ArticleGenetics, selection, evolution : GSE2016
Genomic prediction of breeding values for carcass traits in Nellore cattle.
Article in Genetics, selection, evolution : GSE, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Genetic and genomic insights into morphological defects in nellore cattle: a comprehensive review.Tropical animal health and production · 2026Review
- Recent progress in genomic prediction for Hanwoo cattle and its implications for beef quality: review.Animal bioscience · 2026Article
- Multiple strategies association revealed functional candidate FASN gene for fatty acid composition in cattle.Communications biology · 2025Article
- Tailoring Genomic Selection forGenes · 2024Review
- An overview of recent technological developments in bovine genomics.Veterinary and animal science · 2024Review
- Article
- Genome-Wide Association Study as an Efficacious Approach to Discover Candidate Genes Associated with Body Linear Type Traits in Dairy Cattle.Animals : an open access journal from MDPI · 2024Review
- Estimation of genetic correlations and genomic prediction accuracy for reproductive and carcass traits in Hanwoo cows.Journal of animal science and technology · 2024Article
- Genetic dissection and genomic prediction for pork cuts and carcass morphology traits in pig.Journal of animal science and biotechnology · 2023Article
- Heterogeneity of variance and genetic parameters for milk production in cattle, using Bayesian inference.PloS one · 2023Article
- Integrative analyses of genomic and metabolomic data reveal genetic mechanisms associated with carcass merit traits in beef cattle.Scientific reports · 2022Article
- A Random Forest-Based Genome-Wide Scan Reveals Fertility-Related Candidate Genes and Potential Inter-Chromosomal Epistatic Regions Associated With Age at First Calving in Nellore Cattle.Frontiers in genetics · 2022Article
- Exploring the size of reference population for expected accuracy of genomic prediction using simulated and real data in Japanese Black cattle.BMC genomics · 2021Article
- Accuracy of genomic breeding values and predictive ability for postweaning liveweight and age at first calving in a Nellore cattle population with missing sire information.Tropical animal health and production · 2021Article
- Validation of the Prediction Accuracy for 13 Traits in Chinese Simmental Beef Cattle Using a Preselected Low-Density SNP Panel.Animals : an open access journal from MDPI · 2021Article
- Genomic Prediction of Average Daily Gain, Back-Fat Thickness, and Loin Muscle Depth Using Different Genomic Tools in Canadian Swine Populations.Frontiers in genetics · 2021Article
- Quantitative trait loci-based genomics-assisted prediction for the degree of apple fruit cover color.The plant genome · 2020Article
- Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods.Journal of animal science · 2020Article
- Genome-enable prediction for health traits using high-density SNP panel in US Holstein cattle.Animal genetics · 2020Article
- Incorporating Genome Annotation Into Genomic Prediction for Carcass Traits in Chinese Simmental Beef Cattle.Frontiers in genetics · 2020Article
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15 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe objective of this study was to evaluate the accuracy of genomic predictions for rib eye area (REA), backfat thickness (BFT), and hot carcass weight (HCW) in Nellore beef cattle from Brazilian commercial herds using different prediction models.
methodsPhenotypic data from 1756 Nellore steers from ten commercial herds in Brazil were used. Animals were offspring of 294 sires and 1546 dams, reared on pasture, feedlot finished, and slaughtered at approximately 2 years of age. All animals were genotyped using a 777k Illumina Bovine HD SNP chip. Accuracy of genomic predictions of breeding values was evaluated by using a 5-fold cross-validation scheme and considering three models: Bayesian ridge regression (BRR), Bayes C (BC) and Bayesian Lasso (BL), and two types of response variables: traditional estimated breeding value (EBV), and phenotype adjusted for fixed effects (Y*).
resultsThe prediction accuracies achieved with the BRR model were equal to 0.25 (BFT), 0.33 (HCW) and 0.36 (REA) when EBV was used as response variable, and 0.21 (BFT), 0.37 (HCW) and 0.46 (REA) when using Y*. Results obtained with the BC and BL models were similar. Accuracies increased for traits with a higher heritability, and using Y* instead of EBV as response variable resulted in higher accuracy when heritability was higher.
conclusionsOur results indicate that the accuracy of genomic prediction of carcass traits in Nellore cattle is moderate to high. Prediction of genomic breeding values from adjusted phenotypes Y* was more accurate than from EBV, especially for highly heritable traits. The three models considered (BRR, BC and BL) led to similar predictive abilities and, thus, either one could be used to implement genomic prediction for carcass traits in Nellore cattle.
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