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Despite its cultivation as a source of food, fibre and medicine, and its global status as the most used illicit drug, the genus Cannabis has an inconclusive taxonomic organization and evolutionary history. Drug types of Cannabis (marijuana), which contain high amounts of the psychoactive cannabinoid Δ9-tetrahydrocannabinol (THC), are used for medical purposes and as a recreational drug. Hemp types are grown for the production of seed and fibre, and contain low amounts of THC. Two species or gene pools (C. sativa and C. indica) are widely used in describing the pedigree or appearance of cultivated Cannabis plants. Using 14,031 single-nucleotide polymorphisms (SNPs) genotyped in 81 marijuana and 43 hemp samples, we show that marijuana and hemp are significantly differentiated at a genome-wide level, demonstrating that the distinction between these populations is not limited to genes underlying THC production. We find a moderate correlation between the genetic structure of marijuana strains and their reported C. sativa and C. indica ancestry and show that marijuana strain names often do not reflect a meaningful genetic identity. We also provide evidence that hemp is genetically more similar to C. indica type marijuana than to C. sativa strains.

Cannabis (Cannabis sativa) plants produce and accumulate a terpene-rich resin in glandular trichomes, which are abundant on the surface of the female inflorescence. Bouquets of different monoterpenes and sesquiterpenes are important components of cannabis resin as they define some of the unique organoleptic properties and may also influence medicinal qualities of different cannabis strains and varieties. Transcriptome analysis of trichomes of the cannabis hemp variety 'Finola' revealed sequences of all stages of terpene biosynthesis. Nine cannabis terpene synthases (CsTPS) were identified in subfamilies TPS-a and TPS-b. Functional characterization identified mono- and sesqui-TPS, whose products collectively comprise most of the terpenes of 'Finola' resin, including major compounds such as β-myrcene, (E)-β-ocimene, (-)-limonene, (+)-α-pinene, β-caryophyllene, and α-humulene. Transcripts associated with terpene biosynthesis are highly expressed in trichomes compared to non-resin producing tissues. Knowledge of the CsTPS gene family may offer opportunities for selection and improvement of terpene profiles of interest in different cannabis strains and varieties.

Cannabis use is decreasing in England and Wales, while demand for cannabis treatment in addiction services continues to rise. This could be partly due to an increased availability of high-potency cannabis. Adults residing in the UK were questioned about their drug use, including three types of cannabis (high potency: skunk; low potency: other grass, resin). Cannabis types were profiled and examined for possible associations between frequency of use and (i) cannabis dependence, (ii) cannabis-related concerns. Frequent use of high-potency cannabis predicted a greater severity of dependence [days of skunk use per month: b = 0.254, 95% confidence interval (CI) 0.161-0.357, p < 0.001] and this effect became stronger as age decreased (b = -0.006, 95% CI -0.010 to -0.002, p = 0.004). By contrast, use of low-potency cannabis was not associated with dependence (days of other grass use per month: b = 0.020, 95% CI -0.029 to 0.070, p = 0.436; days of resin use per month: b = 0.025, 95% CI -0.019 to 0.067, p = 0.245). Frequency of cannabis use (all types) did not predict severity of cannabis-related concerns. High-potency cannabis was clearly distinct from low-potency varieties by its marked effects on memory and paranoia. It also produced the best high, was preferred, and most available. High-potency cannabis use is associated with an increased severity of dependence, especially in young people. Its profile is strongly defined by negative effects (memory, paranoia), but also positive characteristics (best high, preferred type), which may be important when considering clinical or public health interventions focusing on cannabis potency.

