Back to Search
Start Over
Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping
- Source :
- Frontiers in Plant Science, Vol 12 (2021), Frontiers in Plant Science
- Publication Year :
- 2021
- Publisher :
- Frontiers Media SA, 2021.
-
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.
- Subjects :
- business.industry
fungi
Plant culture
food and beverages
Plant Science
Biology
equipment and supplies
complex mixtures
SB1-1110
Biotechnology
Crop
Data set
sparse phenotyping
response to selection
multi-environment trials
bacteria
business
correlations between environments
genomic prediction
Selection (genetic algorithm)
Original Research
Uncategorized
Subjects
Details
- ISSN :
- 1664462X
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- Frontiers in Plant Science
- Accession number :
- edsair.doi.dedup.....9fb89e530cb0ca0ca920c3ee0a48dbf3
- Full Text :
- https://doi.org/10.3389/fpls.2021.735285