# Unraveling the genetic diversity of Cannabis sativa L.: Phenotypic and molecular assessment, drivers of variation, and implications

Chaymae Benkirane, Farid Mansouri, Larbi Rhazi, Alicia Ayerdi Gotor, Hana Serghini-Caid, Ahmed Elamrani, Malika Abid

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Chaymae Benkirane, Farid Mansouri, Larbi Rhazi, Alicia Ayerdi Gotor, Hana Serghini-Caid, et al. Unraveling the genetic diversity of Cannabis sativa L.: Phenotypic and molecular assessment, drivers of variation, and implications. Industrial Crops and Products, 2026, 240, pp.122575. 10.1016/j.indcrop.2025.122575. hal-05508645

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# Unraveling the genetic diversity of *Cannabis sativa* L.: Phenotypic and molecular assessment, drivers of variation, and implications

Chaymae Benkirane<sup>a</sup>, Farid Mansouri<sup>a,b</sup>, Larbi Rhazi<sup>c</sup>, Alicia Ayerdi Gotor<sup>d,*</sup>, Hana Serghini-Caid<sup>a</sup>, Ahmed Elamrani<sup>a</sup>, Malika Abid<sup>a</sup>

## ARTICLE INFO

**Keywords:**

*Cannabis sativa* L.

Genetic diversity

Phenotypic variation

Molecular markers

Breeding

Conservation

Environmental adaptation

## ABSTRACT

Cannabis (*Cannabis sativa* L.) is a highly diverse species exhibiting significant phenotypic and genotypic variation. This review provides a comprehensive overview of current literature on cannabis genetic diversity. We begin by outlining key morphological and phenotypic traits -such as plant height, leaf morphology, flowering time, and cannabinoid composition- that are commonly used to distinguish between cultivars. We then explore genotypic diversity through molecular marker studies, including Simple Sequence Repeats, Single Nucleotide Polymorphism, and Next-Generation Sequencing approaches. These tools have significantly advanced our understanding of cannabis population structure and genetic relationships. The implications of genetic diversity studies are examined in several contexts, including the development of improved cultivars, the conservation of genetic resources, and the formulation of regulatory frameworks to ensure traceability and genetic integrity. Additionally, we investigate major factors influencing cannabis genetic diversity, such as domestication history, hybridization events, environmental pressures, and regulatory policies. By synthesizing recent findings, this review highlights the need to expand genetic research into underexplored regions like Africa, implement robust conservation strategies (e.g., seed banks) to preserve unique genetic resources, and adopt data-driven breeding approaches to enhance both agronomic traits and phytochemical profiles.

## 1. Introduction

Cannabis is a plant of growing agricultural, economic, and medicinal significance. It belongs to the *Cannabaceae* family, alongside its closest relative, *Humulus* (Kovalchuk et al., 2020). Cannabis is an exceptionally versatile plant with a wide range of applications, depending on the organ used (Crini et al., 2020). Its inflorescences are rich in cannabinoids, compounds valued for their medicinal and recreational properties (Punja et al., 2023). The seeds serve as a highly nutritious source of plant-based proteins and essential fatty acids, while the extracted oil is used for both nutritional and cosmetic benefits, as well as for biofuel production (Li et al., 2010; Taaifi et al., 2021). The plant's stem fibers are valued in the manufacture of textiles, paper, bioplastics, and hempcrete offering sustainable alternatives to traditional industrial materials (Amarasinghe et al., 2022; Small, 2015). Beyond its commercial applications, cannabis also contributes to environmental

sustainability, aiding in phytoremediation of contaminated soils and fitting well into agroecological systems due to its relatively low input requirements (Golia et al., 2023). Cannabis is typically classified based on its primary use into two major types: fiber-type (often referred to as industrial cannabis) and drug-type (commonly known as Marihuana or Marijuana). This practical classification remains relevant today, particularly in agriculture and legislation, where distinction between industrial and drug-type cannabis informs legal regulatory frameworks worldwide (Barcaccia et al., 2020; Torkamineh and Jones, 2022).

In the early stages of botanical classification, scientists categorized cannabis based on morphological differences, leading to the identification of three species: *Cannabis sativa* L. (1753), described by Carl Linnaeus; *Cannabis indica* Lam. (1783), classified by Jean-Baptiste Lamarck; and *Cannabis ruderalis* Janisch. (1924), identified by Janischevsky (Schultes et al., 1974). This polytypic hypothesis of the genus *Cannabis* was later supported by Hillig's (2005) allozyme variation studies.

<footnote><sup>a</sup> Agricultural Production Improvement, Biotechnology and Environment Laboratory, Faculty of Sciences, Mohammed I university, BP-717, Oujda 60000, Morocco</footnote>

<footnote><sup>b</sup> Higher School of Education and Training, Mohammed I university, BP-410, Oujda 60000, Morocco</footnote>

<footnote><sup>c</sup> Institute Polytechnique UniLaSalle, Université d'Artois, ULR 7519, UniLaSalle, 19 rue Pierre Waguet, BP 30313, Beauvais 60026, France</footnote>

<footnote><sup>d</sup> Institut Polytechnique UniLaSalle, AGHYLE, UP 2018.C101, 19 rue Pierre Waguet, BP 13 30313, Beauvais 60026, France</footnote>

<footnote>* Corresponding author.
E-mail address: alicia.ayerdi-gotor@unilasalle.fr (A.A. Gotor).</footnote>
However, this multi-species classification has been a subject of ongoing debate. Recent genetic research supports a more unified approach, favoring the classification of *Cannabis* as a monotypic genus comprising a single, highly variable species: *Cannabis sativa* L. (Lapierre et al., 2023). Within this species, two primary subspecies are generally recognized: *Cannabis sativa* subsp. *sativa* and *Cannabis sativa* subsp. *indica*, each encompassing both wild and domesticated forms (Barcaccia et al., 2020; Mcpartland, 2018). Some researchers also acknowledge a third subspecies, *Cannabis sativa* subsp. *ruderalis* (Zhang et al., 2018). Despite these taxonomic distinctions, cannabis classification remains complex, particularly due to widespread use of commercial labels such as "sativa," "indica," and "hybrid" in the drug cannabis market (Mcpartland and Guy, 2017). These terms lack a formal taxonomic basis and often do not align with the homonymous scientific taxa.

Cannabis (*Cannabis sativa* L.) has been cultivated for millennia and exhibits remarkable diversity, shaped by a long history of domestication, human-driven selection, and adaptation to diverse ecological environments. This diversity is evident in the wide range of existing varieties and ecotypes, which differ significantly in both morphological traits and chemical composition. Understanding the genetic variability of cannabis is essential for the conservation of genetic resources, addressing agro- nomic and environmental challenges, and enhancing its industrial, medicinal, and therapeutic potential (Borin et al., 2021; Dehnavi et al., 2025; Soler et al., 2017). Historically, assessments of cannabis diversity relied on phenotypic traits, but these traditional methods have been significantly advanced by molecular genetic tools that analyze vari- ability at the DNA level. The integration of these approaches offers a deeper understanding of cannabis's genetic structure and its capacity for adaptation.

This review explores the genetic diversity of *Cannabis sativa* L., emphasizing the phenotypic and molecular methods used for its assessment. Additionally, it discusses the natural and anthropogenic factors driving this variability and considers the broader implications for

research, cultivation, and application across multiple fields.

## 1.1. General description of the cannabis plant

*Cannabis sativa* L. is an annual herbaceous plant, with distinctive morphological features. For illustrative purposes, figures presented in this section depict specimens from the local Moroccan cannabis population known as “Beldia” collected from the Rif region (Jebha). Cannabis is characterized by an erect and rigid stem that can reach 1–6 m in height, depending on the variety (Raman et al., 2017). It is longitudinally grooved, hispid, and usually hollow (Figure1. A-B-C). The root system typically consists of a taproot in open-field crops, ranging from 30 to 60 cm in depth, with varieties penetrating up to 2.5 m (Farag and Kayser, 2017), while in potted environments, the root development is more restricted, exhibiting a fibrous appearance (Figure 1. D). The leaves of cannabis are highly distinctive, serving as the plant's symbolic and characteristic feature. They are palmate, consisting of several lobes (leaflets) that range from 3 to 13 with inequal sizes. The initial true leaves have a single lobe, and as new pairs of leaves emerge at the nodes, the number of leaflets progressively increases until reaching their maximum number. Leaves are petiolate, and each leaflet is serrated, with a pointed apex and pinnate venations. The upper surface of the leaves is dark green, while the underside is lighter green (Figure 1.E). Cannabis leaves have generally a decussate opposite arrangement at lower nodes and an alternate pattern at upper ones (Small, 2015).

Cannabis is a dioecious plant, with only a few varieties being monoecious. Male plants are generally more slender, taller, and less leafy than female plants (Rahimi et al., 2019). Male (staminate) flowers are typically grouped in panicles that are oriented downwards to facilitate pollen dispersal (Figure 2.A1). Each male flower has a small green bract and a green floral stalk, which starts short and elongates as the flower matures (Figure 2.A2). Male flowers lack petals but feature five pale-green sepals covered with trichomes and five free, pendulous,

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Stem, roots and leaves of *Cannabis sativa* L. plant (Moroccan Beldia specimen). Longitudinal (A) and transverse (B) stem sections, anatomical structure of the stem section after green-carmine staining observed at 40 × magnification, showing non-lignified tissues in pink and lignified tissues in dark green (C), fibrous roots (D) and palmated leaves with abaxial (left) and adaxial (right) surfaces (E). Images produced by the authors.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Inflorescences and flowers of *Cannabis sativa* L. male (A) and female (B) plants (Moroccan Beldia specimen). Flowering male plant (A1), loosely cymose-paniculate male flowers (A2), male flower with pale-green sepals, five mature stamens, and pollen (A3), 5 anthers with 4 locules of pollen (A4), microscopic observation of pollen grains (A5). Numerous female flowers on leaf axils (B1), portion of isolated sessile female flowers (B2), glandular bract rich in trichomes (B3), female flower without bract (B4) showing an ovary (1) with two styles (2) and two stigmas (3). Images produced by the authors.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Achenes (seeds) of *Cannabis sativa* L. (Moroccan Beldia specimen). General view showing several brownish, ellipsoid seeds (A), close-up of a seed illustrating its characteristic marbled pattern (B), and longitudinal section showing the embryo and surrounding protective shell (C). Images produced by the authors.</figcaption></figure>
episepalous, isodynamous stamens with long anthers and short filaments (Fig. 2.A3). Each anther contains four locules filled with abundant yellow pollen (Fig. 2.A4). Female (pistillate) flowers appear in pairs in the leaf axils and are arranged in racemes (Fig. 2.B1-B2). These sessile flowers lack petals and sepals as protective structures, but are surrounded by a leaf-like, glandular bract rich in trichomes (Fig. 2.B3). Each female flower has a superior ovary with two filamentous styles that lead to two hairy, resinous, brush-shaped stigmas for pollen capture (Fig. 2.B4). The ovary consists of two fused carpels, each containing an anatropous ovule, though one of the carpels typically aborts. The surviving ovule, if fertilized, produces the seed. The cannabis fruit is a small, ovoid, smooth, and indehiscent achene, characterized by distinctive marbling on its surface and containing a single albuminous seed (Fig. 3). The seed is always encased in a protective hard shell, surrounded by a thin pericarp (Farag and Kayser, 2017).

