Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.
SEGES, Pig Research Centre, 1609, Copenhagen, Denmark.
Genet Sel Evol. 2021 Apr 8;53(1):33. doi: 10.1186/s12711-021-00624-3.
In breeding programs, recording large-scale feed intake (FI) data routinely at the individual level is costly and difficult compared with other production traits. An alternative approach could be to record FI at the group level since animals such as pigs are normally housed in groups and fed by a shared feeder. However, to date there have been few investigations about the difference between group- and individual-level FI recorded in different environments. We hypothesized that group- and individual-level FI are genetically correlated but different traits. This study, based on the experiment undertaken in purebred DanBred Landrace (L) boars, was set out to estimate the genetic variances and correlations between group- and individual-level FI using a bivariate random regression model, and to examine to what extent prediction accuracy can be improved by adding information of individual-level FI to group-level FI for animals recorded in groups. For both bivariate and univariate models, single-step genomic best linear unbiased prediction (ssGBLUP) and pedigree-based BLUP (PBLUP) were implemented and compared.
The variance components from group-level records and from individual-level records were similar. Heritabilities estimated from group-level FI were lower than those from individual-level FI over the test period. The estimated genetic correlations between group- and individual-level FI based on each test day were on average equal to 0.32 (SD = 0.07), and the estimated genetic correlation for the whole test period was equal to 0.23. Our results demonstrate that by adding information from individual-level FI records to group-level FI records, prediction accuracy increased by 0.018 and 0.032 compared with using group-level FI records only (bivariate vs. univariate model) for PBLUP and ssGBLUP, respectively.
Based on the current dataset, our findings support the hypothesis that group- and individual-level FI are different traits. Thus, the differences in FI traits under these two feeding systems need to be taken into consideration in pig breeding programs. Overall, adding information from individual records can improve prediction accuracy for animals with group records.
与其他生产性状相比，在育种计划中，常规地在个体水平上记录大规模的采食量（FI）数据既昂贵又困难。一种替代方法可以是在群体水平上记录 FI，因为猪等动物通常是成群饲养的，并且通过共享饲养器进行喂养。然而，迄今为止，关于在不同环境中记录的群体和个体水平 FI 之间的差异，研究甚少。我们假设群体和个体水平 FI 是遗传相关的，但却是不同的性状。本研究基于纯种丹麦长白猪（L）公猪的实验，旨在使用双变量随机回归模型估计群体和个体水平 FI 之间的遗传方差和相关性，并检验通过将个体水平 FI 的信息添加到群体水平 FI 中，是否可以提高记录在群体中的动物的预测准确性。对于双变量和单变量模型，实施了单步基因组最佳线性无偏预测（ssGBLUP）和基于系谱的 BLUP（PBLUP）并进行了比较。
群体记录和个体记录的方差分量相似。在整个测试期内，群体 FI 估计的遗传力低于个体 FI。基于每个测试日的群体和个体 FI 之间的遗传相关性平均等于 0.32（SD=0.07），整个测试期的遗传相关性等于 0.23。我们的结果表明，通过将个体 FI 记录的信息添加到群体 FI 记录中，与仅使用群体 FI 记录（双变量与单变量模型）相比，PBLUP 和 ssGBLUP 的预测准确性分别提高了 0.018 和 0.032。
基于当前数据集，我们的发现支持群体和个体水平 FI 是不同性状的假设。因此，在猪育种计划中需要考虑这两种喂养系统下 FI 性状的差异。总体而言，添加个体记录的信息可以提高具有群体记录的动物的预测准确性。