Cannabis sativa L. is an annual species cultivated since antiquity for different purposes. While, in the past, hemp inflorescences were considered crop residues, at present, they are regarded as valuable raw materials with different applications, among which extraction of the essential oil (EO) has gained increasing interest in many fields. The aim of the present study is the evaluation of the yield and the chemical composition of the EO obtained by hydrodistillation from eleven hemp genotypes, cultivated in the same location for two consecutive growing seasons. The composition of the EOs was analyzed by GC–MS, and then subjected to multivariate statistical analysis. Sesquiterpenes represented the main class of compounds in all the EOs, both in their hydrocarbon and oxygenated forms, with relative abundances ranging from 47.1 to 78.5%; the only exception was the Felina 32 sample collected in 2019, in which cannabinoids predominated. Cannabinoids were the second most abundant class of compounds, of which cannabidiol was the main one, with relative abundances between 11.8 and 51.5%. The statistical distribution of the samples, performed on the complete chemical composition of the EOs, evidenced a partition based on the year of cultivation, rather than on the genotype, with the exception of Uso-31. Regarding the extraction yield, a significant variation was evidenced among both the genotypes and the years of cultivation.

Cannabis sativa has a complex history reflected in both selection on naturally occurring compounds and historical trade routes among humans. Iran is a rich resource of natural populationswhich hold the promise to characterize historical patterns of population structure and genetic diversity within Cannabis . Recent advances in high-throughput DNA sequencing technologies have dramatically increased our ability to produce information to the point that it is now feasible to inexpensively obtain population level genotype information at a large scale. In the present investigation, we have explored the use of Genotyping-By-Sequencing (GBS) in Iranian cannabis. We genotyped 98 cannabis samples 36 from Iranian locations and 26 accessions from two germplasm collections. In total, 24,710 high-quality Single Nucleotide Polymorphisms (SNP) were identified. Clustering analysis by Principal Component Analysis (PCA) identified two genetic clusters among Iranian populations and fineSTRUCTURE analysis identified 19 populations with some geographic partitioning. We defined Iranian cannabis in two main groups using the results of the PCA and discovered some strong signal to define some locations as population according to fineSTRUCTURE analyses. However, single nucleotide variant analysis uncovered a relatively moderate level of variation among Iranian cannabis.

Cannabis sativa is one of the earliest plants to be domesticated for fiber, food and medicine. Seed from Cannabis grown for industrial purposes during the 18th through 20th centuries have escaped production and established feralized populations across the United States. To maximize the potential of feral Cannabis germplasm, determining the genetic structure and cannabinoid profile is crucial for selection and breeding of new compliant regionally adapted hemp cultivars. To resolve this, a collection of feral Cannabis , comprising 760 plants across twelve US states were sequenced using Genotyping-by-Sequencings (GBS), genotyped at the cannabinoid synthase ( CBDAS) gene, and subject to gas chromatography-mass spectrometry (GC-MS) to assess cannabinoid profiles. Clustering analyses by ADMIXTURE and Principal Component Analysis (PCA) stratified the germplasm into five clusters (Mississippi-River, West North Central-b, West North Central-a, New York, and Indiana). The cannabinoid genotyping assay resolved the feral collections into Type I - B2/B2 (6%), Type II - B2/B1 (15%), and Type III - B1/B1 (78%). Total cannabinoid content ranged from 0.21 to 4.73%. The assessment of genetic diversity, population structure, and cannabinoid profile of the US feral Cannabis collection provides critical information and germplasm resources to develop new and improve existing hemp cultivars.

Background Due to its previously illicit nature, Cannabis sativa had not fully reaped the benefits of recent innovations in genomics and plant sciences. However, Canada’s legalization of C. sativa and products derived from its flower in 2018 triggered significant new demand for robust genotyping tools to assist breeders in meeting consumer demands. Early molecular marker-based research on C. sativa focused on screening for plant sex and chemotype, and more recent research has sought to use molecular markers to target traits of agronomic interest, to study populations and to differentiate between C. sativa cultivars. Results In this study, we have conducted whole genome sequencing of 32 cultivars, mined the sequencing data for SNPs, developed a reduced SNP genotyping panel to discriminate between sequenced cultivars, then validated the 20-SNP panel using DNA from the sequenced cultivars and tested the assays on commercially available dried flower. The assay conversion rate was higher in DNA extracted from fresh plant material than in DNA extracted from dried flower samples. However, called genotypes were internally consistent, highlighting discrepancies between genotypes detected using sequencing data and observed using genotyping assays. The primary contributions of this work are to clearly document the process used to develop minimal SNP genotyping panels, the feasibility of using such panels to differentiate between C. sativa cultivars, and outline improvements and goals for future iterations of PCR-based, minimal SNP panels to enable efficient development genotyping tools to identify and screen C. sativa cultivars. Conclusions Our key recommendations are to increase sampling density to account for intra-cultivar variability; leverage higher read length paired-end short-read technology; conduct in-depth pre- and post-processing of reads, mapping, and variant calling data; integrate trait-associated loci to develop multi-purpose panels; and use iterative approaches for in vitro validation to ensure that only the most discriminant and performant SNPs are retained.

