Sequencing candidate genes in African American and Hispanic/ Latino probands with early-onset Atrial Fibrillation
Bibliographic record
Abstract
Abstract Introduction Mutations in cardiac ion channels, structural proteins and signaling molecules have been identified in European whites with early-onset AF (EOAF). However, it remains unclear if genetic variation also contributes to the etiology of EOAF in ethnic minorities. Purpose To determine the prevalence of disease causing variants in candidate AF genes in African American and Hispanic/Latino probands with EOAF. Method In this family-based study, probands of African and Hispanic descent with EOAF (defined as AF ≤65 years) were prospectively enrolled in a clinical-DNA biorepository and underwent targeted sequencing for 60 AF candidate genes. Variants were filtered at 20X read depth and clinically evaluated with American College of Medical Genetics and Genomics and Association for Molecular Pathology (ACMG/AMP) as well as the Association for Clinical Genomic Science (ACGS) criteria for disease-causing mutations. Results Among 227 EOAF probands with mean (SD) age of AF 51.0 (9.9) years, 132 (58.0%) were men and 148 (65.0%) African American and 79 (35.0%) Hispanic/Latino. Sequencing 60 candidate AF genes revealed 90 variants that met filtering criteria and underwent clinical evaluation. We identified 16 (7.0%) EOAF probands with a likely pathogenic or pathogenic variant with the majority being loss of function (62.5%) and located in the TTN gene (50.0%). We confirmed a family history of AF in 24 probands (10.6%) and 6 families with >1 affected member a variant of unknown significance (VUS) in genes encoding for a sodium channel (SCN10A), potassium channel (KCNE5), sarcomeric proteins (MYH6, TTN) and atrial natriuretic peptide (NPPA) co-segregated with AF. Conclusion Gene sequencing in African American and Hispanic/Latinos probands with EOAF identified a small percentage of disease causing variants in patients with EOAF. Our findings not only represent important progress toward molecular phenotyping of EOAF, but also provides insight into the underlying pathophysiology toward targeted mechanism-based therapies for AF in ethnic minorities. Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): American Heart Association
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".