Unveiling Hidden Genetic Resources: Characterization of Key Agronomic Genes in Underutilized and Cultivated African Crops

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Despite their agronomic importance, molecular data on key stress-related genes remain limited, constraining their integration into modern breeding programs. Previously validated primers targeting genes associated with drought tolerance (NAC), disease resistance (Ph-3, Ty-3), starch biosynthesis (CYP79D1), and phytic acid metabolism (MIPS) were evaluated through PCR and Sanger sequencing. Tomato and cassava served as positive controls and phylogenetic anchors. Amplicons were analyzed using standard sequence-quality workflows, multiple sequence alignment, and phylogenetic reconstruction. Most primer pairs produced clear, single amplicons in both crops, with snake tomato exhibiting particularly strong cross-species amplification. Sequencing confirmed locus specificity and revealed conserved regions enabling primer transferability. Phylogenetic analyses grouped Bambara groundnut and snake tomato sequences with their expected legume and cucurbit clades, validating the evolutionary placement of the amplified loci. These outcomes demonstrate that the targeted genes are conserved across species and suitable for molecular characterization. The successful amplification and sequencing of key stress-related genes provide foundational genomic information for Bambara groundnut and snake tomato. These results confirm that conserved gene regions can be exploited for molecular marker development, comparative genomics, and future breeding programs aimed at enhancing stress resilience in underutilized crops. Bambara groundnut snake tomato stress-responsive genes cross-species primers molecular characterization climate-resilient crops Figures Figure 1 Figure 2 Figure 3 Figure 4 1.0 INTRODUCTION Climate change has continued to reshape global agricultural systems through rising temperatures, shifting rainfall patterns, and increasingly frequent extreme weather events (El-Sayed & Kamel, 2020 ). These environmental disruptions have intensified both abiotic and biotic stresses. including drought, heat, pests, and pathogens which contribute to undermining crop productivity, yield stability, and food security. Addressing these challenges require a deeper understanding of plant genetic variation, the allelic diversity within genomes that governs adaptation, resilience, and agronomic performance. Such variation forms the basis for developing improved crop varieties capable of withstanding environmental stressors (Bhandari et al., 2017 ). Underutilized crops represent an important yet often overlooked reservoir of genetic resources for climate-resilient agriculture. Species such as Bambara groundnut ( Vigna subterranea ) and Snake tomato ( Trichosanthes cucumerina ) thrive under marginal conditions where major staples frequently fail. Bambara groundnut, a nutrient-rich Fabaceae crop, is widely cultivated by smallholder farmers because of its drought tolerance, yield stability, and ability to grow in nutrient-poor soils (Oyeyinka et al., 2015 ; Bamshaiye et al., 2011 ; Feldman et al., 2019 ). Its seeds contain high levels of carbohydrates, proteins, and essential amino acids, making it a valuable contributor to food and nutritional security (Mazahib et al., 2013 ). Snake tomato, commonly called the snake gourd is cultivated across humid tropical regions, provides substantial nutritional and medicinal benefits. Its fruits contain essential vitamins, minerals, phenolic compounds, carotenoids, flavonoids, and dietary fiber, and are used both as functional foods and in traditional medicine (Sandhya et al., 2010 ; Sivakumar, 2023 ; Ominowa et al., 2024 ; Raghunathan et al., 2024 ). Despite their resilience and regional importance, both crops remain poorly characterized at the molecular level, restricting their integration into modern breeding programmes. Key agronomic traits such as drought tolerance, disease resistance, starch biosynthesis, and phytic acid metabolism in crops are likely controlled by gene families. Drought stress disrupts photosynthesis, carbon assimilation, osmotic homeostasis, enzymatic activity, and overall plant growth (Ullah et al., 2017 ; Hussain et al., 2018 ; Anjum et al., 2017 ) and transcription factors such as DREB modulate drought tolerance by activating dehydration-responsive genes (Agarwal et al., 2017 ). In relation to starch biosynthesis, which is central to carbohydrate storage and nutritional quality, relies on enzymes and regulatory proteins including GBSSI and bZIPs that coordinate the expression of starch-related genes (Smith et al., 1997 ; Stower et al., 2012; Zhang et al., 2016 ). Additionally. disease resistance in crops is mediated by immune receptors such as nucleotide-binding leucine-rich repeat (NLR) proteins and receptor-like kinases, which detect pathogen effectors and initiate defense responses (Andolfo & Ercolano, 2015 ; Andersen et al., 2018 ; Kourelis & Van der Hoorn, 2018 ). Genes associated with phytic acid metabolism influence phosphorus storage and mineral bioavailability, with implications for both plant physiology and human nutrition (Raboy, 2003 ; Takagi et al., 2020 ). Although these gene families have been extensively described in model crops such as tomato (Mahmoud et al., 2025 ), cassava (Huang et al., 2021 ), maize (Yu et al., 2022 ), soybean (Gillman et al., 2021 ) and rice (Zhang et al., 2021 ), their study and functional composition remain largely unexplored in Bambara groundnut and snake tomato. To address this gap, the present study conducted a PCR and Sanger sequencing-based characterization of selected agronomic genes in the underutilized species. Validated primer sets from previous studies were used, leveraging the evolutionary conservation of these stress-related genes across plant taxa. Tomato and cassava served as positive controls and phylogenetic references, enabling confirmation of amplification specificity, comparative sequence analysis, and accurate interpretation of gene orthology. Through this approach, the study identified, amplified, sequenced, and characterized key abiotic- and biotic-stress–related genes involved in drought tolerance, disease resistance, starch biosynthesis, and phytic acid metabolism in Bambara groundnut and snake tomato. The resulting genomic information was intended to support future breeding initiatives, guide molecular marker development, and enhance the utilization of these underexplored crops in climate-resilient agriculture. 2.0 MATERIALS AND METHODS 2.1 Plant Material In all, 10 samples comprising of two cultivated African crops (four cultivated tomato and three cultivated cassava genotypes) and two underutilized African crops (two bambara groundnut and one snake tomato genotypes). The germplasms with the exception of snake tomato which was sourced from CSIR- Crops Research Institute were sampled from the environs of Techiman and Kintampo where their propagating materials were collected from local folks for this study (Table 1 ). Samples were established in the screen house of the Biotechnology unit at Council for Scientific and Industrial Research-Crops Research Institute (CSIR-CRI), Fumesua-Kumasi. Table 1 Samples used for the study S/N Sample ID Crop Type 1 Adomano off white Bambara Groundnut 2 Adomano Red Bambara Groundnut 3 Nkentenma Cassava 4 Ahenewa Cassava 5 Ampenkyene Cassava 6 Local Tomato 7 Adwoa Nenewa Tomato 8 Peto Tomato 9 Rhino Power Tomato 10 SN Snake Tomato 2.2 Sampling for Total Genomic DNA Isolation The nursed genotypes in the screen house were sampled towards DNA isolation. The leaf of each sample was collected using a pair of forceps into 2 ml Eppendorf tubes and immediately placed into liquid nitrogen. Approximately 0.2 g of such sample was homogenized with liquid nitrogen in an Eppendorf tube for DNA isolation. 2.3 Extraction of DNA The DNA isolation was done using (Cetyltrimethylammonium bromide) protocol (Doyle and Doyle, 1990 ). One ml of freshly prepared CTAB (20mM Tris HCl, 50mM EDTA, 2M NaCl, 2% CTAB, 3% β- mercaptoethanol) extraction buffer was added to the homogenised leaf in 2 ml Eppendorf tube, vortexed for one min and incubated in water bath at 65°C for 10 mins with intermittent mixing by inverting the tubes. The tubes were cooled for five mins and 600 µl phenol chloroform isoamyl alcohol (25:24:1) was added and mixed gently by inversion until mixture turned milky. The tubes were centrifuged at 13,000 rpm for 10 mins using centrifuge (GenFuge 24D) and then 450 µl aqueous transferred into newly labelled 2 ml tubes, without disturbing the middle layer. Isopropanol (350 µl) was added to supernatant, mixed gently and incubated at -20°C for an hour. Samples were then centrifuge at 13,000 rpm for 10 mins, supernatant was discarded, pellet washed with 80% ethanol and the pellets were dried for 30 mins. Pellets were dissolved in 50 µl low salt buffer and 10 µl RNase A (20 mg/ml) was added and then incubated at 37°C for 45 mins in a water bath. The genomic DNA was quantified using a Nanodrop 2000C Spectrophotometer (Thermos Scientific, USA) and the quality checked on 0.8% agarose gel. 2.4 Validation of Isolated DNA using Actin (housekeeping gene) Extracted samples were further accessed for their suitability for PCR using Actin gene primer. This was done to provide the utmost confidence that the samples are void of any inhibitors that could potentially affect PCR activities. 