A Multi-marker Genomic Approach to Decipher the Divergence and Diversity in Selected Allium sativum L. cultivars

A Multi-marker Genomic Approach to Decipher the Divergence and Diversity in Selected Allium sativum L. cultivars | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Multi-marker Genomic Approach to Decipher the Divergence and Diversity in Selected Allium sativum L. cultivars Narayana Chellaiya Johnson Packia Lekshmi, Duraisamy Mahamuni, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3978989/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The genus Allium comprises plants of significant economic and medical importance, including onion, garlic, and leek plants. The genetic diversity of garlic plants ( Allium sativum ) is vital for improving agricultural practices, developing resilient crops, preserving genetic resources, and exploring the full range of culinary and medicinal potential within this important plant species. In this research, we investigated the results of genetic barcoding, focusing on the internal transcribed spacer (ITS) region; four distinct barcoding regions, matK, rbcL, and trnH-psbA; and the trnL and Inter Simple Sequence Repeats (ISSR) regions of Allium sativum L. (Amaryllidaceae), which were collected from three diverse cultivation sites. Our findings revealed significant interspecific diversity and intraspecific divergence among the three cultivars examined. Interestingly, the results from different genetic markers were consistent, with BDUT 1451 and 1452 consistently grouping together, while BDUT 1450 diverged. These findings emphasize the effectiveness of the multi-marker approach for exploring intricate genetic landscapes. Furthermore, they highlight the importance of genetic studies in understanding the diversity of breeding and the potential utility of this economically and medicinally important nutraceutical crop. garlic ITS barcoding ISSR profiling incomplete lineage sorting breeding Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Introduction Estimating Earth's species diversity has always been a contentious issue, with suggested numbers ranging from approximately 1.5 million (described thus far) to more than 5 billion species (Larsen et al. 2017 ). On the other hand, reports indicate that numerous species are disappearing and that more than a million are under the threat of extinction (Tollefson 2019 ). However, some studies suggest that nearly two-thirds of the existing species were described already (Appeltans et al. 2012 ; Costello et al. 2013 ; Li and Wiens 2023 ). Despite an increase in the number of taxonomists working on biodiversity, there has been a decline in the identification of new species (Costello et al. 2013 ). Notwithstanding considerable technological advancements in taxonomy, Engel et al. ( 2021 ) highlight persistent concerns regarding a dearth of qualified taxonomists, inadequate funding, insufficient training programs, and limited job opportunities within the field. Conventional phytomorphological and plant anatomical studies limit our understanding of evolutionary taxonomy and species identification. These approaches involve four significant limitations. First, phenotypic plasticity and genetic diversity within the characteristics used for species identification can lead to erroneous determinations. Second, this method often overlooks phenotypically cryptic taxa, which are prevalent in many species groups (Knowlton 1993 ; Jarman and Elliott 2000 ; Aryakia et al. 2016 ). Third, phenotypic keys are often effective only for a specific life stage, leaving a substantial portion of individuals unidentified. Finally, while modern interactive tools represent a significant advancement, the use of keys often requires a high level of expertise, increasing the likelihood of misdiagnosis (Hebert et al. 2003 ). These limitations, along with the declining rate of new species identification, necessitate innovative approaches in taxonomic identification. In the late 1990s and early 21st century, the use of a microgenomic identification system emerged as a promising avenue for determining biological diversity. Leveraging the power of genetic analysis through the examination of small fragments of an organism's genome (Hebert et al. 2003 ), this system utilizes genetic barcodes present in every cell, akin to universal product codes used for retail product identification. Despite the relative simplicity of determining life diversity through genomic barcoding, this approach provides a more efficient and precise means of categorizing and understanding the incredible variety of life forms on our planet. DNA barcoding is a transformative methodology that empowers taxonomists by expanding their ability to complete a global inventory of diversity (Hebert and Gregory 2005 ). DNA barcoding not only benefits taxonomists but also scientists in diverse fields, such as forensic science, biotechnology, the food industry, and animal diet research (Creer et al. 2016 ; Deiner et al. 2017 ; Yang et al. 2020 ; Fernandes et al. 2021 ; Gostel and Kress 2022 ). In summary, genomic barcoding provides an expansive and versatile tool for untangling the intricate web of biological diversity, akin to how product barcodes streamline the identification of consumer goods. Efforts to develop universal genomic databases and accessions involve coordination among research organizations, government bodies, scientific societies, and others associated with genomic studies. Initiatives such as the Barcode of Life Initiative (BOLI), established in 2003, aim to improve the applicability and cost-effectiveness of genomic taxon identification (Costa and Carvalho 2010 ). Similarly, the International Barcode of Life (iBOL), established in 2008, seeks to transform biodiversity science by building DNA barcode reference libraries, sequencing facilities, informatics platforms, and analytical protocols through international collaboration (iBOL 2024 ). Despite the potential benefits of DNA barcoding for both practitioners and users of classification, there is controversy within various systematic circles (DeSalle and Goldstein 2019 ; Honeycutt 2021 ; Pfeiler 2023 ). Although employing a single gene region as a DNA barcode does not guarantee complete taxonomic resolution, it offers a significant degree of proximity. Numerous studies on diverse animal groups suggest that DNA barcoding can achieve high levels of species resolution (Garg and Biju 2021 ; Dincă et al. 2023) and provides promising taxon identification and species determination in plant communities (Besse et al. 2021 ; Raskoti and Ale 2021 ; Sawarkar et al. 2021 ; Chac and Thinh 2023 ). The Consortium of Barcode of Life (CBOL) recommends a 2-locus combination comprising the ribulose-1,5 bisphosphate carboxylase oxygenase large subunit (rbcL) and maturase K (matK) as the standard plant barcode (CBOL 2009). These two regions of chloroplast DNA were chosen for their efficient recovery of high-quality sequences and their ability to distinguish between species effectively (Burgess et al. 2011 ). In recent decades, researchers have increasingly utilized rbcL and matK sequences for both species identification (Starr et al. 2009 ; Asahina et al. 2010 ; Bieniek et al. 2015 ; Ho et al. 2021 ) and phylogenetic analysis (Asahina et al. 2010 ; Kuo et al. 2011 ; Maulidya et al. 2020 ; Cetiz et al. 2023 ). Additionally, the spacer between tRNA-His and photosystem II protein D1 (trnH-psbA spacer) and the ITS2 are widely employed for similar purposes (Chen et al. 2010 ; Gao et al. 2010 ; Fu et al. 2011 ; Bieniek et al. 2015 ; Intharuksa et al. 2020 ; Jiang et al. 2023 ). However, it is important to note that the application of universal barcode markers has its own limitations and is subject to further advances (Tekle and Wood 2018 ; Piper et al. 2019 ; Manzoor et al. 2022 ). Accurate identification of crucial agricultural crops is beneficial for DNA barcoding, and insights into the evolutionary background of these crops have been obtained. However, the study of genetic diversity in Allium species has faced challenges due to the absence of readily portable codominant molecular markers during the early stages of this technology (McCallum 2007 ). While a variety of molecular marker methods have successfully addressed questions related to genetic diversity and species relatedness within Allium species, the search for robust and informative markers specific to Allium species has proven to be considerably more demanding (Xie et al. 2020 ). Despite being economically important crops, some Amaryllidaceae crops, such as bulb onions and garlic, remain understudied compared to other major crops (Zhang et al. 2023). Allium sativum , an essential aromatic and nutraceutical ingredient, offers a wide array of health benefits (Wang et al. 2014 ; Rauf et al. 2022 ). Understanding the taxonomic evolution of functional traits requires exploring the molecular breeding and genetic diversity of this important food crop. With these considerations in mind, the present research aimed to explore the relationships among three distinct cultivars of garlic plants ( Allium sativum ) using ITS sequence and DNA barcoding, employing primers for matK, rbcL, trnH-psbA, and trnL. This study provides valuable insights into the genetic diversity and relatedness of these cultivars, contributing to our understanding of this important crop species. Materials and Methods We collected three distinct local cultivars of Allium sativum from different locations in Tamil Nadu, India: Poomparai (BDUT 1450) in Kodaikanal District, Vadugapatti (BDUT 1451) in Theni District, and Pannaikadu (BDUT 1452) in Kodaikanal District. Applying the CTAB method (Doyle and Doyle 1987 ), DNA extraction was conducted with previously washed and sterilized fresh, young roots, each weighing 200 mg per cultivar. Subsequently, a spin column kit was used to purify the DNA extracts from the garlic cultivars. Evaluation of the ethidium bromide-stained DNA extracts revealed the formation of bands upon UV exposure. To evaluate the sequence information of the chosen cultivars, we targeted specific regions: a spacer DNA (ITS) region, four candidate DNA coding regions (rbcL, matK, PsbA, and trnL) and an inter simple sequence repeater (ISSR) of the microsatellite region. Amplification of these regions was achieved through polymerase chain reaction (PCR) using the universal primers outlined in Table 1 . Table 1 Primers used in the barcoding and sequencing of Allium species Genomic Target Primers Primer Sequence 5’-3’ Reference Internal Transcribed Spacer Region ITS1 TCCGTAGGTGAACCTGCGG White et al., 1990 ITS4 TCCTCCGCTTATTGATATGC Barcoding Regions rbcLa-F ATGTCACCACAAACAGAGACTAAAGC Bafeel