ArticleFrontiers in plant science2021
Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Genomic prediction in quinoa across contrasting environments using statistical and machine learning models.The plant genome · 2026Article
- Large scale wheat data integration improves genomic prediction accuracy with the potential to facilitate international breeding collaborations.Communications biology · 2026Article
- Sparse Phenotyping and Haplotype-Based Models for Genomic Prediction in Rice.Rice (New York, N.Y.) · 2023Article
- Improving predictive ability in sparse testing designs in soybean populations.Frontiers in genetics · 2023Article
- Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea.The plant genome · 2022Article
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Authors and funding
6 authors.
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Abstract
Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders' main interest - response to selection - was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs.
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