Trichomes are abundant across almost the entire plant and are of two main types: glandular (capitate-sessile, capitate-stalked, and bulbous) and non-glandular (Raman et al., 2017). Glandular trichomes are specialized structures that store cannabinoids, the primary bioactive compounds in cannabis. These trichomes are most prominent in the female inflorescences, particularly on the glandular bracts. Non-glandular trichomes (hair-like structure) are mainly found on the stems and leaves and are responsible of the rough texture of the plant.

Cannabinoids are natural chemical compounds in cannabis, with tetrahydrocannabinol (THC) and cannabidiol (CBD) being the most well-known (Pattnaik et al., 2022). THC is the primary psychoactive compound responsible for euphoric effects. In contrast, CBD is a non-psychoactive molecule associated with various therapeutic properties, including anxiolytic, antiemetic, anticancer, and anti-inflammatory effects (Nethengwe et al., 2024). Other cannabinoids, such as cannabigerol (CBG) and cannabinol (CBN), are present in smaller amounts and are actually under investigation for their potential medicinal benefits.

## 1.2. Assessment of cannabis diversity using phenotypic markers

Early studies of cannabis genetic diversity relied primarily on phenotypic approaches, conducted before the use of advanced molecular techniques. Phenotypic markers refer to observable or measurable traits (morphological, physiological, or biochemical) that reveal underlying genetic variation within or between populations. Traditional botanical classification of the genus *Cannabis* into *sativa*, *indica*, and *ruderalis* was based exclusively on these phenotypic characteristics. For instance, *Cannabis sativa* typically is known for its tall stature, narrow leaves, and relatively high CBD content. In contrast, *Cannabis indica* is more compact, with broader leaves and higher THC levels. Both *sativa* and *indica* exhibit photoperiod-dependent flowering, whereas *ruderalis* is distinguished by its ability to auto-flower (Gloss, 2015).

Table 1 summarizes the primary phenotypic parameters commonly examined in cannabis research. Multiple morphological criteria have been used to study cannabis diversity, including stem height, internode length, number of leaflets, leaf size, seed size, and more. In addition, some physiological properties, like growth rate, disease resistance, and flowering time, have also been used as important phenotypic markers. With the advancement of analytical techniques, chemical traits -particularly those related to the cannabinoid profile- have emerged as critical markers for distinguishing between cannabis varieties. Based on the ratio between CBD and THC molecules, three primary chemotypes have been proposed: chemotype I (high THC content), chemotype II (balanced CBD:THC ratio), and chemotype III (high CBD content) (Cascini and Boschi, 2017). Additionally, two less prevalent chemotypes have been identified: chemotype IV (cannabigerol-dominant strains with moderate to low levels of both THC and CBD) and chemotype V (strains with minimal cannabinoid content) (Mandolino and Carboni, 2004; Onofri and Mandolino, 2017).

Numerous studies have focused on investigating the phenotypic

<table><caption>**Table 1**</caption><thead><tr><th colspan="2">Phenotypic traits</th><th>Reference</th></tr></thead><tbody><tr><td rowspan="6">Morphological traits</td><td>General architecture</td><td>Plant height, growth habit (erect, bushy), number of primary branches per plant, branch length</td><td>(Amarasinghe et al., 2022; Bachir et al., 2024)</td></tr><tr><td>Stem</td><td>Diameter at the base, the number of nodes, length of internodes, color, pith diameter, number of grooves</td><td>(Amarasinghe et al., 2022; Jin et al., 2021; Younas et al., 2024)</td></tr><tr><td>Root</td><td>Dry weight</td><td>(Murovec et al., 2022; Shams et al., 2020)</td></tr><tr><td>Leaf</td><td>Number of leaves, number of leaflets, length and width of central leaflet, distance from the base of the central leaflet to the widest point of the leaflet, presence of trichomes, color, number of primary and secondary serrations on the central leaflet.</td><td>(Hesami et al., 2023; Jin et al., 2021; Murovec et al., 2022; Vergara et al., 2021; Younas et al., 2024)</td></tr><tr><td>Petiole</td><td>Length, width, color</td><td>(Jin et al., 2021; Younas et al., 2024)</td></tr><tr><td>Inflorescence</td><td>Number of inflorescences, Fresh and dry weight, density of bracts (perigonal bract-to-leaf index), position (axillary/terminal), color of calyx</td><td>(Amarasinghe et al., 2022; Bachir et al., 2024; Jin et al., 2021; Shams et al., 2020)</td></tr><tr><td rowspan="2">Achene/Seed</td><td>Yield per plant, weight, color, presence of marbled patterns, width, length, area size, perimeter and length-to-width ratio, thickness of the hull, percent perianth cover, basal attenuation</td><td>(Naraine et al., 2020; Piluzza et al., 2013)</td></tr><tr><td>Color, type (glandular, non-glandular), number, trichome stalk length, glandular head diameter</td><td>(Hesami et al., 2023; Punja et al., 2023; Sutton et al., 2023)</td></tr><tr><td>Trichome</td><td>Shape, size, Length of Polar axis, Length of Equatorial axis, Polar axis/equatorial axis, Exine thickness, Pore diameter, Number of granules</td><td>(Lone et al., 2023)</td></tr><tr><td>Physiological traits</td><td>Duration of the vegetative phase, number of days to flowering, resistance to diseases, Photosynthetic measurements (e.g. Net photosynthesis, transpiration, stomatal conductance)</td><td>(Jin et al., 2021; Murovec et al., 2022; Rahimi et al., 2019)</td></tr><tr><td>Biochemical traits</td><td>Concentration of cannabinoids, terpenes, and phenolic compounds, nutrients, chlorophyll and carotenoid content, fatty acid profile</td><td>(Alsaleh and Yilmaz, 2025; Benkirane et al., 2023; Shams et al., 2020; Taaifi et al., 2021; Vergara et al., 2021)</td></tr></tbody></table>

Overview of the main phenotypic traits used to study the genetic diversity of *Cannabis sativa* L.
variation of cannabis across various global regions. For instance, Bachir et al. (2024) examined herbarium-preserved cannabis specimens from the Maghreb (mainly Morocco and Algeria), collected prior to the widespread hybridization. Their morphological analysis revealed that Maghrebi cannabis closely resembles European “industrial hemp,” except for its female inflorescence, which is denser and richer in glan- dular trichomes. Significant agro-morphological differences have also been reported among 35 wild cannabis in Pakistan (Younas et al., 2024) and among 10 Iranian landraces (Shams et al., 2020). In a more comprehensive assessment, Babaei et al. (2024) assessed 12 phenolog- ical and 14 morphological traits across 25 indigenous Iranian cannabis populations, identifying seven distinct morphological clusters and five flowering time categories ranging from early-to-late. Broader morpho- logical diversity has also been documented by Petit et al. (2020), who assessed 28 traits across 123 fiber cannabis accessions cultivated in three different European climatic zones.

Furthermore, the relationship between morphology and chemotype has been a focal point of several studies. Jin et al. (2021) investigated 21 commercial cultivars representing the three primary chemotypes and identified distinct morphological markers associated with each group through correlation analyses. CBD-dominant cultivars displayed nar- row, light green leaflets with pronounced serration, loosely structured inflorescences, dense and resinous trichomes, and notable resistance to Botrytis cinerea. Intermediate cultivars displayed medium-sized, dark green leaflets with marked serration, moderately compact in- florescences, less dense and resinous trichomes, and also showed resis- tance to Botrytis cinerea. THC-dominant cultivars, on the other hand, exhibited broad, dark green leaflets, large and compact inflorescences, dense and resinous trichomes, but were more susceptible to Botrytis cinerea. However, the predictive power of morphology alone is limited. Murovec et al. (2022) demonstrated that visual traits are not sufficient to accurately determine chemotypes, as morphologically similar plants can have markedly different cannabinoid profiles. Similarly, Vergara et al. (2021) examined several phenotypic traits in a group of 297 hybrid cannabis plants crossed from two divergent lineages and found no sig- nificant relationship between most traits, such as between leaf shape and cannabinoid content.

Several studies have investigated more specific morphological vari- ations within cannabis, highlighting the plant's remarkable phenotypic diversity. For instance, Lone et al. (2023) reported pollen morphological variations among wild Indian populations. Naraine et al. (2020) analyzed seed morphology across various cannabis types, including ruderal, fiber, oilseed, and drug varieties. In addition, Punja et al. (2023) investigated changes in trichome morphology throughout the matura- tion process in drug-type varieties, while Amarasinghe et al. (2022) focused on stem morphological and anatomical structure of several in- dustrial varieties, as well as their fiber quality.

The application of advanced techniques has significantly refined the study of cannabis morphological traits, enhancing precision and repro- ducibility. Gjorgievska et al. (2024) used infrared spectroscopy com- bined with image processing to differentiate commercial cannabis seeds from those of wild populations, focusing on size and macronutrient composition. Balant et al. (2024) introduced a rapid and cost-effective approach based on intra-foliar modeling of leaflet shape to predict the plant identity and its developmental stages. Similarly, Naim-feil et al. (2022) conducted an extensive analysis of 14 inflorescence morpho- logical traits by imaging 478 cannabis plants representing 119 geno- types, including THC-dominant and intermediate chemotypes. Their research resulted in a predictive equation for estimating plant biomass based on inflorescence width, offering a valuable tool for improving varietal selection. In a related advancement, Sutton et al. (2023) applied automated machine learning to characterize trichome morphology and correlate trichome color (transparent, milky, or brown) with maturation stages, thereby enhancing the accuracy of harvest timing decisions.

Despite these advances, phenotypic traits alone are insufficient to draw reliable and precise conclusions about plant diversity, primarily

due to their susceptibility to environmental factors, such as altitude, temperature, and humidity, as well as agricultural practices (Kaur et al., 2023; Naim-feil et al., 2022; Stambouli et al., 2005; Younas et al., 2024). Indeed, cannabis demonstrated marked phenotypic plasticity, i.e. it has the ability to modify morphological, physiological, or biochemical characteristics in response to environmental conditions, without genetic changes. For instance, a plant growing under low-light conditions will typically develop an elongated stem and larger leaves to enhance light capture for photosynthesis, while root morphology can be shaped by water availability and soil texture. Moreover, the biosynthesis of bioactive compounds, like cannabinoids and phenolic compounds, can increase under biotic or abiotic stresses. Additionally, multiple and recurrent hybridization between varieties with differing traits can result in intermediate phenotypes, complicating comparisons based solely on phenotypic criteria. Another challenge in phenotypic assessments is their dependence on the plant's developmental stage. Currently, there is no standardized reference regarding the precise time when morpho- logical measurements should be taken to ensure accurate and consistent comparisons across studies. Hesami et al. (2023) monitored numerous morphological traits of a drug-type variety throughout its growth cycle and found significant morphological transformations, influenced by photoperiod shifts and the transition from vegetative to reproductive phases. In the same context, Stack et al. (2023) reported that some cultivars exhibited intra-plant variation in cannabinoid concentration, with high amounts in the upper plant section. Despite all these chal- lenges, phenotypic markers remain essential for cannabis characteriza- tion and selection and should be integrated in conjunction with molecular markers.

