Polygenic risk scores of several subtypes of epilepsies in a founder population
Bibliographic record
Abstract
ABSTRACT Importance Epilepsy is defined as a group of neurological disorders characterized by epileptic seizures, brief episodes of symptoms that are caused by abnormal or excessive neuronal activity in the brain. Epilepsy affects around 3 percent of individuals. In the past 10 years, many groups have been working to better understand the complex genetic mechanisms underlying epilepsy. Together, they studied many different genetic mechanisms, but there is still a substantial missing heritability component in epilepsy genetics. Objective Here, we used polygenic risk scores (PRS) to quantify the cumulative effects of a number of variants, which may individually have a very small effect on susceptibility. Design We calculated PRS in 522 French-Canadian epilepsy patients divided into seven subtypes and French-Canadian controls. Setting All study participants (cases and controls) were selected based on their French-Canadian ancestry. Participants The epilepsy cohort was composed of families of at least three affected individuals with Idiopathic Generalized Epilepsy (IGE) or Non-acquired Focal Epilepsy (NAFE) previously collected and diagnosed by neurologists following the International League Against Epilepsy (ILAE) criteria. Exposures All samples were processed on a common genotyping array. Main outcomes and Results We show that the area under the curve (AUC) is almost always slightly greater than 0.5, especially in patients with IGE and subtypes. We also looked at the association of the PRS with the different phenotypes using a linear mixed effects model estimated by generalized estimating equation (GEE) with the pairwise identity-by-descent (IBD) matrix as a random effect. P-values of GEE were consistent with AUC calculations. Conclusions and Relevance Globally, we support the notion that PRS and SNP-based heritability provide reliable measures to rightfully estimate the contribution of genetic factors to the pathophysiological mechanism of epilepsies, but further studies are needed on PRS 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".