Polygenic risk score for hypertrophic cardiomyopathy predicts population disease risk, penetrance in sarcomeric rare variant carriers and survival in cases
Bibliographic record
Abstract
Abstract Background Hypertrophic cardiomyopathy (HCM) is an important cause of mortality, caused by rare pathogenic variants (sarcomere-positive) in around one third of cases. Recent large genome-wide association studies (GWAS) highlight the important contributions of common genetic variation on HCM risk and heritability1-3. Polygenic scores (PGS) quantify the cumulative individual risk from common genetic variation, and may provide important clinical utility. Purpose The aim of this study is to generate PGS for HCM, and evaluate its performance in predicting HCM in the general population, HCM penetrance in sarcomere-positive carriers, and risk of adverse outcomes in individuals with HCM. Methods The PGS was generated using a Bayesian framework (PRS-CS) with individual SNP effect estimates derived from the largest published HCM GWAS (5900 cases and 68359 controls of European ancestry from 7 cohorts) and multi-trait analysis of GWAS (MTAG) (incorporating GWAS of genetically correlated cardiac magnetic resonance imaging (CMR) traits from 36203 White British individuals in the UK Biobank [UKB])1. Genome-wide PGS were calculated using an additive model for participants in two cohorts (UKB and 100,000 Genomes Project [GeL]). To evaluate the effect of PGS on penetrant HCM in sarcomere-positive carriers, we identified individuals with pathogenic or likely pathogenic variants in 8 definitive HCM-causing genes (MYBPC3, MYH7, TNNT2, TNNI3, TPM1, ACTC1, MYL3, and MYL2) in UKB and GeL. Results In 343,182 unrelated White British ancestry participants from the UK Biobank (UKB), PGS was associated with an increased risk of HCM (OR per PGS SD 2.3, P<2x10-16), with 75% of HCM cases having a PGS above the population mean (Figure 1A). Individuals with PGS in the top centile had a substantially increased risk of HCM compared with those in the median (OR 14.5, P<2x10-16) and bottom centile (OR 36.6, 3x10-25) (Figure 1B). PGS had significant effects on stratifying HCM penetrance in 640 sarcomere-positive carriers in the UKB (top vs. middle quintile HCM OR: 3.7, P 0.009), and risk of being a HCM case in 599 sarcomere-positive carriers in GeL (top vs. middle quintile HCM OR 9.5, P 4x10-5) (Figure 1C), highlighting the important interactive effect of common and rare genetic variants. Finally, PGS predicted risk of all-cause mortality and major adverse cardiovascular events (MACE) after HCM diagnosis in 382 cases in UKB (all-cause mortality: top vs. bottom quintile: HR 3.9, P 0.013; MACE: top vs. bottom quintile: HR 3.5, P 4.x10-4), and all-cause mortality in 683 cases in GeL (top vs. bottom quintile: HR 6.3, P 1x10-6) (Figure 1D). Conclusions We derive a PGS for HCM risk prediction, and demonstrate potential clinical utility in stratifying risk of penetrant HCM in sarcomere-positive carriers, and in predicting risk of adverse outcomes in individuals with HCM.Figure 1
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".