Article (Scientific journals)
Use of regularized quantile regression to predict the genetic merit of pigs for asymmetric carcass traits
Mendes dos Santos, Patrícia; Campana Nascimento, Ana Carolina; Nascimento, Moysés et al.
2018In Pesquisa Agropecuaria Brasileira, 53 (9), p. 1011-1017
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Keywords :
Sus scrofa; Blasso; Shrinkage
Abstract :
[en] 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.
Disciplines :
Animal production & animal husbandry
Author, co-author :
Mendes dos Santos, Patrícia
Campana Nascimento, Ana Carolina
Nascimento, Moysés
Fonseca e Silva, Fabyano
Ferreira Azevedo, Camila
REIS MOTA, Rodrigo ;  Université de Liège - ULiège > Agronomie, Bio-ingénierie et Chimie (AgroBioChem) > Ingénierie des productions animales et nutrition
Facioni Guimarães, Simone Eliza
Sávio Lopes, Paulo
Language :
English
Title :
Use of regularized quantile regression to predict the genetic merit of pigs for asymmetric carcass traits
Publication date :
September 2018
Journal title :
Pesquisa Agropecuaria Brasileira
ISSN :
0100-204X
eISSN :
1678-3921
Publisher :
Embrapa Informação Tecnológica, Brasilia, Brazil
Volume :
53
Issue :
9
Pages :
1011-1017
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 01 November 2018

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