et al., 2011 rbcLa-R GTAAAATCAAGTCCACCRCG MatK-3F KIM CGTACAGTACTTTTGTGTTTACGAG Costion et al., 2011 MatK-1R KIM ACCCAGTCCATCTGGAAATCTTGGTTC psbA3_f GTTATGCATGAACGTAATGCTC Kress et al., 2005 trnHf_05 CGCGCATGGTGGATTCACAATCC trnL F CGAAATCGGTAGACGCTACG Taberlet et al., 1991 trnL R GGGGATAGAGGGACTTGAAC Inter Simple Sequence Repeats ISSR8US G(AACA)4 Jabbes et al. ( 2011 ) ISSR9 HVH(TC)8 ISHY 1b (GA)8YT ISHY 2 (AC)8YG ISHY 3 (AG)8YT ISHY 4 (CA)8RG ISSR a GCV(TG)8 ISSR8US G(AACA)4 For each PCR mixture, we included 10 µl of Taq premix, 4 µl of water, 1.5 µl of each of the forward and reverse primers, and 3 µl of DNA. The PCR Thermal Cycler Program was executed using an Eppendorf ProS (Hamburg, Germany). The thermal cycling programme for all sequence analyses comprised an initial cycle of 5 min at 94°C, followed by 35 cycles of 30 sec at 94°C, 30 sec at 58°C, and 60 sec at 72°C. The amplification was completed with a final cycle of 10 min at 72°C. To visualize the PCR products, all the samples were separated on a 1.2% agarose gel in 1× TAE buffer through electrophoresis at 60 V for 90 min. Subsequently, the separation was examined using a UV transilluminator. Before sequencing, the PCR products were subjected to an additional purification step. The Sanger dideoxy method of DNA sequencing was used for the PCR products, and the obtained data were imported and aligned using Molecular Evolutionary Genetics Analysis (MEGA v6.2.2). Key sequence statistics, covering nucleotide frequencies, transition/transversion bias (R = s/v), and variability across various sequence regions, were analysed. For sequence data analysis, both phenetic and cladistic methods were employed. The phenetic analysis utilized the neighbour‒joining (NJ) method, while the cladistic approach employed the maximum parsimony (MP) method. These two complementary methods provided comprehensive insights into the genetic relationships and patterns present in the sequence data. For each primer, the genetic sequence of the ISSR region was indexed to identify and compare polymorphic loci (P) among the cultivars. This information was subsequently utilized to compute the Shannon information index (I) (Lewontin 1972 ) and Nei’s standard genetic distance (D) (Nei 1972 ). The allele diversity data obtained from these indices were used to evaluate the discriminative performance of each primer. Subsequently, the genetic distance information was used to construct a dendrogram with POPGENE v32 software. Finally, a population model was established through Bayesian analysis of the ISSR data using STRUCTURE v3.2.2 software. Results and Discussion Various primers were used to amplify and sequence distinct genomic regions of three chosen Allium cultivars. The number of base pairs in each sequenced region slightly differed among the three cultivars, with the trnL region exhibiting shorter lengths than the other barcoding regions analysed in this study (Table 2 ). Table 2 Lengths of the sequenced genome regions of three selected Allium sativum cultivars for different molecular markers used in this study (number in base pairs). Garlic Cultivar Internal Transcribed Spacer Region Barcoding Regions ITS matK trnH-psbA rbcL trnL BDUT 1450 645 851 650 537 287 BDUT 1451 651 853 646 540 291 BDUT 1452 630 851 640 547 259 The substitution pattern and rates for the ITS sequence were estimated using the Tamura and Nei ( 1993 ) model with a discrete gamma distribution to account for evolutionary rate differences among sites (5 categories, [+ G]). In the ITS region analysis, the shape parameter for the discrete Gamma distribution was estimated to be 200.0000. The mean evolutionary rates in these categories were 0.90, 0.96, 1.00, 1.04, and 1.10 substitutions per site, suggesting moderate evolution of these cultivars. The maximum log likelihood for this computation was − 2594.439, indicating a good fit with the Tamura–Nei model, significant heterogeneity, and a moderate evolutionary rate. The nucleotide frequencies of the ITS region observed in this Tamura–Nei model were A = 22.54%, T = 30.58%, C = 20.37%, and G = 26.51%. The transition/transformation bias (R) for the ITS region was 1.09, indicating a bias higher than the expected level. Additionally, when the Kimura ( 1980 ) 2-parameter model was used for substitution patterns and rates, the nucleotide frequencies were A = 25.00%, T = 25.00%, C = 25.00%, and G = 25.00%. For estimating maximum likelihood (ML) values, a tree topology was automatically computed, and the maximum log likelihood for this Kimura 2-parameter computation was − 2617.110. Both models exhibit a typical ITS region with AT richness, which might contribute to the higher R value than expected. When the phenetic neighbor–joining (NJ) method was applied to the ITS sequence, the phylogeny of the three cultivars resulted in a divergence of BDUT 1451 from that of the other two cultivars, BDUT 1452 and BDUT 1450, which were found to be in a separate cluster. In contrast, according to the cladistic MP method, BDUT 1450 and BDUT 1452 formed a single cluster, while BDUT 1451 diverged into a separate cluster (Figs. 1 and 2 ). The discrepancy in clustering patterns could conceivably be attributed to the high rate of heterogeneity, where the NJ method relies on overall sequence similarity, whereas the MP method focuses on shared derived characters. Interestingly, both methods consistently placed BDUT 1451 in a separate cluster, suggesting intraspecific divergence. The phenetic neighbor-joining (NJ) method of ITS sequence analysis revealed that BDUT 1451 was closely linked to A. sativum gi228015332, and BDUT 1452 diverged from this cluster. BDUT 1450 was closely related to a cluster of A. ampeloprasum var. harmone gi338191565 and A. ampeloprasum var. harmone gi338191566. According to the cladistic method of ITS, BDUT 1450 and BDUT 1452 formed a single clade, whereas BDUT 1451 was closely linked to A. sativum gi228015332. The overall pairwise distance between them was 6.153. A dendrogram created by Baghalian et al. ( 2006 ) using 24 ecotypes revealed five main groups. Thirteen of the 24 ecotypes were included in cluster group A, two ecotypes in group B and seven ecotypes in group C. They found that there was no relationship between genetic divergence and geographical origin in the post planting stage, as genotypes from the same origin entered into different clusters, and genotypes from different origins also entered the same cluster. In the study by Figliuolo et al. ( 2001 ), molecular analysis confirmed the differentiation of A. sativum and A. ampeloprasum in the sequence cluster. The A. ampeloprasum cluster diverged from A. sativum through the use of single-nucleotide sequence variation in both the ITS1 locus and the RAPD locus. Sequence analysis of ITS1 DNA revealed detailed variation within the A. ampeloprasum complex and monomorphism within A. sativum in the study by Figliuolo and Di Stefano ( 2007 ). Several other studies have revealed intricate phylogenetic relationships, and the whole-genome sequence of Allium was generated through ITS analysis (Ipek et al. 2008 , Hirschegger et al. 2010 and Nguyen et al. 2008 ). One striking characteristic of the ITS data is the strangely large intrageneric genetic distances within Allium species. Kimura distances greater than 40% were found in the Friesen et al. ( 2000 ) study and in the Dubouzet and Shinoda ( 1999 ) study; Kimura distances often characterize the most distant genera within subfamilies or even families (Baldwin et al. 1995 ; Blattner and Kadereit 1999 ; Hsiao et al. 1999 ; Noyes and Rieseberg 1999 ). Intrageneric distances in other plant families are mostly less than 10% (Baldwin et al. 1995 ). These findings make Allium species either extraordinarily rapidly evolving taxa or ancient in origin, and molecular evolution is not accompanied by the increase in comparable numbers of taxonomic categories. Moreover, a phylogenetic plot showed that all three cultivars belonging to the same species fell into distinct clusters, signalling their genetic divergence. Fredotović et al. ( 2014 ) performed phylogenetic analyses of the ITS1-5.8S-ITS2 region of 35S rDNA and the non-transcribed spacer (NTS) region of 5S rDNA of A. xcornutum and its relatives to the section A . cepa . After the ITS region was sequenced, the phylogenetic relationships of the barcoding gene sequences, such as matK, rbcL, trnH-psbA and trnL, were determined via cladistic methods. In the analysis of the matK region, the shape parameter for the discrete Gamma distribution was estimated to be 0.0500. The mean evolutionary rates in these categories were 0.00, 0.00, 0.00, 0.03, and 4.97 substitutions per site. The fifth category stands out as remarkably divergent compared with the others. These results imply the presence of a conserved matK region with a low rate of evolution and significant intraspecific divergence. The nucleotide frequencies observed were A = 32.28%, T = 37.92%, C = 15.24%, and G = 14.57%, which confirmed the characteristic AT-rich chloroplast DNA. The maximum log likelihood for this computation was − 1210.897. Additionally, the estimated transition/transformation bias (R) is 0.11, and the maximum log likelihood for this computation was − 1287.698. According to the phenetic neighbor–joining (NJ) method of phylogenetic analysis for matK, all three cultivars formed a single cluster, with BDUT 1450 and 1451 closely linked. According to the cladistic MP method, BDUT 1450 and 1452 were found in a single cluster that diverged from BDUT 1451 (Figs. 3 and 4 ). According to the NJ method of matK analysis performed in this study, all three cultivars were found in a single clade, and the overall pairwise distance between them was 0.012. Both phenetic and cladistic methods concur that BDUT 1451 diverged from BDUT 1450 and 1452. The matK region had a high evolutionary rate (4.97), suggesting the potential accumulation of mutations, possibly driven by neutral evolutionary processes, adaptive evolution, or founder effects, in BDUT 1451. Conversely, BDUT 1450 and 1452 are closely related according to cladistic analysis, consistent with the overall low evolutionary rates observed in the matK region. Overall, the analysis of the matK region revealed high conservation with subtle divergence among the three cultivars. The estimated shape parameter of the rbcL region was 28.0140 for the discrete gamma distribution, suggesting that the heterogeneity in the rbcL region was significantly greater than that in the matK region. The mean evolutionary rates in these categories were 0.75, 0.89, 0.99, 1.09, and 1.28 substitutions per site, implying a moderate evolutionary rate with high heterogeneity. The nucleotide frequencies were A = 