Robust SNP genotyping by multiplex PCR and arrayed primer extension
Bibliographic record
Abstract
BACKGROUND: Arrayed primer extension (APEX) is a microarray-based rapid minisequencing methodology that may have utility in 'personalized medicine' applications that involve genetic diagnostics of single nucleotide polymorphisms (SNPs). However, to date there have been few reports that objectively evaluate the assay completion rate, call rate and accuracy of APEX. We have further developed robust assay design, chemistry and analysis methodologies, and have sought to determine how effective APEX is in comparison to leading 'gold-standard' genotyping platforms. Our methods have been tested against industry-leading technologies in two blinded experiments based on Coriell DNA samples and SNP genotype data from the International HapMap Project. RESULTS: In the first experiment, we genotyped 50 SNPs across the entire 270 HapMap Coriell DNA sample set. For each Coriell sample, DNA template was amplified in a total of 7 multiplex PCRs prior to genotyping. We obtained good results for 41 of the SNPs, with 99.8% genotype concordance with HapMap data, at an automated call rate of 94.9% (not including the 9 failed SNPs). In the second experiment, involving modifications to the initial DNA amplification so that a single 50-plex PCR could be achieved, genotyping of the same 50 SNPs across each of 49 randomly chosen Coriell DNA samples allowed extremely robust 50-plex genotyping from as little as 5 ng of DNA, with 100% assay completion rate, 100% call rate and >99.9% accuracy. CONCLUSION: We have shown our methods to be effective for robust multiplex SNP genotyping using APEX, with 100% call rate and >99.9% accuracy. We believe that such methodology may be useful in future point-of-care clinical diagnostic applications where accuracy and call rate are both paramount.
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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 | 0.000 | 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".