Premium
Genome‐enabled prediction models for black tea ( Camellia sinensis ) quality and drought tolerance traits
Author(s) -
Koech Robert K.,
Malebe Pelly M.,
Nyarukowa Christopher,
Mose Richard,
Kamunya Samson M.,
Loots Theodor,
Apostolides Zeno
Publication year - 2020
Publication title -
plant breeding
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.583
H-Index - 71
eISSN - 1439-0523
pISSN - 0179-9541
DOI - 10.1111/pbr.12813
Subject(s) - biology , camellia sinensis , quantitative trait locus , kegg , population , camellia , trait , genome , phenotypic trait , drought tolerance , genetics , selection (genetic algorithm) , computational biology , gene , phenotype , botany , machine learning , gene expression , demography , transcriptome , sociology , computer science , programming language
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 F 1 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.