Extensive and breed-specific linkage disequilibrium in <i>Canis familiaris</i>
Bibliographic record
Abstract
The 156 breeds of registered dogs in the United States offer a unique opportunity to map genes important in disease susceptibility, morphology, and behavior. Linkage disequilibrium (LD) is of current interest for its application in whole genome association mapping, since the extent of LD determines the feasibility of such studies. We have measured LD at five genomic intervals, each 5 Mb in length and composed of five clusters of sequence variants spaced 800 kb-1.6 Mb apart. These intervals are located on canine chromosomes 1, 2, 3, 34, and 37, and none is under obvious selective pressure. Approximately 20 unrelated dogs were assayed from each of five breeds: Akita, Bernese Mountain Dog, Golden Retriever, Labrador Retriever, and Pekingese. At each genomic interval, SNPs and indels were discovered and typed by resequencing. Strikingly, LD in canines is much more extensive than in humans: D' falls to 0.5 at 400-700 kb in Golden Retriever and Labrador Retriever, 2.4 Mb in Akita, and 3-3.2 Mb in Bernese Mountain Dog and Pekingese. LD in dog breeds is up to 100x more extensive than in humans, suggesting that a correspondingly smaller number of markers will be required for association mapping studies in dogs compared to humans. We also report low haplotype diversity within regions of high LD, with 80% of chromosomes in a breed carrying two to four haplotypes, as well as a high degree of haplotype sharing among breeds.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".