Whole genome amplification of buccal cell DNA: genotyping concordance before and after multiple displacement amplification
Bibliographic record
Abstract
While buccal cells provide an easily accessible source of genomic DNA, the amount extracted may be insufficient for many studies. Whole genome amplification (WGA) using multiple displacement amplification (MDA) may optimize buccal cell genomic DNA yield. We compared the usefulness, in epidemiological surveys, of DNA derived from buccal cells collected by alcohol mouthwash and amplified by WGA protocol and standard protocols. Buccal cell collection kits were mailed to 300 randomly selected members of a large cohort study, and 189 subjects returned buccal cell samples. We determined: (i) which QIAamp DNA Blood Mini Kit extraction protocol (tissue or blood) produced more DNA; and (ii) whether it is feasible to use MDA to prepare DNA for single nucleotide polymorphism (SNP) genotyping of markers such as the methylenetetrahydrofolate reductase (MTHFR) and vitamin D receptor (VDR) genes. The two DNA extraction protocols were tested on 20 different patient samples each. The tissue protocol yielded more DNA than the blood protocol (15.4+/-8.6 vs. 7.6+/-7.1 microg, p<0.0001). The 20 DNA samples extracted using the tissue protocol were then subjected to pre- and post-MDA genotyping using amplicons for the MTHFR SNP at C677T and the intron 8 VDR SNP. No genotyping discrepancies were detected in pair-wise comparisons of pre- and post-MDA. Genotyping DNA from MDA-based WGA is indistinguishable from routine polymerase chain reaction and offers a stable DNA source for genomic research and clinical diagnosis.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".