Array-Based Whole-Genome Survey of Dog Saliva DNA Yields High Quality SNP Data
Bibliographic record
Abstract
BACKGROUND: Genome-wide association scans for genetic loci underlying both Mendelian and complex traits are increasingly common in canine genetics research. However, the demand for high-quality DNA for use on such platforms creates challenges for traditional blood sample ascertainment. Though the use of saliva as a means of collecting DNA is common in human studies, alternate means of DNA collection for canine research have instead been limited to buccal swabs, from which dog DNA is of insufficient quality and yield for use on most high-throughput array-based systems. We thus investigated an animal-based saliva collection method for ease of use and quality of DNA obtained and tested the performance of saliva-extracted canine DNA on genome-wide genotyping arrays. METHODOLOGY/PRINCIPAL FINDINGS: Overall, we found that saliva sample collection using this method was efficient. Extractions yielded high concentrations ( approximately 125 ng/ul) of high-quality DNA that performed equally well as blood-extracted DNA on the Illumina Infinium canine genotyping platform, with average call rates >99%. Concordance rates between genotype calls of saliva- versus blood-extracted DNA samples from the same individual were also >99%. Additionally, in silico calling of copy number variants was successfully performed and verified by PCR. CONCLUSIONS/SIGNIFICANCE: Our findings validate the use of saliva-obtained samples for genome-wide association studies in canines, highlighting an alternative means of collecting samples in a convenient and non-invasive manner.
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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 | 0.001 |
| 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) | 0.000 | 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".