Inexpensive fine mapping and positional cloning in plants using visible, mapped transgenes
Bibliographic record
Abstract
Vast numbers of crop, fungal, and animal accessions as well as insect vectors and evolving eukaryotic pathogens await molecular analysis. Inexpensive methods are required to make map-based gene isolation accessible to more of the world’s researchers. Today, positional cloning relies on genotyping and phenotyping large numbers of progeny to detect chromosome recombination events that break linkage between the trait of interest and flanking molecular markers following meiosis. In the postgenome era, positional cloning will no longer be limited by the availability of high-density molecular markers but rather by the skilled labour and the expense of genotyping and phenotyping 103−104progeny to detect rare recombination events in a narrow chromosome block flanking the target gene of interest. Here, we review how linked, mapped transgenes that encode dominant, visible traits such as green fluorescent protein can be used to preselect meiotic recombinants inexpensively, thus reducing progeny genotyping and phenotyping requirements by >95% during positional cloning. Because transgene markers such as green fluorescent protein are genotype independent, transgenes created in one inbred line may be used to fine-map genetic variation in large numbers of genotypes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".