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• W2991252968 abstract "Summary Genomic selection in tea ( 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 F 1 progenies and 56 individual tea cultivars in validation population genotyped using 1 421 DArTseq markers. Two-fold cross-validation was used for training the prediction models in discovery population, and the best prediction models were consequently, fitted to the validation population. Of all the four based prediction approaches, putative QTLs (Quantitative Trait Loci) + annotated proteins + KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathway-based prediction approach, showed robustness and usefulness in prediction of phenotypes. Extreme Learning Machine model had better prediction ability for catechin, astringency, brightness, briskness, and colour based on putative QTLs + annotated proteins + KEGG pathway approach. The percent variables of importance of putatively annotated proteins and KEGG pathways were associated with the phenotypic traits. The findings has for the first time opened up a new avenue for future application of genomic selection in tea breeding." @default.
• W2991252968 created "2019-12-05" @default.
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• W2991252968 date "2019-11-22" @default.
• W2991252968 modified "2023-09-26" @default.
• W2991252968 title "Genome-enabled prediction models for black tea (Camellia sisnesnsis) quality and drought tolerance traits" @default.
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• W2991252968 doi "https://doi.org/10.1101/850792" @default.
• W2991252968 hasPublicationYear "2019" @default.
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