Use of regularized quantile regression to predict the genetic merit of pigs for asymmetric carcass traits

Abstract

The objective of this work was to evaluate the use of regularized quantile regression (RQR) to predict the genetic merit of pigs for asymmetric carcass traits, compared with the Bayesian lasso (Blasso) method. The genetic data of the traits carcass yield, bacon thickness, and backfat thickness from a F2 population composed of 345 individuals, generated by crossing animals from the Piau breed with those of a commercial breed, were used. RQR was evaluated considering different quantiles (τ = 0.05 to 0.95). The RQR model used to estimate the genetic merit showed accuracies higher than or equal to those obtained by Blasso, for all studies traits. There was an increase of 6.7 and 20.0% in accuracy when the quantiles 0.15 and 0.45 were considered in the evaluation of carcass yield and bacon thickness, respectively. The obtained results are indicative that the regularized quantile regression presents higher accuracy than the Bayesian lasso method for the prediction of the genetic merit of pigs for asymmetric carcass variables.

Quantile regression in genomic selection for oligogenic traits in autogamous plants: A simulation study

“…Generally speaking, the breeding process by RQR can be equal to or faster than by the standard GS methodologies. Although to date little explored in breeding, the RQR method has been shown to be very promising for genomic selection and association studies, in both plant and animal breeding [ 27 – 30 , 48 ]. In this study, RQR (τ = 0.50) fixed the favorable alleles in the fourth generation ( F 4 ) in the scenario with a heritability of 0.4 and selection intensity of 10%.…”

“…According to the recommendation of Santos et al [ 30 ], the penalty parameter λ of RQR was defined as half the penalty parameter resulting from the BLASSO method.…”

“…The results indicated the viability of this alternative for GWS analysis, even in scenarios without skewness of the phenotype distribution. The approaches RQR and BLASSO were also used by Santos et al [ 30 ] to estimate the genetic merit in pigs for asymmetric traits related to the pig carcass, and observed equally or more accurate results by RQR than BLASSO for all evaluated traits.…”

Research Article Regression trees in genomic selection for carcass traits in pigs

“…We can cite Teixeira et al (2016), who proposed and evaluated the use of factor analysis in the same dataset and found that the accuracy in the selection (predictive ability divided by the square root of the heritability of the trait) obtained by BLASSO outperformed the other methods considered by them. Also, Santos et al (2018) evaluated three asymmetric traits of this same dataset using quantile regression and compared it with the BLASSO, presenting the BLASSO with accuracy in the selection similar to those obtained by quantile regression. Additionally, the good performance of the BLASSO was expected, according with the results observed in the study conducted by de los Campos et al (2009).…”