Identification and development of pathogen- and pest-specific defense–resistance-associated ssr marker candidates assisted by machine learning and discovery of putative QTL hotspots in camellia sinensis

dc.contributor.authorEminoğlu, Ayşenur
dc.date.accessioned2026-03-12T07:59:50Z
dc.date.issued2026
dc.departmentRTEÜ, Fen - Edebiyat Fakültesi, Biyoloji Bölümü
dc.description.abstractIn this study, a targeted SSR (Simple Sequence Repeat) marker resource was developed based on genes and protein families associated with pathogen- and pest-related defense–resistance mechanisms in Camellia sinensis. Forty-one genes and protein families reported to show upregulation, increased expression, or functional validation under disease and pest stress were selected, and the corresponding 195 loci were mapped onto the Camellia sinensis cv. Shuchazao genome. SSR screening within gene bodies and gene-flanking regions (±5 kb) identified 5197 SSR loci. Putative QTL hotspot regions were defined using locus-based sliding-window analysis, Z-score calculations, and permutation tests, yielding 633 SSRs filtered at the 99% and 95% significance thresholds. Proteome-wide scans based on conserved amino acid motifs identified multiple loci within the WRKY, NAC, LRR, PRX, and CHI families, and Random Forest analysis was used to prioritize SSRs within these families. Finally, 386 SSR primer sets were designed and evaluated by in silico PCR across six tea genomes. Of these, 245 primers produced amplicons in more than one genome, and 124 exhibited polymorphic information content values greater than 0.500. Overall, the developed SSR panels represent a biologically contextualized and experimentally transferable marker resource targeting defense–resistance-associated genic and gene-proximal regions.
dc.identifier.citationEminoğlu, A. (2026). Identification and Development of Pathogen- and Pest-Specific Defense–Resistance-Associated SSR Marker Candidates Assisted by Machine Learning and Discovery of Putative QTL Hotspots in Camellia sinensis. Plants, 15(3), 454. https://doi.org/10.3390/plants15030454
dc.identifier.doi10.3390/plants15030454
dc.identifier.issn2223-7747
dc.identifier.issue3
dc.identifier.scopus2-s2.0-105030243122
dc.identifier.scopusqualityQ1
dc.identifier.startpage454
dc.identifier.urihttps://doi.org/10.3390/plants15030454
dc.identifier.urihttps://hdl.handle.net/11436/12507
dc.identifier.volume15
dc.indekslendigikaynakScopus
dc.institutionauthorEminoğlu, Ayşenur
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofPlants
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCamellia sinensis
dc.subjectdefense and resistance genes
dc.subjectmachine learning-assisted prioritization
dc.subjectmarker development
dc.subjectputative QTL hotspots
dc.subjectsimple sequence repeats (SSRs)
dc.titleIdentification and development of pathogen- and pest-specific defense–resistance-associated ssr marker candidates assisted by machine learning and discovery of putative QTL hotspots in camellia sinensis
dc.typeArticle

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