Genomic Selection in Winter Wheat Breeding Using a Recommender Approach
Achieving optimal predictive ability is key to increasing the relevance of implementing genomic selection (GS) approaches in plant breeding programs. The potential of an item-based collaborative filtering (IBCF) recommender system in the context of multi-trait, multi-environment GS has been explored...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Genes |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4425/11/7/779 |
| _version_ | 1797562712245403648 |
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| author | Dennis N. Lozada Arron H. Carter |
| author_facet | Dennis N. Lozada Arron H. Carter |
| author_sort | Dennis N. Lozada |
| collection | DOAJ |
| description | Achieving optimal predictive ability is key to increasing the relevance of implementing genomic selection (GS) approaches in plant breeding programs. The potential of an item-based collaborative filtering (IBCF) recommender system in the context of multi-trait, multi-environment GS has been explored. Different GS scenarios for IBCF were evaluated for a diverse population of winter wheat lines adapted to the Pacific Northwest region of the US. Predictions across years through cross-validations resulted in improved predictive ability when there is a high correlation between environments. Using multiple spectral traits collected from high-throughput phenotyping resulted in better GS accuracies for grain yield (GY) compared to using only single traits for predictions. Trait adjustments through various Bayesian regression models using genomic information from SNP markers was the most effective in achieving improved accuracies for GY, heading date, and plant height among the GS scenarios evaluated. Bayesian LASSO had the highest predictive ability compared to other models for phenotypic trait adjustments. IBCF gave competitive accuracies compared to a genomic best linear unbiased predictor (GBLUP) model for predicting different traits. Overall, an IBCF approach could be used as an alternative to traditional prediction models for important target traits in wheat breeding programs. |
| first_indexed | 2024-03-10T18:31:50Z |
| format | Article |
| id | doaj.art-a8ddd7e65da5458e921fade3763da65b |
| institution | Directory Open Access Journal |
| issn | 2073-4425 |
| language | English |
| last_indexed | 2024-03-10T18:31:50Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Genes |
| spelling | doaj.art-a8ddd7e65da5458e921fade3763da65b2023-11-20T06:32:34ZengMDPI AGGenes2073-44252020-07-0111777910.3390/genes11070779Genomic Selection in Winter Wheat Breeding Using a Recommender ApproachDennis N. Lozada0Arron H. Carter1Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USADepartment of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USAAchieving optimal predictive ability is key to increasing the relevance of implementing genomic selection (GS) approaches in plant breeding programs. The potential of an item-based collaborative filtering (IBCF) recommender system in the context of multi-trait, multi-environment GS has been explored. Different GS scenarios for IBCF were evaluated for a diverse population of winter wheat lines adapted to the Pacific Northwest region of the US. Predictions across years through cross-validations resulted in improved predictive ability when there is a high correlation between environments. Using multiple spectral traits collected from high-throughput phenotyping resulted in better GS accuracies for grain yield (GY) compared to using only single traits for predictions. Trait adjustments through various Bayesian regression models using genomic information from SNP markers was the most effective in achieving improved accuracies for GY, heading date, and plant height among the GS scenarios evaluated. Bayesian LASSO had the highest predictive ability compared to other models for phenotypic trait adjustments. IBCF gave competitive accuracies compared to a genomic best linear unbiased predictor (GBLUP) model for predicting different traits. Overall, an IBCF approach could be used as an alternative to traditional prediction models for important target traits in wheat breeding programs.https://www.mdpi.com/2073-4425/11/7/779Bayesian modelsgenomic BLUP (GBLUP)grain yieldheading datehigh-throughput phenotypingitem-based collaborative filtering (IBCF) |
| spellingShingle | Dennis N. Lozada Arron H. Carter Genomic Selection in Winter Wheat Breeding Using a Recommender Approach Genes Bayesian models genomic BLUP (GBLUP) grain yield heading date high-throughput phenotyping item-based collaborative filtering (IBCF) |
| title | Genomic Selection in Winter Wheat Breeding Using a Recommender Approach |
| title_full | Genomic Selection in Winter Wheat Breeding Using a Recommender Approach |
| title_fullStr | Genomic Selection in Winter Wheat Breeding Using a Recommender Approach |
| title_full_unstemmed | Genomic Selection in Winter Wheat Breeding Using a Recommender Approach |
| title_short | Genomic Selection in Winter Wheat Breeding Using a Recommender Approach |
| title_sort | genomic selection in winter wheat breeding using a recommender approach |
| topic | Bayesian models genomic BLUP (GBLUP) grain yield heading date high-throughput phenotyping item-based collaborative filtering (IBCF) |
| url | https://www.mdpi.com/2073-4425/11/7/779 |
| work_keys_str_mv | AT dennisnlozada genomicselectioninwinterwheatbreedingusingarecommenderapproach AT arronhcarter genomicselectioninwinterwheatbreedingusingarecommenderapproach |