A single nucleotide polymorphism assay sheds light on the extent and distribution of genetic diversity, population structure and functional basis of key traits in cultivated north American cannabis
Bibliographic record
Abstract
BACKGROUND: The taxonomic classification of Cannabis genus has been delineated through three main types: sativa (tall and less branched plant with long and narrow leaves), indica (short and highly branched plant with broader leaves) and ruderalis (heirloom type with short stature, less branching and small thick leaves). While still under discussion, particularly whether the genus is polytypic or monotypic, this broad classification reflects putative geographical origins of each group and putative chemotype and pharmacologic effect. METHODS: Here we describe a thorough investigation of cannabis accessions using a set of 23 highly informative and polymorphic SNP (Single Nucleotide Polymorphism) markers associated with important traits such as cannabinoid and terpenoid expression as well as fibre and resin production. The assay offers insight into cannabis population structure, phylogenetic relationship, population genetics and correlation to secondary metabolite concentrations. We demonstrate the utility of the assay for rapid, repeatable and cost-efficient genotyping of commercial and industrial cannabis accessions for use in product traceability, breeding programs, regulatory compliance and consumer education. RESULTS: We identified 5 clusters in the sample set, including industrial hemp (K5) and resin hemp, which likely underwent a bottleneck to stabilize cannabidiolic acid (CBDA) accumulation (K2, Type II & III). Tetrahydrocannabinolic acid (THCA) resin (Type I) makes up the other three clusters with terpinolene (K4 - colloquial "sativa" or "Narrow Leaflet Drug" (NLD), myrcene/pinene (K1) and myrcene/limonene/linalool (K3 - colloquial "indica", "Broad Leaflet Drug" (BLD), which also putatively harbour an active version of the cannabichrometic acid Synthase gene (CBCAS). CONCLUSION: The final chemical compositions of cannabis products have key traits related to their genetic identities. Our analyses in the context of the NCBI Cannabis sativa Annotation Release 100 allows for hypothesis testing with regards to secondary metabolite production. Genetic markers related to secondary metabolite production will be important in many sectors of the cannabis marketplace. For example, markers related to THC production will be important for adaptable and compliant large-scale seed production under the new US Domestic Hemp Production Program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".