Accuracy of genomic prediction of maternal traits in pigs using Bayesian variable selection methods.
J Anim Breed Genet
; 139(6): 654-665, 2022 Nov.
Article
em En
| MEDLINE
| ID: mdl-35758628
ABSTRACT
The aim of this study was to compare three methods of genomic prediction GBLUP, BayesC and BayesGC for genomic prediction of six maternal traits in Landrace sows using a panel of 660 K SNPs. The effects of different priors for the Bayesian methods were also investigated. GBLUP does not take the genetic architecture into account as all SNPs are assumed to have equally sized effects and relies heavily on the relationships between the animals for accurate predictions. Bayesian approaches rely on both fitting SNPs that describe relationships between animals in addition to fitting single SNP effects directly. Both the relationship between the animals and single SNP effects are important for accurate predictions. Maternal traits in sows are often more difficult to record and have lower heritabilities. BayesGC was generally the method with the higher accuracy, although its accuracy was for some traits matched by that of GBLUP and for others by that of BayesC. For piglet mortality within 3 weeks, BayesGC achieved up to 9.2% higher accuracy. For many of the traits, however, the methods did not show significant differences in accuracies.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Genoma
/
Genômica
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
Idioma:
En
Revista:
J Anim Breed Genet
Assunto da revista:
GENETICA
/
MEDICINA VETERINARIA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Noruega