The Utility of Genome-Wide Association Studies in Inherited Arrhythmias and Cardiomyopathies
Bibliographic record
Abstract
Inherited arrhythmias and cardiomyopathies are a group of potentially lethal genetic cardiac disorders which are often passed down through generations and pose risks to several family members. While individually rare, these conditions are collectively common and pose significant challenges for clinical management given their variable severity, age of onset, and response to treatments. Earlier genetic analyses revealed crucial insights into the main genetic culprits of these disorders, such as SCN5A for Brugada syndrome, and MYH7 and MYBPC3 for hypertrophic cardiomyopathy, which have revolutionized diagnosis, risk stratification, and medical management. Nonetheless, issues such as variable expressivity and penetrance, low yield of genetic testing, and relative lack of disease-modifying therapies remain significant hurdles for clinical management. The revolution of genome-wide association studies GWASs has transformed our understanding of inherited arrhythmias and cardiomyopathies, shifting the view of these disorders from a monogenic Mendelian inheritance towards a more complex, often polygenic inheritance with nuanced interplay between genetics and environment. Moreover, GWASs have enabled the quantification of polygenic predisposition to disease using polygenic risk scores, which are often complementary to and independent of monogenic risk. In this review, we highlight how GWASs have transformed the field of inherited arrhythmias and cardiomyopathies, with a particular focus on the polygenic risk scores developed and their clinical utility for the four disorders which have been impacted by GWASs—hypertrophic cardiomyopathy, dilated cardiomyopathy, Brugada syndrome, and long QT syndrome.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".