Genetic insights into agronomic and morphological traits of drug-type cannabis revealed by genome-wide association studies
Bibliographic record
Abstract
Cannabis sativa L., previously concealed by prohibition, is now a versatile and promising plant, thanks to recent legalization, opening doors for medical research and industry growth. However, years of prohibition have left the Cannabis research community lagging behind in understanding Cannabis genetics and trait inheritance compared to other major crops. To address this gap, we conducted a comprehensive genome-wide association study (GWAS) of nine key agronomic and morphological traits, using a panel of 176 drug-type Cannabis accessions from the Canadian legal market. Utilizing high-density genotyping-by-sequencing (HD-GBS), we successfully generated dense genotyping data in Cannabis, resulting in a catalog of 800 K genetic variants, of which 282 K common variants were retained for GWAS analysis. Through GWAS analysis, we identified 18 markers significantly associated with agronomic and morphological traits. Several identified markers exert a substantial phenotypic impact, guided us to putative candidate genes that reside in high linkage-disequilibrium (LD) with the markers. These findings lay a solid foundation for an innovative cannabis research, leveraging genetic markers to inform breeding programs aimed at meeting diverse needs in the industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".