Genomic prediction and genome wide association mapping of quality traits in tea (Camellia sinensis (L.) O. Kuntze)
Conventional tea breeding consisting of recurrent cycles of crossing, field evaluation and phenotypic selection is the main breeding technique for tea (Camellia sinensis (L.) O. Kuntze). However, it is a time consuming process, that result in slow genetic gain. In order to accelerate tea breeding, t...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
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2020
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| Online Access: | https://eprints.nottingham.ac.uk/59775/ |
| _version_ | 1848799672867487744 |
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| author | Lubanga, Nelson Mandela |
| author_facet | Lubanga, Nelson Mandela |
| author_sort | Lubanga, Nelson Mandela |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Conventional tea breeding consisting of recurrent cycles of crossing, field evaluation and phenotypic selection is the main breeding technique for tea (Camellia sinensis (L.) O. Kuntze). However, it is a time consuming process, that result in slow genetic gain. In order to accelerate tea breeding, the use of modern breeding methods is required. In this regard, genomic selection (GS) and genome wide association studies (GWAS) has been considered most promising for genetic improvement of complex traits. The main aim of our study was to investigate the applicability of GS and GWAS in tea breeding. A training population consisting of 103 tea genotypes located at two sites were genotyped using genotyping by sequencing (GBS). Twelve biochemical traits known to influence tea quality were evaluated using Nuclear Magnetic resonance (NMR) spectroscopy. Hierarchical cluster and principle component analyses distinguished the 103 genotypes based on their biochemical properties. Additionally, specific biochemical compounds correlated with sensory properties; mouthfeel and taste correlated with ECG and EGCG, respectively. This implies that biochemical compounds could be used for selecting high quality teas objectively at the seedlings stage while the genotypes are still in the nursery, hence saving time. We also concluded that an optimized miniature process could be used for manufacturing different tea varieties into black tea, however technologies that could optimally control withering and fermentation steps for the different tea varieties developed in a breeding programme could be explored further. Using GWAS, we identified 64 significant SNP markers and candidate genes associated with the biochemical traits. The potential candidate genes identified included transferases, cytochrome P450 704C1 like proteins, E3 ubiquitin protein ligases, ATP dependent zinc metalloprotease and exopolygalacturonases. The candidate genes and the associated SNPs provide valuable resources for future studies to breed high quality tea varieties and to understand the genetic basis of tea quality at a chemical level, to complement the current sensory method of tea tasting. The identified SNP markers could be further fine mapped to evaluate their potential involvement in tea quality. Among the 2779 sequence tags, only 929 SNPs were mapped to each of the two published draft genomes. In addition,311 sequences had blast hits while 217 sequences were annotated and were assigned to biological processes, cellular component and molecular functions. We also compared the prediction accuracies of 5 GS models using a 5 fold cross validation approach. However, the performance of all the GS models were almost the same, with RRBLUP, BayesLASSO and BayesA performing slightly better than BayesB and BayesCπ. Traits with high GS accuracies were Epigallocatechin gallate (ECGG), Theanine, Epicatechin (EC), Epicatechin gallate (EGC) and theobromine, while those with low prediction accuracies were Gallocatechin (GC), catechin and Gallic acid (GA). We conclude that implementing GWAS and GS in tea breeding would help to improve the prediction accuracies and benefit from rapid genetic gains from selection of high quality teas. |
