Comparison of Univariate and Multivariate Applications of GBLUP and Artificial Neural Network for Genomic Prediction of Growth and Carcass Traits in the Brangus Heifer Population
Data for growth (birth, weaning and yearling weights) and carcass (longissimus muscle area, intramuscular fat percentage and depth of rib fat) traits and 50K SNP marker data to calculate the genomic relationship matrix were collected from 738 Brangus heifers. Univariate and multivariate genomic best...
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MDPI AG
2025-04-01
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| author | Sunday O. Peters Kadir Kızılkaya Mahmut Sinecen Milt G. Thomas |
| author_facet | Sunday O. Peters Kadir Kızılkaya Mahmut Sinecen Milt G. Thomas |
| author_sort | Sunday O. Peters |
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| description | Data for growth (birth, weaning and yearling weights) and carcass (longissimus muscle area, intramuscular fat percentage and depth of rib fat) traits and 50K SNP marker data to calculate the genomic relationship matrix were collected from 738 Brangus heifers. Univariate and multivariate genomic best linear unbiased prediction models based on the genomic relationship matrix and univariate and multivariate artificial neural networks models with 1 to 10 neurons, as well as the learning algorithms of Bayesian Regularization, Levenberg–Marquardt and Scaled Conjugate Gradient and transfer function combinations of tangent sigmoid–linear and linear–linear in the hidden-output layers, including the inputs from genomic relationship matrix, were created and applied for the analysis of growth and carcass data. Pearson’s correlation coefficients were used to evaluate the predictive performances of univariate and multivariate genomic best linear unbiased prediction and artificial neural networks models. The overall predictive abilities of genomic best linear unbiased prediction and artificial neural network models were low in the univariate and multivariate analysis. However, the predictive performances of models in the univariate analysis were significantly higher than those from models in the multivariate analysis. In the univariate analysis, models with Bayesian Regularization and the tangent sigmoid–linear or linear–linear transfer function combination yielded higher predictive performances than models with learning algorithms and genomic best linear unbiased prediction models. In addition, predictive performances of models with tangent sigmoid–linear transfer functions were better than those with linear–linear transfer functions in the univariate analysis. |
| format | Article |
| id | doaj-art-c6cb63b090a444dbb0f65f4f36be43b2 |
| institution | DOAJ |
| issn | 2673-933X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Ruminants |
| spelling | doaj-art-c6cb63b090a444dbb0f65f4f36be43b22025-08-20T03:16:39ZengMDPI AGRuminants2673-933X2025-04-01521610.3390/ruminants5020016Comparison of Univariate and Multivariate Applications of GBLUP and Artificial Neural Network for Genomic Prediction of Growth and Carcass Traits in the Brangus Heifer PopulationSunday O. Peters0Kadir Kızılkaya1Mahmut Sinecen2Milt G. Thomas3Department of Animal Science, Berry College, Mount Berry, GA 30149, USADepartment of Animal Science, Faculty of Agriculture, Aydin Adnan Menderes University, Aydin 09100, TurkeyDepartment of Computer Engineering, Faculty of Engineering, Aydin Adnan Menderes University, Aydin 09100, TurkeyDepartment of Animal Science, Texas A&M AgriLife Research, Beeville, TX 78102, USAData for growth (birth, weaning and yearling weights) and carcass (longissimus muscle area, intramuscular fat percentage and depth of rib fat) traits and 50K SNP marker data to calculate the genomic relationship matrix were collected from 738 Brangus heifers. Univariate and multivariate genomic best linear unbiased prediction models based on the genomic relationship matrix and univariate and multivariate artificial neural networks models with 1 to 10 neurons, as well as the learning algorithms of Bayesian Regularization, Levenberg–Marquardt and Scaled Conjugate Gradient and transfer function combinations of tangent sigmoid–linear and linear–linear in the hidden-output layers, including the inputs from genomic relationship matrix, were created and applied for the analysis of growth and carcass data. Pearson’s correlation coefficients were used to evaluate the predictive performances of univariate and multivariate genomic best linear unbiased prediction and artificial neural networks models. The overall predictive abilities of genomic best linear unbiased prediction and artificial neural network models were low in the univariate and multivariate analysis. However, the predictive performances of models in the univariate analysis were significantly higher than those from models in the multivariate analysis. In the univariate analysis, models with Bayesian Regularization and the tangent sigmoid–linear or linear–linear transfer function combination yielded higher predictive performances than models with learning algorithms and genomic best linear unbiased prediction models. In addition, predictive performances of models with tangent sigmoid–linear transfer functions were better than those with linear–linear transfer functions in the univariate analysis.https://www.mdpi.com/2673-933X/5/2/16genomic predictiongenomic relationshipartificial neural networklearning algorithmtransfer function |
| spellingShingle | Sunday O. Peters Kadir Kızılkaya Mahmut Sinecen Milt G. Thomas Comparison of Univariate and Multivariate Applications of GBLUP and Artificial Neural Network for Genomic Prediction of Growth and Carcass Traits in the Brangus Heifer Population Ruminants genomic prediction genomic relationship artificial neural network learning algorithm transfer function |
| title | Comparison of Univariate and Multivariate Applications of GBLUP and Artificial Neural Network for Genomic Prediction of Growth and Carcass Traits in the Brangus Heifer Population |
| title_full | Comparison of Univariate and Multivariate Applications of GBLUP and Artificial Neural Network for Genomic Prediction of Growth and Carcass Traits in the Brangus Heifer Population |
| title_fullStr | Comparison of Univariate and Multivariate Applications of GBLUP and Artificial Neural Network for Genomic Prediction of Growth and Carcass Traits in the Brangus Heifer Population |
| title_full_unstemmed | Comparison of Univariate and Multivariate Applications of GBLUP and Artificial Neural Network for Genomic Prediction of Growth and Carcass Traits in the Brangus Heifer Population |
| title_short | Comparison of Univariate and Multivariate Applications of GBLUP and Artificial Neural Network for Genomic Prediction of Growth and Carcass Traits in the Brangus Heifer Population |
| title_sort | comparison of univariate and multivariate applications of gblup and artificial neural network for genomic prediction of growth and carcass traits in the brangus heifer population |
| topic | genomic prediction genomic relationship artificial neural network learning algorithm transfer function |
| url | https://www.mdpi.com/2673-933X/5/2/16 |
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