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.

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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.

Tea (Camellia sinensis) has enthralled both consumers and researchers, and its popularity has increased, due to its taste, aroma and its medicinal attributes, owed largely to its metabolites.The catechins and theaflavins in green and black tea, respectively, have been documented to possess anti-oxidant, anti-inflammatory, anti-cancer, as well as cardiovascular diseasepreventing properties. Tea consumers concern themselves with the quality of tea, in particular, its taste and aroma based on which consumers are willing to pay premium prices for the best quality teas. In turn, the quality of tea is undeniably affected by variations in its metabolite composition. In this study, two groups of black tea cultivars, one commercial and the second non-commercial, were compared using a metabolomics approach. Data were generated via untargeted GC-MS and semi-targeted 1 H-NMR. The GC-MS results differentiated between the two groups, based on arabinose, sucrose, phloroglucinol and xylonic acid. The 1 H-NMR results differentiated between the two groups, based on caffeine, catechin, EC, EGC, and the amino acids alanine, isoleucine, leucine, theanine, and valine. These metabolites applicability in the discrimination of newly developed cultivars into potentially commercialisable and non-commercialisable groups at an early stage in the tea improvement programme is demonstrated. This information may also help tea breeders to select promising high quality black and drought tolerant improved tea cultivars either for release or further field evaluations.

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

Due to the unpredictable natural droughts that occur, causing tea farmers significant losses in tea estates, a two-day method for distinguishing between drought tolerant (DT) and drought susceptible (DS) Camellia sinensis cultivars was developed. This work was based on known cultivars developed at the Tea Research Institute in Kenya and the Tea Research Foundation for Central Africa in Malawi. This paper contains an in-depth description of the application of the SWAPDT method on four 60-year old, C. sinensis seedling fields in Kenya. The in-filling history of the four fields due to drought-related deaths was obtained from historical records. The SWAPDT method scores correlated very well with the historical records. It has been indicated, from the results obtained in this study, that a sample size of 20 tea trees is sufficient to accurately determine the drought susceptibility of a large tea field of approximately 5-20 hectares, containing 50 000-200 000 tea trees, were the difference between their mean values, as measured by the SWAPDT method, is approximately 10%.

Tea producers are in demand of new high yielding cultivars, which produce high quality tea liquors. To breed for these phenotypic traits is challenging due to their polygenic disposition and influence by environment. Two C. sinensis populations, namely Comm cultivars from open pollinated field selections, and NComm cultivars from the reciprocal cross of two parents were used. These cultivars were employed to identify the metabolites responsible for distinguishing Comm cultivars, with high yield, high quality and DT from NComm cultivars that did not show these traits. PCA and PLS-DA models were constructed on UPLC/DAD data, which showed clear separation between the Comm and NComm cultivars. CHAID decision trees constructed aimed to classify the 303 genotypes as either Comm or NComm cultivars using subset of compounds. Breeders can predict the quality of new selections from mature seedling fields by employing CHAID decision trees, or the CAF/EC ratio, as predictors.