Phenotypic expression of rare progressive cardiac conduction disease variants in the general population
Bibliographic record
Abstract
AIMS: Familial progressive cardiac conduction disease (PCCD) is a heritable condition leading to conduction defects that may require pacemaker implantation. The penetrance of rare PCCD variants in general populations and relationship with electrocardiogram (ECG) trait polygenic risk scores (PRS) is unknown. We investigated the prevalence and phenotypic expression of rare variants linked with PCCD in a population cohort and to establish whether ECG-trait PRSs improve risk prediction. METHODS AND RESULTS: Carriers of known rare pathogenic/likely pathogenic (P/LP) PCCD variants, and variants of uncertain significance (VUS) were identified in 469 511 UK Biobank participants. Primary (any conduction disease) and secondary (high-grade AV block and pacemaker implantation) outcomes were evaluated in lifetime-risk Cox proportional hazard models including rare variant status, sex, and age. Additional models including PR and QRS PRSs were tested. There were 25 P/LP carriers (5 genes) and 3174 VUS carriers (4 genes). Conduction disease was more prevalent in P/LP individuals compared with non-carriers (28% vs. 5.3%, P < 0.001) with a hazard ratio (HR) of 6.60 (95% CI = 3.14-13.8) over 6.5 million person-years of follow-up and C-index 0.602 (0.599-0.605). This was driven by AV block (HR 23.2 [8.7-61.8]) and pacemaker implantation (HR 13.4 [6.01-29.8]). All individuals were aged >50 at diagnosis. Combined with P/LP status, PR-PRS and QRS-PRS improved model performance (C-index 0.618 [0.615-0.622]). CONCLUSION: In a population-based cohort, PCCD P/LP variant carriers were at greater risk of conduction disease. Including PRSs for the PR and QRS improved risk prediction, supporting the combination of rare and common variants in risk assessment.
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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".