30.11%, T = 28.62%, C = 21.73%, and G = 19.55%, which indicated slight GC bias, contrasting with the AT bias observed in the matK analysis. The maximum log likelihood for this computation was − 2070.782. The estimated transition/transformation bias ( R ) is 0.41. The maximum log likelihood for this computation was − 2093.020. Analysis of the rbcL region of the tested cultivars revealed that BDUT 1451 and BDUT 1452 were closely related to each other and formed a single cluster according to both methods of phylogenetic analysis. BDUT 1450 was found in a separate cluster (Figs. 5 and 6 ). Both the phenetic and cladistic methods for rbcL placed BDUT 1450 in a separate cluster, while BDUT 1451 and 1452 formed a closer cluster, aligning with the matK analysis. However, the rbcL analysis revealed closer relationships between BDUT 1451 and 1452 than between BDUT 1451 and matK, potentially indicating incomplete lineage sorting in the rbcL gene, where ancestral polymorphisms have not yet been sorted across lineages. According to the trnH - psbA region analysis, the estimated value of the shape parameter for the discrete Gamma distribution is 2.6231. The mean evolutionary rates in these categories were 0.32, 0.61, 0.88, 1.22, and 1.97 substitutions per site. These results suggest moderate rate variation across sites in the trnH-psbA region. In addition, these findings indicate a mix of conserved and faster-evolving regions compared to the matK and rbcL barcode regions. The nucleotide frequencies were A = 31.48%, T = 33.73%, C = 17.95%, and G = 16.84%, which indicated a rich AT-bias similar to that of matK, which features characteristic chloroplast DNA. The maximum log likelihood for this computation was − 8919.082. The estimated R bias is 0.68, which is comparatively greater than that of matK and rbcL, potentially reflecting differences in structural constraints in this region. The maximum log likelihood for R bias computation was − 9236.580. In the phenetic NJ and cladistic MP methods of phylogenetic analysis of trnH-psbA , BDUT 1451 and BDUT 1452 were linked in a single cluster, and this cluster diverged from the cultivar BDUT 1450 (Figs. 7 and 8 ). The overall pairwise distance among the cultivars was 1.289 for the trnH–pspA sequence. This strengthens the confidence in the observed divergence and suggests a fundamental split between BDUT 1450 and the other two cultivars. The closer relationship between BDUT 1451 and BDUT 1452 in rbcL and trnH-psbA, compared to the more distinct separation in matK, suggested potential incomplete lineage sorting (ILS) in these regions. According to the results of the trnL sequence analysis, the estimated shape parameter for the discrete Gamma distribution was 28.2892, similar to that of rbcL, indicating considerable rate of variation across sites. Similarly, the mean evolutionary rates in these categories resembled those of the rbcL region and were 0.75, 0.89, 0.99, 1.09, and 1.28 substitutions per site, suggesting comparable evolutionary dynamics in these regions. The nucleotide frequencies were A = 37.97%, T = 27.16%, C = 15.57%, and G = 19.31%, similar to those of matK and trnH-psbA. The maximum log likelihood for this computation was − 1030.978. The estimated transition/transformation bias ( R ) is 0.53. The maximum log likelihood for transition/transformation computation was − 1074.046. The trnL sequence analysis showed that BDUT 1450 and 1452 formed a single cluster, and BDUT 1451 was found in a separate cluster via the phenetic neighbor–joining (NJ) method and the cladistic difference–platelet method. Similarly, BDUT 1452 diverged from the BDUT 1451 cluster, and BDUT 1450 diverged from BDUT 1452 (Figs. 9 and 10 ). The overall pairwise distance between them was 1.845. Thus, trnL uniquely clusters BDUT 1450 and 1452 together, contrasting with the groupings observed for matK, rbcL, and trnH-psbA. Species discrimination was successful in an earlier study with 72% of the cases, with the remaining species being matched to groups of congeneric species with 100% success using rbcL and matK. According to the trnH-psbA and rbcL sequence analyses, BDUT 1451 and BDUT 1452 were closely related to each other. BDUT 1450 evolved from a separate clade with A. textile as a closely related species according to the cladistic method and diverged from a cluster with A. textile JX848396 and A. sikkimense isolate LHXJ0489 according to the phenetic method of rbcL analysis. According to the phenetic method, for trnL , BDUT 1452 and BDUT 1450 were found in a single cluster, which showed that they were closely related, and BDUT 1451 was found in a separate clade related to A. linearifolium JF262666; therefore, these genes diverged from a large clade with different Allium species. The ISSR primers used in this study produced a total of 91 scorable bands with sizes ranging from ~ 250 bp to 1500 bp (Figs. 11 –13). The total number of bands for each primer ranged from zero to 21, with an average of 13 (Table 3 ). The total number of polymorphic alleles and the percentage of polymorphisms were 11 and 21.09%, respectively. This implies that low genetic diversity and limited polymorphism occur among the three cultivars. The ISSR9 and ISHY4 primers did not produce any scorable fragments in BDUT 1450, and ISSRa was observed to be an inefficient primer for amplifying all three garlic clones. ISHY 1b and ISHY2 produced the maximum number of amplified fragments (21 bands). Table 3 Characteristics of the ISSR primers Primer Primer sequences 5’-3’ Annealing Temperature (°C) AF PF % P ISSR8US G(AACA)4 50 14 4 28.57 ISSR9 HVH(TC)8 50 11 3 27.27 ISHY 1b (GA)8YT 39 21 3 14.28 ISHY 2 (AC)8YG 43 21 4 19.0 ISHY 3 (AG)8YT 39 19 3 15.79 ISHY 4 (CA)8RG 44 5 1 20.00 ISSR a GCV(TG)8 51 - - - V = G; A; C H = A; C D + G; A; T B = G; C; T Y = C; T R = A;G AF-Total amplified fragments; PF- Number of polymorphic amplicons; % P- Percentage of polymorphism The highest percentage of polymorphisms was observed for ISSR8US, followed by ISSR9 (28.57% and 27.27%), suggesting that these regions offer potentially informative markers for differentiating these cultivars. The statistics of genetic variation for all polymorphic loci were 2 for the observed number of alleles (na), 1.6831 ± 0.22 for the effective number of alleles (ne), 0.3969 ± 0.79 for Nei’s gene diversity (h) (1972) and 0.5837 ± 0.086 for Shannon’s information index (I). These results indicate moderate genetic variation across the analysed loci. In this study, a UPGMA dendrogram of garlic cultivars was constructed on the basis of ISSR data obtained by using POPGENE v32 software; BDUT 1450 and BDUT 1452 were linked together in a single cluster, whereas BDUT 1451 evolved as an outgroup from the BDUT 1450 and BDUT 1452 clusters. The average distance between individuals in the same cluster was determined to be 0.5862 for BDUT 1450 and BDUT 1452 and 0.5838 for BDUT 1451. This finding aligns somewhat with the clustering observed in the trnL barcoding region but contrasts with the consistent grouping in the matK, rbcL, and trnH-psbA regions. The estimated Ln probability of the data was − 10.3, the mean value of the maximum likelihood was − 9.8, the variance in the maximum likelihood was 1.0, and the mean value of the alpha was 0.3288. The Bayesian proportion of individual plants for a K = 3 population model was identified by STRUCTURE software and is indicated in Fig. 14 . The proportions suggest a combination of divergence and heterogeneity among the cultivars. Jabbes et al. ( 2011 ) revealed that, on the basis of the ISSR primers used, 7 to 21 polymorphic fragments were pragmatic, with an average of 12 fragments, which correlates with our results with the same primers used in this study. The presence of heterologous genomes was reflected by the accessibility of a comparatively high number of polymorphic ISSR markers (Jabbes et al. 2011 ). The results of Sadeghi and Cheghanirza (2012) demonstrated that ISSR analysis can be used not only to study the high diversity among cultivars but also to study polymorphisms. Additionally, they confirmed that ISSR techniques are advantageous for studying genetic diversity and are robust methods for characterizing plant genomes. ISSR analysis has been extensively used to determine genetic diversity in important crops and fruits and for biodiversity conservation. Furthermore, ISSRs can be used to evaluate relationships at the interspecific level (Huang and Sun 2000 ). Hao et al. ( 2002 ) provided a clear picture of the relationships among closely related congeneric species. Jabbes et al. ( 2011 ) reported that the diversity between accessions is always greater than the diversity within accessions. The discriminative ability of each marker was evaluated by the diversity index value. A higher diversity index indicates more marker information (Kandasamy et al. 2013 ). The authors concluded that the ISSR marker system was highly informative and efficient for observing the genetic diversity of flowering plants via analytical methods. A total of 99.47% of the bands obtained in the Mukherjee et al. ( 2013 ) study were polymorphic. Nagaoka and Ogihara ( 1997 ) reported that ISSR primers produce reliable and reproducible bands. Conclusion The DNA barcoding results of this study provided us with insight into the genetic diversity of three selected cultivars of Allium sativum . Among the cultivars, BDUT 1450 and 1452 seemed to be closely related, while BDUT 1451 diverged significantly. Discrepancies in clustering patterns among different markers suggest incomplete lineage sorting (ILS), where ancestral traits have not been fully separated. Furthermore, moderate interspecific diversity and intraspecific divergence were observed with complex genetic relationships. However, further studies with advanced methods and additional information from various cultivars from different geographical locations are needed to fully understand the evolutionary history of this important aromatic cum nutraceutical crop. Understanding this genetic landscape presents exciting opportunities for garlic breeding programs, allowing for the development of cultivars with enhanced nutritional profiles, improved disease resistance, and improved adaptation to specific environments. Declarations Funding & Competing Interests The authors have no competing interests to declare that they are relevant to the content of this article. Author Contributions All the authors substantially contributed to the conception and design of the project. Packia Lekshmi and Rajesh produced, assembled and analysed the data. The manuscript was interpreted and written by Packia Lekshmi and Mahamuni, with valuable contributions from Rajesh and Brindha. All the authors reviewed and approved the current version of the manuscript. Data Availability GenBank Accession Numbers: KF769491, KF769492, KF769493, KF769497, KF769498, KF769499, KF769503, KF769504, KF769505, KF769509, KF769510, KF769511, KF779159, KF779160, KF779161. Acknowledgements The first author gratefully acknowledges Dr. M.B. Viswanathan, former Professor and Head, Department of Botany, Bharathidasan University, for providing the laboratory facility for DNA and PCR analyses, and all the authors gratefully acknowledge Mr. R. Anand for his assistance in sample collection. References Appeltans W, Ahyong ST, Anderson G, Angel MV, Artois T, Bailly N, Bamber R, Barber A, Bartsch I, Berta A, Błażewicz-Paszkowycz M (2012) The magnitude of global marine species diversity. Curr Biol 22:2189-2202. https://doi.org/10.1016/j.cub.2012.09.036 Aryakia E, Karimi HR, Naghavi MR et al. (2016) Morphological characterization of intra-and interspecific diversity in some Iranian wild Allium species. Euphytica 211:185-200. https://doi.org/10.1007/s10681-016-1729-8 Asahina H, Shinozaki J, Masuda K, Morimitsu Y, Satake M (2010) Identification of medicinal Dendrobium species by phylogenetic analyses using matK and rbcL sequences. 