2.5 Primer validation A total of five primers designed to target different traits across the three crops studied were selected from literature and in-silico validated to determine their suitability for the study (Appendix A). They were further validated via PCR to determine their optimal annealing temperature and cycling conditions (Ruan & Lai, 2007 ; Kumar et al., 2025 ). 2.6 Polymerase Chain Reaction Polymerase Chain Reaction (PCR) was performed using 2 X Hotstart mastermx Thermoscientific PCR kit. The PCR amplification reaction of 20 µl towards PCR amplification and sequencing contained final concentrations of 10 µl of 2 X Hotstart mastermix, (Thermoscientific), 1 µl each of forward and reverse primer, 4 µl of 50 ng DNA template and 4 µl of Nuclease Free Sterile Water (NFSW). The PCR amplification was carried out in a 96-well PCR thermal cycler (Veriti (R) AB Biosystems) with different PCR cycling profiles (Appendices B1 and B2). Six times (6X) bromophenol blue dye (4ul) was added to the products generated after the PCR. Amplified products (5 µl) were run on 1.5% agarose gel in Tris Boric acid EDTA (TBE) buffer, stained with Ethidium bromide and captured using Alpha Imager HP (protein simple, USA) to confirm the presence of a clear distinct band per sample towards sequencing. 2.7 Sangar Sequencing PCR products (15) with clear distinct bands were plated along with their corresponding primers as requested by the sequencing company (Functional Biosciences, Inc, Wisconsin, USA). The required concentration of primers as well as that of products were adhered to by following strictly the instructions of the sequencing company. 2.8 Data Analysis Sequence data obtained from Functional Biosciences, Inc, Wisconsin, USA was accessed for accurate base calling and correction of bases called using BioEdit version 5.0.9 (Supplementary file 1 (S1). Raw chromatogram files obtained from the sequencing company were initially visualized and edited using BioEdit version 5.0.9. Chromatogram peaks were manually proofread to verify the accuracy of base calling, and ambiguous bases at both ends were trimmed. The data was filtered to obtain a final set of data and were subjected to BLASTn query on the NCBI database using Geneious Prime version 2025.0.3 to identify closely related reference sequences. The top-scoring reference sequences associated with each sample were retrieved and used for phylogenetic analysis. Multiple sequence alignment of the filtered data and their corresponding reference sequences was performed using the Clusta W algorithm implemented in the Alignment Explorer module of MEGA version 12 (Tamura et al., 2021 ; Kumar et al., 2024 ). The Alignment Explorer was used for manual inspection and adjustment of the alignments. Evolutionary relationships among the data were inferred using the Maximum Likelihood (ML) method based on the Kimura 2-Parameter model (Kimura, 1980 ). The tree with the highest log likelihood was selected as the best representation of evolutionary relationships. The robustness of the branching patterns was assessed using 1,000 bootstrap replications (Felsenstein, 1985 ). The initial tree for the heuristic search was automatically selected based on the superior log likelihood value between a Neighbor-Joining (NJ) tree (Saitou and Nei, 1986 ) and a Maximum Parsimony (MP) tree. The NJ tree was generated from a pairwise distance matrix computed using the p-distance method, while the MP tree was derived from 10 searches initiated with random starting trees. The final phylogenetic tree was visualized and annotated in MEGA version 12 and iTOL version 7.2.2 (Letunic and Bork, 2007 ). Analyses were conducted using all codon positions (1st, 2nd, and 3rd) as well as non-coding regions. All evolutionary computations utilized up to four parallel computing threads. Phylogenetic trees were rooted using an orthologous Physcomitrium patens reference sequence (AAQ88111.1) as a phylogenetically external outgroup because it lies outside most focal angiosperm families. For multivariate and population-level analyses, the curated, trimmed alignment was imported into R version 4.4.2. Pairwise genetic distances were calculated using p-distance using the ape package. Ordination analysis was performed through Principal Coordinates Analysis (PCoA) using classical multidimensional scaling (cmdscale). Metadata describing Group (species/source) and Trait (gene functional class) were merged with the genetic distance matrix to support hypothesis-driven analyses. Differences in genetic composition among Groups and Traits were tested using PERMANOVA- (Permutation-based multivariate analyses) ( adonis2 , vegan package) with 999 permutations, providing a robust, permutation-based assessment of group structure. Variance partitioning at the population level was conducted using AMOVA-(Analysis of Molecular Variance) within poppr , with a genind object derived from the aligned sequences. AMOVA significance was evaluated with 999 permutations. Analyses were performed on complete-case datasets, and results were summarized as variance components, Φ statistics, and permutation-based p-values. 3.0 RESULTS 3.1 Assessment of Extracted DNA and QC via Actin Gene PCR Validation The extracted DNA samples were assessed for their quality, concentration and purity. The quality of the 15 samples was determined on a 0.8% agarose gel (Fig. 1 ) and the concentration as well as purity were determined using the nanodrop spectrophotometer 2000 C (Table 2 ). Most of the samples had quality DNA with concentration and purity ranging from 360.9– 2267.9 and 1.63–1.89 respectively. In addition, samples were accessed with Actin primer where all samples amplified for the gene indicating their suitability for sequencing (Fig. 2 ). Key: 1KB = Ladder, 1 = Cassava- Efe, 2 = Nkentenma, 3 = Ahenewa, 4 = Local, 5 = Adwoa Nenewa, 6 = Peto, 7 = Rhino Power, 8 = Adomano off white, 9 = Adomano Red, 10 = Snake Tomato Table 2 Concentration and Purity of samples to be sequenced S/N Sample ID Conc(ng/µl) Purity 1 Cassava- Efe 1444.9 1.66 2 Cassava- Nkentenma 1269.6 1.80 3 Cassava- Ahenewa 1746.5 1.63 4 Tomato- Local 748.4 1.71 5 Tomato- Adwoa Nenewa 360.9 1.69 6 Tomato- Peto 752.1 1.77 7 Tomato- Rhino Power 1055.8 1.82 8 Bambara - Adomano off white 2214.4 1.93 9 Bambara- Adomano Red 1777.9 1.89 10 Snake Tomato 2267.9 1.83 Figure 2 : Gel image (1.5% agarose) of the 15 samples using ACTIN house-keeping primer Key: 100bp = ladder1 = Cassava- Efe, 2 = Nkentenma, 3 = Ahenewa, 4 = Local, 5 = Adwoa Nenewa, 6 = Peto, 7 = Rhino Power, 8 = Adomano off white, 9 = Adomano Red, 10 = Snake Tomato C = No Template Control (NTC) 3.2 PCR and Sample Prep towards Sequencing Five (5) primer pairs were used to screen the 10 samples for various traits. The primer pairs generated amplicons with clear distinct single bands per sample per primer after PCR screening. These primers generated a total of 18 reactions which were prepped up towards sequencing. The 18 reactions had band sizes spanning about 150 bp to 1800 bp on the agarose gel (Appendix C). 3.3 Sanger Sequencing In order to know the order of sequences for each amplicon per trait obtained during PCR, sequencing of the 15 reactions was conducted via target sequencing. All 15 reactions were sequenced with their respective primer sequences (Appendix E and F). Out of the 15 reactions reads obtained, 12 of them representing 80% passed QC based on the filtering parameters selected (Table 3 and Appendix D) for five primers sent for sequencing. The reads which passed the quality thresholds were retained for downstream analysis. Table 3 Summary statistics of sequenced data Metric Summary Total number of reads 15 Average quality score (all reads) 30 Quality trimming parameters Sliding window = 20; Threshold = 20 High-quality (HQ) reads obtained 12 Average HQ read quality 41.6 Average HQ read length 471 bp Quality filtering criteria applied HQ Reads ≥ 50, Trim Q Ave ≥ 25, Trim Length ≥ 100 3.4 Sequence Identification and Phylogenetic Analysis BLASTn analysis of the filtered sequences returned top matches to known gene sequences, including Ph-3, Ty-3–associated loci, CYP79D1, MIPS-related sequences, and other annotated plant genes. All high-quality reads aligned to biologically relevant reference sequences, confirming successful amplification of the intended genomic regions. Multiple sequence alignment produced a final curated dataset of approximately 4,282 nucleotides after removal of positions containing gaps and missing data. The aligned sequences showed conserved and variable regions suitable for phylogenetic evaluation. Maximum Likelihood phylogenetic analysis generated a resolved tree with strong bootstrap support across major branches. Distinct clustering patterns were observed among the sequences. Ado-Off-White and Ado-Red grouped closely with the Phaseolus vulgaris MIPS-related reference sequence AM941723. Peto Ty-3Caps, Rhino Power, and SN Ty-3Caps formed a cluster with XM_010323869, an RNA-dependent RNA polymerase associated with TY-1/TY-3 resistance. Additionally, Ahenewa, Ampenkyene, Nkentenma, Adwoa Ph-3, Local Ph-3, and Peto Ph-3 clustered with the PV426896 and CYP79D1 (AF140613) reference sequences. Bootstrap values ranging from 58% to over 90% supported the stability of these groupings, and the final phylogenetic tree was visualized and annotated using MEGA and iTOL. 3.5 Principal Coordinates Analysis (PCoA) PCoA based on p-distance revealed clear patterns of genetic differentiation among the sampled sequences. The first two principal coordinates captured the majority of the variation, with PC1 explaining 47.5% and PC2 explaining 27.3% of the total genetic variation. The ordination plot (Fig. 2 ) showed that samples clustered predominantly according to species (Group), with distinct separation along PC1. Samples sharing similar gene functional classes (Trait) also tended to cluster together, though with some overlap. 