Predicting the Size of the Progeny Mapping Population Required to Positionally Clone a Gene
Bibliographic record
Abstract
A key frustration during positional gene cloning (map-based cloning) is that the size of the progeny mapping population is difficult to predict, because the meiotic recombination frequency varies along chromosomes. We describe a detailed methodology to improve this prediction using rice (Oryza sativa L.) as a model system. We derived and/or validated, then fine-tuned, equations that estimate the mapping population size by comparing these theoretical estimates to 41 successful positional cloning attempts. We then used each validated equation to test whether neighborhood meiotic recombination frequencies extracted from a reference RFLP map can help researchers predict the mapping population size. We developed a meiotic recombination frequency map (MRFM) for approximately 1400 marker intervals in rice and anchored each published allele onto an interval on this map. We show that neighborhood recombination frequencies (R-map, >280-kb segments) extracted from the MRFM, in conjunction with the validated formulas, better predicted the mapping population size than the genome-wide average recombination frequency (R-avg), with improved results whether the recombination frequency was calculated as genes/cM or kb/cM. Our results offer a detailed road map for better predicting mapping population size in diverse eukaryotes, but useful predictions will require robust recombination frequency maps based on sampling more progeny.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".