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2","display":"","copyAsset":false,"role":"figure","size":84948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCladistic method (MP) tree based on the ITS region of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAllium sativum\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3978989/v1/66f2cb772912280ea065befd.png"},{"id":52409711,"identity":"75a0f85a-fe9f-4de5-9512-c763ba4e1005","added_by":"auto","created_at":"2024-03-11 09:57:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":112696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNJ (phenetic method) tree based on the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ematK\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e region of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAllium 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09:57:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":122377,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNJ (phenetic method) tree based on the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003erbcL\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e region of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAllium sativum\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3978989/v1/b59df88e38efdf8f480bcd3f.png"},{"id":52409824,"identity":"efc8b6df-1e15-403c-a2d2-735c1d5b49bb","added_by":"auto","created_at":"2024-03-11 10:05:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":100528,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCladistic method (MP) tree based on the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003erbcL\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e region of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAllium sativum\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3978989/v1/17f0a8d499aa093e88846ae4.png"},{"id":52409948,"identity":"6959af5c-52fa-4a8d-a116-d66925e0dd7b","added_by":"auto","created_at":"2024-03-11 10:13:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":86622,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic method (NJ) tree based on the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003etrnH-psbA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e region of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAllium sativum\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3978989/v1/bc4a66d4b736942dbfa11c14.png"},{"id":52411003,"identity":"6487a96f-4bf0-4b7e-b51d-024d310227e4","added_by":"auto","created_at":"2024-03-11 10:21:19","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":81147,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCladistic method (MP) tree based on the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003etrnH-psbA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e region of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAllium sativum\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3978989/v1/33c8534bfbeeb19d4631e6ce.png"},{"id":52409457,"identity":"17dc1283-2373-4bb2-8f01-bd2d4da6fb8c","added_by":"auto","created_at":"2024-03-11 09:49:19","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":131387,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic method (NJ) tree based on the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003etrnL\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e region of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAllium sativum\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3978989/v1/6acf757fd0d43cc94064b0d3.png"},{"id":52409458,"identity":"06a55b0d-dc8c-4fe1-bfdc-973ca67ffea8","added_by":"auto","created_at":"2024-03-11 09:49:19","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":117073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCladistic method (MP) tree based on the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003etrnL\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e region of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAllium sativum\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3978989/v1/5860fd8921772a84b4ee6166.png"},{"id":52409715,"identity":"dc01be6e-7bc0-47c6-8232-c61b50a91055","added_by":"auto","created_at":"2024-03-11 09:57:19","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":237770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAmplified products of garlic cultivars for the ISSR8US and ISSR9 primers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003eLane 1- DNA ladder; Lanes 2, 3 and 4- Amplified products of BDUT 1450, BDUT 1451 and BDUT 1452 for the primer pair ISSR8US; Lanes 5, 6 and 7- Amplified products of BDUT 1450, BDUT 1451 and BDUT 1452 for the primer pair ISSR9\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-3978989/v1/10913f1fb5a712e2abf6fdb9.png"},{"id":52409460,"identity":"0ef5cd29-9f8e-4bda-bb03-97bfdce00442","added_by":"auto","created_at":"2024-03-11 09:49:19","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":402057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAmplified products of garlic cultivars for ISHY 1b, ISHY 2 and ISHY 3 primers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(Lane 1- DNA ladder; Lanes 2, 3 and 4 – Amplified products of BDUT 1450, BDUT 1451 and BDUT 1452 for the primer ISHY 1b; Lanes 5, 6 and 7 – Amplified products of BDUT 1450, BDUT 1451 and BDUT 1452 for the primer ISHY 2; Lanes 8, 9 and 10 – Amplified products of BDUT 1450, BDUT 1451 and BDUT 1452 for the primer ISHY 3)\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-3978989/v1/edb5175709d760dabe9dd98c.png"},{"id":52409459,"identity":"9fab9c94-aa53-410b-a410-23fcba247f40","added_by":"auto","created_at":"2024-03-11 09:49:19","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":506277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAmplified products of garlic cultivars for ISHY4 and ISSRa primers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLane 1- DNA ladder\u003c/p\u003e\n\u003cp\u003eLanes 2, 3 and 4 – Amplified products of BDUT 1450, BDUT 1451 and BDUT 1452, respectively, for the primer ISHY 4\u003c/p\u003e\n\u003cp\u003eLanes 5, 6 and 7 – Amplified products of BDUT 1450, BDUT 1451 and BDUT 1452 for the primer pair ISSR a\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-3978989/v1/1bfcb08d016bbf988b15cee3.png"},{"id":52409827,"identity":"9839d676-b2cd-4af6-98b0-3ab7845bb324","added_by":"auto","created_at":"2024-03-11 10:05:19","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":38260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBayesian proportions of individual plants for the K=3 population model. The populations identified by STRUCTURE software are indicated in different colours.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-3978989/v1/59ea072e704ffdf27d45424d.png"},{"id":56936243,"identity":"9b56840b-e51f-425f-ad4c-63fe285ee8b3","added_by":"auto","created_at":"2024-05-22 11:11:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3077023,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3978989/v1/93ca2765-6991-4bda-8d5c-9b393e5cd224.pdf"}],"financialInterests":"","formattedTitle":"A Multi-marker Genomic Approach to Decipher the Divergence and Diversity in Selected Allium sativum L. cultivars","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEstimating Earth's species diversity has always been a contentious issue, with suggested numbers ranging from approximately 1.5\u0026nbsp;million (described thus far) to more than 5\u0026nbsp;billion species (Larsen et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). On the other hand, reports indicate that numerous species are disappearing and that more than a million are under the threat of extinction (Tollefson \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, some studies suggest that nearly two-thirds of the existing species were described already (Appeltans et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Costello et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Li and Wiens \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite an increase in the number of taxonomists working on biodiversity, there has been a decline in the identification of new species (Costello et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Notwithstanding considerable technological advancements in taxonomy, Engel et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) highlight persistent concerns regarding a dearth of qualified taxonomists, inadequate funding, insufficient training programs, and limited job opportunities within the field.\u003c/p\u003e \u003cp\u003eConventional phytomorphological and plant anatomical studies limit our understanding of evolutionary taxonomy and species identification. These approaches involve four significant limitations. First, phenotypic plasticity and genetic diversity within the characteristics used for species identification can lead to erroneous determinations. Second, this method often overlooks phenotypically cryptic taxa, which are prevalent in many species groups (Knowlton \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Jarman and Elliott \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Aryakia et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Third, phenotypic keys are often effective only for a specific life stage, leaving a substantial portion of individuals unidentified. Finally, while modern interactive tools represent a significant advancement, the use of keys often requires a high level of expertise, increasing the likelihood of misdiagnosis (Hebert et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). These limitations, along with the declining rate of new species identification, necessitate innovative approaches in taxonomic identification. In the late 1990s and early 21st century, the use of a microgenomic identification system emerged as a promising avenue for determining biological diversity. Leveraging the power of genetic analysis through the examination of small fragments of an organism's genome (Hebert et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), this system utilizes genetic barcodes present in every cell, akin to universal product codes used for retail product identification. Despite the relative simplicity of determining life diversity through genomic barcoding, this approach provides a more efficient and precise means of categorizing and understanding the incredible variety of life forms on our planet.