3.6 PERMANOVA PERMANOVA, at 999 permutations formally tested differences in genetic composition among Groups, Traits, and their combined effects. Results revealed that Group membership explained a significant proportion of genetic variation (R² = 0.570, F = 5.309, p = 0.001), confirming the strong species-level structure observed in PCoA (Table 4 ). Similarly, Trait categories were also significant (R² = 0.597, F = 9.625, p = 0.001) (Table 5 ), indicating that functional gene class contributes to the observed genetic variation, although to a slightly lesser degree than species identity. The combined model (Group + Trait) explained 66.7% of the total variation (F = 5.505, p = 0.001) with residual variation at ~ 33% (Table 6 ). Table 4 PERMANOVA for Group Source Df Sum of Squares R² F-value p-value Group 3 1.6133 0.57031 5.3089 0.001 Residual 12 1.2155 0.42969 Total 15 2.8288 1.00000 Table 5 PERMANOVA for Trait Source Df Sum of Squares R² F-value p-value Trait 2 1.6885 0.5969 9.6248 0.001 Residual 13 1.1403 0.4031 Total 15 2.8288 1.0000 Table 6 PERMANOVA for Group + Trait Source Df Sum of Squares R² F-value p-value Model (Group + Trait) 4 1.88646 0.66687 5.505 0.001 Residual 11 0.94238 0.33313 Total 15 2.82883 1.00000 3.7 AMOVA AMOVA provided a complementary hierarchical assessment of genetic variation. The partitioning of variance indicated that 42.5% of the total genetic variance was attributable to differences among Groups, whereas 57.5% resided within groups (Table 7 ). The observed Φ statistic (Φ = 0.425, p = 0.001) (Table 8 ) after assessment was highly significant, reinforcing the strong genetic differentiation among Groups. Table 7 AMOVA Variance Partitioning Source of Variation df Sum Sq Mean Sq Variance Component % Variation Between Groups 3 473.6726 157.89087 31.2076 42.53% Within Groups 12 505.9524 42.16270 42.1627 57.47% Total 15 979.6250 65.30833 73.3703 100% Table 8 AMOVA Significance Test (999 permutations) Statistic Observed Expected Variance p-value Φ (samples–total) 0.4253 0.1183 41.9080 0.001 4.0 DISCUSSION This study examined agronomically important and stress-related genes across cassava, tomato, snake tomato, and Bambara groundnut with the broader goal of generating molecular resources for improving both cultivated and underutilized African crops. As climate change intensifies drought, heat, pest, and pathogen pressures, understanding allelic diversity within functional gene families is critical for developing resilient varieties (El-Sayed & Kamel, 2020 ; Bhandari et al., 2017 ). Genes involved in drought tolerance, disease resistance, starch metabolism, and phytic acid regulation are of particular interest because of their central role in crop performance under environmental stress. The high-quality genomic DNA obtained across species provided a solid foundation for downstream analyses, with concentration and purity values falling within acceptable limits for PCR and sequencing. Consistent amplification of the ACTIN housekeeping gene reinforced template suitability, consistent with reports across other plant systems where ACTIN amplification is used as a benchmark for DNA integrity (Healey et al., 2014 ; Pokharel et al., 2023 ; Dong et al., 2024 ; Amoako et al., 2024 ; Bosompem et al., 2025 ). A key finding of this work was the cross-species utility of primers designed originally for cassava and tomato. Both in silico and experimental validation confirmed that primers targeting Ph-3, Ty-3, MIPS, NAC, and CYP79D1 successfully amplified orthologous loci in Bambara groundnut and snake tomato. This reflects the evolutionary conservation of coding regions within resistance, stress-response, and metabolic pathways, an observation widely reported for R-genes (Shen et al., 2006 ; Leister, 2004 ), NAC transcription factors (Liu et al., 2011 ; Nuruzzaman et al., 2013 ), abiotic stress pathways (Liu et al., 2011 ; Liu et al., 2022 ; Khan et al., 2022 ), the MIPS/phytic acid pathway (Majee et al., 2011; Jagal Kishore et al., 2020 ), and cyanogenic glucoside genes such as CYP79 (Lin et al., 2000; Cheng et al., 2025 ; Koleva et al., 2025 ). The strong, single-band products obtained, particularly in snake tomato are consistent with studies demonstrating that conserved exonic regions frequently enable interspecific primer transferability (Andolfo & Ercolano, 2015 ; Kourelis & van der Hoorn, 2018 ). Most amplicons produced high-quality Sanger reads, and BLASTn identifications confirmed that recovered sequences corresponded to biologically meaningful loci associated with defense, metabolism, and stress tolerance (Roychowdhury et al., 2023 ). The successful retrieval of Ty-3 (viral resistance), Ph-3 (fungal resistance), MIPS (inositol biosynthesis), and CYP79D1 (cyanogenic glucoside production), all well-documented functional genes (Raboy, 2003 ; Feldman et al., 2019 ; Takagi et al., 2020 ) which adds valuable genomic information for under-characterized crops. Phylogenetic analysis revealed clear clustering patterns consistent with expected evolutionary relationships. Bambara groundnut grouped with legume-derived MIPS sequences, reflecting conservation within the inositol pathway (Ghosh et al., 2025 ). Tomato and snake tomato sequences formed Solanaceae-aligned Ty-3 clades associated with TY-1/TY-3 viral resistance, whereas cassava sequences clustered with CYP79D1 references, consistent with known variation in cyanogenic glucoside biosynthetic genes (Martinez & Diaz, 2024 ). The moderate-to-high bootstrap support observed (37–100%) aligns with prior functional gene phylogenies (Zhang et al., 2016 ; Andersen et al., 2018 ). Multivariate genetic analyses provided quantitative support for these patterns. PCoA revealed distinct clustering primarily driven by species identity, with functional gene class contributing secondary structure. Significant PERMANOVA and AMOVA statistics confirmed that genetic differentiation is structured both among species and among trait classes, with substantial within-group variation also present. Similar patterns have been documented in conserved gene families under purifying selection, where functional constraints limit excessive divergence while species-specific signatures remain detectable (Ullah et al., 2017 ; Hussain et al., 2018 ). Collectively, the molecular validation, phylogenetic relationships, and multivariate structure analyses demonstrate that the amplified loci capture both conserved and informative variation suitable for comparative genomics, marker development, and trait-focused studies. This is particularly important for underutilized African crops such as Bambara groundnut and snake tomato, which remain poorly represented in genomic databases (Mazahib et al., 2013 ; Oyeyinka et al., 2015 ; Sivakumar, 2023 ). Overall, the study provides validated primer sets, functional gene sequences, and comparative genetic insights that strengthen the molecular foundation for improving neglected crops. By integrating gene-targeted amplification with phylogenetic and multivariate analyses, the work contributes resources that can support marker-assisted breeding, trait dissection, and the broader conservation of genetic diversity, critical steps toward enhancing climate resilience and nutritional quality in African agricultural systems. CONCLUSION This study demonstrated the successful cross-species amplification and characterization of agronomically important genes associated with drought tolerance, disease resistance, starch biosynthesis, and phytic acid metabolism in Bambara groundnut and snake tomato. By applying previously validated primers from related species, we confirmed that several stress-responsive loci, including NAC , Ph-3 , Ty-3 , MIPS , and CYP79D1 , are sufficiently conserved to enable reliable PCR-based detection in these underutilized crops. The clear and reproducible amplicons obtained across diverse landraces highlight the potential of these primer sets as foundational tools for future genomics-assisted breeding. This work provides an initial molecular resource for two under-researched crops and lays the groundwork for expanded sequencing, marker development, and comparative genomics aimed at enhancing climate-resilient agriculture in sub-Saharan Africa. Declarations Author Contributions Francis Anti Amoako: Conceptualization, methodology, analysis, investigation, writing – original draft. Evans Kpobi: Co-lead research design, supervision, validation, writing – review and editing. Caroline Edem Anani: Wet laboratory experimentation, sample processing. Eunice Amponsah: Field sampling and material collection. David Amedorme: Data analysis and interpretation. Lily Batsa: Wet laboratory experimentation. Agnes Nimo Bosompem: Wet laboratory