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Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping

Authors :
Sang He
Yong Jiang
Rebecca Thistlethwaite
Matthew J. Hayden
Richard Trethowan
Hans D. Daetwyler
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.

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