Genomic selection for slaughter age in pigs using the Cox frailty model

Abstract

ABSTRACT. The aim of this study was to compare genomic selection methodologies using a linear mixed model and the Cox survival model. We used data from an F2 population of pigs, in which the response variable was the time in days from birth to the culling of the animal and the covariates were 238 markers [237 single nucleotide polymorphism (SNP) plus the halothane gene]. The data were corrected for fixed effects, and the accuracy of the method was determined based on the correlation of the ranks of predicted genomic breeding values (GBVs) in both models with the corrected phenotypic values. The analysis was repeated with a subset of SNP markers with largest absolute effects. The results were in agreement with the GBV prediction and the estimation of marker effects for both models for uncensored data and for normality. However, when considering censored data, the Cox model with a normal random effect (S1) was more appropriate. Since there was no agreement between the linear mixed model and the imputed data (2015) (L2) for the prediction of genomic values and the estimation of marker effects, the model S1 was considered superior as it took into account the latent variable and the censored data. Marker selection increased correlations between the ranks of predicted GBVs by the linear and Cox frailty models and the corrected phenotypic values, and 120 markers were required to increase the predictive ability for the characteristic analyzed.

Genomic prediction for additive and dominance effects of censored traits in pigs

“…In the linear model, the fit is performed directly on the time variable, while in the Cox model, the fit is based on the hazard of the animal with regards to obtaining the desired weight at slaughter; i.e., the best animals in the linear models were those with the lowest predicted TGVs and GBVs, since they reached the desired slaughter weight in a shorter period of time. In the Cox models, the best animals were those with the greatest predicted TGVs and GBVs; thus, the risk function grows rapidly, thereby indicating that the animal's weight also grows quickly (Hou et al, 2009;Giolo and Demétrio, 2011;Santos et al, 2015).…”

“…This occurred because of the greater distance between the predicted genetic values and the adjusted phenotypes of the censored times. Although they presented with the smallest predictive abilities, these models are conceptually more appropriate, since they express the correlations with the phenotypes in the latent scale (Santos et al, 2015).…”

“…Thus, residual variance cannot be directly obtained. An alternative was proposed by Yazdi et al (2002) and was used by Schneider et al (2005) and Santos et al (2015). Under this approach, the residual variance is replaced by 1/(1-c) where c is the proportion of censored data.…”

“…In the context of GS, Santos et al (2015) were the pioneers when it came to performing the genomic prediction of censored phenotypes while using the Cox survival model. As such, the model with only additive effects was considered, and was fitted by using the coxme function of the R software, since the relationship matrix based on pedigree was replaced by the relationship matrix based on markers.…”

“…Thus, we aimed to build upon the method proposed by Santos et al (2015) to include dominance effects and evaluate their performance. In addition, we also aimed to compare the proposed model with alternative linear models in the presence and absence of censored observations.…”