Enhancing the interpretation of genetic observations in KCNQ1 in unselected populations: relevance to secondary findings
Bibliographic record
Abstract
AIMS: Rare variants in the KCNQ1 gene are found in the healthy population to a much greater extent than the prevalence of Long QT Syndrome type 1 (LQTS1). This observation creates challenges in the interpretation of KCNQ1 rare variants that may be identified as secondary findings in whole exome sequencing.This study sought to identify missense variants within sub-domains of the KCNQ1-encoded Kv7.1 potassium channel that would be highly predictive of disease in the context of secondary findings. METHODS AND RESULTS: We established a set of KCNQ1 variants reported in over 3700 patients with diagnosed or suspected LQTS sent for clinical genetic testing and compared the domain-specific location of identified variants to those observed in an unselected population of 140 000 individuals. We identified three regions that showed a significant enrichment of KCNQ1 variants associated with LQTS at an odds ratio (OR) >2: the pore region, and the adjacent 5th (S5) and 6th (S6) transmembrane (TM) regions. An additional segment within the carboxyl terminus of Kv7.1, conserved region 2 (CR2), also showed an increased OR of disease association. Furthermore, the TM spanning S5-Pore-S6 region correlated with a significant increase in cardiac events. CONCLUSION: Rare missense variants with a clear phenotype of LQTS have a high likelihood to be present within the pore and adjacent TM segments (S5-Pore-S6) and a greater tendency to be present within CR2. This data will enhance interpretation of secondary findings within the KCNQ1 gene. Further, our data support a more severe phenotype in LQTS patients with variants within the S5-Pore-S6 region.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".