Genome‐enabled prediction models for black tea (Camellia sinensis) quality and drought tolerance traits

Genomic selection in tea plant (Camellia sinensis) breeding has the potential to accelerate efficiency of choosing parents with desirable traits at the seedling stage. The study evaluated different genome‐enabled prediction models for black tea quality and drought tolerance traits in discovery and v...

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Bibliographic Details
Published in:Plant breeding 2020-10, Vol.139 (5), p.1003-1015
Main Authors: Koech, Robert K., Malebe, Pelly M., Nyarukowa, Christopher, Mose, Richard, Kamunya, Samson M., Loots, Theodor, Apostolides, Zeno, Léon, Jens
Format: Article
Language:English
Subjects:
Black tea
black tea quality
Camellia sinensis
Cultivars
DArTseq
Drought resistance
drought tolerance
Encyclopedias
Gene mapping
Genetic crosses
Genomes
genomic prediction
Plant breeding
Population
Populations
Prediction models
Progeny
Quantitative trait loci
Seedlings
Tea
tea breeding
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Summary:Genomic selection in tea plant (Camellia sinensis) breeding has the potential to accelerate efficiency of choosing parents with desirable traits at the seedling stage. The study evaluated different genome‐enabled prediction models for black tea quality and drought tolerance traits in discovery and validation populations. The discovery population comprised of two segregating tea populations (TRFK St. 504 and TRFK St. 524) with 255 F1 progeny and 56 individual tea cultivars in validation population genotyped using 1,421 DArTseq markers. Twofold cross‐validation was used for training the prediction models in the discovery population on eight different phenotypic traits. The best prediction models in the discovery population were consequently fitted to the validation population. Of all the four model‐based prediction approaches, putative QTLs (Quantitative Trait Loci) + annotated proteins + KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathway‐based prediction approach showed more robustness. The findings have for the first time opened up a new avenue for future application of genomic selection in tea breeding.
ISSN:0179-9541
1439-0523
DOI:10.1111/pbr.12813