### 1.3. Assessment of cannabis genetic diversity using molecular markers

Several molecular tools have been developed to accurately assess the genetic diversity of cannabis. Early research relied on classical markers such as RAPD (Random Amplified Polymorphic DNA) and AFLP (Amplified Fragment Length Polymorphism), which provided a rapid and cost-effective method for evaluating genetic variability. These were later complemented by more powerful markers like SSRs (Simple Sequence Repeats), which provided improved resolution and repro- ducibility. In recent years, advances in high-throughput sequencing have profoundly transformed cannabis genomics. Approaches such as whole-genome sequencing (WGS) and genotyping-by-sequencing (GBS), now enable the detection of thousands to millions of polymorphic sites across the genome. These technologies have facilitated the discovery of diverse sequence-based markers, particularly SNPs (single nucleotide polymorphisms) and SSRs identified through in silico mining. It is important to note that SSRs can be generated either through traditional hybridization-based genomic enrichment methods or by direct mining of sequencing datasets. In contrast, SNPs, representing single-nucleotide polymorphisms, require sequencing and high-throughput genotyping platforms for their detection. Table 2 presents a summary of the most commonly used molecular tools in cannabis genetic diversity studies, detailing their underlying principles, key advantages, and potential drawbacks.

The data obtained from molecular markers are analyzed to assess the genetic diversity and structure of populations. Table 3 highlights representative investigations on genetic diversity of Cannabis sativa L. from different regions of the world using a range of molecular markers, each providing specific diversity measurements, such as the percentage of polymorphic loci, Shannon's index, allele number, and heterozygosity. High-throughput technologies now enable genome-wide analyses, incorporating SNP-based diversity indices, nucleotide diversity, and linkage disequilibrium patterns. In addition, molecular markers allow for the study of genetic structure, the construction of phylogenetic trees, and the assessment of genetic variability within and between pop- ulations, through methods such as Analysis of Molecular Variance (AMOVA).
<table><caption>**Table 2**
Comparison of the frequently used molecular markers and techniques in genetic diversity studies of *Cannabis sativa* L.</caption><thead><tr><th>Molecular tools</th><th>Principle</th><th>Advantages</th><th>Limitations</th></tr></thead><tbody><tr><td><strong>*Molecular markers</strong></td><td></td><td></td><td></td></tr><tr><td>RAPD (Random Amplified Polymorphic DNA)</td><td>PCR amplification of random DNA segments using short primers</td><td>Fast, simple, low-cost, no prior sequence knowledge needed</td><td>Low reproducibility, dominant marker</td></tr><tr><td>AFLP (Amplified Fragment Length Polymorphism)</td><td>Selective PCR amplification of restriction fragments</td><td>High polymorphism detection, no prior sequence knowledge needed</td><td>Technically demanding, dominant marker, complex banding patterns</td></tr><tr><td>SSR (Simple Sequence Repeats)</td><td>PCR-based amplification of tandem repeat regions</td><td>Highly polymorphic, co-dominant, good reproducibility</td><td>High development cost, requires prior sequence knowledge</td></tr><tr><td>ISSR (Inter-Simple Sequence Repeats)</td><td>PCR amplification of DNA regions between microsatellites</td><td>No prior sequence knowledge needed, highly polymorphic</td><td>Dominant marker, low reproducibility</td></tr><tr><td>SNP (Single Nucleotide Polymorphism)</td><td>Single base pair variations detected using sequencing or hybridization techniques</td><td>High abundance, co-dominant, high-throughput</td><td>Expensive initial setup, requires reference genome</td></tr><tr><td><strong>*Genotyping/ sequencing techniques</strong></td><td></td><td></td><td></td></tr><tr><td>WGS (Whole genome sequencing)</td><td>Complete sequencing of the entire genome</td><td>Provides the most comprehensive genetic information, detects all variants</td><td>Very expensive, requires significant computational resources</td></tr><tr><td>GBS (Genotyping by Sequencing)</td><td>Reduced-representation sequencing approach using restriction enzymes to identify genome-wide SNPs</td><td>High precision, high-throughput, cost-effective for SNP discovery, no prior genome needed</td><td>High cost, requires sequencing facilities, Requires bioinformatics expertise, missing data issues</td></tr></tbody></table>

## 1.4. RAPD and AFLP markers

Early genetic studies of cannabis employed dominant classical markers, specifically RAPD (Random Amplified Polymorphic DNA) and AFLP (Amplified Fragment Length Polymorphism). RAPD amplifies random DNA fragments using short arbitrary primers, while AFLP combines DNA digestion with restriction enzymes followed by the selective amplification of specific fragment subsets.

The first application of RAPD markers in cannabis was conducted by Faeti et al. (1996), who used 10 markers on 13 fiber-type cultivars of different sex, originating from Korea and five European countries. Their analysis revealed high genetic diversity, identifying 201 polymorphic fragments, enabling the distinction of accessions based on their origin. Similarly, Pinarkara et al. (2009) assessed the genetic diversity of 29 drug-type Cannabis accessions seized in Turkey using 22 RAPD primers. They generated 264 bands, 93 % of which were polymorphic, and revealing clear population differentiation by region. Piluzza et al. (2013) examined both drug and fiber accessions from various countries, including Colombia, Mexico, California, Bolivia, Thailand, Afghanistan, Serbia, Hungary, South Africa, and different regions of China, using 6 RAPD markers. Their analysis yielded 61 polymorphic fragments and found that 74 % of the total variation was inter-accession, clearly distinguishing between fiber and drug types. Furthermore, Shao et al. (2003) used 15 RAPD markers on 10 adult plants collected from different regions of China to explore sexual polymorphism in cannabis. They identified three DNA fragments uniquely associated with female plants. AFLP markers have also been employed to investigate Cannabis genetic diversity. Hu et al. (2012) analyzed 13 populations from China and reported especially high diversity in the Yunnan population, indicated by 88.82 % polymorphic bands, Nei's gene diversity (h = 0.3011), and Shannon's index (I = 0.4571). Additionally, Datwyler and Weiblen (2006) detected 1206 AFLP bands –88 % polymorphic- and found that 27.2 % of the total genetic variation was attributable to differences between fiber and drug-type accessions.

Classical markers such as RAPDs and AFLPs are cost-effective tools for genetic analysis, with the advantage of requiring neither species-specific primers nor prior genomic sequence information for primer construction. However, they are poorly reproducible for RAPDs and technically complex and labor-intensive for AFLPs. Additionally, they are dominant, i.e. they cannot differentiate between homozygous and heterozygous alleles at a given locus. Therefore, they do not assess heterozygosity, which is an essential parameter in evaluating genetic diversity.

## 1.5. SSR and ISSR markers

Simple Sequence Repeats (SSRs), also known as STRs (Short Tandem Repeats) or microsatellites, are codominant markers valued for their high genetic resolution and ability to assess heterozygosity. These markers consist of short, tandemly repeated DNA motifs (e.g., ATATAT) that are abundant and widely distributed throughout the genome (Nadeem et al., 2018). Due to their high polymorphism, reproducibility, and cost-effectiveness, SSRs are widely employed in studies of genetic diversity. The initial development of SSR markers relied on a genomic enrichment technique, which isolates and amplifies microsatellite sequences from genomic DNA libraries enriched in short repeats. Using this approach, Alghanim and Almirall (2003), Gilmore et al. (2003), Gilmore and Peakall (2003), and Hsieh et al. (2003) pioneered the first SSRs and applied them to investigate genetic diversity in cannabis across various contexts.

Advancements in sequencing technologies have paved the way for new approaches that directly explore the cannabis genome and transcriptome, enabling the design of SSRs with much broader genome coverage. The first genome and transcriptome sequencing of cannabis was published in 2011, focusing on the high-THC cultivar Purple Kush (Van Bakel et al., 2011). Using this transcriptome, Gao et al. (2014) identified 4 577 expressed sequence tag-derived SSRs (EST-SSRs), of which 117 were tested across 24 cannabis strains, with 87 demonstrating high levels of polymorphism. Subsequent studies have developed *de novo* genomic SSR primers directly from the cannabis genome. For example, Soler et al. (2016) designed 11 SSRs for genetic and forensic applications. In a follow-up study, Soler et al. (2017) selected six markers from a panel of 29 dinucleotide SSRs to genotype 154 individuals from 22 cannabis accessions. Similarly, Schwabe and Mcglaughlin (2019) developed 10 novel SSRs to investigate genetic diversity across 30 cannabis strains collected from three US states. Combining both genomic and transcriptomic data, Galasso and Ponzoni (2015) identified 407 491 SSRs in the genome and 15 655 in the transcriptome. From these, 15 SSRs (10 genomic and 5 transcriptomic) were selected for PCR validation, with four tested on 10 industrial cannabis cultivars to assess their effectiveness in detecting genetic variations. More recently, Zhang et al. (2020) identified 92 409 SSRs from a refined cannabis genome assembly and developed 63 699 SSR primer pairs. Of these, 80 were tested in a genetic diversity study with 59 demonstrating high polymorphism and successfully differentiating 199 cannabis germplasm samples into two distinct subgroups. Additionally, Barcaccia et al. (2020) developed 126 593 perfect SSRs and 12 017 composed SSRs from the cs10 genome, selecting 41 SSRs for further analysis. Borin et al.

