Estimating genetic parameters of digital behavior traits and their relationship with production traits in purebred pigs

IF 3.6 1区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE Genetics Selection Evolution Pub Date : 2024-04-16 DOI:10.1186/s12711-024-00902-w
Mary Kate Hollifield, Ching-Yi Chen, Eric Psota, Justin Holl, Daniela Lourenco, Ignacy Misztal
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Abstract

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.
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估计纯种猪数字行为特征的遗传参数及其与生产特征的关系
随着数字表型和高通量数据的引入,以前难以或无法直接测量的性状变得容易获得,这为提高动物生产的效率和遗传增益率提供了机会。在性能测试期间,评估行为性状与生产性状之间的间接关系很有意义。本研究旨在评估从逐日视频记录中提取的行为数据的质量,并估算行为性状的遗传参数及其与猪生产性状的表型和遗传相关性。对 2008 头雌性纯种猪的行为进行了记录,从 10 周龄左右的开测开始,到离测结束,共记录了 70 天,共计 119,812 条日行为记录。行为特征包括进食、饮水、侧卧、胸卧、坐、站和行走距离米数。质量控制程序用于算法培训和调整、记录时间标准化、剔除淘汰动物以及过滤不真实的记录。生产性状包括平均日增重(ADG)、背脂肪厚度(BF)和腰围深度(LD)。单性状线性模型用于估计行为性状的遗传率,双性状线性模型用于估计行为性状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,遗传率估计值从 0.19 到 0.57 不等,并且与生产性状的表型和遗传相关性较低到中等。此外,还使用双性状线性模型来比较记录期间不同时间间隔的性状。为了分析记录期间行为数据的冗余性,比较了行为和生产性状不同记录时间间隔的平均值。总体而言,55-68 天记录间隔的平均值与生产性状的表型和遗传相关性估计值最强。数字表型是一种记录行为表型的低成本新方法,但需要彻底的数据清理程序。在不同的时间间隔评估行为性状,可以更深入地了解行为性状在整个生长期的变化及其与生产性状的关系,而生产性状的记录频率可能较低。
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来源期刊
Genetics Selection Evolution
Genetics Selection Evolution 生物-奶制品与动物科学
CiteScore
6.50
自引率
9.80%
发文量
74
审稿时长
1 months
期刊介绍: Genetics Selection Evolution invites basic, applied and methodological content that will aid the current understanding and the utilization of genetic variability in domestic animal species. Although the focus is on domestic animal species, research on other species is invited if it contributes to the understanding of the use of genetic variability in domestic animals. Genetics Selection Evolution publishes results from all levels of study, from the gene to the quantitative trait, from the individual to the population, the breed or the species. Contributions concerning both the biological approach, from molecular genetics to quantitative genetics, as well as the mathematical approach, from population genetics to statistics, are welcome. Specific areas of interest include but are not limited to: gene and QTL identification, mapping and characterization, analysis of new phenotypes, high-throughput SNP data analysis, functional genomics, cytogenetics, genetic diversity of populations and breeds, genetic evaluation, applied and experimental selection, genomic selection, selection efficiency, and statistical methodology for the genetic analysis of phenotypes with quantitative and mixed inheritance.
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