Animal Genomics and Improvement Laboratory, Agricultural Research Service, United States Department of Agriculture (USDA), Beltsville, MD 20705-2350.
Council on Dairy Cattle Breeding, 4201 Northview Drive, Suite 302, Bowie, MD 20716.
J Dairy Sci. 2021 May;104(5):5111-5124. doi: 10.3168/jds.2020-19777. Epub 2021 Mar 11.
Genetic selection has been a very successful tool for the long-term improvement of livestock populations, and the rapid adoption of genomic selection over the last decade has doubled the rate of gain in some populations. Breeding programs seek to identify genetically superior parents of the next generation, typically as a function of an index that combines information about many economically important traits into a single number. In the United States, the data that drive this system are collected through the national dairy herd improvement program that began more than a century ago. The resulting information about animal performance, pedigree, and genotype is used to compute genomic evaluations for comparing and ranking animals for selection. However, the full expression of genetic potential requires that animals are placed in environments that can support such performance. The Agricultural Research Service of the US Department of Agriculture and the Council on Dairy Cattle Breeding collaborate to deliver state-of-the-art genomic evaluations to the dairy industry. Today, most breeding stock are selected and marketed using the net merit dollars (NM$) selection index, which evolved from 2 traits in 1926 (milk and fat yield) to a combination of 36 individual traits following the last NM$ update in 2018. Updates to NM$ require the estimation of many different values, and it can be difficult to achieve consensus from stakeholders on what should be added to, or removed from, the index at each review, and how those traits should be weighted. Over time, the majority of the emphasis in the index has shifted from yield traits to fertility, health, and fitness traits. Phenotypes for some of these new traits are difficult or expensive to measure, or require changes to on-farm habits that have not been widely adopted. This is driving interest in sensor-based systems that provide continuous measurements of the farm environment, individual animal performance, and detailed milk composition. There is also a need to capture more detailed data about the environment in which animals perform, including information about feeding, housing, milking systems, and infectious and parasitic load. However, many challenges accompany these new technologies, including a lack of standardization or validation, need for high-speed internet connections, increased computational requirements, and interpretations that are often not backed by direct observations of biological phenomena. This work will describe how US selection objectives are developed, as well as discuss opportunities and challenges associated with new technologies for measuring and recording animal performance.
遗传选择一直是长期提高家畜种群的非常成功的工具，而在过去十年中基因组选择的快速采用将一些种群的增益速度提高了一倍。育种计划旨在确定下一代具有遗传优势的父母，通常是将许多重要经济性状的信息组合成一个数字的指标的函数。在美国，推动这一系统的数据是通过一个多世纪前开始的国家奶牛群改良计划收集的。关于动物性能、血统和基因型的结果信息用于计算基因组评估，以比较和对动物进行选择排名。然而，要充分发挥遗传潜力，就需要将动物置于能够支持这种性能的环境中。美国农业部农业研究局和奶牛育种理事会合作，为乳业提供最先进的基因组评估。如今，大多数种畜都是使用净效益美元（NM$）选择指数进行选择和销售的，该指数从 1926 年的 2 个性状（牛奶和脂肪产量）演变而来，在 2018 年最后一次 NM$更新后，发展成为 36 个个体性状的组合。NM$的更新需要估计许多不同的值，并且在每次审查时，利益相关者很难就应该在指数中添加什么或删除什么以及如何对这些性状进行加权达成共识。随着时间的推移，指数中的大部分重点已经从产量性状转移到了生育力、健康和适应性性状。一些新性状的表型很难或昂贵，或者需要对尚未广泛采用的农场习惯进行改变。这就推动了对基于传感器的系统的兴趣，这些系统提供了农场环境、个体动物性能和详细牛奶成分的连续测量。还需要捕获有关动物表现环境的更详细数据，包括有关喂养、住房、挤奶系统以及传染性和寄生虫负荷的信息。然而，这些新技术伴随着许多挑战，包括缺乏标准化或验证、需要高速互联网连接、增加计算要求以及通常没有直接观察到生物现象支持的解释。这项工作将描述美国选择目标是如何制定的，并讨论与测量和记录动物性能相关的新技术的机会和挑战。
