Unraveling the role of non-coding rare variants in epilepsy
Bibliographic record
Abstract
Abstract Importance Despite the use of very large cohorts, the discovery of new variants has leveled off in recent years in epilepsy studies and consequently, most of the heritability is still unexplained. Rare non-coding variants have been largely ignored in studies on epilepsy, although non-coding single nucleotide variants can have a significant impact on gene expression. Objective To determine if rare non-coding deleterious variants are associated with epilepsy. Design This is a case-control study made possible by the CENet cohort. Setting This was initially a multicenter study (families and trios), although the sequencing was processed at the same facility and for the present case-control study, only unrelated individuals were drawn. Participants Patients used in this study are affected either by genetic generalized epilepsy (GGE), non-acquired focal epilepsy (NAFE) or are called ‘mixed’ (phenotype that differs from other affected relatives). Controls are unaffected parents from developmental and epileptic encephalopathy trios. Main Outcomes and Measures To assess the functional impact of non-coding variants, ExPecto, a deep learning algorithm was used. A binomial logistic regression was performed to compare the burden of rare non-coding deleterious variants between cases and controls. Results We had access to WGS from 123 GGE, 112 NAFE and 12 mixed for a total of 247 patients, as well as 377 controls. Rare non-coding highly deleterious variants were associated with GGE (OR 2.74; 95% CI 1.20-6.22), but not with NAFE (OR 0.85; 95% CI 0.27-2.67) or all epilepsy cases (OR 1.54; 95% CI 0.77-3.11) when compared with controls. Conclusion and Relevance In this study we showed that rare non-coding deleterious variants are associated with epilepsy, specifically with GGE. Larger WGS epilepsy cohort will be needed to investigate those effects at a greater resolution. Nevertheless, we demonstrated the importance of studying non-coding regions in epilepsy, a disease where new discoveries are scarce, and a high proportion of the heritability is yet to be explained. Key points Question Are non-coding single nucleotide variants (SNV) associated with epilepsy? Findings In this study we showed that patients with generalized genetic epilepsy (GGE) had significantly more rare non-coding deleterious variants than controls and non-acquired focal epilepsy (NAFE) patients. The study included 247 epilepsy patients and 377 controls who were sequenced for the whole genome. Meaning Rare non-coding SNV are associated with epilepsy, more specifically with GGE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".