High Microsatellite and SNP Genotyping Success Rates Established in a Large Number of Genomic DNA Samples Extracted From Mouth Swabs and Genotypes
Bibliographic record
Abstract
Abstract In this article, we present the genomic DNA yield and the microsatellite and single nucleotide polymorphism (SNP) genotyping success rates of genomic DNA extracted from a large number of mouth swab samples. In total, the median yield and quality was determined in 714 individuals and the success rates in 378,480 genotypings of 915 individuals. The median yield of genomic DNA per mouth swab was 4.1 μg (range 0.1–42.2 μg) and was not reduced when mouth swabs were stored for at least 21 months prior to extraction. A maximum of 20 mouth swabs is collected per participant. Mouth swab samples showed in, respectively, 89% for 390 microsatellites and 99% for 24 SNPs a genotyping success rate higher than 75%. A very low success rate of genotyping (0%–10%) was obtained for 3.2% of the 915 mouth swab samples using microsatellite markers. Only 0.005% of the mouth swab samples showed a geno-typing success rate lower than 75% (range 58%–71%) using SNPs. Our results show that mouth swabs can be easily collected, stored by our conditions for months prior to DNA extraction and result in high yield and high-quality DNA appropriate for genotyping with high success rate including whole genome searches using microsatellites or SNPs.
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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 | 0.005 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".