\u003c/p\u003e \u003cp\u003eDNA barcoding is a transformative methodology that empowers taxonomists by expanding their ability to complete a global inventory of diversity (Hebert and Gregory \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). DNA barcoding not only benefits taxonomists but also scientists in diverse fields, such as forensic science, biotechnology, the food industry, and animal diet research (Creer et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Deiner et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fernandes et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gostel and Kress \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In summary, genomic barcoding provides an expansive and versatile tool for untangling the intricate web of biological diversity, akin to how product barcodes streamline the identification of consumer goods. Efforts to develop universal genomic databases and accessions involve coordination among research organizations, government bodies, scientific societies, and others associated with genomic studies. Initiatives such as the Barcode of Life Initiative (BOLI), established in 2003, aim to improve the applicability and cost-effectiveness of genomic taxon identification (Costa and Carvalho \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Similarly, the International Barcode of Life (iBOL), established in 2008, seeks to transform biodiversity science by building DNA barcode reference libraries, sequencing facilities, informatics platforms, and analytical protocols through international collaboration (iBOL \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the potential benefits of DNA barcoding for both practitioners and users of classification, there is controversy within various systematic circles (DeSalle and Goldstein \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Honeycutt \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pfeiler \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although employing a single gene region as a DNA barcode does not guarantee complete taxonomic resolution, it offers a significant degree of proximity. Numerous studies on diverse animal groups suggest that DNA barcoding can achieve high levels of species resolution (Garg and Biju \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Dincă et al. 2023) and provides promising taxon identification and species determination in plant communities (Besse et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Raskoti and Ale \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sawarkar et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chac and Thinh \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Consortium of Barcode of Life (CBOL) recommends a 2-locus combination comprising the ribulose-1,5 bisphosphate carboxylase oxygenase large subunit (rbcL) and maturase K (matK) as the standard plant barcode (CBOL 2009). These two regions of chloroplast DNA were chosen for their efficient recovery of high-quality sequences and their ability to distinguish between species effectively (Burgess et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In recent decades, researchers have increasingly utilized rbcL and matK sequences for both species identification (Starr et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Asahina et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Bieniek et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ho et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and phylogenetic analysis (Asahina et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kuo et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Maulidya et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cetiz et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, the spacer between tRNA-His and photosystem II protein D1 (trnH-psbA spacer) and the ITS2 are widely employed for similar purposes (Chen et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fu et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bieniek et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Intharuksa et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, it is important to note that the application of universal barcode markers has its own limitations and is subject to further advances (Tekle and Wood \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Piper et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Manzoor et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccurate identification of crucial agricultural crops is beneficial for DNA barcoding, and insights into the evolutionary background of these crops have been obtained. However, the study of genetic diversity in \u003cem\u003eAllium\u003c/em\u003e species has faced challenges due to the absence of readily portable codominant molecular markers during the early stages of this technology (McCallum \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). While a variety of molecular marker methods have successfully addressed questions related to genetic diversity and species relatedness within \u003cem\u003eAllium\u003c/em\u003e species, the search for robust and informative markers specific to \u003cem\u003eAllium\u003c/em\u003e species has proven to be considerably more demanding (Xie et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite being economically important crops, some Amaryllidaceae crops, such as bulb onions and garlic, remain understudied compared to other major crops (Zhang et al. 2023). \u003cem\u003eAllium sativum\u003c/em\u003e, an essential aromatic and nutraceutical ingredient, offers a wide array of health benefits (Wang et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rauf et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Understanding the taxonomic evolution of functional traits requires exploring the molecular breeding and genetic diversity of this important food crop. With these considerations in mind, the present research aimed to explore the relationships among three distinct cultivars of garlic plants (\u003cem\u003eAllium sativum\u003c/em\u003e) using ITS sequence and DNA barcoding, employing primers for matK, rbcL, trnH-psbA, and trnL. This study provides valuable insights into the genetic diversity and relatedness of these cultivars, contributing to our understanding of this important crop species.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eWe collected three distinct local cultivars of \u003cem\u003eAllium sativum\u003c/em\u003e from different locations in Tamil Nadu, India: Poomparai (BDUT 1450) in Kodaikanal District, Vadugapatti (BDUT 1451) in Theni District, and Pannaikadu (BDUT 1452) in Kodaikanal District. Applying the CTAB method (Doyle and Doyle \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), DNA extraction was conducted with previously washed and sterilized fresh, young roots, each weighing 200 mg per cultivar. Subsequently, a spin column kit was used to purify the DNA extracts from the garlic cultivars. Evaluation of the ethidium bromide-stained DNA extracts revealed the formation of bands upon UV exposure. To evaluate the sequence information of the chosen cultivars, we targeted specific regions: a spacer DNA (ITS) region, four candidate DNA coding regions (rbcL, matK, PsbA, and trnL) and an inter simple sequence repeater (ISSR) of the microsatellite region. Amplification of these regions was achieved through polymerase chain reaction (PCR) using the universal primers outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003ePrimers used in the barcoding and sequencing of \u003cem\u003eAllium\u003c/em\u003e species\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenomic Target\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimer Sequence 5\u0026rsquo;-3\u0026rsquo;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInternal Transcribed Spacer Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eITS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCCGTAGGTGAACCTGCGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWhite et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1990\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eITS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCCTCCGCTTATTGATATGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eBarcoding Regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003erbcLa-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATGTCACCACAAACAGAGACTAAAGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBafeel et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003erbcLa-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTAAAATCAAGTCCACCRCG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatK-3F KIM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGTACAGTACTTTTGTGTTTACGAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCostion et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatK-1R KIM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACCCAGTCCATCTGGAAATCTTGGTTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epsbA3_f\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTTATGCATGAACGTAATGCTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKress et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2005\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etrnHf_05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGCGCATGGTGGATTCACAATCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etrnL F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGAAATCGGTAGACGCTACG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTaberlet et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1991\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etrnL R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGGGATAGAGGGACTTGAAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eInter Simple