Cannabis sativa L. is divided into three main groups: drug-type, intermediate-type, and fiber-type. The presence of tetrahydrocannabinol (THC) exceeding 0.2–0.3% in drug-type and intermediate Cannabis that utilized for recreational and medicinal purposes renders them illegal due to potential mental health implications. Fiber-type contains high cannabidiol (CBD) and low THC, making it suitable for household use such as textiles and animal feed. Accurate classification is essential to prevent misuse of the plant. High-performance thin-layer chromatography (HPTLC) and ultra-performance liquid chromatography (UPLC), used respectively for the qualitative and quantitative analyses of THC and CBD particularly in female inflorescences, categorized 85 samples of 46 cultivars used in this study into three distinct chemotypes. While chemotype analysis of a very specific organ of the plants accurately identifies Cannabis groups, it requires time-consuming plant development to maturity. Genotype analysis targeting tetrahydrocannabinolic acid synthase (THCAS) and cannabidiolic acid synthase (CBDAS) genes offers a faster alternative for classifying Cannabis types, allowing for sample determination from any part at any developmental stage of the plant. DNA sequencing allowed a phylogenetic analysis based on these genes, classifying all 85 samples of 46 cultivars into the same three groups identified by chemotype analysis. This study is the first to successfully examine the relationship between chemotype and genotype in 85 samples of 46 cultivars. Rapid identification of Cannabis types through genotype analysis lays the groundwork for future development of detection kits.

A rugged handheld sensor for rapid in-field classification of cannabis samples based on their THC content using ultra-compact near-infrared spectrometer technology is presented. The device is designed for use by the Austrian authorities to discriminate between legal and illegal cannabis samples directly at the place of intervention. Hence, the sensor allows direct measurement through commonly encountered transparent plastic packaging made from polypropylene or polyethylene without any sample preparation. The measurement time is below 20 s. Measured spectral data are evaluated using partial least squares discriminant analysis directly on the device’s hardware, eliminating the need for internet connectivity for cloud computing. The classification result is visually indicated directly on the sensor via a colored LED. Validation of the sensor is performed on an independent data set acquired by non-expert users after a short introduction. Despite the challenging setting, the achieved classification accuracy is higher than 80%. Therefore, the handheld sensor has the potential to reduce the number of unnecessarily confiscated legal cannabis samples, which would lead to significant monetary savings for the authorities.

The widespread legalization of Cannabis has opened the industry to using contemporary analytical techniques for chemotype analysis. Chemotypic data has been collected on a large variety of oil profiles inherent to the cultivars that are commercially available. The unknown gene regulation and pharmacokinetics of dozens of cannabinoids offer opportunities of high interest in pharmacology research. Retailers in many medical and recreational jurisdictions are typically required to report chemical concentrations of at least some cannabinoids. Commercial cannabis laboratories have collected large chemotype datasets of diverse Cannabis cultivars. In this work a data set of 17,600 cultivars tested by Steep Hill Inc., is examined using machine learning techniques to interpolate missing chemotype observations and cluster cultivars into groups based on chemotype similarity. The results indicate cultivars cluster based on their chemotypes, and that some imputation methods work better than others at grouping these cultivars based on chemotypic identity. Due to the missing data and to the low signal to noise ratio for some less common cannabinoids, their behavior could not be accurately predicted. These findings have implications for characterizing complex interactions in cannabinoid biosynthesis and improving phenotypical classification of Cannabis cultivars.