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

Patricia Mendes dos Santos, Ana Carolina Campana Nascimento, Moysés Nascimento, Fabyano Fonseca e Silva, Camila Ferreira Azevedo, Rodrigo Reis Mota, Simone Eliza Facioni Guimarães, Paulo Sávio Lopes
2018 Pesquisa Agropecuária Brasileira  
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
more » ... 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.
doi:10.1590/s0100-204x2018000900004 fatcat:zhvuy7hwt5bsfplhfzlhwkfpe4