Cannabinoid Inheritance Relies on Complex Genetic Architecture
Bibliographic record
Abstract
Introduction: Understanding the inheritance of cannabinoid compounds in Cannabis sativa will facilitate effective crop breeding and careful regulation of controlled substances. The production of two key cannabinoids, Δ-9-tetrahydrocannabinol (THC) and cannabidiol (CBD), is partially controlled by two additive loci. Here, we present the first study to search for evidence of alternate genetic models describing the inheritance and expression of cannabinoids. Materials and Methods: Using an information-theoretic approach, we estimated composite genetic effects (CGEs) of four cultivars with pure CBD or pure THC chemotypes, their F 1 and F 2 hybrid progeny, to identify genetic models that explain cannabinoid inheritance patterns. We also estimated the effective number of genetic factors that control differences in cannabinoid concentration (THC, CBD, and cannabichromene [CBC]). Results: Unlike previous research, we note nonadditive components of cannabinoid inheritance. Concentration of THC is a polygenic trait (three to four genetic factors). Both additive and dominance CGEs best explained THC expression patterns. In contrast, cytoplasmic genomes and additive genes may influence CBD concentration. Maternal additive effects and additive genetic effects apparently influence CBC expression. Conclusions: Cannabinoid inheritance is more complex than previously appreciated; among other genetic effects, cytogenetic and maternal contributions may be undervalued influences on cannabinoid ratios and concentrations. Further research on the environmental sensitivity of cannabinoid production is advised.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".