Genetic architecture of phenological, morphological, and phytochemical traits in <i>Cannabis</i> landraces
Bibliographic record
Abstract
Abstract Despite its long history of cultivation and diverse applications, Cannabis sativa remains underexplored at the genomic level, particularly in landrace populations that harbor untapped genetic diversity. In this study, we investigated the genetic architecture of 145 Iranian cannabis landrace accessions, including both male and female plants, using 233K common SNPs and genome‐wide association studies. Our analysis revealed three genetically distinct subpopulations shaped by geography, climate, and traditional cultivation practices. We identified 91 significant genomic regions associated with 40 phenological, morphological, and phytochemical traits, including 15 key loci with pleiotropic effects linked to multiple traits, including flowering time, plant architecture, biomass accumulation, and cannabinoid biosynthesis. These findings highlight the complex interplay between developmental and metabolic pathways in cannabis. The high heritability of most traits and rapid linkage disequilibrium decay underscore the potential of these landraces for high‐resolution mapping and genetic improvement. This work provides a valuable genomic resource for marker‐assisted selection, supporting the development of improved cultivars with tailored cannabinoid profiles and agronomic traits.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".