Tea (Camellia sinensis) contain polyphenols and caffeine which have been found to be of popular interest in tea quality. Tea production relies on well distributed rainfall which influence tea quality. Phenotypic data for two segregating tea populations TRFK St 504 and TRFK St 524, were collected and used to identify the quantitative trait loci (QTL) influencing tea biochemical and drought stress traits based on a consensus genetic map constructed using the DArTseq platform. The populations comprised 261 F1 clonal progeny. The map consisted of 15 linkage groups which corresponds to chromosome haploid number of tea plant (2n = 2x = 30) and spanned 1260.1 cM with a mean interval of 1.1 cM between markers. A total of 16 phenotypic traits were assessed in the two populations. Both interval and multiple QTL mapping revealed a total of 47 putative QTL in the 15 LGs associated with tea quality and percent relative water content at a significant genomewide threshold of 5%. In total, six caffeine QTL, 25 catechins QTL, three theaflavins QTL, nine QTL for tea taster score and three QTL for percent relative water contents were detected. Out of these 47 QTL, 19 QTL were identified for ten traits in three main regions on LG01, LG02, LG04, LG12, LG13 and LG14. The QTL associated with caffeine, individual catechins, and theaflavins were clustered mostly in LG02 and LG04 but in different regions on the map. The explained variance by each QTL in the population ranged from 5.5 to 56.6%, with an average of 9.9%. Identification of QTL that are tightly linked to markers associated with black tea quality coupled with UPLC assay may greatly accelerate development of novel tea cultivars owing to its amenability at seedling stage. In addition, validated molecular markers will contribute greatly to adoption of marker-assisted selection (MAS) for drought tolerance and tea quality improvement.
The understanding of black tea quality and percent relative water content (%RWC) traits in tea (Camellia sinensis) by a quantitative trait loci (QTL) approach can be useful in elucidation and identification of candidate genes underlying the QTL which has remained to be difficult. The objective of the study was to identify putative QTL controlling black tea quality and percent relative water traits in two tea populations and their F1 progeny. A total of 1,421 DArTseq markers derived from the linkage map identified 53 DArTseq markers to be linked to black tea quality and %RWC. All 53 DArTseq markers with unique best hits were identified in the tea genome. A total of 5,592 unigenes were assigned gene ontology (GO) terms, 56% comprised biological processes, cellular component (29%) and molecular functions (15%), respectively. A total of 84 unigenes in 15 LGs were assigned to 25 different Kyoto Encyclopedia of Genes and Genomes (KEGG) database pathways based on categories of secondary metabolite biosynthesis. The three major enzymes identified were transferases (38.9%), hydrolases (29%) and oxidoreductases (18.3%). The putative candidate proteins identified were involved in flavonoid biosynthesis, alkaloid biosynthesis, ATPase family proteins related to abiotic/biotic stress response. The functional annotation of putative QTL identified in this current study will shed more light on the proteins associated with caffeine and catechins biosynthesis and % RWC. This study may help breeders in selection of parents with desirable DArTseq markers for development of new tea cultivars with desirable 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 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.
2 7 astringency, brightness, briskness, and colour based on putative QTLs + annotated 2 8 proteins + KEGG pathway approach.2 9• The percent variables of importance of putatively annotated proteins and KEGG 3 0 pathways were associated with the phenotypic traits. The findings has for the first 3 1
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