experimentation. Kwaku Boateng: Plant material establishment and wet laboratory experimentation. Kwame Boadu: Wet laboratory experimentation. David Pukinka: Plant material establishment. Francis Badu: Plant material establishment. Benard Tawiah: Plant material establishment. Ruth Prempeh: Laboratory supervision and resource management. All authors contributed to the study conception and design. Material preparation, data collection, and data analysis were performed by Francis Anti Amoako, Evans Kopbi, Caroline Anani Edem, David Amedrome, and Lily Batsa. The first draft of the manuscript was written by Francis Anti Amoako, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical Approval Not applicable. Data Availability All data generated or analyzed during this study are included in this published article and its supplementary materials. Additional materials can be provided by the corresponding author on reasonable request. References Adzawla W, Donkoh SA, Nyarko G, O’Reilly P, Mayes S. 2016. Use patterns and perceptions about the attributes of Bambara groundnut (Vigna subterranea (L.) Verdc.) in Northern Ghana. Ghana J Sci Technol Dev 4 (2), 56–71 (DOI: 10.47881/88.967x ) Agarwal, P. K., Gupta, K., Lopato, S., & Agarwal, P. (2017). Dehydration responsive element binding transcription factors and their applications for the engineering of stress tolerance. Journal of Experimental Botany , 68 (9), 2135–2148. (DOI: 10.1093/jxb/erx118 ) Amoako, F. K., Amoako, F. 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16:27:21","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":203358,"visible":true,"origin":"","legend":"","description":"","filename":"e5a8bb17b3f54ccdb37d51f0ccb525161structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8343122/v1/60024b5141a120052f51a842.xml"},{"id":99156582,"identity":"a0ba76b0-f2c2-439b-b948-7a9504da981d","added_by":"auto","created_at":"2025-12-29 11:48:20","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":219899,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8343122/v1/f014267032da26db0873c8a8.html"},{"id":99156563,"identity":"e46a742d-22fe-418e-a074-312aeb773043","added_by":"auto","created_at":"2025-12-29 11:48:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":184294,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eQuality check gel (0.8 % agarose) image of the 10 selected samples Key: 1KB= Ladder, 1= Cassava- Efe, 2= Nkentenma, 3=Ahenewa, 4= Local, 5= Adwoa Nenewa, 6= Peto, 7= Rhino Power, 8= Adomano off white, 9= Adomano Red, 10= Snake Tomato\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8343122/v1/8a4d6683929d1df3ae6849d6.png"},{"id":99156564,"identity":"778dc7fc-d5d3-4869-9e9f-c3f6a2c7337b","added_by":"auto","created_at":"2025-12-29 11:48:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGel image (1.5 % agarose) of the 15 samples using ACTIN house-keeping primer Key: 100bp= ladder1= Cassava- Efe, 2= Nkentenma, 3=Ahenewa, 4= Local, 5= Adwoa Nenewa, 6= Peto, 7= Rhino Power, 8= Adomano off white, 9= Adomano Red, 10= Snake Tomato C= No Template Control (NTC)\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8343122/v1/a018d3970d6347d229d78fb4.png"},{"id":99156567,"identity":"d2d098e7-7391-49ad-a6d3-de6ca21a137b","added_by":"auto","created_at":"2025-12-29 11:48:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":443879,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1: Phylogenetic tree of samples and references generated using MEGA version 12 and iTOL version 7.2.2\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8343122/v1/77749d0b487c497a71e4c52b.png"},{"id":99156569,"identity":"c34e3c24-d64e-471b-95eb-2286afcdd31c","added_by":"auto","created_at":"2025-12-29 11:48:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":417299,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2: Principal Coordinates Analysis (PCoA) plot of sequenced data for the study\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8343122/v1/cc0c4fcec1de2184bc94c172.png"},{"id":103765441,"identity":"ae95f852-d177-4281-a352-7e623872d6e2","added_by":"auto","created_at":"2026-03-02 16:01:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2728561,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8343122/v1/e5c7dc50-5862-4143-bca2-29d0b169f06f.pdf"},{"id":99317683,"identity":"6dffbd76-cb61-415e-91d8-b6abb5e93905","added_by":"auto","created_at":"2025-12-31 16:30:35","extension":"fas","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9887,"visible":true,"origin":"","legend":"","description":"","filename":"Data.fas","url":"https://assets-eu.researchsquare.com/files/rs-8343122/v1/aa1ea58b034cdcb6b8b2b15a.fas"},{"id":99315311,"identity":"0a7e4b3a-a590-4100-9504-babf2cc6f861","added_by":"auto","created_at":"2025-12-31 16:26:47","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":349604,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8343122/v1/9aa6726a89136bc089c157d4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eUnveiling Hidden Genetic Resources: Characterization of Key Agronomic Genes in Underutilized and Cultivated African Crops\u003c/p\u003e","fulltext":[{"header":"1.0 INTRODUCTION","content":"\u003cp\u003eClimate change has continued to reshape global agricultural systems through rising temperatures, shifting rainfall patterns, and increasingly frequent extreme weather events (El-Sayed \u0026amp; Kamel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These environmental disruptions have intensified both abiotic and biotic stresses. including drought, heat, pests, and pathogens which contribute to undermining crop productivity, yield stability, and food security. Addressing these challenges require a deeper understanding of plant genetic variation, the allelic diversity within genomes that governs adaptation, resilience, and agronomic performance. Such variation forms the basis for developing improved crop varieties capable of withstanding environmental stressors (Bhandari et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderutilized crops represent an important yet often overlooked reservoir of genetic resources for climate-resilient agriculture. Species such as Bambara groundnut (\u003cem\u003eVigna subterranea\u003c/em\u003e) and Snake tomato (\u003cem\u003eTrichosanthes cucumerina\u003c/em\u003e) thrive under marginal conditions where major staples frequently fail. Bambara groundnut, a nutrient-rich Fabaceae crop, is widely cultivated by smallholder farmers because of its drought tolerance, yield stability, and ability to grow in nutrient-poor soils (Oyeyinka et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bamshaiye et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Feldman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Its seeds contain high levels of carbohydrates, proteins, and essential amino acids, making it a valuable contributor to food and nutritional security (Mazahib et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Snake tomato, commonly called the snake gourd is cultivated across humid tropical regions, provides substantial nutritional and medicinal benefits. Its fruits contain essential vitamins, minerals, phenolic compounds, carotenoids, flavonoids, and dietary fiber, and are used both as functional foods and in traditional medicine (Sandhya et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sivakumar, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ominowa et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Raghunathan et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite their resilience and regional importance, both crops remain poorly characterized at the molecular level, restricting their integration into modern breeding programmes.\u003c/p\u003e \u003cp\u003eKey agronomic traits such as drought tolerance, disease resistance, starch biosynthesis, and phytic acid metabolism in crops are likely controlled by gene families. Drought stress disrupts photosynthesis, carbon assimilation, osmotic homeostasis, enzymatic activity, and overall plant growth (Ullah et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hussain et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Anjum et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and transcription factors such as DREB modulate drought tolerance by activating dehydration-responsive genes (Agarwal et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In relation to starch biosynthesis, which is central to carbohydrate storage and nutritional quality, relies on enzymes and regulatory proteins including GBSSI and bZIPs that coordinate the expression of starch-related genes (Smith et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Stower et al., 2012; Zhang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally. disease resistance in crops is mediated by immune receptors such as nucleotide-binding leucine-rich repeat (NLR) proteins and receptor-like kinases, which detect pathogen effectors and initiate defense responses (Andolfo \u0026amp; Ercolano, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Andersen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kourelis \u0026amp; Van der Hoorn, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Genes associated with phytic acid metabolism influence phosphorus storage and mineral bioavailability, with implications for both plant physiology and human nutrition (Raboy, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Takagi et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although these gene families have been extensively described in model crops such as tomato (Mahmoud et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), cassava (Huang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), maize (Yu et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), soybean (Gillman et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and rice (Zhang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), their study and functional composition remain largely unexplored in Bambara groundnut and snake tomato.