| first_indexed | 2025-11-14T20:39:24Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-59775 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:39:24Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-597752025-02-28T14:45:58Z https://eprints.nottingham.ac.uk/59775/ Genomic prediction and genome wide association mapping of quality traits in tea (Camellia sinensis (L.) O. Kuntze) Lubanga, Nelson Mandela Conventional tea breeding consisting of recurrent cycles of crossing, field evaluation and phenotypic selection is the main breeding technique for tea (Camellia sinensis (L.) O. Kuntze). However, it is a time consuming process, that result in slow genetic gain. In order to accelerate tea breeding, the use of modern breeding methods is required. In this regard, genomic selection (GS) and genome wide association studies (GWAS) has been considered most promising for genetic improvement of complex traits. The main aim of our study was to investigate the applicability of GS and GWAS in tea breeding. A training population consisting of 103 tea genotypes located at two sites were genotyped using genotyping by sequencing (GBS). Twelve biochemical traits known to influence tea quality were evaluated using Nuclear Magnetic resonance (NMR) spectroscopy. Hierarchical cluster and principle component analyses distinguished the 103 genotypes based on their biochemical properties. Additionally, specific biochemical compounds correlated with sensory properties; mouthfeel and taste correlated with ECG and EGCG, respectively. This implies that biochemical compounds could be used for selecting high quality teas objectively at the seedlings stage while the genotypes are still in the nursery, hence saving time. We also concluded that an optimized miniature process could be used for manufacturing different tea varieties into black tea, however technologies that could optimally control withering and fermentation steps for the different tea varieties developed in a breeding programme could be explored further. Using GWAS, we identified 64 significant SNP markers and candidate genes associated with the biochemical traits. The potential candidate genes identified included transferases, cytochrome P450 704C1 like proteins, E3 ubiquitin protein ligases, ATP dependent zinc metalloprotease and exopolygalacturonases. The candidate genes and the associated SNPs provide valuable resources for future studies to breed high quality tea varieties and to understand the genetic basis of tea quality at a chemical level, to complement the current sensory method of tea tasting. The identified SNP markers could be further fine mapped to evaluate their potential involvement in tea quality. Among the 2779 sequence tags, only 929 SNPs were mapped to each of the two published draft genomes. In addition,311 sequences had blast hits while 217 sequences were annotated and were assigned to biological processes, cellular component and molecular functions. We also compared the prediction accuracies of 5 GS models using a 5 fold cross validation approach. However, the performance of all the GS models were almost the same, with RRBLUP, BayesLASSO and BayesA performing slightly better than BayesB and BayesCπ. Traits with high GS accuracies were Epigallocatechin gallate (ECGG), Theanine, Epicatechin (EC), Epicatechin gallate (EGC) and theobromine, while those with low prediction accuracies were Gallocatechin (GC), catechin and Gallic acid (GA). We conclude that implementing GWAS and GS in tea breeding would help to improve the prediction accuracies and benefit from rapid genetic gains from selection of high quality teas. 2020-02-22 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/59775/1/Nelson%20Lubanga%20Thesis.pdf Lubanga, Nelson Mandela (2020) Genomic prediction and genome wide association mapping of quality traits in tea (Camellia sinensis (L.) O. Kuntze). PhD thesis, University of Nottingham. tea quality genomic selection genome wide association studies biochemical breeding technique |
| spellingShingle | tea quality genomic selection genome wide association studies biochemical breeding technique Lubanga, Nelson Mandela Genomic prediction and genome wide association mapping of quality traits in tea (Camellia sinensis (L.) O. Kuntze) |
| title | Genomic prediction and genome wide association mapping of quality traits in tea (Camellia sinensis (L.) O. Kuntze) |
| title_full | Genomic prediction and genome wide association mapping of quality traits in tea (Camellia sinensis (L.) O. Kuntze) |
| title_fullStr | Genomic prediction and genome wide association mapping of quality traits in tea (Camellia sinensis (L.) O. Kuntze) |
| title_full_unstemmed | Genomic prediction and genome wide association mapping of quality traits in tea (Camellia sinensis (L.) O. Kuntze) |
| title_short | Genomic prediction and genome wide association mapping of quality traits in tea (Camellia sinensis (L.) O. Kuntze) |
| title_sort | genomic prediction and genome wide association mapping of quality traits in tea (camellia sinensis (l.) o. kuntze) |
| topic | tea quality genomic selection genome wide association studies biochemical breeding technique |
| url | https://eprints.nottingham.ac.uk/59775/ |