Discovery of major QTL and a massive haplotype associated with cannabinoid biosynthesis in drug‐type <i>Cannabis</i>
Bibliographic record
Abstract
Cannabis (Cannabis sativa L.), once sidelined by decades of prohibition, has now gained recognition as a multifaceted and promising plant in both medical research and commercial applications following its recent legalization. This study leverages a genome-wide association study (GWAS) on 174 drug-type Cannabis accessions from the legal Canadian market, focusing on identifying quantitative trait loci (QTL) and candidate genes associated with eleven cannabinoid traits using 282K common single-nucleotide polymorphisms. This approach aims to transform our understanding of Cannabis genetics. We have pinpointed 33 significant markers that significantly influence cannabinoid production, promising to drive the development of Cannabis varieties with specific cannabinoid profiles. Among the notable findings is a massive haplotype of ∼60 Mb on chromosome 7 in Type I (i.e., tetrahydrocannabinol [THC]-dominant) accessions, highlighting a major genetic influence on cannabinoid profiles. These insights offer valuable guidance for Cannabis breeding programs, enabling the use of precise genetic markers to select and refine promising Cannabis varieties. This approach promises to speed up the breeding process, reduce costs significantly compared to traditional methods, and ensure that the resulting Cannabis varieties are optimized for specific medical and recreational needs. This study marks a significant stride toward fully integrating Cannabis into modern agricultural practices and genetic research, paving the way for future innovations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".