Evaluation of genomic and phenomic prediction for application in apple breeding
Abstract Background Apple breeding schemes can be improved by using genomic prediction models to forecast the performance of breeding material. The predictive ability of these models depends on factors like trait genetic architecture, training set size, relatedness of the selected material to the tr...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
|
| Series: | BMC Plant Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12870-025-06104-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585954219524096 |
|---|---|
| author | Michaela Jung Marius Hodel Andrea Knauf Daniela Kupper Markus Neuditschko Simone Bühlmann-Schütz Bruno Studer Andrea Patocchi Giovanni AL Broggini |
| author_facet | Michaela Jung Marius Hodel Andrea Knauf Daniela Kupper Markus Neuditschko Simone Bühlmann-Schütz Bruno Studer Andrea Patocchi Giovanni AL Broggini |
| author_sort | Michaela Jung |
| collection | DOAJ |
| description | Abstract Background Apple breeding schemes can be improved by using genomic prediction models to forecast the performance of breeding material. The predictive ability of these models depends on factors like trait genetic architecture, training set size, relatedness of the selected material to the training set, and the validation method used. Alternative genotyping methods such as RADseq and complementary data from near-infrared spectroscopy could help improve the cost-effectiveness of genomic prediction. However, the impact of these factors and alternative approaches on predictive ability beyond experimental populations still need to be investigated. In this study, we evaluated 137 prediction scenarios varying the described factors and alternative approaches, offering recommendations for implementing genomic selection in apple breeding. Results Our results show that extending the training set with germplasm related to the predicted breeding material can improve average predictive ability across eleven studied traits by up to 0.08. The study emphasizes the usefulness of leave-one-family-out cross-validation, reflecting the application of genomic prediction to a new family, although it reduced average predictive ability across traits by up to 0.24 compared to 10-fold cross-validation. Similar average predictive abilities across traits indicate that imputed RADseq data could be a suitable genotyping alternative to SNP array datasets. The best-performing scenario using near-infrared spectroscopy data for phenomic prediction showed a 0.35 decrease in average predictive ability across traits compared to conventional genomic prediction, suggesting that the tested phenomic prediction approach is impractical. Conclusions Extending the training set using germplasm related with the target breeding material is crucial to improve the predictive ability of genomic prediction in apple. RADseq is a viable alternative to SNP array genotyping, while phenomic prediction is impractical. These findings offer valuable guidance for applying genomic selection in apple breeding, ultimately leading to the development of breeding material with improved quality. |
| format | Article |
| id | doaj-art-e98b1a0d6ea344bbaa4fc6bde6fabd6a |
| institution | Kabale University |
| issn | 1471-2229 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Plant Biology |
| spelling | doaj-art-e98b1a0d6ea344bbaa4fc6bde6fabd6a2025-01-26T12:23:19ZengBMCBMC Plant Biology1471-22292025-01-0125111910.1186/s12870-025-06104-wEvaluation of genomic and phenomic prediction for application in apple breedingMichaela Jung0Marius Hodel1Andrea Knauf2Daniela Kupper3Markus Neuditschko4Simone Bühlmann-Schütz5Bruno Studer6Andrea Patocchi7Giovanni AL Broggini8AgroscopeAgroscopeAgroscopeAgroscopeAgroscopeAgroscopeMolecular Plant Breeding, Institute of Agricultural Sciences, ETH ZurichAgroscopeMolecular Plant Breeding, Institute of Agricultural Sciences, ETH ZurichAbstract Background Apple breeding schemes can be improved by using genomic prediction models to forecast the performance of breeding material. The predictive ability of these models depends on factors like trait genetic architecture, training set size, relatedness of the selected material to the training set, and the validation method used. Alternative genotyping methods such as RADseq and complementary data from near-infrared spectroscopy could help improve the cost-effectiveness of genomic prediction. However, the impact of these factors and alternative approaches on predictive ability beyond experimental populations still need to be investigated. In this study, we evaluated 137 prediction scenarios varying the described factors and alternative approaches, offering recommendations for implementing genomic selection in apple breeding. Results Our results show that extending the training set with germplasm related to the predicted breeding material can improve average predictive ability across eleven studied traits by up to 0.08. The study emphasizes the usefulness of leave-one-family-out cross-validation, reflecting the application of genomic prediction to a new family, although it reduced average predictive ability across traits by up to 0.24 compared to 10-fold cross-validation. Similar average predictive abilities across traits indicate that imputed RADseq data could be a suitable genotyping alternative to SNP array datasets. The best-performing scenario using near-infrared spectroscopy data for phenomic prediction showed a 0.35 decrease in average predictive ability across traits compared to conventional genomic prediction, suggesting that the tested phenomic prediction approach is impractical. Conclusions Extending the training set using germplasm related with the target breeding material is crucial to improve the predictive ability of genomic prediction in apple. RADseq is a viable alternative to SNP array genotyping, while phenomic prediction is impractical. These findings offer valuable guidance for applying genomic selection in apple breeding, ultimately leading to the development of breeding material with improved quality.https://doi.org/10.1186/s12870-025-06104-wGenomic selectionPhenomic selectionMalus × domesticaQuantitative traitsApple REFPOP |
| spellingShingle | Michaela Jung Marius Hodel Andrea Knauf Daniela Kupper Markus Neuditschko Simone Bühlmann-Schütz Bruno Studer Andrea Patocchi Giovanni AL Broggini Evaluation of genomic and phenomic prediction for application in apple breeding BMC Plant Biology Genomic selection Phenomic selection Malus × domestica Quantitative traits Apple REFPOP |
| title | Evaluation of genomic and phenomic prediction for application in apple breeding |
| title_full | Evaluation of genomic and phenomic prediction for application in apple breeding |
| title_fullStr | Evaluation of genomic and phenomic prediction for application in apple breeding |
| title_full_unstemmed | Evaluation of genomic and phenomic prediction for application in apple breeding |
| title_short | Evaluation of genomic and phenomic prediction for application in apple breeding |
| title_sort | evaluation of genomic and phenomic prediction for application in apple breeding |
| topic | Genomic selection Phenomic selection Malus × domestica Quantitative traits Apple REFPOP |
| url | https://doi.org/10.1186/s12870-025-06104-w |
| work_keys_str_mv | AT michaelajung evaluationofgenomicandphenomicpredictionforapplicationinapplebreeding AT mariushodel evaluationofgenomicandphenomicpredictionforapplicationinapplebreeding AT andreaknauf evaluationofgenomicandphenomicpredictionforapplicationinapplebreeding AT danielakupper evaluationofgenomicandphenomicpredictionforapplicationinapplebreeding AT markusneuditschko evaluationofgenomicandphenomicpredictionforapplicationinapplebreeding AT simonebuhlmannschutz evaluationofgenomicandphenomicpredictionforapplicationinapplebreeding AT brunostuder evaluationofgenomicandphenomicpredictionforapplicationinapplebreeding AT andreapatocchi evaluationofgenomicandphenomicpredictionforapplicationinapplebreeding AT giovannialbroggini evaluationofgenomicandphenomicpredictionforapplicationinapplebreeding |