Gorssen Wim, Winters Carmen, Meyermans Roel, Chapard Léa, Hooyberghs Katrijn, Janssens Steven, Huisman Abe, Peeters Katrijn, Mulder Han, Buys Nadine
Center for Animal Breeding and Genetics, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30 - Box 2472, 3001, Leuven, Belgium.
Laboratory for Biological Psychology, KU Leuven, Tiensestraat 102 - Box 3714, 3000, Leuven, Belgium.
J Anim Sci Biotechnol. 2023 Aug 1;14(1):101. doi: 10.1186/s40104-023-00901-9.
Increasing resilience is a priority in modern pig breeding. Recent research shows that general resilience can be quantified via variability in longitudinal data. The collection of such longitudinal data on weight, feed intake and feeding behaviour in pigs has been facilitated by the development of technologies such as automated feeding stations. The goal of this study was to investigate resilience traits, which were estimated as deviations from longitudinal weight, feed intake and feeding behaviour data during the finishing phase. A dataset with 324,207 records between the age of 95 and 155 days on 5,939 Piétrain pigs with known pedigree and genomic information was used. We provided guidelines for a rigid quality control of longitudinal body weight data, as we found that outliers can significantly affect results. Gompertz growth curve analysis, linear modelling and trajectory analyses were used for quantifying resilience traits.
To our knowledge, this is the first study comparing resilience traits from longitudinal body weight, feed intake and feeding behaviour data in pigs. We demonstrated that the resilience traits are lowly to moderately heritable for deviations in body weight (h = 2.9%-20.2%), in feed intake (9.4%-23.3%) and in feeding behaviour (16.2%-28.3%). Additionally, these traits have good predictive abilities in cross-validation analyses. Deviations in individual body weight and feed intake trajectories are highly correlated (r = 0.78) with low to moderate favourable genetic correlations with feed conversion ratio (r = 0.39-0.49). Lastly, we showed that some resilience traits, such as the natural logarithm of variances of observed versus predicted body weights (lnvar), are more robust to lower observation frequencies and are repeatable over three different time periods of the finishing phase.
Our results will help future studies investigating resilience traits and resilience-related traits. Moreover, our study provides first results on standardization of quality control and efficient data sampling from automated feeding station data. Our findings will be valuable for breeding organizations as they offer evidence that pigs' general resilience can be selected on with good accuracy. Moreover, this methodology might be extended to other species to quantify resilience based on longitudinal data.
提高恢复力是现代养猪育种的一个优先事项。最近的研究表明，一般恢复力可以通过纵向数据的变异性来量化。自动饲喂站等技术的发展促进了猪体重、采食量和采食行为等纵向数据的收集。本研究的目的是调查恢复力性状，这些性状被估计为育肥阶段纵向体重、采食量和采食行为数据的偏差。使用了一个数据集，该数据集包含5939头具有已知系谱和基因组信息的皮特兰猪在95至155日龄之间的324207条记录。我们为纵向体重数据的严格质量控制提供了指导方针，因为我们发现异常值会显著影响结果。采用冈珀茨生长曲线分析、线性建模和轨迹分析来量化恢复力性状。
据我们所知，这是第一项比较猪纵向体重、采食量和采食行为数据中恢复力性状的研究。我们证明，体重偏差（h = 2.9%-20.2%）、采食量偏差（9.4%-23.3%）和采食行为偏差（16.2%-28.3%）的恢复力性状具有低到中等的遗传力。此外，这些性状在交叉验证分析中具有良好的预测能力。个体体重和采食量轨迹的偏差与饲料转化率具有高度相关性（r = 0.78），与饲料转化率的遗传相关性为低到中等有利（r = 0.39-0.49）。最后，我们表明，一些恢复力性状，如观察体重与预测体重方差的自然对数（lnvar），对较低的观察频率更具稳健性，并且在育肥阶段的三个不同时间段内是可重复的。
我们的结果将有助于未来研究调查恢复力性状和与恢复力相关的性状。此外，我们的研究提供了关于质量控制标准化和从自动饲喂站数据进行高效数据采样的初步结果。我们的发现对育种组织将是有价值的，因为它们提供了证据表明可以准确地选择猪的一般恢复力。此外，这种方法可能会扩展到其他物种，以根据纵向数据量化恢复力。
