Genome-enabled prediction models for black tea (Camellia sisnesnsis) quality and drought tolerance traits

Abstract

SummaryO_LIGenomic selection in tea (Camellia sinensis) breeding has the potential to accelerate efficiency of choosing parents with desirable traits at the seedling stage. C_LIO_LIThe 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 progenies and 56 individual tea cultivars in validation population genotyped using 1 421 DArTseq markers. C_LIO_LITwo-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. C_LIO_LIOf 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. C_LIO_LIExtreme Learning Machine model had better prediction ability for catechin, astringency, brightness, briskness, and colour based on putative QTLs + annotated proteins + KEGG pathway approach. C_LIO_LIThe 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. C_LI