\u003c/p\u003e \u003cp\u003eTo address this gap, the present study conducted a PCR and Sanger sequencing-based characterization of selected agronomic genes in the underutilized species. Validated primer sets from previous studies were used, leveraging the evolutionary conservation of these stress-related genes across plant taxa. Tomato and cassava served as positive controls and phylogenetic references, enabling confirmation of amplification specificity, comparative sequence analysis, and accurate interpretation of gene orthology. Through this approach, the study identified, amplified, sequenced, and characterized key abiotic- and biotic-stress\u0026ndash;related genes involved in drought tolerance, disease resistance, starch biosynthesis, and phytic acid metabolism in Bambara groundnut and snake tomato. The resulting genomic information was intended to support future breeding initiatives, guide molecular marker development, and enhance the utilization of these underexplored crops in climate-resilient agriculture.\u003c/p\u003e"},{"header":"2.0 MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant Material\u003c/h2\u003e \u003cp\u003eIn all, 10 samples comprising of two cultivated African crops (four cultivated tomato and three cultivated cassava genotypes) and two underutilized African crops (two bambara groundnut and one snake tomato genotypes). The germplasms with the exception of snake tomato which was sourced from CSIR- Crops Research Institute were sampled from the environs of Techiman and Kintampo where their propagating materials were collected from local folks for this study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Samples were established in the screen house of the Biotechnology unit at Council for Scientific and Industrial Research-Crops Research Institute (CSIR-CRI), Fumesua-Kumasi.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSamples used for the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrop Type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdomano off white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBambara Groundnut\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdomano Red\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBambara Groundnut\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNkentenma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCassava\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAhenewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCassava\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmpenkyene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCassava\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdwoa Nenewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhino Power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSnake Tomato\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sampling for Total Genomic DNA Isolation\u003c/h2\u003e \u003cp\u003eThe nursed genotypes in the screen house were sampled towards DNA isolation. The leaf of each sample was collected using a pair of forceps into 2 ml Eppendorf tubes and immediately placed into liquid nitrogen. Approximately 0.2 g of such sample was homogenized with liquid nitrogen in an Eppendorf tube for DNA isolation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Extraction of DNA\u003c/h2\u003e \u003cp\u003eThe DNA isolation was done using (Cetyltrimethylammonium bromide) protocol (Doyle and Doyle, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). One ml of freshly prepared CTAB (20mM Tris HCl, 50mM EDTA, 2M NaCl, 2% CTAB, 3% β- mercaptoethanol) extraction buffer was added to the homogenised leaf in 2 ml Eppendorf tube, vortexed for one min and incubated in water bath at 65\u0026deg;C for 10 mins with intermittent mixing by inverting the tubes. The tubes were cooled for five mins and 600 \u0026micro;l phenol chloroform isoamyl alcohol (25:24:1) was added and mixed gently by inversion until mixture turned milky. The tubes were centrifuged at 13,000 rpm for 10 mins using centrifuge (GenFuge 24D) and then 450 \u0026micro;l aqueous transferred into newly labelled 2 ml tubes, without disturbing the middle layer. Isopropanol (350 \u0026micro;l) was added to supernatant, mixed gently and incubated at -20\u0026deg;C for an hour. Samples were then centrifuge at 13,000 rpm for 10 mins, supernatant was discarded, pellet washed with 80% ethanol and the pellets were dried for 30 mins. Pellets were dissolved in 50 \u0026micro;l low salt buffer and 10 \u0026micro;l RNase A (20 mg/ml) was added and then incubated at 37\u0026deg;C for 45 mins in a water bath. The genomic DNA was quantified using a Nanodrop 2000C Spectrophotometer (Thermos Scientific, USA) and the quality checked on 0.8% agarose gel.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Validation of Isolated DNA using Actin (housekeeping gene)\u003c/h2\u003e \u003cp\u003eExtracted samples were further accessed for their suitability for PCR using Actin gene primer. This was done to provide the utmost confidence that the samples are void of any inhibitors that could potentially affect PCR activities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Primer validation\u003c/h2\u003e \u003cp\u003eA total of five primers designed to target different traits across the three crops studied were selected from literature and in-silico validated to determine their suitability for the study (Appendix A). They were further validated via PCR to determine their optimal annealing temperature and cycling conditions (Ruan \u0026amp; Lai, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Polymerase Chain Reaction\u003c/h2\u003e \u003cp\u003ePolymerase Chain Reaction (PCR) was performed using 2 X Hotstart mastermx Thermoscientific PCR kit. The PCR amplification reaction of 20 \u0026micro;l towards PCR amplification and sequencing contained final concentrations of 10 \u0026micro;l of 2 X Hotstart mastermix, (Thermoscientific), 1 \u0026micro;l each of forward and reverse primer, 4 \u0026micro;l of 50 ng DNA template and 4 \u0026micro;l of Nuclease Free Sterile Water (NFSW). The PCR amplification was carried out in a 96-well PCR thermal cycler (Veriti\u003csup\u003e(R)\u003c/sup\u003e AB Biosystems) with different PCR cycling profiles (Appendices B1 and B2). Six times (6X) bromophenol blue dye (4ul) was added to the products generated after the PCR. Amplified products (5 \u0026micro;l) were run on 1.5% agarose gel in Tris Boric acid EDTA (TBE) buffer, stained with Ethidium bromide and captured using Alpha Imager HP (protein simple, USA) to confirm the presence of a clear distinct band per sample towards sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Sangar Sequencing\u003c/h2\u003e \u003cp\u003ePCR products (15) with clear distinct bands were plated along with their corresponding primers as requested by the sequencing company (Functional Biosciences, Inc, Wisconsin, USA). The required concentration of primers as well as that of products were adhered to by following strictly the instructions of the sequencing company.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Data Analysis\u003c/h2\u003e \u003cp\u003eSequence data obtained from Functional Biosciences, Inc, Wisconsin, USA was accessed for accurate base calling and correction of bases called using BioEdit version 5.0.9 (Supplementary file 1 (S1). Raw chromatogram files obtained from the sequencing company were initially visualized and edited using BioEdit version 5.0.9. Chromatogram peaks were manually proofread to verify the accuracy of base calling, and ambiguous bases at both ends were trimmed. The data was filtered to obtain a final set of data and were subjected to BLASTn query on the NCBI database using Geneious Prime version 2025.0.3 to identify closely related reference sequences. The top-scoring reference sequences associated with each sample were retrieved and used for phylogenetic analysis.\u003c/p\u003e \u003cp\u003eMultiple sequence alignment of the filtered data and their corresponding reference sequences was performed using the Clusta W algorithm implemented in the Alignment Explorer module of MEGA version 12 (Tamura et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Alignment Explorer was used for manual inspection and adjustment of the alignments.\u003c/p\u003e \u003cp\u003eEvolutionary relationships among the data were inferred using the Maximum Likelihood (ML) method based on the Kimura 2-Parameter model (Kimura, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). The tree with the highest log likelihood was selected as the best representation of evolutionary relationships. The robustness of the branching patterns was assessed using 1,000 bootstrap replications (Felsenstein, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). The initial tree for the heuristic search was automatically selected based on the superior log likelihood value between a Neighbor-Joining (NJ) tree (Saitou and Nei, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) and a Maximum Parsimony (MP) tree. The NJ tree was generated from a pairwise distance matrix computed using the p-distance method, while the MP tree was derived from 10 searches initiated with random starting trees.