1Departamento de Estatística,Universidade Federal de Viçosa,Av. Peter Henry Rolfs,s/n, Campus Universitário,Viçosa,MG 36570-977,Brazil.
2Department of Animal Science,Iowa State University,1221 Kildee Hall,Ames,IA 50011-3150,USA.
Animal. 2019 May;13(5):1009-1019. doi: 10.1017/S1751731118002616. Epub 2018 Oct 11.
Growth rate is a major component of feed efficiency when estimating residual feed intake (RFI). Quantile regression (QR) methodology can be used to identify animals with different growth trajectories. The objective of this study was to evaluate the use of QR to identify phenotypic and genetic differences in pigs selected for low RFI. Using performance data on 750 Yorkshire pigs selected for low RFI, individual average daily gain (ADG), average daily feed intake (ADFI), RFI and Gompertz growth curve parameters (asymptotic weight (a), inflection point (b) and decay parameter (c)) were estimated for each pig. Using QR methodology, three Gompertz growth curves were estimated for the whole population for three quantiles (0.1, 0.5 and 0.9) of the BW data. Each animal was classified into one of the quantile regression groups (QRG) based on their overall Euclidian distance between each observed and estimated BW from the quantile growth curves. These three curves were also estimated using only part of the data (generations -1 to 3, and -1 to 4) in order to evaluate the agreement classification rate of animals from later generations into QRGs. We evaluated the effect of QRG on growth parameters and performance traits. Genetic parameters were estimated for these traits, as well as for QRG. In addition, genetic trends for each QRG were estimated. Three distinct growth curves were observed for animals classified into either quantiles 0.1 (QRG0.1), 0.5 (QRG0.5) or 0.9 (QRG0.9). When only part of the data was used to estimate quantile growth curves, all animals from QRG0.1 were correctly classified in their group. Animals in QRG0.1 had significantly lower ADFI, ADG and RFI, and greater a, b and c than animals in the other groups. Quantile regression groups analysed as a trait was highly heritable (0.41) and had high (0.8) and moderate (0.46) genetic correlations with ADG and RFI, respectively. Selection for reduced RFI increased the number of animals classified as QRG0.1 in the population. Overall, downward genetic trends were observed for all traits as a function of selection for reduced RFI. However, QRG0.1 was the only group that had a positive genetic trend for ADG. Altogether, these results indicate that selection for reduced RFI changes the shape of growth curves in Yorkshire in pigs, and that QR methodology was able to identify animals having different genetic potential for feed efficiency, bringing a new opportunity to improve selection for reduced RFI.
生长速度是估计剩余采食量 (RFI) 时饲料效率的主要组成部分。分位数回归 (QR) 方法可用于识别具有不同生长轨迹的动物。本研究的目的是评估 QR 用于识别低 RFI 选择猪的表型和遗传差异。使用 750 头约克夏猪的性能数据,对每头猪估计个体平均日增重 (ADG)、平均日采食量 (ADFI)、RFI 和 Gompertz 生长曲线参数 (渐近体重 (a)、拐点 (b) 和衰减参数 (c))。使用 QR 方法,根据 BW 数据的三个分位数 (0.1、0.5 和 0.9) 为整个群体估计了三个 Gompertz 生长曲线。根据每个观察到的和从分位数生长曲线估计的 BW 之间的总体欧几里得距离,每个动物都被分类到一个分位数回归组 (QRG) 中。还使用数据的一部分 (世代 -1 到 3 和 -1 到 4) 来估计这些曲线,以评估后代动物进入 QRG 的分类率。我们评估了 QRG 对生长参数和性能特征的影响。还估计了这些特征以及 QRG 的遗传参数。此外,还估计了每个 QRG 的遗传趋势。分为分位数 0.1(QRG0.1)、0.5(QRG0.5)或 0.9(QRG0.9)的动物观察到了三个不同的生长曲线。当仅使用部分数据来估计分位数生长曲线时,QRG0.1 中的所有动物都正确地归入其组中。在 QRG0.1 中的动物的 ADFI、ADG 和 RFI 显著较低,而 a、b 和 c 则大于其他组的动物。作为一个特征分析的分位数回归组具有高度的遗传性 (0.41),与 ADG 和 RFI 的遗传相关性分别为高 (0.8) 和中度 (0.46)。选择减少 RFI 增加了群体中被归类为 QRG0.1 的动物数量。总体而言,由于选择减少 RFI,所有性状的遗传趋势都呈下降趋势。然而,QRG0.1 是唯一具有 ADG 正遗传趋势的组。总的来说,这些结果表明,选择减少 RFI 改变了约克夏猪生长曲线的形状,QR 方法能够识别具有不同饲料效率遗传潜力的动物,为提高选择减少 RFI 提供了新的机会。