<footnote>* "Molecular markers" refer to the genetic variants used for diversity analysis, whereas "genotyping or sequencing techniques" (e.g., GBS, WGS) refer to the platforms or methods used to discover or generate markers.</footnote>
<table><caption>**Table 3**
Evidence of genetic diversity of *Cannabis sativa* L. using different molecular markers.</caption><thead><tr><th>Molecular marker/approach</th><th>Plant material</th><th>Origin</th><th>Key findings of genetic variability</th><th>Reference</th></tr></thead><tbody><tr><td>22 RAPD</td><td>29 Drug seized samples (seeds)</td><td>Turkey</td><td><ul><li>2 clusters according to geographical regions (western part and eastern part of Turkey)</li><li>AMOVA: 79.77 % of the genetic variation is among accessions within each cluster</li></ul></td><td>(Pinarkara et al., 2009)</td></tr><tr><td>6 RAPD</td><td>19 Accessions of wild and domesticated hemp</td><td>Colombia, Mexico, California, Bolivia, Thailand, Afghanistan, Serbia, Hungary, South Africa and different regions of China</td><td><ul><li>6-15 bands per locus</li><li>AMOVA: 74 % variation among accessions and 26 % within accessions</li><li>Cluster analysis: 8 clusters</li><li>Good discrimination of the fiber and drug types</li></ul></td><td>(Piluzza et al., 2013)</td></tr><tr><td>10 RAPD</td><td>54 hemp samples</td><td>Italy, Hungary, Poland, Korea, Romania, Czechoslovakia</td><td><ul><li>201 informative bands. Each primer ranged from 5 to 13 bands.</li></ul></td><td>(Faeti et al., 1996)</td></tr><tr><td>15 RAPD</td><td>10 adult individuals (5 males & 5 females)</td><td>China</td><td><ul><li>Cluster analysis: good agreement with the origin</li></ul></td><td>(Shao et al., 2003)</td></tr><tr><td>AFLP (8 selected primer combinations)</td><td>2 hemp accessions (parental lines) + segregating progeny of 80 plants (F<sub>2</sub>)</td><td>Genebank in Germany</td><td><ul><li>Identification of female-associated DNA fragments: 3 reproducible RAPD bands present in all tested female plants, absent in all tested males.</li></ul></td><td>(Flachowsky et al., 2001)</td></tr><tr><td>10 AFLP</td><td>3 populations of fiber type and 1 potent cultivar of marijuana</td><td>Netherlands, Canada, USA</td><td><ul><li>17 male specific fragments detected in one accession and 16 in another.</li><li>Demonstration that sex in hemp is genetically determined by sex chromosomes (XY system)</li><li>1206 bands (88 % were polymorphic)</li><li>18 bands distinguished fiber populations from the drug cultivar.</li></ul></td><td>(Datwyler and Weiblen, 2006)</td></tr><tr><td>20 ISSR</td><td>43 industrial hemp genotypes</td><td>Mainly from Turkey + 6 foreign cultivars + 5 with unknown origin</td><td><ul><li>AMOVA: 52 % variation within populations</li><li>Turkish females had higher diversity (N<sub>a</sub> 1.507, N<sub>e</sub> 1.226, H<sub>e</sub> 0.154, %Polymorphism 74.91 %).</li><li>AMOVA: 92 % of variation within populations</li><li>STRUCTURE: 3 main genetic clusters associated with gender rather than geographical origin</li></ul></td><td>(Alsaleh and Yilmaz, 2025)</td></tr><tr><td>18 ISSR</td><td>33 accessions (23 drug seized samples, 9 hemp type, 1 unknown)</td><td>Turkey</td><td><ul><li>ISSR880bp785: male-specific marker</li><li>Differentiation between drug and fiber type accessions.</li><li>205 bands, 93.7 % were polymorphic</li><li>Hemp-type has high levels of variation than drug-type</li></ul></td><td>(Hakki et al., 2007)</td></tr><tr><td>28 ISSR tested and 15 used</td><td>14 samples (drug-type / fiber-type)</td><td>Northern India (collected from roadside sites along national highways)</td><td><ul><li>152 fragments generated (32 were polymorphic)</li><li>Average number of bands per primer 10.14</li><li>Similarity index (Jaccard's coefficient): 0.17-0.67</li><li>A total of 31 unique bands were generated</li><li>UPGMA / PCA: clustering into two main groups (with sub-clusters); clustering only "partially" matched their collection area.</li></ul></td><td>(Khatak et al., 2016)</td></tr><tr><td>13 ISSR and 2 SCAR</td><td>26 accessions (5 males and 5 females from each accession)</td><td>Iran, Afghanistan</td><td><ul><li>ISSR amplified 143 polymorphic bands (5-15 loci/marker); only 10 were significantly sex-linked</li><li>For ISSRs: N<sub>e</sub> 1.15-1.41, H<sub>e</sub> 0.09-0.24, I 0.14-0.36</li><li>The SCAR "MADC5" was a polymorphic marker and showed a significant relation with sex.</li></ul></td><td>(Rahimi et al., 2019)</td></tr><tr><td>25 ISSR tested and 14 used</td><td>10 landraces / local hemp populations</td><td>Iran</td><td><ul><li>231 bands produced, of which 198 (85 %) were polymorphic</li><li>UPGMA clustering and STRUCTURE: six genetic clusters</li><li>Gene flow (N<sub>m</sub>) 0.87, F<sub>st</sub> 0.364</li><li>10 alleles per locus (range 2-28), H<sub>e</sub> 0.68 (range 0.28-0.94)</li></ul></td><td>(Shams et al., 2020)</td></tr><tr><td>15 SSR</td><td>48 samples (5 fiber accessions)</td><td>—</td><td><ul><li>Significant genetic differentiation between accessions (F<sub>st</sub> 0.12)</li><li>52 alleles detected (4.7 alleles per locus)</li><li>H<sub>e</sub> (gene diversity) per locus: 0.368-0.710</li><li>27 unique genotypes identified among the 41 samples</li></ul></td><td>(Gilmore and Peakall, 2003)</td></tr><tr><td>11 SSR</td><td>41 samples (diverse marijuana plants)</td><td>—</td><td><ul><li>Very low Probability of Identity: 1.8 × 10<sup>-7</sup> → suitability of markers for individual identification / DNA typing</li><li>106 alleles (14 unique to drug-type and 30 unique to fiber-type)</li></ul></td><td>(Alghanim and Almirall, 2003)</td></tr><tr><td>10 SSR</td><td>510 drug samples<br>57 fiber samples</td><td>Australia</td><td></td><td>(Howard et al., 2009)</td></tr></tbody><tfoot><tr><td colspan="5" style="text-align: right;">(continued on next page)</td></tr></tfoot></table>
<table><caption>Table 3 (continued)</caption><thead><tr><th>Molecular marker/approach</th><th>Plant material</th><th>Origin</th><th>Key findings of genetic variability</th><th>Reference</th></tr></thead><tbody><tr><td>45 EST-SSR</td><td>115 cannabis genotypes</td><td>18 provinces of China, Ukraine, Poland, and France</td><td><ul><li>314 different genotypes detected.</li><li>Unique genotypes were found in all fiber samples; 55% of drug samples shared genotypes</li></ul></td><td>(Gao et al., 2014)</td></tr><tr><td>13 SSR</td><td>1324 Cannabis samples: 24 hemp varieties + 15 drug varieties</td><td>Hungary, France, Ukraine, Poland, Netherlands, Italy, Spain, Romania, + unknown origin</td><td><ul><li>Structure: 4 groups based on geography: Northern China, Europe, Central China, and Southern China.</li><li>N<sub>e</sub> 1.34–1.8, I 0.27–0.58, H<sub>e</sub> 0.18–0.36, % polymorphic loci 41–85 %</li><li>Highest level of genetic diversity in the Central China group</li></ul></td><td>(Dufresnes et al., 2017)</td></tr><tr><td>11 SSR</td><td>10 varieties (4 hemp + 6 marijuana)</td><td>Germany, Spain, Finland, France</td><td><ul><li>PCA, STRUCTURE, F<sub>ST</sub>: Strong genetic differentiation of hemp vs drug-type</li><li>Hemp varieties: allelic richness AR 4.0 ± 0.8, H<sub>e</sub> 0.59 ± 0.10, F<sub>IS</sub> 0.19 ± 0.05, F<sub>ST</sub> 0.15 ± 0.07.</li><li>Drug-type varieties: AR 2.3 ± 0.9, H<sub>e</sub> 0.41 ± 0.15, F<sub>ST</sub> 0.39 ± 0.16.</li></ul></td><td>(Soler et al., 2016)</td></tr><tr><td>6 SSR</td><td>154 individuals of 22 accessions</td><td>—</td><td><ul><li>64 alleles detected</li><li>Values per locus: Na: 2–11, H<sub>e</sub>: 0–0.70, H<sub>e</sub>: 0.24–0.89</li><li>PCoA: Clear differentiation between hemp-type and marijuana-type</li><li>DNA extracted from embryos had quality and concentration comparable to leaf-derived DNA</li></ul></td><td>(Soler et al., 2017)</td></tr><tr><td>13-locus SSR Multiplex system</td><td>101 samples (marijuana)</td><td>Seized US samples</td><td><ul><li>17 alleles and 23.8 genotypes per locus.</li><li>Average values of H<sub>e</sub> 0.753, H<sub>e</sub> 0.429, F<sub>IS</sub> 0.587.</li><li>AMOVA: variation among cultivars 31.94 %, among individuals 37.11 %, within individuals 30.96</li></ul></td><td>(Soler et al., 2017)</td></tr><tr><td>10 SSR</td><td>122 plants, belonging to 30 commercially drug-type “strains,” marketed as Sativa/Indica/Hybrid</td><td>multiple dispensaries across 3 US states</td><td><ul><li>Structure: 14 genetic clusters</li><li>The multiplex generates balanced, interpretable profiles</li><li>The multiplex is species specific (for Cannabis)</li><li>Heterozygous peak height ratio: 0.83 across loci</li><li>Combined power of discrimination: 1 in 55 million</li></ul></td><td>(Houston et al., 2017)</td></tr><tr><td>10 SSR</td><td>49 samples:<br/>- Wild/feral hemp,<br/>- Cultivated hemp,<br/>- “Research-grade marijuana”<br/>federally produced (NIDA),<br/>- Commercially available drug-type accessions (high-THC/ high-CBD)</td><td>Samples from U.S. sources</td><td><ul><li>PCoA / STRUCTURE: two major genetic groups, but not matching the commonly used “Sativa / Indica / Hybrid” categories.</li><li>Pairwise relatedness: samples from different strains sometimes more related than those within the same strain → mislabeling or misidentification in the supply chain</li></ul></td><td>(Schwabe and Mcglaughlin, 2019)</td></tr><tr><td>Two 13-loci SSR multiplex systems</td><td>94 seized samples within Brazil in 3 different years</td><td>Northeastern Brazil, Paraguay, Europe, or unknown</td><td><ul><li>PCoA, STRUCTURE: clear separation between hemp-type and drug-type Cannabis</li><li>The “federally produced” samples (NIDA) share a genetic affinity with hemp</li><li>Pairwise genetic differentiation: F<sub>ST</sub> 0.023–0.394</li><li>Within the drug-type samples: no consistent genetic differentiation matching commercial “strain type” labels</li><li>Private alleles across many loci: specifically, for the “wild hemp” group (12) and the NIDA group (4) → unique genetic variation not shared with retail varieties</li></ul></td><td>(Schwabe et al., 2021)</td></tr><tr><td>6 SSR</td><td>56 individuals (2 populations Beldia and Critical)</td><td>Morocco</td><td><ul><li>The SSR multiplex systems were effective in individualizing each sample</li><li>PCoA: sample discrimination according to geographic origin / seizure group</li><li>AMOVA: 22–28 % variation among regions, 2–5 % among populations, 6–9 % among individuals, and 61–67 % within individuals</li><li>Structure (using 19 SSR): 3 clusters consistent with their geographic origin and likely trafficking routes</li></ul></td><td>(De De Oliveira Pereira Ribeiro et al., 2020)</td></tr><tr><td></td><td></td><td></td><td><ul><li>111 alleles detected (18.5 alleles/locus)</li><li>Local population (Beldia): N<sub>a</sub> 14.5, N<sub>e</sub> 7.6, N<sub>p</sub> 7.5, H<sub>e</sub> 0.782</li><li>Introduced modern variety (Critical): N<sub>a</sub> 11, N<sub>e</sub> 4.7, N<sub>p</sub> 4, H<sub>e</sub> 0.703</li></ul></td><td>(Benkirane et al., 2024)</td></tr></tbody></table>