\u003c/p\u003e \u003cp\u003eThe final phylogenetic tree was visualized and annotated in MEGA version 12 and iTOL version 7.2.2 (Letunic and Bork, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Analyses were conducted using all codon positions (1st, 2nd, and 3rd) as well as non-coding regions. All evolutionary computations utilized up to four parallel computing threads. Phylogenetic trees were rooted using an orthologous \u003cem\u003ePhyscomitrium patens\u003c/em\u003e reference sequence (AAQ88111.1) as a phylogenetically external outgroup because it lies outside most focal angiosperm families.\u003c/p\u003e \u003cp\u003eFor multivariate and population-level analyses, the curated, trimmed alignment was imported into R version 4.4.2. Pairwise genetic distances were calculated using p-distance using the \u003cem\u003eape\u003c/em\u003e package. Ordination analysis was performed through Principal Coordinates Analysis (PCoA) using classical multidimensional scaling (cmdscale). Metadata describing Group (species/source) and Trait (gene functional class) were merged with the genetic distance matrix to support hypothesis-driven analyses.\u003c/p\u003e \u003cp\u003eDifferences in genetic composition among Groups and Traits were tested using PERMANOVA- (Permutation-based multivariate analyses) (\u003cem\u003eadonis2\u003c/em\u003e, \u003cem\u003evegan\u003c/em\u003e package) with 999 permutations, providing a robust, permutation-based assessment of group structure. Variance partitioning at the population level was conducted using AMOVA-(Analysis of Molecular Variance) within \u003cem\u003epoppr\u003c/em\u003e, with a genind object derived from the aligned sequences. AMOVA significance was evaluated with 999 permutations. Analyses were performed on complete-case datasets, and results were summarized as variance components, Φ statistics, and permutation-based p-values.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Assessment of Extracted DNA and QC via Actin Gene PCR Validation\u003c/h2\u003e \u003cp\u003eThe extracted DNA samples were assessed for their quality, concentration and purity. The quality of the 15 samples was determined on a 0.8% agarose gel (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and the concentration as well as purity were determined using the nanodrop spectrophotometer 2000 C (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Most of the samples had quality DNA with concentration and purity ranging from 360.9\u0026ndash; 2267.9 and 1.63\u0026ndash;1.89 respectively. In addition, samples were accessed with Actin primer where all samples amplified for the gene indicating their suitability for sequencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eKey: 1KB\u0026thinsp;=\u0026thinsp;Ladder, 1\u0026thinsp;=\u0026thinsp;Cassava- Efe, 2\u0026thinsp;=\u0026thinsp;Nkentenma, 3\u0026thinsp;=\u0026thinsp;Ahenewa, 4\u0026thinsp;=\u0026thinsp;Local, 5\u0026thinsp;=\u0026thinsp;Adwoa Nenewa, 6\u0026thinsp;=\u0026thinsp;Peto, 7\u0026thinsp;=\u0026thinsp;Rhino Power, 8\u0026thinsp;=\u0026thinsp;Adomano off white, 9\u0026thinsp;=\u0026thinsp;Adomano Red, 10\u0026thinsp;=\u0026thinsp;Snake Tomato\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConcentration and Purity of samples to be sequenced\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConc(ng/\u0026micro;l)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePurity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCassava- Efe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1444.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCassava- Nkentenma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1269.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCassava- Ahenewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1746.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTomato- Local\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e748.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTomato- Adwoa Nenewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e360.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTomato- Peto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e752.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTomato- Rhino Power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1055.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBambara - Adomano off white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2214.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBambara- Adomano Red\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1777.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSnake Tomato\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2267.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e: \u003cb\u003eGel image (1.5% agarose) of the 15 samples using ACTIN house-keeping primer\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eKey: 100bp\u0026thinsp;=\u0026thinsp;ladder1\u0026thinsp;=\u0026thinsp;Cassava- Efe, 2\u0026thinsp;=\u0026thinsp;Nkentenma, 3\u0026thinsp;=\u0026thinsp;Ahenewa, 4\u0026thinsp;=\u0026thinsp;Local, 5\u0026thinsp;=\u0026thinsp;Adwoa Nenewa, 6\u0026thinsp;=\u0026thinsp;Peto, 7\u0026thinsp;=\u0026thinsp;Rhino Power, 8\u0026thinsp;=\u0026thinsp;Adomano off white, 9\u0026thinsp;=\u0026thinsp;Adomano Red, 10\u0026thinsp;=\u0026thinsp;Snake Tomato C\u0026thinsp;=\u0026thinsp;No Template Control (NTC)\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 PCR and Sample Prep towards Sequencing\u003c/h2\u003e \u003cp\u003eFive (5) primer pairs were used to screen the 10 samples for various traits. The primer pairs generated amplicons with clear distinct single bands per sample per primer after PCR screening. These primers generated a total of 18 reactions which were prepped up towards sequencing. The 18 reactions had band sizes spanning about 150 bp to 1800 bp on the agarose gel (Appendix C).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sanger Sequencing\u003c/h2\u003e \u003cp\u003eIn order to know the order of sequences for each amplicon per trait obtained during PCR, sequencing of the 15 reactions was conducted via target sequencing. All 15 reactions were sequenced with their respective primer sequences (Appendix E and F). Out of the 15 reactions reads obtained, 12 of them representing 80% passed QC based on the filtering parameters selected (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Appendix D) for five primers sent for sequencing. The reads which passed the quality thresholds were retained for downstream analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics of sequenced data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSummary\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal number of reads\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage quality score (all reads)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuality trimming parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSliding window\u0026thinsp;=\u0026thinsp;20; Threshold\u0026thinsp;=\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh-quality (HQ) reads obtained\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage HQ read quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage HQ read length\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e471 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuality filtering criteria applied\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHQ Reads\u0026thinsp;\u0026ge;\u0026thinsp;50, Trim Q Ave\u0026thinsp;\u0026ge;\u0026thinsp;25, Trim Length\u0026thinsp;\u0026ge;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Sequence Identification and Phylogenetic Analysis\u003c/h2\u003e \u003cp\u003eBLASTn analysis of the filtered sequences returned top matches to known gene sequences, including Ph-3, Ty-3\u0026ndash;associated loci, CYP79D1, MIPS-related sequences, and other annotated plant genes. All high-quality reads aligned to biologically relevant reference sequences, confirming successful amplification of the intended genomic regions.\u003c/p\u003e \u003cp\u003eMultiple sequence alignment produced a final curated dataset of approximately 4,282 nucleotides after removal of positions containing gaps and missing data. The aligned sequences showed conserved and variable regions suitable for phylogenetic evaluation.\u003c/p\u003e \u003cp\u003eMaximum Likelihood phylogenetic analysis generated a resolved tree with strong bootstrap support across major branches. Distinct clustering patterns were observed among the sequences. Ado-Off-White and Ado-Red grouped closely with the \u003cem\u003ePhaseolus vulgaris\u003c/em\u003e MIPS-related reference sequence AM941723. Peto Ty-3Caps, Rhino Power, and SN Ty-3Caps formed a cluster with XM_010323869, an RNA-dependent RNA polymerase associated with TY-1/TY-3 resistance. Additionally, Ahenewa, Ampenkyene, Nkentenma, Adwoa Ph-3, Local Ph-3, and Peto Ph-3 clustered with the PV426896 and CYP79D1 (AF140613) reference sequences. Bootstrap values ranging from 58% to over 90% supported the stability of these groupings, and the final phylogenetic tree was visualized and annotated using MEGA and iTOL.