Targeted Deep Sequencing Reveals No Definitive Evidence for Somatic Mosaicism in Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Studies of ≤15 atrial fibrillation (AF) patients have identified atrial-specific mutations within connexin genes, suggesting that somatic mutations may account for sporadic cases of the arrhythmia. We sought to identify atrial somatic mutations among patients with and without AF using targeted deep next-generation sequencing of 560 genes, including genetic culprits implicated in AF, the Mendelian cardiomyopathies and channelopathies, and all ion channels within the genome. METHODS AND RESULTS: Targeted gene capture and next-generation sequencing were performed on DNA from lymphocytes and left atrial appendages of 34 patients (25 with AF). Twenty AF patients had undergone cardiac surgery exclusively for pulmonary vein isolation and 17 had no structural heart disease. Sequence alignment and variant calling were performed for each atrial-lymphocyte pair using the Burrows-Wheeler Aligner, the Genome Analysis Toolkit, and MuTect packages. Next-generation sequencing yielded a median 265-fold coverage depth (interquartile range, 64-369). Comparison of the 3 million base pairs from each atrial-lymphocyte pair revealed a single potential somatic missense mutation in 3 AF patients and 2 in a single control (12 versus 11%; P=1). All potential discordant variants had low allelic fractions (range, 2.3%-7.3%) and none were detected with conventional sequencing. CONCLUSIONS: Using high-depth next-generation sequencing and state-of-the art somatic mutation calling approaches, no pathogenic atrial somatic mutations could be confirmed among 25 AF patients in a comprehensive cardiac arrhythmia genetic panel. These findings indicate that atrial-specific mutations are rare and that somatic mosaicism is unlikely to exert a prominent role in AF pathogenesis.
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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 | 0.000 | 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) | 0.001 | 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".