(continued on next page)
<table><caption>Table 3 (continued)</caption><thead><tr><th>Molecular marker/approach</th><th>Plant material</th><th>Origin</th><th>Key findings of genetic variability</th><th>Reference</th></tr></thead><tbody><tr><td>20 SSR</td><td>104 individuals (11 varieties either dioecious or monoecious)</td><td>Italy, Finland, Hungary, France, Poland, and Netherlands</td><td><ul><li>301 alleles detected</li><li>Depending on variety: N<sub>e</sub> 1.89–4.43, H<sub>e</sub> 0.48–0.74, I 0.56–1.49, F<sub>IS</sub> 0.02–0.35</li><li>Population structure: 8 genetic groups, clustering individuals based on geographical origins and sexual behavior</li><li>2 SSR markers linked to sex detection</li><li>Development of 92 409 SSR motifs and 63 707 complementary SSR primer, 80 pairs were randomly selected, and 59 were polymorphic.</li><li>72 loci identified</li><li>N<sub>a</sub> 2–8, N<sub>e</sub> 1.38–5.32, H<sub>e</sub> 0.28–0.81, I 0.5–1.78</li><li>Structure analysis: two subgroups.</li><li>Three "haplogroups": H, M, L corresponding to high-latitude, middle-latitude, and low-latitude origin zones.</li><li>High species-level haplotype diversity (Hd = 0.848)</li><li>Overall F<sub>ST</sub> among the three haplogroups: 0.695.</li><li>Most molecular variation is between groups (69.48 %), rather than among populations within groups (14.43 %) or within populations (16.1 %)</li><li>4 haplotypes were identified</li><li>Distinguish marijuana from hemp crop type</li><li>361 multi-locus genotypes were identified</li><li>Structure: 5 clusters (H<sub>e</sub>: 0.13–0.32, I: 2.8–5.5)</li><li>Discrimination between drug-type and fiber-type</li><li>PCA &amp; ADMIXTURE: Samples clustered into five genetic groups: "Mississippi-River", "West North Central-a", "West North Central-b", "New York", and "Indiana"</li><li>F<sub>ST</sub> between inferred groups: 0.07–0.39</li><li>Average H<sub>o</sub>, H<sub>e</sub>, F<sub>IS</sub>: 0.20, 0.20, and –0.03</li><li>AMOVA: 9 % between groups, 20 % between accessions within groups and 71 % within accessions</li><li>The Iranian collection is structured into five distinct genetic groups.</li><li>H<sub>e</sub> and H<sub>e</sub> by geographic group ranged from 0.204 to 0.25 and 0.264–0.296</li><li>F<sub>ST</sub> among Iranian regional populations: (0.024–0.09)</li><li>Comparison with global cannabis collections: Iranian populations are genetically distinct, but overall are closer to marijuana than to hemp</li><li>F<sub>ST</sub> between Iranian collection (as a whole) vs global hemp collection: 0.086; vs global marijuana collection: 0.062.</li><li>Structure: 2 groups based on cannabis type (hemp, Marijuana)</li><li>Heterozygosity (genetic diversity) significantly higher in hemp than in marijuana</li><li>Genome-wide genetic distinction not limited to THC-synthesis genes.</li><li>Average F<sub>ST</sub> between hemp and marijuana: 0.156</li><li>Chinese cannabis: Five groups based on geographical source and ecotype</li><li>Cannabis cultivated for fiber in northeastern China was genetically close to the European cannabis for fiber</li><li>Cannabis in southwestern China was genetically similar to marijuana</li><li>Genes related to flowering time, seed germination, seed size, embryogenesis, growth, and stress responses were identified</li><li>Mean heterozygosity = 0.15 across Iranian samples</li><li>Iranian samples cluster more closely with "marijuana" accessions than with hemp</li></ul></td><td>(Borin et al., 2021)</td></tr><tr><td>59 SSR (from WGS)</td><td>199 individuals from 12 populations</td><td>20 regions across China, Germany, Hungary, Poland, Ukraine, Kazakhstan, Russia, Uzbekistan, the United States, Lithuania, Bosnia and Herzegovina</td><td></td><td>(Zhang et al., 2020)</td></tr><tr><td>5 chloroplast DNA regions</td><td>645 individuals from 52 accessions: 25 wild populations + 27 domesticated populations / cultivars</td><td>China and other countries (the Netherlands, France, Hungary, Italy, Russia, Nigeria, Korea, USA)</td><td></td><td>(Zhang et al., 2018)</td></tr><tr><td>9 chloroplast SNP markers</td><td>122 individuals (including 16 blind samples)</td><td>European samples and Chilean &amp; USA/ Mexico seizures</td><td></td><td>(Di nunzio et al., 2024)</td></tr><tr><td>23 SNP</td><td>420 samples</td><td>North America</td><td></td><td>(Henry et al., 2020)</td></tr><tr><td>21 037 SNP (from GBS)</td><td>760 feral plants</td><td>12 US states</td><td></td><td>(Aina et al., 2025)</td></tr><tr><td>23 266 SNP (from GBS)</td><td>228 plants (from 35 natural populations)</td><td>Iran</td><td></td><td>(Dehnavi et al., 2025)</td></tr><tr><td>14 031 SNP (From GBS)</td><td>81 Marijuana and 43 hemp samples</td><td>—</td><td></td><td>(Sawler et al., 2015)</td></tr><tr><td>22.98 million SNPs (from WGS)</td><td>52 samples: Wild/Feral accessions + landraces + cultivated cultivars</td><td>China + (5 samples from USA, Finland, and Ukraine)</td><td></td><td>(Chen et al., 2022)</td></tr><tr><td>24 710 SNPs (From GBS)</td><td>98 samples (36 from Iranian locations + 26 accessions from two germplasm collections + a few from Afghanistan)</td><td>Iran + global germplasm accessions from collections for comparison</td><td></td><td>(Soorni et al., 2017)</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 3 (continued)**</caption><thead><tr><th>Molecular marker/approach</th><th>Plant material</th><th>Origin</th><th>Key findings of genetic variability</th><th>Reference</th></tr></thead><tbody><tr><td>SNPs (From GBS and WGS)</td><td>340 varieties (landraces, fiber and seed oil hemp, drug-types)</td><td>—</td><td><ul><li>Within Iranian cannabis: PCA (2 clusters) and Fine Structure (19 clusters with some geographic partitioning)</li><li>AMOVA (on 2 & 19 clusters): within populations (93–96 % variation) and only ~1 % between populations</li><li>Three major genetic groups identified: hemp, narrow-leaflet drug-types (NLDT), broad-leaflet drug-types (BLDT)</li><li>Genetic diversity higher in NLDT than in BLDT</li><li>There is evidence of hybridization across groups</li></ul></td><td>(Lynch et al., 2016)</td></tr><tr><td>8.5 million SNPs (from WGS)</td><td>190 samples: Feralized populations, domesticated accessions (fiber-type and drug-type)</td><td>USA, other worldwide collections (Europe, Asia)</td><td><ul><li>PCA and Admixture: clear sub-population structure (e.g. European, Asian, US feral, and Marijuana groups).</li><li>“Asian” group presented higher proportion of segregating sites → “out of Asia” origin</li><li>Pairwise F<sub>ST</sub> analysis: distant clustering of US feral samples from all other samples (particularly with Marijuana samples F<sub>ST</sub> = 0.116).</li><li>Identification of loci under balancing and positive selection</li></ul></td><td>(Woods et al., 2023)</td></tr><tr><td>12 million SNPs (from WGS)</td><td>110 accessions: feral/wild plants, landraces, cultivars (hemp and drug-type)</td><td>Broad global sampling: fields or stores in Switzerland, China, India, Pakistan, and Peru + germplasm collections (Russia)</td><td><ul><li>PCA and admixture: four well-separated genetic groups → feral/landrace, Hemp-type cultivars, Drug-type cultivars, and a group of drug-type feral</li><li>Phylogenetic + demographic inference: single domestication origin in East Asia in early Neolithic times</li><li>All modern hemp and drug cultivars descend from the ancestral gene pool of basal feral/landrace accessions.</li><li>Selection signatures (e.g. F<sub>ST</sub>, π-ratio): some candidate genes under divergent selection in hemp vs. drug lines</li></ul></td><td>(Ren et al., 2021)</td></tr></tbody></table>

RAPD (Random Amplified Polymorphic DNA), AFLP (Amplified Fragment Length Polymorphism), ISSR (Inter Simple Sequence Repeat), SCAR (Sequence Characterized Amplified Region), SSR (Simple Sequence Repeat), SNP (Single Nucleotide Polymorphism), AMOVA (Analysis of Molecular Variance), PCA (Principal component analysis), PCoA (principal coordinate analysis), N<sub>a</sub> (number of alleles), N<sub>e</sub> (Number of effective alleles), N<sub>p</sub> (number of private alleles), I (Shannon Index), H<sub>e</sub> (expected heterozygosity), F<sub>st</sub> (fixation index), F<sub>is</sub> (inbreeding coefficient), UPGMA (unweighted pair group method with arithmetic average).

(2021) validated 20 of these SSRs across 104 cannabis individuals from 11 accessions, demonstrating their highly polymorphism and ability to distinguish accessions based on their geographical origin.

Several studies have applied previously developed SSRs to different cannabis populations tailoring their use to specific research objectives and contexts (Benkirane et al., 2024; Rode et al., 2005; Schwabe et al., 2021). In some cases, researchers have even combined SSRs from different studies to assemble optimized panels for detecting genetic variability without incurring additional marker development costs (De De Oliveira Pereira Ribeiro et al., 2020; Dufresnes et al., 2017; Houston et al., 2017; Howard et al., 2009; Marcińska et al., 2022; Presinszka et al., 2015; Vyhnánek et al., 2020). The widespread adoption of SSRs across these studies highlights their robustness, reliability, and effectiveness in genetic diversity research.

Inter simple sequence repeat (ISSR) markers were introduced as rapid and cost-effective alternatives to SSRs, although they are dominant rather than codominant markers. ISSRs amplify regions between microsatellites and do not require prior sequencing. These markers have been employed in several studies to investigate the genetic diversity of Cannabis (Alsaleh and Yılmaz, 2025; Hakki et al., 2007; Khatak et al., 2016; Rahimi et al., 2019; Shams et al., 2020). For instance, Shams et al. (2020) used ISSR markers to study the genetic structure of 10 Iranian cannabis populations, revealing six distinct clusters and suggesting the presence of populations with shared genetic backgrounds. More recently, Alsaleh and Yılmaz (2024) selected 20 ISSR markers in a study of Turkish and foreign cannabis accessions, observing clear genetic differentiation by sex and significant diversity within female Turkey

plants (Key diversity metrics included an average number of alleles per locus of 1.507, Shannon's Index of 0.258, and 74.91 % of polymorphic loci).

## 1.6. SNP and sequencing-based methods

Since the first sequencing of the *Cannabis* genome in 2011, numerous studies have further refined genome assemblies and annotations (Braich et al., 2020; Gao et al., 2020; Grassa et al., 2021; Laverty et al., 2019). These advancements have enabled more detailed investigations into the genetic distinctions between high-THC and high-CBD varieties, while also providing deeper insights into the global genetic diversity of cultivated *Cannabis* strains.

Whole genome sequencing (WGS) is a powerful tool for investigating genetic variability, as it enables comprehensive genome-wide comparisons between individuals or populations. This approach provides high-resolution insights into genetic variations, particularly through the identification of single nucleotide polymorphisms (SNPs), which are single-base changes that serve as highly informative genetic markers. Despite their high accuracy, SNP-based analyses were historically limited by high costs and the extensive bioinformatic resources required to process and interpret large datasets. However, with recent advancements in sequencing technologies and data analysis, WGS has become more accessible; and as a result, it is increasingly being employed in genetic diversity studies of *Cannabis sativa*. Using WGS, Busta et al. (2022) identified 9.3 million high-quality SNPs in feral U.S. populations from Nebraska, revealing their genetic affinity to fiber-type and basal
lineages, with little geographic structure but notable chemotypic vari-
ability. Similarly, Chen et al. (2022) detected 22.98 million SNPs across
wild and cultivated Chinese accessions, classifying them into five ge-
netic groups shaped by ecotype and geographic origin. These findings
highlight the power of WGS to uncover fine-scale genetic variation,
clarify population structure, and reveal evolutionary patterns in
*Cannabis sativa* L.

