Ascertainment of uninterrupted CAG repeat length and disease-modifying variants in fragment-based genetic testing for Huntington Disease
Bibliographic record
Abstract
Purpose In Huntington disease (HD), synonymous variants causing loss or duplication of the interrupting CAA codon in the HTT CAG repeat modify disease onset. These variants are undetectable during HD genetic testing, resulting in inaccurate diagnostic reporting of uninterrupted CAG repeat length. Inaccurate reporting of CAG repeat length results in misdiagnosis of individuals with alleles near diagnostic cut-offs. We present a method to identify variant alleles during CAG repeat genotyping, allowing accurate diagnostic reporting of uninterrupted CAG repeat length. Methods We used triplet-primed PCR (TP-PCR) to amplify HTT CAG repeat alleles with canonical or noncanonical repeat interruptions and leveraged differences in peak amplification patterns to develop a screening method based on peak height ratio (PHR). We used PHR to screen blood DNA from a cohort of symptomatic individuals with diagnostic CAG repeat lengths of 40 to 41. Results TP-PCR enables accurate reporting of uninterrupted CAG repeat length in diagnostic testing by detecting HD alleles with loss or duplication of the CAG repeat interruption. Conclusion PHR screening of TP-PCR traces is a cost-effective screening method for detection, ascertainment of uninterrupted HTT CAG repeat length, and accurate diagnostic reporting for individuals with disease-modifying noncanonical CAG repeat interruptions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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 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".