With the introduction of digital phenotyping and high-throughput data, traits that were previously difficult or impossible to measure directly have become easily accessible, offering the opportunity to enhance the efficiency and rate of genetic gain in animal production. It is of interest to assess how behavioral traits are indirectly related to the production traits during the performance testing period. The aim of this study was to assess the quality of behavior data extracted from day-wise video recordings and estimate the genetic parameters of behavior traits and their phenotypic and genetic correlations with production traits in pigs. Behavior was recorded for 70 days after on-test at about 10 weeks of age and ended at off-test for 2008 female purebred pigs, totaling 119,812 day-wise records. Behavior traits included time spent eating, drinking, laterally lying, sternally lying, sitting, standing, and meters of distance traveled. A quality control procedure was created for algorithm training and adjustment, standardizing recording hours, removing culled animals, and filtering unrealistic records. Production traits included average daily gain (ADG), back fat thickness (BF), and loin depth (LD). Single-trait linear models were used to estimate heritabilities of the behavior traits and two-trait linear models were used to estimate genetic correlations between behavior and production traits. The results indicated that all behavior traits are heritable, with heritability estimates ranging from 0.19 to 0.57, and showed low-to-moderate phenotypic and genetic correlations with production traits. Two-trait linear models were also used to compare traits at different intervals of the recording period. To analyze the redundancies in behavior data during the recording period, the averages of various recording time intervals for the behavior and production traits were compared. Overall, the average of the 55- to 68-day recording interval had the strongest phenotypic and genetic correlation estimates with the production traits. Digital phenotyping is a new and low-cost method to record behavior phenotypes, but thorough data cleaning procedures are needed. Evaluating behavioral traits at different time intervals offers a deeper insight into their changes throughout the growth periods and their relationship with production traits, which may be recorded at a less frequent basis.

中文翻译：

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纯种猪数字行为性状遗传参数的估计及其与生产性状的关系


随着数字表型分析和高通量数据的引入，以前难以或不可能直接测量的性状变得容易获得，从而为提高动物生产中遗传增益的效率和速率提供了机会。在性能测试期间评估行为特征如何与生产特征间接相关是很有意义的。本研究的目的是评估从每日视频记录中提取的行为数据的质量，并估计猪行为性状的遗传参数及其表型和遗传与生产性状的相关性。 2008 只雌性纯种猪的行为在大约 10 周龄时进行了 70 天的行为记录，并在非测试时结束，总计 119,812 条日记录。行为特征包括吃饭、喝水、侧卧、胸卧、坐着、站立和行走距离的时间。创建了质量控制程序，用于算法训练和调整、标准化记录时间、删除扑杀动物以及过滤不切实际的记录。生产性状包括平均日增重（ADG）、背膘厚度（BF）和腰部深度（LD）。单性状线性模型用于估计行为性状的遗传力，双性状线性模型用于估计行为和生产性状之间的遗传相关性。结果表明，所有行为性状都是可遗传的，遗传力估计范围为0.19至0.57，并且与生产性状表现出低至中等的表型和遗传相关性。还使用两个性状线性模型来比较记录期间不同间隔的性状。 为了分析记录期间行为数据的冗余，比较了行为和生产性状的不同记录时间间隔的平均值。总体而言，55 至 68 天记录间隔的平均值与生产性状具有最强的表型和遗传相关性估计。数字表型分析是一种记录行为表型的新型低成本方法，但需要彻底的数据清理程序。在不同时间间隔评估行为特征可以更深入地了解它们在整个生长期间的变化以及它们与生产特征的关系，这些特征可能以较低的频率记录。