Genotyping by sequencing (GBS) offers a cost-effective alternative to
whole genome sequencing (WGS) for studying genetic diversity by tar-
geting specific genomic regions through the use of restriction enzymes.
This approach has been widely adopted to characterize the genetic di-
versity of cannabis across various geographic regions. A landmark study
by Sawler et al. (2015) applied GBS to 81 marijuana and 43 industrial
samples, detecting 14 031 SNPs. Their findings confirmed that drug- and
fiber-types represent genetically distinct gene pools, with differences
distributed throughout the genome rather than being confined to THC
biosynthesis-related genes. Soorni et al. (2017) analyzed 98 *Cannabis
sativa* samples, including 36 from various regions of Iran and 26 from
germplasm collections. They identified 24 710 high-quality SNPs, and
revealed a genetic structure composed of 19 populations, suggesting
partial geographic structuring within Iranian cannabis populations.
More recently, Dehnavi et al. (2025) applied GBS to a natural collection
of *Cannabis sativa* in Iran, sequencing 228 individuals from 35 pop-
ulations across diverse climatic zones. Their study identified 23 266
high-quality SNPs and delineated five distinct genetic clusters. Notably,
some Iranian populations exhibited genetic similarity to marijuana va-
ieties, while others clustered with fiber-type, highlighting the region's
extensive cannabis diversity. Similar studies have also been conducted
in Canada and America. De Ronne et al. (2024) studied 176 drug-type
cannabis accessions from the Canadian legal market, identifying
approximately 800 000 genetic variants, revealing key elements of ge-
netic diversity within commercialized varieties. Meanwhile, Aina et al.
(2025) genotyped 760 feral individuals from 12 U.S. states, identifying
five distinct genetic groups and notable variations in cannabinoid pro-
files (78 % samples high in CBD, 6 % High in THC, 15 % intermediate),
suggesting patterns of regional adaptation. Collectively, these studies
illustrate the value of GBS as a powerful tool for exploring genetic di-
versity, population structure, and evolutionary dynamics within
*Cannabis sativa*.

1.7. *Organelle DNA markers*

Organelle DNA, specifically mitochondrial and chloroplast genomes,
has also been used to develop molecular markers for exploring Cannabis
genetic diversity (Di nunzio et al., 2024; Gilmore et al., 2007; Houston
et al., 2018; Multi-Ullah et al., 2023; Roman et al., 2019). For example,
Roman et al. (2019) identified 58 polymorphisms and seven variable
regions within *Cannabis sativa* chloroplast DNA. Their analysis of
*rpl32-trnL* and *trnS-trnG* regions in fiber- and drug-type samples from
United States, Canada, Mexico, and Chile revealed genetic differentia-
tion linked to both crop type and biogeographic origin. Similarly, Mul-
ti-Ullah et al. (2023) also used 3 chloroplast markers (*rbcL*, *matK*, and
*trnH-psbA*) as DNA barcodes to examine the genetic variability of
cannabis populations in Pakistan. While their results revealed relatively
high degree of genetic homogeneity among Pakistani samples, notable
differences were observed when compared to sequences from other re-
gions of the world. More recently, Di nunzio et al. (2024) used 9 chlo-
roplast SNPs to identify 4 different haplotypes among 122 Cannabis
samples, further highlighting the importance of organelle markers in
differentiating populations based on their geographical origin and crop
type.

In summary, the evolution of molecular markers has significantly
refined the analyses of genetic diversity in cannabis, moving from early
dominant and less reproducible markers to more robust and precise
tools. The adoption of SNP-based analyses and high-throughput
sequencing have greatly enhanced the resolution of genetic analyses,

enabling deeper insights into cannabis variability. It is important to
note, however, that molecular studies may report different diversity
patterns, as reflected in Table 3, depending on the type of markers used,
genome coverage, sample size, and the geographic origin of accessions.
Analytical and computational approaches, such as the choice of popu-
lation structure or clustering methods, can further influence the inter-
pretation of genetic diversity. These factors explain why results can
differ across studies, while collectively providing complementary in-
sights into the genomic variation of the species. Such diversity has
important implications for practical applications in varietal selection,
genetic resource conservation, and forensic identification.

## 2. Implications of genetic diversity studies

The available molecular studies on cannabis genetic diversity have
significant implications across various fields, depending on the specific
context and findings of each investigation. This section will explore the
broad applicability of cannabis genetic diversity, offering key insights
for the development of improved cultivars, forensic investigations,
biodiversity conservation, domestication history, and taxonomy.

2.1. *Genetic improvement and cultivar development*

Research into genetic diversity enables the identification and char-
acterization of available genetic resources, essential to the development
of new cannabis varieties and overall genetic improvement. Character-
ized germplasm can be leveraged to breed varieties tailored for specific
needs, such as adaptation to climate change, disease resistance, or
enhanced yields across multiple industries. For instance, breeding ef-
forts have targeted the development of CBD-rich varieties for pharma-
ceutical and medical applications, fiber-rich varieties for the textile and
rope industries, and seed varieties for seed oil production. Several
studies of genetic variability have highlighted the importance of intra-
population and intra-individual variability as key contributors to plant
improvement efforts (Alsaleh and Yılmaz, 2025; Benkirane et al., 2024;
Datwyler and Weiblen, 2006; Soler et al., 2017).

Additionally, several studies on genetic diversity have identified
highly heritable traits, highlighting their potential for effective genetic
selection. Indeed, heritability gives insights into the extent to which
phenotypic variation is determined by genetic factors. Petit et al. (2020)
reported strong broad-sense heritability for traits related to bast fiber
content, flowering time, and cell wall composition, with weak genotype
× environment (G×E) interactions. Similarly, Babaei et al. (2024) found
a high heritability for almost all the morphological and phenological
studied traits, helping valuable direction for breeding strategies. Ad-
vancements in plant improvement and genetic selection are increasingly
facilitated by molecular marker-assisted selection (MAS) and
Genome-wide association studies (GWAS). GWAS is used to identify
genetic markers linked to important phenotypic traits, which can sub-
sequently be applied in MAS to guide targeted breeding and accelerate
the genetic improvement process. These advances are fundamentally
rooted in genetic diversity studies. For example, Chen et al. (2022)
identified genes involved in key agronomic traits such as flowering, seed
germination, seed size, embryogenesis, growth, and stress responses,
highlighting promising targets for breeding initiatives. Notably, their
study highlighted the role of Flowering locus T-like (FT-like) genes in
*Cannabis* adaptation to varying photoperiods, a key factor in tailoring
cultivars suited to diverse environments and industrial needs. Further
supporting this, De Ronne et al. (2024) identified 18 molecular markers
significantly associated with key agronomic and morphological traits,
some demonstrating strong phenotypic effects and showing notable
linkage disequilibrium with candidate genes. More recently, Dehnavi
et al. (2025) uncovered novel loci associated with yield and chemotype
traits, providing valuable insights for future *Cannabis sativa* improve-
ment programs.

Another critical aspect extensively explored in cannabis genetic
diversity studies is “sexual polymorphism”. *Cannabis sativa* L. possess a diploid genome (2 n = 20) with nine autosomes and one pair of sex chromosomes (XX in females, XY in males). The haploid genome size has been estimated at approximately 818 Mb in females and 843 Mb in males, with the Y chromosome being larger (Braich et al., 2020). Since cannabinoid production in female inflorescences decreases following fertilization, it is critical for farmers to detect and remove male plants early to prevent pollination and ensure optimal cannabinoid yields. This approach aims to identify male plants during the vegetative phase to help reduce production costs, while maximizing the yield of therapeutic or recreational cannabinoids. Several molecular approaches have been employed to facilitate early sex determination. Using RAPD primers, Shao et al. (2003) identified three female-specific polymorphic DNA fragments (870 bp and 1160 bp amplified by primer OPA-04, and 1680 bp by primer OPF-05), which were absent in male genomes. Additionally, Rode et al. (2005) detected three alleles across two SSR markers (out of 15 tested), revealing polymorphism not only between X and Y chromosomes but also among different X chromosomes. Rahimi et al. (2019) applied ISSR and SCAR (Sequence Characterized Amplified Regions) markers, and found that 10 ISSR bands exhibited a significant relationship with plant sex. The SCAR marker “MADC5”, in particular, demonstrated strong correlation, suggesting its utility in sex identification. In a similar study, Borin et al. (2021) selected two SSR markers that successfully discriminated cannabis accessions by sex and were linked to the SCAR119 locus involved in sex determination. More recently, Alsaleh and Yılmaz (2024) investigated Turkish cannabis accessions using 20 ISSR primers. They identified significant genetic differentiation by sex and selected a 785-bp fragment (ISSR880bp785) as a male-specific molecular marker. These studies demonstrate the growing potential of sex-linked polymorphisms for early sex identification in *Cannabis sativa*, ultimately enhancing breeding efficiency, through optimized crosses and reduced labor and cultivation costs. They are also valuable for commercial cultivation, enabling more effective optimization of cannabinoid production.

## 2.2. Forensic applications

Genetic diversity studies play a pivotal role in forensic science, particularly in combating the illicit cannabis trade. Several studies have focused on developing rapid identification kits and effective molecular markers to differentiate cannabis from other species and even to distinguish between drug-type and fiber-type varieties.

Notably, Hsieh et al. (2003) developed a test based on a specific SSR marker (NMI01), capable of exclusively identifying cannabis species among 20 plant species, including closely related taxa, such as *Humulus japonicus* and *Nicotiana tabacum*. This high level of specificity has important forensic and regulatory implications: a reliable DNA-based assay enables the authentication of seized plant material, supports species-level traceability in legal supply chains, and assists authorities in distinguishing cannabis from morphologically similar non-controlled species. Such tools therefore contribute directly to strengthening forensic frameworks and ensuring consistent legal regulation of cannabis materials.

Several studies have since employed SSR markers to reliably differentiate between fiber- and drug-type cannabis. For example, Schwabe et al. (2021) applied 10 SSR markers across 49 samples, revealing clear differentiation between both types. Soler et al. (2016) established a fast and efficient forensic method using 11 polymorphic SSRs and *Cannabis* embryo DNA, bypassing the need for leaf tissue. Similarly, Dufresnes et al. (2017) selected a panel of 13 SSR markers, and demonstrated strong genetic structure between drug and fiber types, enabling confident classification of unknown samples. Houston et al. (2017), (2015) further refined a 13-locus SSR multiplex system, achieving high quality standards comparable to conventional short tandem repeat (STR) systems used in human forensic identification. Beyond SSRs, other molecular markers, including RAPDs (Piluzza et al., 2013), AFLPs (Datwyler

and Weiblen, 2006), ISSRs (Hakki et al., 2007), SNPs (Henry et al., 2020), and chloroplast SNPs (Di nunzio et al., 2024) have also been employed to differentiate between cannabis types.

In parallel, other genetic diversity studies have effectively discriminated seized cannabis samples by origin using various molecular markers (De De Oliveira Pereira Ribeiro et al., 2020; Pinarkara et al., 2009), enabling traceability in forensic contexts. Köhnemann et al. (2012) combined 15 STR markers in a single multiplex PCR assay to trace drug distribution networks and link cannabis plants to crime scenes. Indeed, many cannabis growers use clonal propagation (stem cuttings) from selected individuals to produce plants with high THC content. Since the mother plant and its clones share identical genetic profiles, DNA analysis can trace plant material back to a common source, aiding law enforcement in drug eradication efforts (Dufresnes et al., 2017; Howard et al., 2009). In this context, Alghanim and Almirall (2003) developed 11 STR markers suitable for genetic parentage analysis, with forensic applications in tracking clonal marijuana material.