Sequence Repeats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eISSR8US\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG(AACA)4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eJabbes et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eISSR9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHVH(TC)8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eISHY 1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(GA)8YT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eISHY 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(AC)8YG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eISHY 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(AG)8YT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eISHY 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(CA)8RG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eISSR a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCV(TG)8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eISSR8US\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG(AACA)4\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\u003eFor each PCR mixture, we included 10 \u0026micro;l of Taq premix, 4 \u0026micro;l of water, 1.5 \u0026micro;l of each of the forward and reverse primers, and 3 \u0026micro;l of DNA. The PCR Thermal Cycler Program was executed using an \u003cem\u003eEppendorf ProS\u003c/em\u003e (Hamburg, Germany). The thermal cycling programme for all sequence analyses comprised an initial cycle of 5 min at 94\u0026deg;C, followed by 35 cycles of 30 sec at 94\u0026deg;C, 30 sec at 58\u0026deg;C, and 60 sec at 72\u0026deg;C. The amplification was completed with a final cycle of 10 min at 72\u0026deg;C. To visualize the PCR products, all the samples were separated on a 1.2% agarose gel in 1\u0026times; TAE buffer through electrophoresis at 60 V for 90 min. Subsequently, the separation was examined using a UV transilluminator.\u003c/p\u003e \u003cp\u003eBefore sequencing, the PCR products were subjected to an additional purification step. The Sanger dideoxy method of DNA sequencing was used for the PCR products, and the obtained data were imported and aligned using Molecular Evolutionary Genetics Analysis (MEGA v6.2.2). Key sequence statistics, covering nucleotide frequencies, transition/transversion bias (R\u0026thinsp;=\u0026thinsp;s/v), and variability across various sequence regions, were analysed. For sequence data analysis, both phenetic and cladistic methods were employed. The phenetic analysis utilized the neighbour‒joining (NJ) method, while the cladistic approach employed the maximum parsimony (MP) method. These two complementary methods provided comprehensive insights into the genetic relationships and patterns present in the sequence data.\u003c/p\u003e \u003cp\u003eFor each primer, the genetic sequence of the ISSR region was indexed to identify and compare polymorphic loci (P) among the cultivars. This information was subsequently utilized to compute the Shannon information index (I) (Lewontin \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) and Nei\u0026rsquo;s standard genetic distance (D) (Nei \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). The allele diversity data obtained from these indices were used to evaluate the discriminative performance of each primer. Subsequently, the genetic distance information was used to construct a dendrogram with POPGENE v32 software. Finally, a population model was established through Bayesian analysis of the ISSR data using STRUCTURE v3.2.2 software.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eVarious primers were used to amplify and sequence distinct genomic regions of three chosen \u003cem\u003eAllium\u003c/em\u003e cultivars. The number of base pairs in each sequenced region slightly differed among the three cultivars, with the trnL region exhibiting shorter lengths than the other barcoding regions analysed in this study (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLengths of the sequenced genome regions of three selected \u003cem\u003eAllium sativum\u003c/em\u003e cultivars for different molecular markers used in this study (number in base pairs).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGarlic Cultivar\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInternal Transcribed Spacer Region\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eBarcoding Regions\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eITS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ematK\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003etrnH-psbA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003erbcL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003etrnL\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBDUT 1450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBDUT 1451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBDUT 1452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe substitution pattern and rates for the ITS sequence were estimated using the Tamura and Nei (\u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e) model with a discrete gamma distribution to account for evolutionary rate differences among sites (5 categories, [+\u0026thinsp;G]). In the ITS region analysis, the shape parameter for the discrete Gamma distribution was estimated to be 200.0000. The mean evolutionary rates in these categories were 0.90, 0.96, 1.00, 1.04, and 1.10 substitutions per site, suggesting moderate evolution of these cultivars. The maximum log likelihood for this computation was \u0026minus;\u0026thinsp;2594.439, indicating a good fit with the Tamura\u0026ndash;Nei model, significant heterogeneity, and a moderate evolutionary rate. The nucleotide frequencies of the ITS region observed in this Tamura\u0026ndash;Nei model were A\u0026thinsp;=\u0026thinsp;22.54%, T\u0026thinsp;=\u0026thinsp;30.58%, C\u0026thinsp;=\u0026thinsp;20.37%, and G\u0026thinsp;=\u0026thinsp;26.51%. The transition/transformation bias (R) for the ITS region was 1.09, indicating a bias higher than the expected level. Additionally, when the Kimura (\u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e) 2-parameter model was used for substitution patterns and rates, the nucleotide frequencies were A\u0026thinsp;=\u0026thinsp;25.00%, T\u0026thinsp;=\u0026thinsp;25.00%, C\u0026thinsp;=\u0026thinsp;25.00%, and G\u0026thinsp;=\u0026thinsp;25.00%. For estimating maximum likelihood (ML) values, a tree topology was automatically computed, and the maximum log likelihood for this Kimura 2-parameter computation was \u0026minus;\u0026thinsp;2617.110.\u003c/p\u003e\n\u003cp\u003eBoth models exhibit a typical ITS region with AT richness, which might contribute to the higher R value than expected. When the phenetic neighbor\u0026ndash;joining (NJ) method was applied to the ITS sequence, the phylogeny of the three cultivars resulted in a divergence of BDUT 1451 from that of the other two cultivars, BDUT 1452 and BDUT 1450, which were found to be in a separate cluster. In contrast, according to the cladistic MP method, BDUT 1450 and BDUT 1452 formed a single cluster, while BDUT 1451 diverged into a separate cluster (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The discrepancy in clustering patterns could conceivably be attributed to the high rate of heterogeneity, where the NJ method relies on overall sequence similarity, whereas the MP method focuses on shared derived characters. Interestingly, both methods consistently placed BDUT 1451 in a separate cluster, suggesting intraspecific divergence.\u003c/p\u003e\n\u003cp\u003eThe phenetic neighbor-joining (NJ) method of ITS sequence analysis revealed that BDUT 1451 was closely linked to \u003cem\u003eA. sativum\u003c/em\u003e gi228015332, and BDUT 1452 diverged from this cluster. BDUT 1450 was closely related to a cluster of \u003cem\u003eA. ampeloprasum\u003c/em\u003e var. \u003cem\u003eharmone\u003c/em\u003e gi338191565 and \u003cem\u003eA. ampeloprasum\u003c/em\u003e var. \u003cem\u003eharmone\u003c/em\u003e gi338191566. According to the cladistic method of ITS, BDUT 1450 and BDUT 1452 formed a single clade, whereas BDUT 1451 was closely linked to \u003cem\u003eA. sativum\u003c/em\u003e gi228015332. The overall pairwise distance between them was 6.153. A dendrogram created by Baghalian et al. (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) using 24 ecotypes revealed five main groups. Thirteen of the 24 ecotypes were included in cluster group A, two ecotypes in group B and seven ecotypes in group C. They found that there was no relationship between genetic divergence and geographical origin in the post planting stage, as genotypes from the same origin entered into different clusters, and genotypes from different origins also entered the same cluster. In the study by Figliuolo et al. (\u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e), molecular analysis confirmed the differentiation of \u003cem\u003eA. sativum\u003c/em\u003e and \u003cem\u003eA. ampeloprasum\u003c/em\u003e in the sequence cluster. The \u003cem\u003eA. ampeloprasum\u003c/em\u003e cluster diverged from \u003cem\u003eA. sativum\u003c/em\u003e through the use of single-nucleotide sequence variation in both the ITS1 locus and the RAPD locus. Sequence analysis of ITS1 DNA revealed detailed variation within the \u003cem\u003eA. ampeloprasum\u003c/em\u003e complex and monomorphism within \u003cem\u003eA. sativum\u003c/em\u003e in the study by Figliuolo and Di Stefano (\u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Several other studies have revealed intricate phylogenetic relationships, and the whole-genome sequence of Allium was generated through ITS analysis (Ipek et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e, Hirschegger et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e and Nguyen et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eOne striking characteristic of the ITS data is the strangely large intrageneric genetic distances within \u003cem\u003eAllium\u003c/em\u003e species. Kimura distances greater than 40% were found in the Friesen et al. (\u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e) study and in the Dubouzet and Shinoda (\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e) study; Kimura distances often characterize the most distant genera within subfamilies or even families (Baldwin et al. \u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e; Blattner and Kadereit \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e; Hsiao et al. \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e; Noyes and Rieseberg \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). Intrageneric distances in other plant families are mostly less than 10% (Baldwin