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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: serao@iastate.edu
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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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References

Cai, W , Casey, DS and Dekkers, JCM 2008. Selection response and genetic parameters for residual feed intake in Yorkshire swine. Journal of Animal Science 86, 287–298.10.2527/jas.2007-0396Google Scholar
Cai, W , Wu, H and Dekkers, JCM 2011. Longitudinal analysis of body weight and feed intake in selection lines for residual feed intake in pigs. Asian-Aust. Journal of Animal Science 24, 17–27.Google Scholar
Cai, W , Kaiser, MS and Dekkers, JCM 2012. Bayesian analysis of the effect of selection for residual feed intake on growth and feed intake curves in Yorkshire swine. Journal of Animal Science 90, 127–141.10.2527/jas.2011-4293Google Scholar
Coyne, M , Sevon-Aimonen, ML , Berry, DP , Mäntysaari, EA , Juga, J and McHugh, N 2014. Estimation of genetic (Co)variances of von Bertalanffy and Gompertz Growth Function Parameters in Pigs. In Proceedings of the 10th World Congress of Genetics Applied to Livestock Production, 17–22 August 2014, Vancouver, Canada, 604 pp.Google Scholar
Gilbert, H , Bidanel, JP , Gruand, J , Caritez, JC , Billon, Y , Guillouet, P , Lagant, H , Noblet, J and Sellier, P 2007. Genetic parameters for residual feed intake in growing pigs, with emphasis on genetic relationships with carcass and meat quality traits. Journal of Animal Science 85, 3182–3190.10.2527/jas.2006-590Google Scholar
Gilmour, AR , Gogel, BJ , Cullis, BR and Thompson, R 2009. ASReml user guide release 3.0. Retrieved on 1 September 2017 from http://www.vsni.co.uk/downloads/asreml/release3/UserGuide.pdf.Google Scholar
Haraldsen, M , Ødegard, J , Olsen, D , Vangen, O , Ranberg, IMA and Meuwissen, THE 2009. Prediction of genetic growth curves in pigs. Animal 3, 475–481.10.1017/S1751731108003807Google Scholar
Hoque, A and Suziki, K 2008. Genetic parameters for production traits and measures of residual feed intake in Duroc and Landrace pigs. Journal of Animal Science 79, 543–549.10.1111/j.1740-0929.2008.00562.xGoogle Scholar
Jiao, S , Manteca, C , Gray, KA and Cassady, JP 2014. Feed intake, average daily gain, feed efficiency, and real-time ultrasound traits in Duroc pigs: I. Genetic parameter estimation and accuracy of genomic prediction. Journal of Animal Science 92, 2377–2386.10.2527/jas.2013-7338Google Scholar
Johnson, ZB , Chewning, JJ and Nugent, RA 1999. Genetic parameters for production traits and measures of residual feed in Large White swine. Journal of Animal Science 77, 1679–1685.10.2527/1999.7771679xGoogle Scholar
Koch, RM , Gregory, KE , Chambers, D and Swiger, LA 1963. Efficiency of feed use in beef cattle. Journal of Animal Science 22, 486–494.10.2527/jas1963.222486xGoogle Scholar
Koenker, R 2015. Package ‘quantreg’. Retrieved on 10 June 2016 from https://cran.r-project.org/web/packages/quantreg/quantreg.pdf.Google Scholar
Koenker, R and Bassett, GW 1978. Regression quantiles. Econometrica 46, 33–50.10.2307/1913643Google Scholar
Koenker, R and Beum, JP 1996. An interior point algorithm for nonlinear quantile regression. Econometrica 71, 265–283.10.1016/0304-4076(96)84507-6Google Scholar
Luo, J , Lei, H , Shen, L , Yang, R , Pu, Q , Zhu, K , Li, M , Tang, G , Li, X , Zhang, S and Zhu, Li 2015. Estimation of growth curves and suitable slaughter weight of the Liangshan pig. Asian-Australasian Journal of Animal Sciences 28, 1252–1258.10.5713/ajas.15.0010Google Scholar
Mauch, ED , Young, JM , Serão, NVL , Hsu, WL , Patience, JF , Kerr, BJ , Weber, TE , Gabler, NK and Dekkers, JCM 2018. Effect of a low-energy, higher-fiber diets on pigs divergently selected for residual feed intake when fed higher-energy, lower-fiber diets. Journal of Animal Science 96, 1221–1236.10.1093/jas/sky065Google Scholar
Nascimento, M , Nascimento, ACC , Cai, W , Dekkers, JCM and Serão, NVL 2017. Genetic parameters and trends for residual feed intake (RFI), performance traits, and growth curve parameters in pigs selected for low RFI with contrasting growth trajectories. Journal of Animal Sciences 95 (suppl. 5), 15.Google Scholar
Patience, JF 2012. The influence of dietary energy on feed efficiency in grow-finish swine. In Feed efficiency in swine (ed. JF Patience), pp. 101–129. Wageningen Academic Press, Wageningen, The Netherlands.10.3920/978-90-8686-756-1_5Google Scholar
Patience, JF , Serão, MCR and Gutiérrez, NA 2015. A review of feed efficiency in swine: biology and applications. Journal of Animal Science and Biotechnology 6, 33.10.1186/s40104-015-0031-2Google Scholar
R Core Team 2016. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved on 2 October 2018 from http://www.R-project.org/.Google Scholar
Saintilan, R , Brossard, L , Vautier, B , Sellier, P , Bidanel, J , van Milgen, J and Gilbert, H 2015. Phenotypic and genetic relationships between growth and feed intake curves and feed efficiency and amino acid requirements in the growing pig. Animal 9, 18–27.10.1017/S1751731114002171Google Scholar
Silva, FF , Resende, MDV , Rocha, GS , Duarte, DAS , Lopes, PS , Brustolini, OJB , Thus, S , Viana, JMS and Guimarães, SEF 2013. Genomic growth curves of an outbred pig population. Genetics and Molecular Biology 36, 520–527.10.1590/S1415-47572013005000042Google Scholar
Von Felde, A , Roehe, R , Looft, H and Kalm, E 1996. Genetic association between feed intake behaviour at different stages of growth of group-housed boars. Livestock Production Science 47, 11–22.10.1016/S0301-6226(96)01006-8Google Scholar
Webb, AJ 1998. Objectives and strategies in pig improvement: an applied perspective. Journal of Dairy Science 81 (suppl. 2), 36–46.10.3168/jds.S0022-0302(98)70152-3Google Scholar
West, GB , Brown, JH and Enquist, BJ 2001. A general model for ontogenetic growth. Nature 413, 628–631.10.1038/35098076Google Scholar
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