Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks

Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks

The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumin...

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Veröffentlicht in:Journal of animal breeding and genetics (1986) 2020-09, Vol.137 (5), p.438-448
Hauptverfasser: Brito Lopes, Fernando, Magnabosco, Cláudio U., Passafaro, Tiago L., Brunes, Ludmilla C., Costa, Marcos F. O., Eifert, Eduardo C., Narciso, Marcelo G., Rosa, Guilherme J. M., Lobo, Raysildo B., Baldi, Fernando
Format: Artikel
Sprache:eng
Schlagworte:
animal breeding
Animals
Artificial neural networks
Bayes Theorem
Bayesian analysis
Bayesian regression models
Beef cattle
Breeding - statistics & numerical data
Cattle
Chromosomes
deep learning
Gene Frequency
Genome - genetics
genomic selection
Genomics - statistics & numerical data
Genotype
Genotypes
Linkage disequilibrium
Linkage Disequilibrium - genetics
Livestock breeding
Mathematical models
Meat
Meat - analysis
Meat - statistics & numerical data
Neural networks
Neural Networks, Computer
Pedigree
Performance prediction
Polymorphism, Single Nucleotide
Predictions
Quality control
Quantitative Trait, Heritable
Regression analysis
Regression models
Single-nucleotide polymorphism
Zebu
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Beschreibung
Zusammenfassung:The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non‐autosomal chromosomes, with minor allele frequency
ISSN:0931-2668
1439-0388
DOI:10.1111/jbg.12468