Polygenic risk scores of several subtypes of epilepsies in a founder population
Bibliographic record
Abstract
Objective Polygenic risk scores (PRSs) are used to quantify the cumulative effects of a number of genetic variants, which may individually have a very small effect on susceptibility to a disease; we used PRSs to better understand the genetic contribution to common epilepsy and its subtypes. Methods We first replicated previous single associations using 373 unrelated patients. We then calculated PRSs in the same French Canadian patients with epilepsy divided into 7 epilepsy subtypes and population-based controls. We fitted a logistic mixed model to calculate the variance explained by the PRS using pseudo-R2 statistics. Results We show that the PRS explains more of the variance in idiopathic generalized epilepsy than in patients with nonacquired focal epilepsy. We also demonstrate that the variance explained is different within each epilepsy subtype. Conclusions Globally, we support the notion that PRSs provide a reliable measure to rightfully estimate the contribution of genetic factors to the pathophysiologic mechanism of epilepsies, but further studies are needed on PRSs before they can be used clinically.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".