Genomic prediction ability for carcass composition indicator traits in Nellore cattle

2021 ◽  
Vol 245 ◽  
pp. 104421
Author(s):  
Rosiane P. Silva ◽  
Rafael Espigolan ◽  
Mariana P. Berton ◽  
Raysildo B. Lôbo ◽  
Cláudio U. Magnabosco ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Carcass Composition ◽  
Prediction Ability ◽  
Nellore Cattle ◽  
Indicator Traits

Genomic regions and enrichment analyses associated with carcass composition indicator traits in Nellore cattle

2018 ◽  
Vol 136 (2) ◽  
pp. 118-133 ◽  
Author(s):  
Rosiane P. Silva ◽  
Mariana P. Berton ◽  
Laís Grigoletto ◽  
Felipe E. Carvalho ◽  
Rafael M. O. Silva ◽  
...  
Keyword(s):  
Carcass Composition ◽  
Nellore Cattle ◽  
Genomic Regions ◽  
Indicator Traits

scholarly journals PSXII-22 Genomic prediction accuracy for feed efficiency related traits using different pseudo-phenotypes, prediction and validation methods in Nellore cattle

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 245-246
Author(s):  
Cláudio U Magnabosco ◽  
Fernando Lopes ◽  
Valentina Magnabosco ◽  
Raysildo Lobo ◽  
Leticia Pereira ◽  
...  
Keyword(s):  
Body Weight ◽  
Weight Gain ◽  
Genomic Prediction ◽  
Feed Efficiency ◽  
Prediction Accuracy ◽  
Body Weight Gain ◽  
Prediction Methods ◽  
Genomic Breeding ◽  
Validation Population ◽  
Nellore Cattle

Abstract The aim of the study was to evaluate prediction methods, validation approaches and pseudo-phenotypes for the prediction of the genomic breeding values of feed efficiency related traits in Nellore cattle. It used the phenotypic and genotypic information of 4,329 and 3,594 animals, respectively, which were tested for residual feed intake (RFI), dry matter intake (DMI), feed efficiency (FE), feed conversion ratio (FCR), residual body weight gain (RG), and residual intake and body weight gain (RIG). Six prediction methods were used: ssGBLUP, BayesA, BayesB, BayesCπ, BLASSO, and BayesR. Three validation approaches were used: 1) random: where the data was randomly divided into ten subsets and the validation was done in each subset at a time; 2) age: the division into the training (2010 to 2016) and validation population (2017) were based on the year of birth; 3) genetic breeding value (EBV) accuracy: the data was split in the training population being animals with accuracy above 0.45; and validation population those below 0.45. We checked the accuracy and bias of genomic value (GEBV). The results showed that the GEBV accuracy was the highest when the prediction is obtained with ssGBLUP (0.05 to 0.31) (Figure 1). The low heritability obtained, mainly for FE (0.07 ± 0.03) and FCR (0.09 ± 0.03), limited the GEBVs accuracy, which ranged from low to moderate. The regression coefficient estimates were close to 1, and similar between the prediction methods, validation approaches, and pseudo-phenotypes. The cross-validation presented the most accurate predictions ranging from 0.07 to 0.037. The prediction accuracy was higher for phenotype adjusted for fixed effects than for EBV and EBV deregressed (30.0 and 34.3%, respectively). Genomic prediction can provide a reliable estimate of genomic breeding values for RFI, DMI, RG and RGI, as to even say that those traits may have higher genetic gain than FE and FCR.

scholarly journals Impact of residual covariance structures on genomic prediction ability in multi-environment trials

PLoS ONE ◽  
2018 ◽  
Vol 13 (7) ◽  
pp. e0201181
Author(s):  
Boby Mathew ◽  
Jens Léon ◽  
Mikko J. Sillanpää
Keyword(s):  
Genomic Prediction ◽  
Covariance Structures ◽  
Prediction Ability ◽  
Residual Covariance

scholarly journals Genomic Prediction with Genotype by Environment Interaction Analysis for Kernel Zinc Concentration in Tropical Maize Germplasm