et al. \u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e). These findings make \u003cem\u003eAllium\u003c/em\u003e species either extraordinarily rapidly evolving taxa or ancient in origin, and molecular evolution is not accompanied by the increase in comparable numbers of taxonomic categories. Moreover, a phylogenetic plot showed that all three cultivars belonging to the same species fell into distinct clusters, signalling their genetic divergence. Fredotović et al. (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) performed phylogenetic analyses of the ITS1-5.8S-ITS2 region of 35S rDNA and the non-transcribed spacer (NTS) region of 5S rDNA of \u003cem\u003eA. xcornutum\u003c/em\u003e and its relatives to the section \u003cem\u003eA\u003c/em\u003e. \u003cem\u003ecepa\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eAfter the ITS region was sequenced, the phylogenetic relationships of the barcoding gene sequences, such as matK, rbcL, trnH-psbA and trnL, were determined via cladistic methods. In the analysis of the matK region, the shape parameter for the discrete Gamma distribution was estimated to be 0.0500. The mean evolutionary rates in these categories were 0.00, 0.00, 0.00, 0.03, and 4.97 substitutions per site. The fifth category stands out as remarkably divergent compared with the others. These results imply the presence of a conserved matK region with a low rate of evolution and significant intraspecific divergence. The nucleotide frequencies observed were A\u0026thinsp;=\u0026thinsp;32.28%, T\u0026thinsp;=\u0026thinsp;37.92%, C\u0026thinsp;=\u0026thinsp;15.24%, and G\u0026thinsp;=\u0026thinsp;14.57%, which confirmed the characteristic AT-rich chloroplast DNA. The maximum log likelihood for this computation was \u0026minus;\u0026thinsp;1210.897. Additionally, the estimated transition/transformation bias (R) is 0.11, and the maximum log likelihood for this computation was \u0026minus;\u0026thinsp;1287.698.\u003c/p\u003e\n\u003cp\u003eAccording to the phenetic neighbor\u0026ndash;joining (NJ) method of phylogenetic analysis for matK, all three cultivars formed a single cluster, with BDUT 1450 and 1451 closely linked. According to the cladistic MP method, BDUT 1450 and 1452 were found in a single cluster that diverged from BDUT 1451 (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). According to the NJ method of \u003cem\u003ematK\u003c/em\u003e analysis performed in this study, all three cultivars were found in a single clade, and the overall pairwise distance between them was 0.012. Both phenetic and cladistic methods concur that BDUT 1451 diverged from BDUT 1450 and 1452. The matK region had a high evolutionary rate (4.97), suggesting the potential accumulation of mutations, possibly driven by neutral evolutionary processes, adaptive evolution, or founder effects, in BDUT 1451. Conversely, BDUT 1450 and 1452 are closely related according to cladistic analysis, consistent with the overall low evolutionary rates observed in the matK region. Overall, the analysis of the matK region revealed high conservation with subtle divergence among the three cultivars.\u003c/p\u003e\n\u003cp\u003eThe estimated shape parameter of the rbcL region was 28.0140 for the discrete gamma distribution, suggesting that the heterogeneity in the rbcL region was significantly greater than that in the matK region. The mean evolutionary rates in these categories were 0.75, 0.89, 0.99, 1.09, and 1.28 substitutions per site, implying a moderate evolutionary rate with high heterogeneity. The nucleotide frequencies were A\u0026thinsp;=\u0026thinsp;30.11%, T\u0026thinsp;=\u0026thinsp;28.62%, C\u0026thinsp;=\u0026thinsp;21.73%, and G\u0026thinsp;=\u0026thinsp;19.55%, which indicated slight GC bias, contrasting with the AT bias observed in the matK analysis. The maximum log likelihood for this computation was \u0026minus;\u0026thinsp;2070.782. The estimated transition/transformation bias (\u003cem\u003eR\u003c/em\u003e) is 0.41. The maximum log likelihood for this computation was \u0026minus;\u0026thinsp;2093.020. Analysis of the \u003cem\u003erbcL\u003c/em\u003e region of the tested cultivars revealed that BDUT 1451 and BDUT 1452 were closely related to each other and formed a single cluster according to both methods of phylogenetic analysis. BDUT 1450 was found in a separate cluster (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Both the phenetic and cladistic methods for rbcL placed BDUT 1450 in a separate cluster, while BDUT 1451 and 1452 formed a closer cluster, aligning with the matK analysis. However, the rbcL analysis revealed closer relationships between BDUT 1451 and 1452 than between BDUT 1451 and matK, potentially indicating incomplete lineage sorting in the rbcL gene, where ancestral polymorphisms have not yet been sorted across lineages.\u003c/p\u003e\n\u003cp\u003eAccording to the trnH\u003cem\u003e-\u003c/em\u003epsbA region analysis, the estimated value of the shape parameter for the discrete Gamma distribution is 2.6231. The mean evolutionary rates in these categories were 0.32, 0.61, 0.88, 1.22, and 1.97 substitutions per site. These results suggest moderate rate variation across sites in the trnH-psbA region. In addition, these findings indicate a mix of conserved and faster-evolving regions compared to the matK and rbcL barcode regions. The nucleotide frequencies were A\u0026thinsp;=\u0026thinsp;31.48%, T\u0026thinsp;=\u0026thinsp;33.73%, C\u0026thinsp;=\u0026thinsp;17.95%, and G\u0026thinsp;=\u0026thinsp;16.84%, which indicated a rich AT-bias similar to that of matK, which features characteristic chloroplast DNA. The maximum log likelihood for this computation was \u0026minus;\u0026thinsp;8919.082. The estimated R bias is 0.68, which is comparatively greater than that of matK and rbcL, potentially reflecting differences in structural constraints in this region. The maximum log likelihood for R bias computation was \u0026minus;\u0026thinsp;9236.580. In the phenetic NJ and cladistic MP methods of phylogenetic analysis of \u003cem\u003etrnH-psbA\u003c/em\u003e, BDUT 1451 and BDUT 1452 were linked in a single cluster, and this cluster diverged from the cultivar BDUT 1450 (Figs. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). The overall pairwise distance among the cultivars was 1.289 for the \u003cem\u003etrnH\u0026ndash;pspA\u003c/em\u003e sequence. This strengthens the confidence in the observed divergence and suggests a fundamental split between BDUT 1450 and the other two cultivars. The closer relationship between BDUT 1451 and BDUT 1452 in rbcL and trnH-psbA, compared to the more distinct separation in matK, suggested potential incomplete lineage sorting (ILS) in these regions.\u003c/p\u003e\n\u003cp\u003eAccording to the results of the trnL sequence analysis, the estimated shape parameter for the discrete Gamma distribution was 28.2892, similar to that of rbcL, indicating considerable rate of variation across sites. Similarly, the mean evolutionary rates in these categories resembled those of the rbcL region and were 0.75, 0.89, 0.99, 1.09, and 1.28 substitutions per site, suggesting comparable evolutionary dynamics in these regions. The nucleotide frequencies were A\u0026thinsp;=\u0026thinsp;37.97%, T\u0026thinsp;=\u0026thinsp;27.16%, C\u0026thinsp;=\u0026thinsp;15.57%, and G\u0026thinsp;=\u0026thinsp;19.31%, similar to those of matK and trnH-psbA. The maximum log likelihood for this computation was \u0026minus;\u0026thinsp;1030.978. The estimated transition/transformation bias (\u003cem\u003eR\u003c/em\u003e) is 0.53. The maximum log likelihood for transition/transformation computation was \u0026minus;\u0026thinsp;1074.046. The \u003cem\u003etrnL\u003c/em\u003e sequence analysis showed that BDUT 1450 and 1452 formed a single cluster, and BDUT 1451 was found in a separate cluster via the phenetic neighbor\u0026ndash;joining (NJ) method and the cladistic difference\u0026ndash;platelet method. Similarly, BDUT 1452 diverged from the BDUT 1451 cluster, and BDUT 1450 diverged from BDUT 1452 (Figs. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). The overall pairwise distance between them was 1.845. Thus, trnL uniquely clusters BDUT 1450 and 1452 together, contrasting with the groupings observed for matK, rbcL, and trnH-psbA.\u003c/p\u003e\n\u003cp\u003eSpecies discrimination was successful in an earlier study with 72% of the cases, with the remaining species being matched to groups of congeneric species with 100% success using rbcL and matK. According to the trnH-psbA and rbcL sequence analyses, BDUT 1451 and BDUT 1452 were closely related to each other. BDUT 1450 evolved from a separate clade with \u003cem\u003eA. textile\u003c/em\u003e as a closely related species according to the cladistic method and diverged from a cluster with \u003cem\u003eA. textile\u003c/em\u003e JX848396 and \u003cem\u003eA. sikkimense\u003c/em\u003e isolate LHXJ0489 according to the phenetic method of \u003cem\u003erbcL\u003c/em\u003e analysis. According to the phenetic method, for \u003cem\u003etrnL\u003c/em\u003e, BDUT 1452 and BDUT 1450 were found in a single cluster, which showed that they were closely related, and BDUT 1451 was found in a separate clade related to \u003cem\u003eA. linearifolium\u003c/em\u003e JF262666; therefore, these genes diverged from a large clade with different \u003cem\u003eAllium\u003c/em\u003e species.\u003c/p\u003e\n\u003cp\u003eThe ISSR primers used in this study produced a total of 91 scorable bands with sizes ranging from ~\u0026thinsp;250 bp to 1500 bp (Figs. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e\u0026ndash;13). The total number of bands for each primer ranged from zero to 21, with an average of 13 (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The total number of polymorphic alleles and the percentage of polymorphisms were 11 and 21.09%, respectively. This implies that low genetic diversity and limited polymorphism occur among the three cultivars. The ISSR9 and ISHY4 primers did not produce any scorable fragments in BDUT 1450, and ISSRa was observed to be an inefficient primer for amplifying all three garlic clones. ISHY 1b and ISHY2 produced the maximum number of amplified fragments (21 bands).