Evaluation of polygenic score for hypertrophic cardiomyopathy in the general population and across clinical settings
Bibliographic record
Abstract
Hypertrophic cardiomyopathy (HCM) is an important cause of morbidity and mortality, with rare pathogenic variants found in about a third of cases (sarcomere-positive). Large-scale genome-wide association studies (GWAS) demonstrate that common genetic variation contributes substantially to HCM risk. Here, we derive polygenic scores (PGS) from HCM GWAS, and multi-trait analysis of GWAS incorporating genetically-correlated traits, and test their performance in the UK Biobank, 100,000 Genomes Project, and across clinical cohorts. Higher PGS substantially increases population risk of HCM, particularly amongst sarcomere-positive carriers where HCM penetrance differs 10-fold between those in the highest and lowest PGS quintiles. In relatives of HCM patients, PGS stratifies risks of developing HCM and adverse outcomes. Finally, PGS strongly predicts risk of adverse outcomes in HCM, with a 4 to 6-fold increase in death between cases in the highest and lowest PGS quintiles. These findings promise broad clinical utility of PGS in the general population, in cases, and in families with HCM, enabling tailored screening and surveillance, and stratification of risk of adverse outcomes.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".