2020 ◽  
Vol 10 (8) ◽  
pp. 2629-2639
Author(s):  
Edna K. Mageto ◽  
Jose Crossa ◽  
Paulino Pérez-Rodríguez ◽  
Thanda Dhliwayo ◽  
Natalia Palacios-Rojas ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Models ◽  
Interaction Analysis ◽  
Genotype By Environment Interaction ◽  
Zn Deficiency ◽  
Environment Interaction ◽  
Prediction Ability ◽  
Association Panel ◽  
Maize Germplasm ◽  
Zn Concentration

Zinc (Zn) deficiency is a major risk factor for human health, affecting about 30% of the world’s population. To study the potential of genomic selection (GS) for maize with increased Zn concentration, an association panel and two doubled haploid (DH) populations were evaluated in three environments. Three genomic prediction models, M (M1: Environment + Line, M2: Environment + Line + Genomic, and M3: Environment + Line + Genomic + Genomic x Environment) incorporating main effects (lines and genomic) and the interaction between genomic and environment (G x E) were assessed to estimate the prediction ability (rMP) for each model. Two distinct cross-validation (CV) schemes simulating two genomic prediction breeding scenarios were used. CV1 predicts the performance of newly developed lines, whereas CV2 predicts the performance of lines tested in sparse multi-location trials. Predictions for Zn in CV1 ranged from -0.01 to 0.56 for DH1, 0.04 to 0.50 for DH2 and -0.001 to 0.47 for the association panel. For CV2, rMP values ranged from 0.67 to 0.71 for DH1, 0.40 to 0.56 for DH2 and 0.64 to 0.72 for the association panel. The genomic prediction model which included G x E had the highest average rMP for both CV1 (0.39 and 0.44) and CV2 (0.71 and 0.51) for the association panel and DH2 population, respectively. These results suggest that GS has potential to accelerate breeding for enhanced kernel Zn concentration by facilitating selection of superior genotypes.

scholarly journals Genomic Regions Associated with Feed Efficiency Indicator Traits in an Experimental Nellore Cattle Population

PLoS ONE ◽  
2016 ◽  
Vol 11 (10) ◽  
pp. e0164390 ◽  
Author(s):  
Bianca Ferreira Olivieri ◽  
Maria Eugênia Zerlotti Mercadante ◽  
Joslaine Noely dos Santos Gonçalves Cyrillo ◽  
Renata Helena Branco ◽  
Sarah Figueiredo Martins Bonilha ◽  
...  
Keyword(s):  
Feed Efficiency ◽  
Cattle Population ◽  
Nellore Cattle ◽  
Genomic Regions ◽  
Indicator Traits

Effect of quality control, density and allele frequency of markers on the accuracy of genomic prediction for complex traits in Nellore cattle

2019 ◽  
Vol 59 (1) ◽  
pp. 48 ◽  
Author(s):  
Tiago Bresolin ◽  
Guilherme Jordão de Magalhães Rosa ◽  
Bruno Dourado Valente ◽  
Rafael Espigolan ◽  
Daniel Gustavo Mansan Gordo ◽  
...  
Keyword(s):  
Quality Control ◽  
Allele Frequency ◽  
Minor Allele Frequency ◽  
Genomic Prediction ◽  
High Density ◽  
Minor Allele ◽  
Call Rate ◽  
Genotyping Assay ◽  
Nellore Cattle ◽  
Genomic Predictions

This study was designed to test the impact of quality control, density and allele frequency of single nucleotide polymorphisms (SNP) markers on the accuracy of genomic predictions, using three traits with different heritabilities and two methods of prediction in a Nellore cattle population genotyped with the Illumina Bovine HD Assay. A total of 1756; 3150 and 3119 records of age at first calving (AFC); weaning weight (WW) and yearling weight (YW), respectively, were used. Three scenarios with different exclusion thresholds for minor allele frequency (MAF), deviation from Hardy–Weinberg equilibrium (HWE) and correlation between SNP pairs (r2) were constructed for all traits: (1) high rigor (S1): call rate <0.98, MAF <0.05, HWE with P <10−5, and r2 >0.999; (2) Moderate rigor (S2): call rate <0.85 and MAF <0.01; (3) Low rigor (S3): only non-autosomal SNP and those mapped on the same position were excluded. Additionally, to assess the prediction accuracy from different markers density, six panels (10K, 50K, 100K, 300K, 500K and 700K) were customised using the high-density genotyping assay as reference. Finally, from the markers available in high-density genotyping assay, six groups (G) with different minor allele frequency bins were defined to estimate the accuracy of genomic prediction. The range of MAF bins was approximately equal for the traits studied: G1 (0.000–0.009), G2 (0.010–0.064), G3 (0.065–0.174), G4 (0.175–0.325), G5 (0.326–0.500) and G6 (0.000–0.500). The Genomic Best Linear Unbiased Predictor and BayesCπ methods were used to estimate the SNP marker effects. Five-fold cross-validation was used to measure the accuracy of genomic prediction for all scenarios. There were no effects of genotypes quality control criteria on the accuracies of genomic predictions. For all traits, the higher density panel did not provide greater prediction accuracies than the low density one (10K panel). The groups of SNP with low MAF (MAF ≤0.007 for AFC, MAF ≤0.009 for WW and MAF ≤0.008 for YW) provided lower prediction accuracies than the groups with higher allele frequencies.