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of the ISSR primers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrimer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrimer sequences 5\u0026rsquo;-3\u0026rsquo;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnnealing\u003c/p\u003e\n \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% P\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISSR8US\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG(AACA)4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISSR9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHVH(TC)8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISHY 1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(GA)8YT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISHY 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(AC)8YG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISHY 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(AG)8YT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISHY 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(CA)8RG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISSR a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGCV(TG)8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;G; A; C H\u0026thinsp;=\u0026thinsp;A; C D\u0026thinsp;+\u0026thinsp;G; A; T B\u0026thinsp;=\u0026thinsp;G; C; T Y\u0026thinsp;=\u0026thinsp;C; T R\u0026thinsp;=\u0026thinsp;A;G\u003c/p\u003e\n \u003cp\u003eAF-Total amplified fragments; PF- Number of polymorphic amplicons; % P- Percentage of polymorphism\u003c/p\u003e\n \u003cp\u003eThe highest percentage of polymorphisms was observed for ISSR8US, followed by ISSR9 (28.57% and 27.27%), suggesting that these regions offer potentially informative markers for differentiating these cultivars. The statistics of genetic variation for all polymorphic loci were 2 for the observed number of alleles (na), 1.6831\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 for the effective number of alleles (ne), 0.3969\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79 for Nei\u0026rsquo;s gene diversity (h) (1972) and 0.5837\u0026thinsp;\u0026plusmn;\u0026thinsp;0.086 for Shannon\u0026rsquo;s information index (I). These results indicate moderate genetic variation across the analysed loci. In this study, a UPGMA dendrogram of garlic cultivars was constructed on the basis of ISSR data obtained by using POPGENE \u003cem\u003ev32\u003c/em\u003e software; BDUT 1450 and BDUT 1452 were linked together in a single cluster, whereas BDUT 1451 evolved as an outgroup from the BDUT 1450 and BDUT 1452 clusters. The average distance between individuals in the same cluster was determined to be 0.5862 for BDUT 1450 and BDUT 1452 and 0.5838 for BDUT 1451. This finding aligns somewhat with the clustering observed in the trnL barcoding region but contrasts with the consistent grouping in the matK, rbcL, and trnH-psbA regions. The estimated Ln probability of the data was \u0026minus;\u0026thinsp;10.3, the mean value of the maximum likelihood was \u0026minus;\u0026thinsp;9.8, the variance in the maximum likelihood was 1.0, and the mean value of the alpha was 0.3288. The Bayesian proportion of individual plants for a K\u0026thinsp;=\u0026thinsp;3 population model was identified by STRUCTURE software and is indicated in Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e. The proportions suggest a combination of divergence and heterogeneity among the cultivars.\u003c/p\u003e\n \u003cp\u003eJabbes et al. (\u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e) revealed that, on the basis of the ISSR primers used, 7 to 21 polymorphic fragments were pragmatic, with an average of 12 fragments, which correlates with our results with the same primers used in this study. The presence of heterologous genomes was reflected by the accessibility of a comparatively high number of polymorphic ISSR markers (Jabbes et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). The results of Sadeghi and Cheghanirza (2012) demonstrated that ISSR analysis can be used not only to study the high diversity among cultivars but also to study polymorphisms. Additionally, they confirmed that ISSR techniques are advantageous for studying genetic diversity and are robust methods for characterizing plant genomes. ISSR analysis has been extensively used to determine genetic diversity in important crops and fruits and for biodiversity conservation. Furthermore, ISSRs can be used to evaluate relationships at the interspecific level (Huang and Sun \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). Hao et al. (\u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e) provided a clear picture of the relationships among closely related congeneric species. Jabbes et al. (\u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e) reported that the diversity between accessions is always greater than the diversity within accessions.\u003c/p\u003e\n \u003cp\u003eThe discriminative ability of each marker was evaluated by the diversity index value. A higher diversity index indicates more marker information (Kandasamy et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). The authors concluded that the ISSR marker system was highly informative and efficient for observing the genetic diversity of flowering plants via analytical methods. A total of 99.47% of the bands obtained in the Mukherjee et al. (\u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) study were polymorphic. Nagaoka and Ogihara (\u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e) reported that ISSR primers produce reliable and reproducible bands.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003e \u003cb\u003eThe\u003c/b\u003e DNA barcoding results of this study provided us with insight into the genetic diversity of three selected cultivars of \u003cem\u003eAllium sativum\u003c/em\u003e. Among the cultivars, BDUT 1450 and 1452 seemed to be closely related, while BDUT 1451 diverged significantly. Discrepancies in clustering patterns among different markers suggest incomplete lineage sorting (ILS), where ancestral traits have not been fully separated. Furthermore, moderate interspecific diversity and intraspecific divergence were observed with complex genetic relationships. However, further studies with advanced methods and additional information from various cultivars from different geographical locations are needed to fully understand the evolutionary history of this important aromatic cum nutraceutical crop. Understanding this genetic landscape presents exciting opportunities for garlic breeding programs, allowing for the development of cultivars with enhanced nutritional profiles, improved disease resistance, and improved adaptation to specific environments.\u003c/p\u003e"},{"header":"Declarations","content":"\n\u003cp\u003e\u003cstrong\u003eFunding \u0026amp; Competing Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that they are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors substantially contributed to the conception and design of the project. Packia Lekshmi and Rajesh produced, assembled and analysed the data. The manuscript was interpreted and written by Packia Lekshmi and Mahamuni, with valuable contributions from Rajesh and Brindha. All the authors reviewed and approved the current version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenBank Accession Numbers:\u003c/strong\u003e KF769491, KF769492, KF769493, KF769497, KF769498, KF769499, KF769503, KF769504, KF769505, KF769509, KF769510, KF769511, KF779159, KF779160, KF779161.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author gratefully acknowledges Dr. M.B. Viswanathan, former Professor and Head, Department of Botany, Bharathidasan University, for providing the laboratory facility for DNA and PCR analyses, and all the authors gratefully acknowledge Mr. R. Anand for his assistance in sample collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAppeltans W, Ahyong ST, Anderson G, Angel MV, Artois T, Bailly N, Bamber R, Barber A, Bartsch I, Berta A, Błażewicz-Paszkowycz M (2012) The magnitude of global marine species diversity. Curr Biol 22:2189-2202. https://doi.org/10.1016/j.cub.2012.09.036\u003c/li\u003e\n\u003cli\u003eAryakia E, Karimi HR, Naghavi MR et al. (2016) Morphological characterization of intra-and interspecific diversity in some Iranian wild \u003cem\u003eAllium\u003c/em\u003e species. Euphytica 211:185-200. https://doi.org/10.1007/s10681-016-1729-8\u003c/li\u003e\n\u003cli\u003eAsahina H, Shinozaki J, Masuda K, Morimitsu Y, Satake M (2010) Identification of medicinal Dendrobium species by phylogenetic analyses using \u003cem\u003ematK\u003c/em\u003e and \u003cem\u003erbcL\u003c/em\u003e sequences. 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Euphytica 220:14. https://doi.org/10.1007/s10681-023-03259-1\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"garlic, ITS, barcoding, ISSR profiling, incomplete lineage sorting, breeding","lastPublishedDoi":"10.21203/rs.3.rs-3978989/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3978989/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe genus \u003cem\u003eAllium\u003c/em\u003e comprises plants of significant economic and medical importance, including onion, garlic, and leek plants. The genetic diversity of garlic plants (\u003cem\u003eAllium sativum\u003c/em\u003e) is vital for improving agricultural practices, developing resilient crops, preserving genetic resources, and exploring the full range of culinary and medicinal potential within this important plant species. In this research, we investigated the results of genetic barcoding, focusing on the internal transcribed spacer (ITS) region; four distinct barcoding regions, matK, rbcL, and trnH-psbA; and the trnL and Inter Simple Sequence Repeats (ISSR) regions of \u003cem\u003eAllium\u003c/em\u003e \u003cem\u003esativum\u003c/em\u003e L. (Amaryllidaceae), which were collected from three diverse cultivation sites. Our findings revealed significant interspecific diversity and intraspecific divergence among the three cultivars examined. Interestingly, the results from different genetic markers were consistent, with BDUT 1451 and 1452 consistently grouping together, while BDUT 1450 diverged. These findings emphasize the effectiveness of the multi-marker approach for exploring intricate genetic landscapes. Furthermore, they highlight the importance of genetic studies in understanding the diversity of breeding and the potential utility of this economically and medicinally important nutraceutical crop.\u003c/p\u003e","manuscriptTitle":"A Multi-marker Genomic Approach to Decipher the Divergence and Diversity in Selected Allium sativum L. cultivars","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 09:49:14","doi":"10.21203/rs.3.rs-3978989/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a876dc36-6a57-4759-82f2-c90078c6c69b","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-27T03:54:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-11 09:49:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3978989","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3978989","identity":"rs-3978989","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}