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Genetic evaluation and selection response for growth in meat-type quail through random regression models using B-spline functions and Legendre polynomials

Published online by Cambridge University Press:  14 August 2017

L. F. M. Mota ,
P. G. M. A. Martins ,
T. O. Littiere ,
L. R. A. Abreu ,
M. A. Silva  and
C. M. Bonafé
L. F. M. Mota
Affiliation:
Department of Animal Science, Faculty of Agricultural and Veterinary Science, State University of São Paulo, Jaboticabal, SP 14884-900, Brazil
P. G. M. A. Martins*
Affiliation:
Federal Institute of Northern Minas Gerais, Almenara, MG 39900-000, Brazil
T. O. Littiere
Affiliation:
Department of Animal Sciences, Federal University of Jequitinhonha and Mucuri Valleys, Diamantina, MG 39100-000, Brazil
L. R. A. Abreu
Affiliation:
School of Veterinary, Federal University of Minas Gerais, Belo Horizonte, MG 30123-970, Brazil
M. A. Silva
Affiliation:
Department of Animal Sciences, Federal University of Jequitinhonha and Mucuri Valleys, Diamantina, MG 39100-000, Brazil
C. M. Bonafé
Affiliation:
Department of Animal Sciences, Federal University of Jequitinhonha and Mucuri Valleys, Diamantina, MG 39100-000, Brazil
*
†E-mail: paulo.martins@ifnmg.edu.br
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Abstract

The objective was to estimate (co)variance functions using random regression models (RRM) with Legendre polynomials, B-spline function and multi-trait models aimed at evaluating genetic parameters of growth traits in meat-type quail. A database containing the complete pedigree information of 7000 meat-type quail was utilized. The models included the fixed effects of contemporary group and generation. Direct additive genetic and permanent environmental effects, considered as random, were modeled using B-spline functions considering quadratic and cubic polynomials for each individual segment, and Legendre polynomials for age. Residual variances were grouped in four age classes. Direct additive genetic and permanent environmental effects were modeled using 2 to 4 segments and were modeled by Legendre polynomial with orders of fit ranging from 2 to 4. The model with quadratic B-spline adjustment, using four segments for direct additive genetic and permanent environmental effects, was the most appropriate and parsimonious to describe the covariance structure of the data. The RRM using Legendre polynomials presented an underestimation of the residual variance. Lesser heritability estimates were observed for multi-trait models in comparison with RRM for the evaluated ages. In general, the genetic correlations between measures of BW from hatching to 35 days of age decreased as the range between the evaluated ages increased. Genetic trend for BW was positive and significant along the selection generations. The genetic response to selection for BW in the evaluated ages presented greater values for RRM compared with multi-trait models. In summary, RRM using B-spline functions with four residual variance classes and segments were the best fit for genetic evaluation of growth traits in meat-type quail. In conclusion, RRM should be considered in genetic evaluation of breeding programs.

Keywords

covariance functionsgenetic correlationsegmented polynomials
Type
Research Article
Information
animal , Volume 12 , Issue 4 , April 2018 , pp. 667 - 674
Copyright
© The Animal Consortium 2017 

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