Obšteter Jana, Jenko Janez, Gorjanc Gregor
Department of Animal Science, Agricultural Institute of Slovenia, Ljubljana, Slovenia.
Geno Breeding and A. I. Association, Hamar, Norway.
Front Genet. 2021 Feb 10;12:637017. doi: 10.3389/fgene.2021.637017. eCollection 2021.
This paper evaluates the potential of maximizing genetic gain in dairy cattle breeding by optimizing investment into phenotyping and genotyping. Conventional breeding focuses on phenotyping selection candidates or their close relatives to maximize selection accuracy for breeders and quality assurance for producers. Genomic selection decoupled phenotyping and selection and through this increased genetic gain per year compared to the conventional selection. Although genomic selection is established in well-resourced breeding programs, small populations and developing countries still struggle with the implementation. The main issues include the lack of training animals and lack of financial resources. To address this, we simulated a case-study of a small dairy population with a number of scenarios with equal available resources yet varied use of resources for phenotyping and genotyping. The conventional progeny testing scenario collected 11 phenotypic records per lactation. In genomic selection scenarios, we reduced phenotyping to between 10 and 1 phenotypic records per lactation and invested the saved resources into genotyping. We tested these scenarios at different relative prices of phenotyping to genotyping and with or without an initial training population for genomic selection. Reallocating a part of phenotyping resources for repeated milk records to genotyping increased genetic gain compared to the conventional selection scenario regardless of the amount and relative cost of phenotyping, and the availability of an initial training population. Genetic gain increased by increasing genotyping, despite reduced phenotyping. High-genotyping scenarios even saved resources. Genomic selection scenarios expectedly increased accuracy for young non-phenotyped candidate males and females, but also proven females. This study shows that breeding programs should optimize investment into phenotyping and genotyping to maximize return on investment. Our results suggest that any dairy breeding program using conventional progeny testing with repeated milk records can implement genomic selection without increasing the level of investment.
本文评估了通过优化表型分型和基因分型投入来最大化奶牛育种遗传进展的潜力。传统育种侧重于对选择候选个体或其近亲进行表型分型，以提高育种者的选择准确性和生产者的质量保证。基因组选择将表型分型和选择分离，因此与传统选择相比，每年的遗传进展有所增加。尽管基因组选择已在资源丰富的育种计划中确立，但小群体和发展中国家在实施方面仍面临困难。主要问题包括缺乏训练动物和财政资源。为了解决这一问题，我们模拟了一个小型奶牛群体的案例研究，设置了多个场景，这些场景拥有相同的可用资源，但表型分型和基因分型的资源使用情况不同。传统的后裔测定场景在每个泌乳期收集11个表型记录。在基因组选择场景中，我们将每个泌乳期的表型分型减少到10至1个表型记录，并将节省的资源投入到基因分型中。我们在表型分型与基因分型的不同相对价格下，以及有无基因组选择的初始训练群体的情况下测试了这些场景。与传统选择场景相比，将部分用于重复产奶记录的表型分型资源重新分配用于基因分型，无论表型分型的数量和相对成本以及初始训练群体的可用性如何，都能增加遗传进展。尽管表型分型减少，但通过增加基因分型，遗传进展仍会增加。高基因分型场景甚至节省了资源。基因组选择场景预期会提高未进行表型分型的年轻候选公母畜以及经产母畜的准确性。本研究表明，育种计划应优化表型分型和基因分型投入，以实现投资回报最大化。我们的结果表明，任何使用带有重复产奶记录的传统后裔测定的奶牛育种计划都可以在不增加投资水平的情况下实施基因组选择。
