UMAP of ethnicity for the epilepsy patients and controls.

The UMAP was made with ‘umap-learn v0.5.1’ and based on the first 5 principal components. (TIF)</p

Number of individuals for each phenotype.

The discovery of new variants has leveled off in recent years in epilepsy studies, despite the use of very large cohorts. 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. We had access to whole genome sequencing (WGS) from 247 epilepsy patients and 377 controls. To assess the functional impact of non-coding variants, ExPecto, a deep learning algorithm was used to predict expression change in brain tissues. We compared the burden of rare non-coding deleterious variants between cases and controls. Rare non-coding highly deleterious variants were significantly enriched in Genetic Generalized Epilepsy (GGE), but not in Non-Acquired Focal Epilepsy (NAFE) or all epilepsy cases when compared with controls. 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.</div

Burden of variants for different CVS thresholds across epilepsy phenotypes with only individuals of European descent.

Odds ratios and p-value were calculated using a binomial logistic regression for variants of different Constraint Violation Score (CVS) thresholds. Lines represent 95% confidence intervals. Comparisons were made for cases and controls (A), Genetic Generalized Epilepsy (GGE) and controls (B), Non-Acquired Focal Epilepsy (NAFE) and controls (C) and GGE and NAFE (D). (TIF)</p

Accuracy of predictions’ directionality on known GTEx eQTLs.

Directionality accuracy was computed according to ExPecto’s predicted magnitude in natural log fold change. (TIF)</p

Burden of variants for different CVS thresholds across epilepsy phenotypes.

Odds ratios and p-value were calculated using a binomial logistic regression for variants of different Constraint Violation Score (CVS) thresholds. Lines represent 95% confidence intervals. Comparisons were made for cases and controls (A), Genetic Generalized Epilepsy (GGE) and controls (B), Non-Acquired Focal Epilepsy (NAFE) and controls (C) and GGE and NAFE (D).</p

Burden of variants for different CVS thresholds across epilepsy phenotypes when using non-neurological tissues.

Odds ratios and p-value were calculated using a binomial logistic regression for variants of different Constraint Violation Score (CVS) thresholds. Lines represent 95% confidence intervals. Comparisons were made for cases and controls (A), Genetic Generalized Epilepsy (GGE) and controls (B), Non-Acquired Focal Epilepsy (NAFE) and controls (C) and GGE and NAFE (D). Tissues that were used are artery aorta, colon transverse and skin of body. (TIF)</p

STROBE statement—checklist of items that should be included in reports of observational studies.

STROBE statement—checklist of items that should be included in reports of observational studies.</p

List of variants included in the final analysis.

The discovery of new variants has leveled off in recent years in epilepsy studies, despite the use of very large cohorts. 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. We had access to whole genome sequencing (WGS) from 247 epilepsy patients and 377 controls. To assess the functional impact of non-coding variants, ExPecto, a deep learning algorithm was used to predict expression change in brain tissues. We compared the burden of rare non-coding deleterious variants between cases and controls. Rare non-coding highly deleterious variants were significantly enriched in Genetic Generalized Epilepsy (GGE), but not in Non-Acquired Focal Epilepsy (NAFE) or all epilepsy cases when compared with controls. 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.</div