Empowering canine genomics: design and validation of a high-density SNP array for Indian dogs
Bibliographic record
Abstract
India harbors a substantial population of 9.43 million dogs, showcasing diverse phenotypes and utility. Initiatives focusing on awareness, conservation, and informed breeding can greatly enhance the recognition and welfare of the unique Indian canine heritage. This study describes the design and development of a high-density SNP array for genomic characterization of Indian dogs. Paired-end (150 bp) DNA sequences of 48 samples from four diverse dog populations were generated with 10× coverage, following the standard pipeline of Axiom Array technology for chip design. More than 23 million raw SNPs were initially identified, with 629 597 SNP markers ultimately tiled on the Indian canine array ( Axiom_Shwaan) after stringent filtering and processing. With an inter-marker distance of 3.8 kb the Axiom_Shwaan greatly increases the canine genome coverage. The array was validated by genotyping 186 samples representing 11 dog breeds/populations from India. The high call rate (99%) of SNPs on the designed chip indicates its suitability for use in Indian dog populations, reflecting sufficient genetic diversity. The principal component and phylogenetic analyses delineated the native dog breeds into discrete groups. This high-density SNP array will empower future applications in population genetics, breed/selection signature identification, development of trait-specific biomarkers, and genome-wide data mining for various canine abilities.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".