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Principal Coordinates Analysis (PCoA)\u003c/h2\u003e \u003cp\u003ePCoA based on p-distance revealed clear patterns of genetic differentiation among the sampled sequences. The first two principal coordinates captured the majority of the variation, with PC1 explaining 47.5% and PC2 explaining 27.3% of the total genetic variation. The ordination plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed that samples clustered predominantly according to species (Group), with distinct separation along PC1. Samples sharing similar gene functional classes (Trait) also tended to cluster together, though with some overlap.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6 PERMANOVA\u003c/h2\u003e \u003cp\u003ePERMANOVA, at 999 permutations formally tested differences in genetic composition among Groups, Traits, and their combined effects. Results revealed that Group membership explained a significant proportion of genetic variation (R\u0026sup2; = 0.570, F\u0026thinsp;=\u0026thinsp;5.309, p\u0026thinsp;=\u0026thinsp;0.001), confirming the strong species-level structure observed in PCoA (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, Trait categories were also significant (R\u0026sup2; = 0.597, F\u0026thinsp;=\u0026thinsp;9.625, p\u0026thinsp;=\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), indicating that functional gene class contributes to the observed genetic variation, although to a slightly lesser degree than species identity. The combined model (Group\u0026thinsp;+\u0026thinsp;Trait) explained 66.7% of the total variation (F\u0026thinsp;=\u0026thinsp;5.505, p\u0026thinsp;=\u0026thinsp;0.001) with residual variation at ~\u0026thinsp;33% (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePERMANOVA for Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.3089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePERMANOVA for Trait\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.6248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePERMANOVA for Group\u0026thinsp;+\u0026thinsp;Trait\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel (Group\u0026thinsp;+\u0026thinsp;Trait)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.88646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.82883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.7 AMOVA\u003c/h2\u003e \u003cp\u003eAMOVA provided a complementary hierarchical assessment of genetic variation. The partitioning of variance indicated that 42.5% of the total genetic variance was attributable to differences among Groups, whereas 57.5% resided within groups (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The observed Φ statistic (Φ\u0026thinsp;=\u0026thinsp;0.425, p\u0026thinsp;=\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) after assessment was highly significant, reinforcing the strong genetic differentiation among Groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAMOVA Variance Partitioning\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of Variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum Sq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Sq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVariance Component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e% Variation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetween Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e473.6726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e157.89087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.2076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.53%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e505.9524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.16270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.1627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.47%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e979.6250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.30833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.3703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAMOVA Significance Test (999 permutations)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΦ (samples\u0026ndash;total)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.9080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 DISCUSSION","content":"\u003cp\u003eThis study examined agronomically important and stress-related genes across cassava, tomato, snake tomato, and Bambara groundnut with the broader goal of generating molecular resources for improving both cultivated and underutilized African crops. As climate change intensifies drought, heat, pest, and pathogen pressures, understanding allelic diversity within functional gene families is critical for developing resilient varieties (El-Sayed \u0026amp; Kamel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bhandari et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Genes involved in drought tolerance, disease resistance, starch metabolism, and phytic acid regulation are of particular interest because of their central role in crop performance under environmental stress.\u003c/p\u003e \u003cp\u003eThe high-quality genomic DNA obtained across species provided a solid foundation for downstream analyses, with concentration and purity values falling within acceptable limits for PCR and sequencing. Consistent amplification of the ACTIN housekeeping gene reinforced template suitability, consistent with reports across other plant systems where ACTIN amplification is used as a benchmark for DNA integrity (Healey et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pokharel et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dong et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Amoako et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bosompem et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA key finding of this work was the cross-species utility of primers designed originally for cassava and tomato. Both in silico and experimental validation confirmed that primers targeting Ph-3, Ty-3, MIPS, NAC, and CYP79D1 successfully amplified orthologous loci in Bambara groundnut and snake tomato. This reflects the evolutionary conservation of coding regions within resistance, stress-response, and metabolic pathways, an observation widely reported for R-genes (Shen et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Leister, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), NAC transcription factors (Liu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Nuruzzaman et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), abiotic stress pathways (Liu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Khan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the MIPS/phytic acid pathway (Majee et al., 2011; Jagal Kishore et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and cyanogenic glucoside genes such as CYP79 (Lin et al., 2000; Cheng et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Koleva et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The strong, single-band products obtained, particularly in snake tomato are consistent with studies demonstrating that conserved exonic regions frequently enable interspecific primer transferability (Andolfo \u0026amp; Ercolano, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kourelis \u0026amp; van der Hoorn, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost amplicons produced high-quality Sanger reads, and BLASTn identifications confirmed that recovered sequences corresponded to biologically meaningful loci associated with defense, metabolism, and stress tolerance (Roychowdhury et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The successful retrieval of Ty-3 (viral resistance), Ph-3 (fungal resistance), MIPS (inositol biosynthesis), and CYP79D1 (cyanogenic glucoside production), all well-documented functional genes (Raboy, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Feldman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Takagi et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) which adds valuable genomic information for under-characterized crops.