Extensive and breed-specific linkage disequilibrium in <i>Canis familiaris</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it