Selective sweep mapping of genes with large phenotypic effects
Bibliographic record
Abstract
Many domestic dog breeds have originated through fixation of discrete mutations by intense artificial selection. As a result of this process, markers in the proximity of genes influencing breed-defining traits will have reduced variation (a selective sweep) and will show divergence in allele frequency. Consequently, low-resolution genomic scans can potentially be used to identify regions containing genes that have a major influence on breed-defining traits. We model the process of breed formation and show that the probability of two or three adjacent marker loci showing a spurious signal of selection within at least one breed (i.e., Type I error or false-positive rate) is low if highly variable and moderately spaced markers are utilized. We also use simulations with selection to demonstrate that even a moderately spaced set of highly polymorphic markers (e.g., one every 0.8 cM) has high power to detect regions targeted by strong artificial selection in dogs. Further, we show that a gene responsible for black coat color in the Large Munsterlander has a 40-Mb region surrounding the gene that is very low in heterozygosity for microsatellite markers. Similarly, we survey 302 microsatellite markers in the Dachshund and find three linked monomorphic microsatellite markers all within a 10-Mb region on chromosome 3. This region contains the FGFR3 gene, which is responsible for achondroplasia in humans, but not in dogs. Consequently, our results suggest that the causative mutation is a gene or regulatory region closely linked to FGFR3.
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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 | 0.001 |
| 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 | 0.000 |
| 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".