Genetic structured antedependence and random regression models applied to the longitudinal feed conversion ratio in growing Large White pigs1

Abstract

The objective of the present study was to compare a random regression model, usually used in genetic analyses of longitudinal data, with the structured antedependence (SAD) model to study the longitudinal feed conversion ratio (FCR) in growing Large White pigs and to propose criteria for animal selection when used for genetic evaluation. The study was based on data from 11,790 weekly FCR measures collected on 1,186 Large White male growing pigs. Random regression (RR) using orthogonal polynomial Legendre and SAD models was used to estimate genetic parameters and predict FCR-based EBV for each of the 10 wk of the test. The results demonstrated that the best SAD model (1 order of antedependence of degree 2 and a polynomial of degree 2 for the innovation variance for the genetic and permanent environmental effects, i.e., 12 parameters) provided a better fit for the data than RR with a quadratic function for the genetic and permanent environmental effects (13 parameters), with Bayesian information criteria values of -10,060 and -9,838, respectively. Heritabilities with the SAD model were higher than those of RR over the first 7 wk of the test. Genetic correlations between weeks were higher than 0.68 for short intervals between weeks and decreased to 0.08 for the SAD model and -0.39 for RR for the longest intervals. These differences in genetic parameters showed that, contrary to the RR approach, the SAD model does not suffer from border effect problems and can handle genetic correlations that tend to 0. Summarized breeding values were proposed for each approach as linear combinations of the individual weekly EBV weighted by the coefficients of the first or second eigenvector computed from the genetic covariance matrix of the additive genetic effects. These summarized breeding values isolated EBV trajectories over time, capturing either the average general value or the slope of the trajectory. Finally, applying the SAD model over a reduced period of time suggested that similar selection choices would result from the use of the records from the first 8 wk of the test. To conclude, the SAD model performed well for the genetic evaluation of longitudinal phenotypes.

New residual feed intake criterion for longitudinal data

“…Instead, animals should be selected based on the trajectory of their TEBV over time. This selection requires a limited number of descriptive parameters of these trajectories, which are usually obtained by eigendecomposition of the genetic covariance matrix [ 15 , 30 ]. It has been shown that the two first eigenvectors obtained with longitudinal models are usually sufficient to describe the trajectory of feed efficiency [ 15 ].…”

“…This selection requires a limited number of descriptive parameters of these trajectories, which are usually obtained by eigendecomposition of the genetic covariance matrix [ 15 , 30 ]. It has been shown that the two first eigenvectors obtained with longitudinal models are usually sufficient to describe the trajectory of feed efficiency [ 15 ]. The corresponding summarised estimated breeding values (SBV1 and SBV2), which are the product of the vector of TEBV with the first or the second eigenvectors, generally correspond to the EBV for average feed efficiency and the EBV for the main slope of the trajectory over time, respectively [ 15 ].…”

“… corresponded to the measurements of BF on animal at week . Outliers, as defined in Huynh-Tran et al [ 15 ], were removed from the analysis. Data for the first week in the growing-finishing room (11 weeks of age), which was considered to be an adaptation period for the animals, and for weeks corresponding to 25 and 26 weeks of age, as the number of pigs weighed per week decreased at the end of the test period (animals already slaughtered), were also removed from the dataset.…”

“…Using automatic self-feeders [ 10 – 14 ], individual feed intake can be recorded whenever an animal accesses its feeder. Since repeated recordings of body weight and body composition are also possible, it is conceivable to compute repeated measurements of RFI at various ages [ 15 ]. Compared to the use of an average RFI over the growing period, the main advantage of such longitudinal RFI data is the ability to describe changes in the trait over time and to provide more accurate estimates of breeding values for genetic selection [ 16 ].…”

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

“…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).…”