Abstracts

Rationale: Epilepsy is one of the most common neurological conditions characterized by recurrent episodes of seizures. The etiology is heterogeneous, however genetic factors are thought to play a major role (Annu Rev Genomics Hum Genet. 2020;21:205-301). In the past decade, next-generation sequencing (NGS) resulted in the identification of novel sequence variants associated with susceptibility to epilepsy (Nat Commun. 2018;9(1):5269). This set of new knowledge has already been used to identify personalized therapies for affected patients (Expert Rev Mol Diagn. 2019;19(3):217-28 and Expert Rev Mol Diagn. 2015;15(12):1531-8). In this study, we sought to discover novel variants associated with epilepsy and identify candidate genes as therapeutic targets.

Methods: Whole genome sequencing (WGS) was performed on 58 DNA samples from patients with confirmed epilepsy. Variant analysis was performed using variant call files (VCFs) computed with DRAGEN (Illumina) on the human reference genome GRCh37. A three-tiered strategy was used: first to analyze variants in known epilepsy genes (SCN1A, WDR45), then to identify mutations within 16 epilepsy-associated genetic loci known in literature (Nat Commun. 2018;9(1):5269), and finally expand to unblinding prospective bioinformatic assessment of all sequence variants. Variant annotation was performed with Ensembl’s Variant Effect Predictor (VEP), version 103.1, for the human genome assembly GRCh37. For Tiers 1 and 2, we annotated variants with reference alternate allele frequencies from 1,000 Genomes project, while for Tier 3, we applied all annotations available in the VEP. Candidate genes in Tier 1 and 2 were validated by overlap with high scoring candidates in literature references. In Tier 3, novel associations were predicted using different metrics and database annotations, such as: MAX_AF, SIFT, PolyPhen, CLIN_SIG, IMPACT, BYOTYPE. Biological pathways and genes associated with the hits were identify using the g:GOSt module of g:Profiler.

Results: We identified clinically relevant genetic variants in known and emerging epilepsy-associated genes (WDR45, SCN1A). In a high-confidence list of genes priorly identified from literature we were able to detect high impact variants in (PCDH7, KCNAB1, ATXN1, GRK1, and KCNN2). To identify novel disease-gene relationships we prioritized genes with high number of variants associated with significant high impact rating (CASKIN2, POMT1), classified as pathogenic (FAM161A, SCN1A, TTN), and probably damaging (ZBP1, CYP2C19, MDP1, CTC1, CBWD5). Finally, we used an enrichment analysis on 200 genes with the highest number of hits to identify candidates involved with neuronal development (NRG1), neuronal cell adhesion (NRXN1, DCC, PTPRD, LRRC4C, CNTNAP2, CTNND2, CTNNA2) and, synapse-related functions (GRID2, NLGN4X).

Conclusions: This study identifies novel genetic variants in patients with confirmed epileptic conditions, extending scopes of clinical diagnosis and providing the groundwork for future functional studies as well as clues for the development of new treatments.

Funding: Not applicable