范围是总结和讨论用于奶牛选择的基因组工具的进展和挑战,以优化育种计划并控制近亲繁殖对生产力的负面影响,从而在食品生产效率方面实现经济有效的进展。特别关注潜在的基因组选择衍生结果,以促进现代奶牛场的精准管理,包括对新型基因组编辑方法的概述,作为对未来的展望。