Development and validation of a minimal SNP genotyping panel for the differentiation of Cannabis sativa cultivars
Bibliographic record
Abstract
BACKGROUND: Due to its previously illicit nature, Cannabis sativa had not fully reaped the benefits of recent innovations in genomics and plant sciences. However, Canada's legalization of C. sativa and products derived from its flower in 2018 triggered significant new demand for robust genotyping tools to assist breeders in meeting consumer demands. Early molecular marker-based research on C. sativa focused on screening for plant sex and chemotype, and more recent research has sought to use molecular markers to target traits of agronomic interest, to study populations and to differentiate between C. sativa cultivars. RESULTS: In this study, we have conducted whole genome sequencing of 32 cultivars, mined the sequencing data for SNPs, developed a reduced SNP genotyping panel to discriminate between sequenced cultivars, then validated the 20-SNP panel using DNA from the sequenced cultivars and tested the assays on commercially available dried flower. The assay conversion rate was higher in DNA extracted from fresh plant material than in DNA extracted from dried flower samples. However, called genotypes were internally consistent, highlighting discrepancies between genotypes detected using sequencing data and observed using genotyping assays. The primary contributions of this work are to clearly document the process used to develop minimal SNP genotyping panels, the feasibility of using such panels to differentiate between C. sativa cultivars, and outline improvements and goals for future iterations of PCR-based, minimal SNP panels to enable efficient development genotyping tools to identify and screen C. sativa cultivars. CONCLUSIONS: Our key recommendations are to increase sampling density to account for intra-cultivar variability; leverage higher read length paired-end short-read technology; conduct in-depth pre- and post-processing of reads, mapping, and variant calling data; integrate trait-associated loci to develop multi-purpose panels; and use iterative approaches for in vitro validation to ensure that only the most discriminant and performant SNPs are retained.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".