Integrating Genetic, Transcriptional, and Functional Analyses to Identify 5 Novel Genes for Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) affects >30 million individuals worldwide and is associated with an increased risk of stroke, heart failure, and death. AF is highly heritable, yet the genetic basis for the arrhythmia remains incompletely understood. METHODS AND RESULTS: To identify new AF-related genes, we used a multifaceted approach, combining large-scale genotyping in 2 ethnically distinct populations, cis-eQTL (expression quantitative trait loci) mapping, and functional validation. Four novel loci were identified in individuals of European descent near the genes NEURL (rs12415501; relative risk [RR]=1.18; 95% confidence interval [CI], 1.13-1.23; P=6.5×10(-16)), GJA1 (rs13216675; RR=1.10; 95% CI, 1.06-1.14; P=2.2×10(-8)), TBX5 (rs10507248; RR=1.12; 95% CI, 1.08-1.16; P=5.7×10(-11)), and CAND2 (rs4642101; RR=1.10; 95% CI, 1.06-1.14; P=9.8×10(-9)). In Japanese, novel loci were identified near NEURL (rs6584555; RR=1.32; 95% CI, 1.26-1.39; P=2.0×10(-25)) and CUX2 (rs6490029; RR=1.12; 95% CI, 1.08-1.16; P=3.9×10(-9)). The top single-nucleotide polymorphisms or their proxies were identified as cis-eQTLs for the genes CAND2 (P=2.6×10(-19)), GJA1 (P=2.66×10(-6)), and TBX5 (P=1.36×10(-5)). Knockdown of the zebrafish orthologs of NEURL and CAND2 resulted in prolongation of the atrial action potential duration (17% and 45%, respectively). CONCLUSIONS: We have identified 5 novel loci for AF. Our results expand the diversity of genetic pathways implicated in AF and provide novel molecular targets for future biological and pharmacological investigation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".