Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers
Abstract Background Genomewide prediction estimates the genomic breeding values of selection candidates which can be utilized for population improvement and cultivar development. Ridge regression and deep learning-based selection models were implemented for yield and agronomic traits of 204 chile pe...
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | BMC Genomic Data |
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| Online Access: | https://doi.org/10.1186/s12863-023-01179-6 |
| _version_ | 1827399307822628864 |
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| author | Dennis N. Lozada Karansher Singh Sandhu Madhav Bhatta |
| author_facet | Dennis N. Lozada Karansher Singh Sandhu Madhav Bhatta |
| author_sort | Dennis N. Lozada |
| collection | DOAJ |
| description | Abstract Background Genomewide prediction estimates the genomic breeding values of selection candidates which can be utilized for population improvement and cultivar development. Ridge regression and deep learning-based selection models were implemented for yield and agronomic traits of 204 chile pepper genotypes evaluated in multi-environment trials in New Mexico, USA. Results Accuracy of prediction differed across different models under ten-fold cross-validations, where high prediction accuracy was observed for highly heritable traits such as plant height and plant width. No model was superior across traits using 14,922 SNP markers for genomewide selection. Bayesian ridge regression had the highest average accuracy for first pod date (0.77) and total yield per plant (0.33). Multilayer perceptron (MLP) was the most superior for flowering time (0.76) and plant height (0.73), whereas the genomic BLUP model had the highest accuracy for plant width (0.62). Using a subset of 7,690 SNP loci resulting from grouping markers based on linkage disequilibrium coefficients resulted in improved accuracy for first pod date, ten pod weight, and total yield per plant, even under a relatively small training population size for MLP and random forest models. Genomic and ridge regression BLUP models were sufficient for optimal prediction accuracies for small training population size. Combining phenotypic selection and genomewide selection resulted in improved selection response for yield-related traits, indicating that integrated approaches can result in improved gains achieved through selection. Conclusions Accuracy values for ridge regression and deep learning prediction models demonstrate the potential of implementing genomewide selection for genetic improvement in chile pepper breeding programs. Ultimately, a large training data is relevant for improved genomic selection accuracy for the deep learning models. |
| first_indexed | 2024-03-08T19:44:02Z |
| format | Article |
| id | doaj.art-f6c562d8811f4a3c9b19270168609ac6 |
| institution | Directory Open Access Journal |
| issn | 2730-6844 |
| language | English |
| last_indexed | 2024-03-08T19:44:02Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomic Data |
| spelling | doaj.art-f6c562d8811f4a3c9b19270168609ac62023-12-24T12:30:39ZengBMCBMC Genomic Data2730-68442023-12-0124111310.1186/s12863-023-01179-6Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppersDennis N. Lozada0Karansher Singh Sandhu1Madhav Bhatta2Department of Plant and Environmental Sciences, New Mexico State UniversityBayer Crop ScienceBayer Crop ScienceAbstract Background Genomewide prediction estimates the genomic breeding values of selection candidates which can be utilized for population improvement and cultivar development. Ridge regression and deep learning-based selection models were implemented for yield and agronomic traits of 204 chile pepper genotypes evaluated in multi-environment trials in New Mexico, USA. Results Accuracy of prediction differed across different models under ten-fold cross-validations, where high prediction accuracy was observed for highly heritable traits such as plant height and plant width. No model was superior across traits using 14,922 SNP markers for genomewide selection. Bayesian ridge regression had the highest average accuracy for first pod date (0.77) and total yield per plant (0.33). Multilayer perceptron (MLP) was the most superior for flowering time (0.76) and plant height (0.73), whereas the genomic BLUP model had the highest accuracy for plant width (0.62). Using a subset of 7,690 SNP loci resulting from grouping markers based on linkage disequilibrium coefficients resulted in improved accuracy for first pod date, ten pod weight, and total yield per plant, even under a relatively small training population size for MLP and random forest models. Genomic and ridge regression BLUP models were sufficient for optimal prediction accuracies for small training population size. Combining phenotypic selection and genomewide selection resulted in improved selection response for yield-related traits, indicating that integrated approaches can result in improved gains achieved through selection. Conclusions Accuracy values for ridge regression and deep learning prediction models demonstrate the potential of implementing genomewide selection for genetic improvement in chile pepper breeding programs. Ultimately, a large training data is relevant for improved genomic selection accuracy for the deep learning models.https://doi.org/10.1186/s12863-023-01179-6Capsicum spp.Genomic predictionGenomic estimated breeding valuesLinkage disequilibriumMachine learningPlant morphology |
| spellingShingle | Dennis N. Lozada Karansher Singh Sandhu Madhav Bhatta Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers BMC Genomic Data Capsicum spp. Genomic prediction Genomic estimated breeding values Linkage disequilibrium Machine learning Plant morphology |
| title | Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers |
| title_full | Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers |
| title_fullStr | Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers |
| title_full_unstemmed | Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers |
| title_short | Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers |
| title_sort | ridge regression and deep learning models for genome wide selection of complex traits in new mexican chile peppers |
| topic | Capsicum spp. Genomic prediction Genomic estimated breeding values Linkage disequilibrium Machine learning Plant morphology |
| url | https://doi.org/10.1186/s12863-023-01179-6 |
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