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

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.

Influences of Dietary Supplementation with Maca (Lepidium meyenii) on Performance, Parameters of Growth Curve and Carcass Characteristics in Japanese Quail

“…There are quite a few differences between the live weight values of Japanese quails due to the domestication, adaptation to cage conditions and genetic selection studies. While body weight values at the age of six weeks have been reported as 100-130 g in some studies [34,35], these averages have been reported to be in the range of 250-300 g in other studies [36,37]. In studies in which the growth of Japanese quails was examined with the Gompertz model, the mature-weight parameter was found in the range of 224-295 g [38,39].…”

Invited review: Advances and applications of random regression models: From quantitative genetics to genomics

“…Thus, because the covariance pattern among the repeated measurements of longitudinal traits is well structured, different statistical methods are required to better account for the trait structure compared with single-measurement traits. Today, analyses of longitudinal data are carried out mainly based on RRM (e.g., Mota et al, 2018a;Padilha et al, 2018;Saghi et al, 2018).…”

“…The popularity of RRM has increased substantially in animal breeding research over the past years, and RRM have been considered the method of choice to genetically evaluate longitudinal traits in various livestock species, including dairy cattle (e.g., Kheirabadi, 2018;Padilha et al, 2018), beef cattle (e.g., Oliveira et al, 2018a), sheep (e.g., Saghi et al, 2018), goats (e.g., Brito et al, 2018), horses (e.g., Bartolomé et al, 2018), swine (e.g., Huynh-Tran et al, 2017), poultry (e.g., Miyumo et al, 2018), quail (e.g., Mota et al, 2018a), and fish (e.g., Zhao et al, 2018).…”

Random regression models using B‐splines functions provide more accurate genomic breeding values for milk yield and lactation persistence in Murrah buffaloes

“…The choice of the covariance function used for modelling the additive genetic and permanent environmental effects is an important step for the implementation of RRM in genetic evaluations. Legendre orthogonal polynomials (LEG) are the most commonly used models in various livestock species (Brito et al, 2018; Lázaro et al, 2021; Li et al, 2020; Meyer, 2005; Mota et al, 2018; Oliveira et al, 2019). In addition, several studies have shown that LEG are appropriate for genetically modelling milk yield in dairy buffaloes (Aspilcueta‐Borquis et al, 2013, 2012; Breda et al, 2010; Hurtado‐Lugo et al, 2015; Lázaro et al, 2021; Sesana et al, 2010; Tonhati et al, 2008a).…”