Saliva as an alternative source of high yield canine genomic DNA for genotyping studies
Bibliographic record
Abstract
BACKGROUND: The domestic dog presents an attractive model system for the study of the genetic basis of disease. The development of resources such as the canine genome sequence and SNP genotyping platforms has allowed for the implementation of canine genetic studies. Successful implementation of such studies depends not only on the quality of individual DNA samples, but also on the number of samples obtained. The latter can be maximized using a non-invasive DNA collection method that can increase study participation. We compared the DNA yield and quality obtained from blood and buccal swabs to those obtained using a non-invasive saliva collection kit (Oragene *ANIMAL kit). We also assessed the success rate of PCR amplification and genotyping accuracy of DNA isolated using these collection methods. FINDINGS: Comparison of DNA yields from matched saliva, blood and buccal swab samples showed that yields from saliva were significantly higher than those from blood (p = 0.0198) or buccal swabs (p = 0.0008). Electrophoretic analysis revealed that blood and saliva produced higher quality DNA than buccal swabs. In addition, a 1.1-kb PCR fragment was successfully amplified using the paired DNA samples and genotyping by PCR-RFLP yielded identical results. CONCLUSION: We demonstrate that DNA yields from canine saliva are higher than those from blood or buccal swabs. The quality of DNA extracted from saliva is sufficient for successful amplification of a 1.1-kb fragment and for accurate SNP genotyping by PCR-RFLP. We conclude that saliva presents a non-invasive alternative source of high quantities of canine genomic DNA suitable for genotyping studies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".