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Impact of early genomic prediction for recurrent selection in an upland rice synthetic population

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Title Impact of early genomic prediction for recurrent selection in an upland rice synthetic population
 
Creator Baertschi, Cédric
Cao, Tuong-Vi
Bartholomé, Jérôme
Ospina Rey, Yolima
Quintero, Constanza
Frouin, Julien
Bouvet, Jean-M.
Grenier, Cécile
 
Subject rice
recurrent selection
genomics
progeny testing
plant breeding
calibration
arroz
selección recurrente
genómica
 
Description Population breeding through recurrent selection is based on the repetition of evaluation and recombination among best-selected individuals. In this type of breeding strategy, early evaluation of selection candidates combined with genomic prediction could substantially shorten the breeding cycle length, thus increasing the rate of genetic gain. The objective of this study was to optimize early genomic prediction in an upland rice (Oryza sativa L.) synthetic population improved through recurrent selection via shuttle breeding in two sites. To this end, we used genomic prediction on 334 S0 genotypes evaluated with early generation progeny testing (S0:2 and S0:3) across two sites. Four traits were measured (plant height, days to flowering, grain yield, and grain zinc concentration) and the predictive ability was assessed for the target site. For days to flowering and plant height, which correlate well among sites (0.51–0.62), an increase of up to 0.4 in predictive ability was observed when the model was trained using the two sites. For grain zinc concentration, adding the phenotype of the predicted lines in the nontarget site to the model improved the predictive ability (0.51 with two-site and 0.31 with single-site model), whereas for grain yield the gain was less (0.42 with two-site and 0.35 with single-site calibration). Through these results, we found a good opportunity to optimize the genomic recurrent selection scheme and maximize the use of resources by performing early progeny testing in two sites for traits with best expression and/or relevance in each specific environment.
 
Date 2021-12-08
2022-09-05T12:26:01Z
2022-09-05T12:26:01Z
 
Type Journal Article
 
Identifier Baertschi, C.; Cao, T.V.; Bartholomé, J., Ospina R.Y.; Quintero, C.; Frouin, J.; Bouvet J.M.; Grenier, C. (2021) Impact of early genomic prediction for recurrent selection in an upland rice synthetic population. G3 Genes Genomes Genetics 11(12): jkab320. ISSN 2160-1836
2160-1836
https://hdl.handle.net/10568/121096
https://doi.org/10.1093/g3journal/jkab320
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format application/pdf
 
Publisher Oxford University Press (OUP)
 
Source G3: Genes
Genomes
Genetics