All the aforementioned studies underscore the significant contribution of genetic diversity research in developing effective tools for cannabis identification and traceability, reinforcing its value within forensic frameworks.

## 2.3. Genetic resource conservation and germplasm management

The study of genetic diversity is crucial for the effective management of cannabis collections and the implementation of conservation strategies. Several studies highlighted the critical role of landraces, feral, and wild populations as a reservoir of genetic diversity, either using phenotypic or molecular markers (Aina et al., 2025; Babaei et al., 2024; Benkirane et al., 2024). These populations offer valuable genetic resources that can be harnessed to improve cannabis varieties adapted to changing environmental conditions. Landraces are domesticated cannabis plants that have adapted over centuries to specific geographic regions through natural selection and traditional human cultivation. Feral cannabis consists of escaped domesticated plants that have naturalized in the wild but originate from cultivated ancestors. True wild cannabis is thought to be naturally occurring and never subjected to domestication. However, it is believed that most “wild” cannabis today is actually “feral” due to extensive human influence (Small, 2015), and the terms “feral” and “wild” are used interchangeably in most cases.

In a study, Zhang et al. (2020) demonstrated that wild cannabis accessions possess greater genetic diversity than local cultivars, emphasizing the need for their conservation. Indeed, selective breeding practices can significantly reduce genetic diversity. This fact is even more obvious with the shift toward modern standardized cultivars, that often marginalize traditional locally adapted populations. Genetic studies play a key role in assessing diversity over time, and in designing cost-effective conservation strategies. For example, Benkirane et al. (2024) assessed cannabis genetic diversity in Morocco and advocated for the implementation of conservation strategies to protect the local Beldia population from genetic erosion due to the widespread introduction of the modern Critical variety. Unfortunately, there are currently no large public seed or clonal gene banks for drug-type cannabis due to legal issues (Torkamaneh and Jones, 2022). In this context, Welling et al. (2016) surveyed previously reported *ex situ* collections (research-institute holdings, private germplasm repositories), summarizing their size, origin, and limitations. Interestingly, the authors highlighted that many collections are incomplete, biased toward certain regions (e.g. Europe), and under-characterized; they proposed the creation of a virtual core collection as an innovative alternative approach for cannabis germplasm conservation. In addition, safeguarding underrepresented cannabis genetic resources requires complementary, practical conservation actions. Priority should be given to documenting and preserving traditional landraces through on-farm seed maintenance, controlled regeneration to prevent introgressions from commercial hybrids, and targeted sampling in regions with high but threatened diversity. At the
*ex-situ* level, long-term seed storage, cryopreservation, and *in vitro* maintenance can help secure unique germplasm until broader legal frameworks permit structured gene-banking and exchange through international collaborative networks. Integrating these measures with genomic characterization would ensure that vulnerable genetic resources are effectively captured and protected.

Furthermore, genetic structure analyses from different studies, such as Soler et al. (2017) and Schwabe et al. (2021), offer valuable insights for managing cannabis germplasm collections. These analyses help identify and remove redundant accessions (genetically identical or highly similar individuals) often resulting from clonal propagation or closely related varieties. This prevents unnecessary duplication, optimizes the efficiency of germplasm collections, and ensures the preservation of a broad and representative genetic base. Such streamlined collections are more valuable for breeding research purposes and long-term conservation efforts.

## 2.4. Phylogeny and domestication history

Genetic diversity plays a crucial role in understanding the phylogeny and domestication history of Cannabis. Using molecular markers, researchers can generate phylogenetic trees that reconstruct the evolutionary relationships between different accessions (Alsaleh and Yılmaz, 2025; Soler et al., 2017). This helps trace common ancestors, elucidate speciation events, and understand migrations and genetic exchanges occurred between populations (Faeti et al., 1996). Genetic diversity studies also allow for the identification of wild populations closely related to cultivated varieties, which may be the ancestors of domesticated plants. Additionally, they can detect genetic "bottlenecks," resulting from human intervention and thus aiding in the identification of centers of origin and diversification of domesticated cannabis varieties.

For example, Zhang et al. (2018) analyzed sequence variations in five chloroplast DNA (cpDNA) regions across 645 individuals from 52 accessions (25 wild and 27 domesticated populations) and revealed three distinct haplogroups, distributed along a latitudinal gradient (high-middle-low), challenging the widely accepted hypothesis of Central Asian origin. Instead, their results suggest a multiregional origin related to low-latitude regions, as the low-latitude haplogroup represents the earliest diverging lineage. Using whole-genome sequencing (WGS) studies, some researchers supported a single domestication origin in East Asia (Ren et al., 2021), while others advocate a broader "out of Asia" origin (Woods et al., 2023). These conflicting results highlight the unresolved nature of Cannabis' evolutionary origins and underscore the need for further investigation. Another example of the usefulness of genetic diversity analysis is the study of Lynch et al. (2016), who studied the nuclear genomic diversity across 340 cannabis varieties, identifying three major genetic groups (hemp, narrow leaflet drug-types (NLDTs) and broad leaflet drug-types (BLDTs)). Their results suggested a more recent domestication of BLDTs, as indicated by their lower diversity and narrower geographic distribution, compared to NLDTs. Furthermore, Borin et al. (2021), through studying the genetic diversity of cannabis accessions from different European regions, challenged the hypothesis that the Italo-Hungarian group evolved from the Northern European group, and rather encouraged the idea of independent introduction of cannabis into Northern and Southern Europe at different times. Collectively, these studies demonstrate the significance of genetic diversity studies in unravelling the evolutionary history and domestication pathways of Cannabis.

## 2.5. Taxonomic classification and genetic consistency

Genetic diversity studies have also helped in evaluating the validity of the traditional classification of the Cannabis genus, although this topic remains a subject of ongoing debate. Several recent studies support the monotypic classification of cannabis (Barcaccia et al., 2020; Lynch et al.,

2016; Zhang et al., 2018). For example, comparative analyses of five plant barcoding regions show that *C. sativa* and *C. indica* differ by only 0.41 %, a divergence level characteristic of subspecies or varieties rather than distinct species (McPartland and Guy, 2014). This low molecular differentiation falls well below species-level thresholds, supporting the interpretation of *Cannabis* as a single species exhibiting subspecific variation. Consistent findings emerge from another study. Zhang et al. (2018), using five chloroplast regions in 645 individuals from 53 accessions, identified 3 distinct haplogroups, but found no evidence of reproductive isolation among them. They likewise concluded that Cannabis is monotypic and best classified as *C. sativa*, with differentiation more appropriately structured into the subspecies: *sativa*, *indica*, and *ruderalis*. Together, these molecular data underpin the widespread adoption of a single-species concept, with subspecies divisions reflecting domestication, ecological adaptation, and breeding history rather than speciation.

Additionally, genetic diversity studies contribute to assessing the consistency of nomenclatures used in practice. Several investigations have shown that the vernacular classification used by breeders and sellers is not always reliable. Schwabe and Mcglaughlin (2019) and Schwabe et al. (2021) demonstrated that there is no clear distinction between the categories Sativa, Hybrid, and Indica in retail marijuana samples. Furthermore, other diversity studies have highlighted genetic inconsistencies among cannabis varieties, underscoring issues of mislabeling and the informal naming practices prevalent in the industry. Indeed, some cannabis strains with different names could found to be genetically similar, while others with identical names could exhibit significant genetic differences as noted by several studies (Dufresnes et al., 2017; Henry et al., 2020; Johnson and Wallace, 2021; McCormick et al., 2024; Punja et al., 2017). For instance, Henry et al. (2020) identified cases of mislabeled accessions with identical multi-locus genotypes, such as "Atomical Haze" with "Tangerine Dream," "UK Cheese" with "Jamaican Ten Speed," and "Original Amnesia" with "Sour Tangie," revealing widespread inconsistencies in strain identification and emphasize the need for standardized naming conventions based on genetic evidence. To address this issue, we recommend (i) developing reference genomic panels representing the major chemotype-genotype groups; (ii) implementing standardized DNA-based barcoding tools for cultivar authentication; (iii) integrating chemical profiling with genomic clustering to create stable cultivar categories; and (iv) encouraging regulatory agencies to adopt genetic certification schemes similar to those used in other crop species. These measures offer practical steps toward a harmonized and scientifically robust classification system.

## 3. Factors influencing the genetic diversity of cannabis

The genetic diversity of cannabis arises from multiple factors framed within basic population genetics principles. In an idealized population at Hardy-Weinberg equilibrium, allele and genotype frequencies remain constant over generations in the absence of evolutionary forces. However, natural and anthropogenic factors disrupt this equilibrium, generating genetic variation and shaping population structure. Natural factors influence genetic diversity without human intervention, including natural selection, spontaneous mutations, and reproductive patterns. Anthropogenic factors, on the other hand, are driven by human actions, such as hybridization, artificial selection, induced mutations, and regulations governing cannabis cultivation. By examining how these evolutionary forces shape allele frequencies heterozygosity, and population differentiation, we can develop a mechanistic understanding of the diversity patterns observed in cannabis. This section will explore and discuss these factors to better understand their impact on cannabis diversity.

### 3.1. Natural and artificial selection

Natural selection is an important concept for understanding the
evolution of populations in a given environment. It favors individuals with traits that improve survival and reproductive success, such as cold tolerance in mountainous regions or early flowering in high latitude populations. Natural selection promotes individuals with specific traits that enhance their survival and reproduction in a specific environment. As a result, individuals with less favorable traits are gradually eliminated, reducing genetic diversity within a population. However, cannabis has historically thrived in a wide range of habitats and, therefore, has been subjected to various natural selection pressures related to different environmental conditions contributing to its high overall genetic diversity.

Additionally, hybridization has been a major anthropogenic driver of *Cannabis* diversity since domestication. Selective breeding for certain traits has led to the emergence of numerous strains suited to different needs (Small, 2015). Fiber cannabis is selected for its taller, slimmer stem with more primary phloem fiber, medicinal cannabis for its high CBD content, and recreational cannabis for its high THC content. Modern marker-assisted selection has further refined this process by enabling targeted breeding for specific traits. Modern *Cannabis* cultivars are often derived from crosses between *C. sativa* and *C. indica*, enhancing the genetic diversity of the species, but also introduced the risk of genetic homogenization. In addition, excessive hybridization particularly when it leads to the replacement of traditional landraces with commercial hybrids, can result in genetic erosion (Mcopartland and Small, 2020). Morphological differences between wild and domesticated forms confirm human intervention on the species' evolution. For instance, wild *Cannabis* achenes have an elongated attachment base that facilitates disarticulation to promote seed dispersal. This trait is absent in domesticated cannabis to facilitate seed harvesting (Small, 2015).

Genetic studies have enhanced our understanding of *C. sativa*'s evolutionary history. Chen et al. (2022) investigated adaptive selection in *Cannabis sativa* by sequencing the whole genome of wild and cultivated accessions in China. The analysis revealed genes associated with domestication traits, including flowering time, seed germination, and environmental stress responses. These results suggest the dual role of environmental and human-driven selection have shaped the genetic diversity of cannabis, contributing to its adaptation to different ecosystems and cropping systems. Similarly, Woods et al. (2023) identified several loci reflecting natural and artificial selection in feral and domesticated *C. sativa* populations from various origins. In US feral populations, six loci linked to stress response were under balancing selection, while one disease resistance gene showed positive selection, indicating local adaptation. In the marijuana population, a gene responsible for trichome regulation was under positive selection, likely due to artificial selection for higher THC yield. Both Aina et al. (2025) and Woods et al. (2023) identified chromosomal inversions in feral *Cannabis sativa* populations in the U.S., suggesting local adaptations. These studies demonstrate how natural and artificial selection have jointly shaped the genetic structure and adaptive potential of *Cannabis*.

