Review: Integrating a semen quality control program and sire fertility at a large artificial insemination organization
Review: Integrating a semen quality control program and sire fertility at a large artificial insemination organization
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DOI：
10.1017/s1751731118000319
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发表时间：
2018-06-01
期刊：
影响因子：
3.6
通讯作者：
DeJarnette, J. M.
中科院分区：
文献类型：
--
作者：
Harstine, B. R.;Utt, M. D.;DeJarnette, J. M.
近年来，可用于评估精子种群特征的技术有了很大进步。销售牛精液的大型人工授精(AI)组织不仅将其中许多技术用于新的研究目的，还将其用于决定是否出售或丢弃产品。在人工智能组织内，精液质量数据的获取、解释和利用通常由质量控制部门执行。一般来说，关于精液销售的质量控制决策通常建立在精液质量和田间繁殖力之间建立的联系上。虽然没有一种个体精子生物测定能够成功地预测父系生育能力，但已有许多研究报道了各种体内生育能力指标之间的相关性。目前可用于评估精液的最强大的技术是高通量，包括计算机辅助精子分析和各种流式细胞仪分析，这些分析量化了荧光染色细胞的属性。然而，所有测量生物参数的技术都要遵循精密度、准确性和重复性的原则。在质量控制和质量保证计划中，了解实验室分析的重复性限制是很重要的。因此，获得与精子质量和雄性受精率有关的大量数据集的人工智能组织处于有利地位，可以检查和评论数据收集和解释。这在父系繁殖力方面尤其如此，在那里，人工智能父系种群经过了高度的生育选择。在奶牛育种委员会2017年12月发布的父系受孕率报告中，93%的荷斯坦公牛(n=2062)的繁殖力偏差在品种平均水平的3%以内。无论采用何种报告制度，对父系生育率的估计都应基于每个父系的适当服务数量。许多用户对这些评估的预测价值抱有不切实际的期望，因为他们不了解从实地来源收集的二项式数据本身就缺乏精确度。基本统计学原理警告我们，在考虑样本量和统计能力的情况下，实验设计、平衡处理、抽样偏差、适当的模型和结果的适当解释的重要性。总体而言，这篇综述试图描述和联系精子在体外生物测定中的使用，人工授精受精率的报告，以及围绕精液质量控制程序实施的管理决策。
The technology available to assess sperm population characteristics has advanced greatly in recent years. Large artificial insemination (AI) organizations that sell bovine semen utilize many of these technologies not only for novel research purposes, but also to make decisions regarding whether to sell or discard the product. Within an AI organization, the acquisition, interpretation and utilization of semen quality data is often performed by a quality control department. In general, quality control decisions regarding semen sales are often founded on the linkages established between semen quality and field fertility. Although no one individual sperm bioassay has been successful in predicting sire fertility, many correlations to various in vivo fertility measures have been reported. The most powerful techniques currently available to evaluate semen are high-throughput and include computer-assisted sperm analysis and various flow cytometric analyses that quantify attributes of fluorescently stained cells. However, all techniques measuring biological parameters are subject to the principles of precision, accuracy and repeatability. Understanding the limitations of repeatability in laboratory analyses is important in a quality control and quality assurance program. Hence, AI organizations that acquire sizeable data sets pertaining to sperm quality and sire fertility are well-positioned to examine and comment on data collection and interpretation. This is especially true for sire fertility, where the population of AI sires has been highly selected for fertility. In the December 2017 sire conception rate report by the Council on Dairy Cattle Breeding, 93% of all Holstein sires (n = 2062) possessed fertility deviations within 3% of the breed average. Regardless of the reporting system, estimates of sire fertility should be based on an appropriate number of services per sire. Many users impose unrealistic expectations of the predictive value of these assessments due to a lack of understanding for the inherent lack of precision in binomial data gathered from field sources. Basic statistical principles warn us of the importance of experimental design, balanced treatments, sampling bias, appropriate models and appropriate interpretation of results with consideration for sample size and statistical power. Overall, this review seeks to describe and connect the use of sperm in vitro bioassays, the reporting of AI sire fertility, and the management decisions surrounding the implementation of a semen quality control program.