scholarly journals Genetic Parameters and Genome-Wide Association Studies for Anti-Müllerian Hormone Levels and Antral Follicle Populations Measured After Estrus Synchronization in Nellore Cattle

Animals ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1185
Author(s):  
Laís Grigoletto ◽  
Miguel Henrique Almeida Santana ◽  
Fabiana Fernandes Bressan ◽  
Joanir Pereira Eler ◽  
Marcelo Fábio Gouveia Nogueira ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Genetic Parameters ◽  
Association Studies ◽  
Antral Follicle ◽  
Genome Wide Association Studies ◽  
Hormone Levels ◽  
Estrous Synchronization ◽  
Nellore Cattle ◽  
Genomic Regions ◽  
Indicator Traits

Reproductive efficiency plays a major role in the long-term sustainability of livestock industries and can be improved through genetic and genomic selection. This study aimed to estimate genetic parameters (heritability and genetic correlation) and identify genomic regions and candidate genes associated with anti-Müllerian hormone levels (AMH) and antral follicle populations measured after estrous synchronization (AFP) in Nellore cattle. The datasets included phenotypic records for 1099 and 289 Nellore females for AFP and AMH, respectively, high-density single nucleotide polymorphism (SNP) genotypes for 944 animals, and 4129 individuals in the pedigree. The heritability estimates for AMH and AFP were 0.28 ± 0.07 and 0.30 ± 0.09, and the traits were highly and positively genetically correlated (rG = 0.81 ± 0.02). These findings indicated that these traits can be improved through selective breeding, and substantial indirect genetic gains are expected by selecting for only one of the two traits. A total of 31 genomic regions were shown to be associated with AMH or AFP, and two genomic regions located on BTA1 (64.9–65.0 Mb and 109.1–109.2 Mb) overlapped between the traits. Various candidate genes were identified to be potentially linked to important biological processes such as ovulation, tissue remodeling, and the immune system. Our findings support the use of AMH and AFP as indicator traits to genetically improve fertility rates in Nellore cattle and identify better oocyte donors.

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

2020 ◽  
Vol 137 (5) ◽  
pp. 438-448 ◽  
Author(s):  
Fernando Brito Lopes ◽  
Cláudio U. Magnabosco ◽  
Tiago L. Passafaro ◽  
Ludmilla C. Brunes ◽  
Marcos F. O. Costa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Meat Tenderness ◽  
Nellore Cattle ◽  
Artificial Neural

scholarly journals Genomic prediction ability for feed efficiency traits using different models and pseudo-phenotypes under several validation strategies in Nelore cattle

animal ◽  
2020 ◽  
pp. 100085
Author(s):  
L.C. Brunes ◽  
F. Baldi ◽  
F.B. Lopes ◽  
M.G. Narciso ◽  
R.B. Lobo ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Feed Efficiency ◽  
Prediction Ability ◽  
Nelore Cattle

scholarly journals Genomic prediction of breeding values for carcass traits in Nellore cattle

2016 ◽  
Vol 48 (1) ◽  
Author(s):  
Gerardo A. Fernandes Júnior ◽  
Guilherme J. M. Rosa ◽  
Bruno D. Valente ◽  
Roberto Carvalheiro ◽  
Fernando Baldi ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Carcass Traits ◽  
Breeding Values ◽  
Nellore Cattle
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