Loss of daylength sensitivity by splice site mutation in <i>Cannabis</i> pseudo‐response regulator
Bibliographic record
Abstract
Photoperiod insensitivity (auto-flowering) in drug-type Cannabis sativa circumvents the need for short day (SD) flowering requirements making outdoor cultivation in high latitudes possible. However, the benefits of photoperiod insensitivity are counterbalanced by low cannabinoid content and poor flower quality in auto-flowering genotypes. Despite recent studies in cannabis flowering, a mechanistic understanding of photoperiod insensitivity is still lacking. We used a combination of genome-wide association study and genetic fine-mapping to identify the genetic cause of auto-flowering in cannabis. We then used gene expression analyses and transient transformation assays to characterize flowering time control. Herein, we identify a splice site mutation within circadian clock gene PSEUDO-RESPONSE REGULATOR 37 (CsPRR37) in auto-flowering cannabis. We show that CsPRR37 represses FT expression and its circadian oscillations transition to a less repressive state during SD as compared to long days (LD). We identify several key circadian clock genes whose expression is altered in auto-flowering cannabis, particularly under non-inductive LD. Research into the pervasiveness of this mutation and others affecting flowering time will help elucidate cannabis domestication history and advance cannabis breeding toward a more sustainable outdoor cultivation system.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".