Evaluating Polygenic Risk Scores in “Lone” Atrial Fibrillation
Bibliographic record
Abstract
BackgroundPolygenic scores incorporating varying numbers of single nucleotide polymorphisms (SNPs) have been demonstrated to exert a prominent role in atrial fibrillation (AF). We sought to compare the relative discriminatory capacities of 2 previously validated polygenic scores in “lone” AF.MethodsA total of 186 lone AF cases of European ancestry underwent SNP genotyping. A genome-wide polygenic score (GPS) and polygenic risk score (PRS) involving 6,730,541 and 1168 SNPs, respectively, were calculated for 186 cases and 423 controls of European ancestry from the 1000 Genomes (1KG) Project. The distribution of the polygenic scores was compared between the cases and controls and their discriminatory capacities were evaluated using receiver operating characteristic (ROC) curves.ResultsA total of 34.4% of patients with lone AF had GPS scores greater than the top 10th percentile of 1KG controls, corresponding to a 4.64-fold increased odds (95% confidence interval [CI], 2.99-7.18; P < 0.001) for AF. A PRS score in the top 10th percentile of 1KG controls was observed in 26.3% of cases, which equated to a 3.16-fold increased odds (95% CI, 2.01-4.98; P < 0.001) for AF. Comparison of C-statistics from ROC curves indicated improved discriminatory capacity of the GPS (0.76) relative to the PRS (0.70) (P = 0.002).ConclusionsOur study evaluating 2 polygenic scores for AF suggests that the GPS, containing more than 6.7 million SNPs, exhibits an improved discriminatory capacity in lone AF compared with a PRS possessing 1168 SNPs. Our findings suggest that genetic risk scores for AF that maximally leverage genomic data may provide improved predictive power.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".