Genetic Insights into Agronomic and Morphological Traits of Drug-Type Cannabis Revealed by Genome-Wide Association Studies
Bibliographic record
Abstract
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 underdeveloped and lacking knowledge about cannabis genetics and trait inheritance. To bridge this gap, we conducted a comprehensive genome-wide association study (GWAS), using a panel of 176 drug-type cannabis accessions, curated to represent the Canadian legal market. This pioneering GWAS harnessed the power of high-density genotyping-by-sequencing (HD-GBS), resulting in an exhaustive catalog of 800K genetic variants. These variants served as the bedrock for a GWAS designed to dissect the genetic foundations of nine key traits. To identify the most robust markers associated with these traits, two sophisticated statistical methodologies were used (SUPER and BLINK), ultimately identifying 33 markers significantly associated with agronomic and morphological traits. Several identified markers exert a substantial phenotypic impact, guided us to a rich trove of putative candidate genes that reside in high linkage-disequilibrium (LD) with the markers. These markers show great promise for revolutionizing cannabis breeding to meet diverse needs. In doing so, they lay the solid foundation for an innovative cannabis industry poised to reshape the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".