Accuracy of genomic selection using stochastic search variable selection in Australian Holstein Friesian dairy cattle.
Genet Res (Camb)
; 91(5): 307-11, 2009 Oct.
Article
em En
| MEDLINE
| ID: mdl-19922694
ABSTRACT
Genomic selection describes a selection strategy based on genomic breeding values predicted from dense single nucleotide polymorphism (SNP) data. Multiple methods have been proposed but the critical issue is how to decide whether an SNP should be included in the predictive set to estimate breeding values. One major disadvantage of the traditional Bayes B approach is its high computational demands caused by the changing dimensionality of the models. The use of stochastic search variable selection (SSVS) retains the same assumptions about the distribution of SNP effects as Bayes B, while maintaining constant dimensionality. When Bayesian SSVS was used to predict genomic breeding values for real dairy data over a range of traits it produced accuracies higher or equivalent to other genomic selection methods with significantly decreased computational and time demands than Bayes B.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Seleção Genética
/
Cruzamento
/
Processos Estocásticos
/
Genômica
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Indústria de Laticínios
Tipo de estudo:
Evaluation_studies
/
Prognostic_studies
Limite:
Animals
País/Região como assunto:
Oceania
Idioma:
En
Revista:
Genet Res (Camb)
Ano de publicação:
2009
Tipo de documento:
Article