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Using quantile regression methodology to evaluate changes in the shape of growth curves in pigs selected for increased feed efficiency based on residual feed intake

Published online by Cambridge University Press:  11 October 2018

M. Nascimento ,
A. C. C. Nascimento ,
J. C. M. Dekkers  and
N. V. L. Serão Open the ORCID record for N. V. L. Serão [Opens in a new window]
M. Nascimento
Affiliation:
Departamento de Estatística, Universidade Federal de Viçosa, Av. Peter Henry Rolfs, s/n, Campus Universitário, Viçosa, MG 36570-977, Brazil
A. C. C. Nascimento
Affiliation:
Departamento de Estatística, Universidade Federal de Viçosa, Av. Peter Henry Rolfs, s/n, Campus Universitário, Viçosa, MG 36570-977, Brazil
J. C. M. Dekkers
Affiliation:
Department of Animal Science, Iowa State University, 1221 Kildee Hall, Ames, IA 50011-3150, USA
N. V. L. Serão*
Affiliation:
Department of Animal Science, Iowa State University, 1221 Kildee Hall, Ames, IA 50011-3150, USA
*
†E-mail: [email protected]
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Abstract

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.

Keywords

animal geneticsgenetic parametersGompertz curvenon-linear modelsSus scrofa
Type
Research Article
Information
animal , Volume 13 , Issue 5 , May 2019 , pp. 1009 - 1019
Copyright
© The Animal Consortium 2018 

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