\u003c/p\u003e \u003cp\u003ePhylogenetic analysis revealed clear clustering patterns consistent with expected evolutionary relationships. Bambara groundnut grouped with legume-derived MIPS sequences, reflecting conservation within the inositol pathway (Ghosh et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Tomato and snake tomato sequences formed Solanaceae-aligned Ty-3 clades associated with TY-1/TY-3 viral resistance, whereas cassava sequences clustered with CYP79D1 references, consistent with known variation in cyanogenic glucoside biosynthetic genes (Martinez \u0026amp; Diaz, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The moderate-to-high bootstrap support observed (37\u0026ndash;100%) aligns with prior functional gene phylogenies (Zhang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Andersen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultivariate genetic analyses provided quantitative support for these patterns. PCoA revealed distinct clustering primarily driven by species identity, with functional gene class contributing secondary structure. Significant PERMANOVA and AMOVA statistics confirmed that genetic differentiation is structured both among species and among trait classes, with substantial within-group variation also present. Similar patterns have been documented in conserved gene families under purifying selection, where functional constraints limit excessive divergence while species-specific signatures remain detectable (Ullah et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hussain et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCollectively, the molecular validation, phylogenetic relationships, and multivariate structure analyses demonstrate that the amplified loci capture both conserved and informative variation suitable for comparative genomics, marker development, and trait-focused studies. This is particularly important for underutilized African crops such as Bambara groundnut and snake tomato, which remain poorly represented in genomic databases (Mazahib et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Oyeyinka et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sivakumar, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the study provides validated primer sets, functional gene sequences, and comparative genetic insights that strengthen the molecular foundation for improving neglected crops. By integrating gene-targeted amplification with phylogenetic and multivariate analyses, the work contributes resources that can support marker-assisted breeding, trait dissection, and the broader conservation of genetic diversity, critical steps toward enhancing climate resilience and nutritional quality in African agricultural systems.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study demonstrated the successful cross-species amplification and characterization of agronomically important genes associated with drought tolerance, disease resistance, starch biosynthesis, and phytic acid metabolism in Bambara groundnut and snake tomato. By applying previously validated primers from related species, we confirmed that several stress-responsive loci, including \u003cem\u003eNAC\u003c/em\u003e, \u003cem\u003ePh-3\u003c/em\u003e, \u003cem\u003eTy-3\u003c/em\u003e, \u003cem\u003eMIPS\u003c/em\u003e, and \u003cem\u003eCYP79D1\u003c/em\u003e, are sufficiently conserved to enable reliable PCR-based detection in these underutilized crops. The clear and reproducible amplicons obtained across diverse landraces highlight the potential of these primer sets as foundational tools for future genomics-assisted breeding. This work provides an initial molecular resource for two under-researched crops and lays the groundwork for expanded sequencing, marker development, and comparative genomics aimed at enhancing climate-resilient agriculture in sub-Saharan Africa.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Francis Anti Amoako: Conceptualization, methodology, analysis, investigation, writing \u0026ndash; original draft.\u003cbr\u003e\u0026nbsp;Evans Kpobi: Co-lead research design, supervision, validation, writing \u0026ndash; review and editing.\u003cbr\u003e\u0026nbsp;Caroline Edem Anani: Wet laboratory experimentation, sample processing.\u003cbr\u003e\u0026nbsp;Eunice Amponsah: Field sampling and material collection.\u003cbr\u003e\u0026nbsp;David Amedorme: Data analysis and interpretation.\u003cbr\u003e\u0026nbsp;Lily Batsa: Wet laboratory experimentation.\u003cbr\u003e\u0026nbsp;Agnes Nimo Bosompem: Wet laboratory experimentation.\u003cbr\u003e\u0026nbsp;Kwaku Boateng: Plant material establishment and wet laboratory experimentation.\u003cbr\u003e\u0026nbsp;Kwame Boadu: Wet laboratory experimentation.\u003cbr\u003e\u0026nbsp;David Pukinka: Plant material establishment.\u003cbr\u003e\u0026nbsp;Francis Badu: Plant material establishment.\u003cbr\u003e\u0026nbsp;Benard Tawiah: Plant material establishment.\u003cbr\u003e\u0026nbsp;Ruth Prempeh: Laboratory supervision and resource management.\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and data analysis were performed by Francis Anti Amoako, Evans Kopbi, Caroline Anani Edem, David Amedrome, and Lily Batsa. The first draft of the manuscript was written by Francis Anti Amoako, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All data generated or analyzed during this study are included in this published article and its supplementary materials. Additional materials can be provided by the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdzawla W, Donkoh SA, Nyarko G, O\u0026rsquo;Reilly P, Mayes S. 2016. Use patterns and perceptions about the attributes of Bambara groundnut (Vigna subterranea (L.) Verdc.) in Northern Ghana. Ghana J Sci Technol Dev 4 (2), 56\u0026ndash;71 (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.47881/88.967x\u003c/span\u003e\u003cspan address=\"10.47881/88.967x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgarwal, P. K., Gupta, K., Lopato, S., \u0026amp; Agarwal, P. (2017). 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Maize endosperm-specific transcription factors O2 and PBF network the regulation of protein and starch synthesis. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e113\u003c/em\u003e(39), 10842\u0026ndash;10847.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Bambara groundnut, snake tomato, stress-responsive genes, cross-species primers, molecular characterization, climate-resilient crops","lastPublishedDoi":"10.21203/rs.3.rs-8343122/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8343122/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBambara groundnut (Vigna subterranea) and snake tomato (Trichosanthes cucumerina) are underutilized African crops valued for their nutritional quality and resilience to harsh environments. Despite their agronomic importance, molecular data on key stress-related genes remain limited, constraining their integration into modern breeding programs. Previously validated primers targeting genes associated with drought tolerance (NAC), disease resistance (Ph-3, Ty-3), starch biosynthesis (CYP79D1), and phytic acid metabolism (MIPS) were evaluated through PCR and Sanger sequencing. Tomato and cassava served as positive controls and phylogenetic anchors. Amplicons were analyzed using standard sequence-quality workflows, multiple sequence alignment, and phylogenetic reconstruction. Most primer pairs produced clear, single amplicons in both crops, with snake tomato exhibiting particularly strong cross-species amplification. Sequencing confirmed locus specificity and revealed conserved regions enabling primer transferability. Phylogenetic analyses grouped Bambara groundnut and snake tomato sequences with their expected legume and cucurbit clades, validating the evolutionary placement of the amplified loci. These outcomes demonstrate that the targeted genes are conserved across species and suitable for molecular characterization. The successful amplification and sequencing of key stress-related genes provide foundational genomic information for Bambara groundnut and snake tomato. These results confirm that conserved gene regions can be exploited for molecular marker development, comparative genomics, and future breeding programs aimed at enhancing stress resilience in underutilized crops.\u003c/p\u003e","manuscriptTitle":"Unveiling Hidden Genetic Resources: Characterization of Key Agronomic Genes in Underutilized and Cultivated African Crops","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-29 11:48:15","doi":"10.21203/rs.3.rs-8343122/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-27T05:34:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-27T05:30:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-26T00:03:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7172142581279577228093983853294613164","date":"2026-01-22T17:55:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249945794146490792816379465850347306731","date":"2026-01-16T16:41:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51688855140478105385329093438168925256","date":"2026-01-16T11:07:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227826825030049957655906521582578183819","date":"2026-01-14T18:06:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31697910399519233115446354400693924208","date":"2026-01-14T17:23:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-25T09:33:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-18T11:39:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-18T11:37:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2025-12-12T07:40:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"46bcf47b-6c12-47e5-82b7-f7eef595010f","owner":[],"postedDate":"December 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:00:31+00:00","versionOfRecord":{"articleIdentity":"rs-8343122","link":"https://doi.org/10.1007/s10722-026-02755-2","journal":{"identity":"genetic-resources-and-crop-evolution","isVorOnly":false,"title":"Genetic Resources and Crop Evolution"},"publishedOn":"2026-02-26 15:57:22","publishedOnDateReadable":"February 26th, 2026"},"versionCreatedAt":"2025-12-29 11:48:15","video":"","vorDoi":"10.1007/s10722-026-02755-2","vorDoiUrl":"https://doi.org/10.1007/s10722-026-02755-2","workflowStages":[]},"version":"v1","identity":"rs-8343122","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8343122","identity":"rs-8343122","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}