## 3.2. Spontaneous and induced mutations

Genetic mutations in cannabis contribute to the emergence of new phenotypic variants and enhance the overall genetic diversity of the species, as is the case with any other plant species. Spontaneous mutations arise from internal mechanisms such as errors during DNA replication or external factors such as radiation or environmental stress. Induced mutations are artificially generated through physical methods (e.g., X-rays, gamma, or UV irradiation), or chemical agents (e.g., ethyl methane sulfonate). Mutations can be neutral, deleterious, or beneficial, with some providing adaptive advantages, especially in response to environmental stresses.

Somatic mutations are frequently reported in cannabis and are an important source that contribute to its genetic diversity. By sequencing different parts of a single mother plant, researchers found significant intra-plant genetic variation, with a higher accumulation of mutations in

the apical regions, supporting the concept of genetic mosaicism (Adamek et al., 2022). These spontaneous mutations have contributed to genotypes with unique and recognizable traits, such as plants having whorled phyllotaxy with three or more leaves at each node, the "Ducksfoot" strain with webbed leaves, and Australian Bastard Cannabis (ABC) characterized by a succulent shrub-like form (Hesami et al., 2022). Furthermore, Casano (2019) described and isolated a spontaneous chimeric mutation responsible for a variegated foliar phenotype in cannabis, proposing the ornamental cultivar "Divina" based on this trait. In addition, using gamma irradiation-induced mutagenesis on the seeds of a fiber cultivar, Fuochi et al. (2002) successfully developed two new cultivars: "Red Petiole", characterized by anthocyanin-pigmented petioles, and "Yellow Apex", marked by yellow distal leaflets. These studies demonstrate the effect of mutations in increasing the phenotypic and genetic diversity of Cannabis.

In addition, modern biotechnological tools such as genome editing enable the artificial induction of "site-specific" mutations in the cannabis genome. These targeted interventions can alter genes of interest, such as genes involved in cannabinoid biosynthesis, plant morphology, or stress response, thereby generating new genetic variations for varietal improvement and functional genomics research. In this context, the pioneering study by Zhang et al. (2021) represents a major breakthrough: the authors established a stable transformation protocol for hemp (*Cannabis sativa* L.) via *Agrobacterium tumefaciens* and, for the first time, successfully induced targeted mutagenesis using the CRISPR/Cas9 system. By targeting the *CsPDS1* gene, they obtained mutant plantlets exhibiting an albino phenotype, which serves as clear evidence of efficient and stable gene editing. This work paves the way for the controlled modification of the cannabis genome, enabling not only the generation of new genetic diversity but also the functional exploration of genes of interest, such as those involved in cannabinoid biosynthesis. Although the application of these technologies is still limited by technical challenges -such as low regeneration rates and the transmission of mutations to subsequent generations- their potential to enrich the genetic diversity of cannabis, particularly for medicinal or industrial purposes, is considerable.

## 3.3. Gene flow

Gene flow, the movement of genetic material between populations, is a key factor that shapes the genetic diversity of *Cannabis* by maintaining or increasing genetic variability. For instance, the observed gene flow in the study of Zhang et al. (2020) was around 5, suggesting that the studied worldwide cannabis resources experience frequent genetic exchange, effectively mitigating the genetic differentiation resulting from genetic drift. Similarly, Soler et al. (2017) documented a relatively high gene flow (1.736) between both botanical varieties (*sativa* and *indica*). However, more restricted gene flow (below 1) was reported between local Iranian populations (Shams et al., 2020), indicating substantial genetic differentiation between them.

In general, cannabis is predominantly a cross-pollinated plant with male and female flowers on separate plants, which promotes heterozygosity and enhances genetic diversity observed across populations. However, in monoecious varieties, bearing both male and female reproductive organs on the same plant, self-pollination is more common, which limits genetic diversity within these populations. Furthermore, cannabis is a wind-pollinated plant, with lightweight pollen grains capable of traveling significant distances, thereby promoting cross-breeding between varieties (Small and Antle, 2003). Additionally, the movement of seeds, either through human transport or by birds and animals, aids in the dispersal of the species and the colonization of new habitats. However, natural gene flow can be limited by geographic, ecological, or urban barriers, leading to isolated populations which can result in the emergence of genetically distinct populations with unique traits, contributing to localized diversity within the species.
### 3.4. Regulatory laws and policies

The prohibition of cannabis in most countries significantly impacted the genetic diversity of the species. Prior to the 20th-century bans, many countries cultivated regionally adapted landraces tailored to local climates and specific uses. However, the global criminalization of cannabis cultivation has severely contributed to the decline of some landraces and forced clandestine cannabis cultivation, often in controlled environments, where selection was primarily focused on producing THC-rich varieties (Sawler et al., 2015; Swift et al., 2013). This resulted in genetic homogenization, with a few dominant hybrid lineages such as Skunk, Haze, and Kush hybrids becoming dominant. Prohibition also curtailed the international exchange of seeds and germplasm reducing opportunities for gene flow and leading to the isolation of previously diverse populations. Over the past few decades, several countries have legalized cannabis for medicinal and/or recreational purposes (Bachir et al., 2022; Ndlangamandla et al., 2024; Roman et al., 2019). Medical use is now permitted in many countries, particularly in Europe and North America. Recreational use was first fully legalized in Uruguay in 2013, followed by Canada in 2018, and several other countries such as the Netherlands, Malta, and many states of the United States and Australia (Simiyu et al., 2022). At the international level, the United Nations removed Cannabis from Schedule IV, its most restrictive classification, in 2020, while it remains controlled under Schedule I. Despite this liberalization, the legal cannabis industry has increasingly been structured around a few standardized strains, mainly modern hybrids created to meet consumer demand (Torkamaneh and Jones, 2022). The focus on specific cannabinoid profiles, such as high THC or CBD content, has marginalized strains containing other bioactive compounds (e.g., CBG, CBC, THCV), thereby limiting the biochemical diversity of cannabis. Moreover, many traditional strains have been supplanted by commercial hybrids optimized for greenhouse or indoor production. Finally, the widespread use of clonal propagation has contributed to further homogenization of cultivated plants, reducing natural genetic mixing. This was confirmed by Henry et al. (2020), who identified 361 multi-locus genotypes representing putative clonal lines in 420 tested samples, and by Howard et al. (2009), who reported genotype sharing in 55 % of drug-type accessions. Similarly, Dufresnes et al. (2017) observed limited genetic diversity among drug-type varieties, further reflecting the use of clonal reproduction.

In summary, these regulatory and market-driven dynamics fueled the evolutionary trajectory of cannabis to generate its variability and plasticity over time.

### 4. Conclusion and future directions

Cannabis exhibits extensive genetic diversity. The analysis of phenotypic traits and molecular markers across a broad range of studies, revealed significant variations between cannabis cultivars, reflecting both natural evolutionary processes and human-breeding efforts. Integrating molecular and phenotypic datasets provides a comprehensive framework for understanding cannabis diversity and enhancing its applied potential for advancing breeding programs, preserving genetic resources, and improving desirable agronomic and biochemical traits. While molecular markers reveal genetic variation, population structure, and allelic richness, phenotypic data capture trait expression under specific environmental conditions, including agronomic performance and phytochemical profiles. Combining these datasets enables the efficient identification of superior genotypes, supports marker-assisted breeding, and informs conservation strategies by guiding the development of core collections that capture both genetic variation and functional diversity, ensuring the preservation of key adaptive traits for future use. Importantly, linking molecular variation to phenotypic traits across environments can reveal genotype × environment interactions, which are critical for developing stable cultivars. However, studies explicitly addressing these interactions in cannabis remain limited,

representing a key knowledge gap that should be prioritized in future research to fully exploit the crop's genetic resources.

In addition, subsequent investigations on cannabis genetic diversity should focus on several priorities to guide research and conservation efforts. First, special attention should be given to expanding geographic representation and exploring underrepresented populations, particularly those from Africa where limited studies have been conducted. This scarcity of published genotyping studies on African germplasm affects the global understanding of *Cannabis sativa* diversity. In fact, current reconstructions of population structure and phylogeny rely predominantly on European, Asian, and North American datasets, which may obscure unique African lineages, misestimate gene flow patterns, or underrepresent adaptive variation shaped by African agroecologies. The lack of African data also limits the completeness of global conservation strategies, as uncharacterized local landraces may harbor valuable genetic resources for breeding, stress tolerance, or phytochemical traits. Addressing this gap will require coordinated sampling strategies, collaborative networks linking African and international research institutions, and the systematic integration of African accessions into publicly accessible genomic repositories to achieve full worldwide representation.

Second, expanding the use of whole-genome sequencing and genome-wide association studies (GWAS), will deepen our understanding of the genetic architecture underlying key traits, such as cannabinoid and terpene biosynthesis, stress tolerance, and yield potential. Additionally, integrating multi-omics approaches (including transcriptomics, metabolomics, and epigenomics) with genetic analyses can provide a deeper understanding of how genes control traits in cannabis. Such an approach can elucidate underlying regulatory networks, connect genetic variation to phenotypic expression, and pinpoint key biological pathways relevant to plant for breeding or to biotechnological research. Integrating these data streams constitutes an important future direction for advancing our understanding of trait architecture and supporting the development of improved cannabis varieties. Future research should also explore the potential of gene editing technologies, which remain scarcely applied in cannabis, to accelerate trait improvement and functional genomics studies.

Furthermore, the need for standardized marker sets and harmonized protocols to permit cross-study comparability is increasingly evident. Although methodological innovation in cannabis genetics is advancing, the absence of common marker panels and unified laboratory and bioinformatic workflows remains a major research gap. The use of heterogeneous marker types, primer sets, sequencing depths, and data-processing pipelines generates results that are often difficult to compare across studies. Such variability complicates the distinction between genuine biological patterns and methodological artefacts, thereby limiting the construction of a cumulative and interoperable understanding of cannabis genetic diversity. Establishing consensus SSR and SNP panels, together with harmonized genotyping and analytical procedures, would not constrain innovation; rather, it would provide a robust framework for integrating data across studies while allowing novel technologies to be incorporated progressively.

Lastly, conservation efforts should also be reinforced to protect landraces and wild populations from genetic erosion, particularly in regions where traditional cannabis cultivation is threatened by modernization, regulatory shifts, or the expansion of commercial hybrid varieties. Effective measures may include the establishment of well-curated *ex situ* collections (e.g., seed banks and cryopreserved tissues), alongside the implementation of on-farm conservation programs that support local farmers in maintaining traditional germplasm.

### CRediT authorship contribution statement

**Hana SERGHINI-CAID:** Writing – review & editing, Visualization.
**Alicia AYERDI GOTOR:** Writing – review & editing. **Malika ABID:** Writing – review & editing, Supervision, Conceptualization. **Ahmed**
**ELAMRANI:** Writing – review & editing. **Chaymae BENKIRANE:** Writing – original draft, Investigation, Conceptualization. **Larbi RHAZI:** Writing – review & editing. **Farid MANSOURI:** Writing – review & editing, Investigation, Conceptualization.

## Funding sources

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data Availability

No data was used for the research described in the article.

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