Epilepsy will affect nearly 3% of people at some point during their lifetime. Copy number variants (CNVs) have been implicated in many diseases including neurological pathologies. Previous CNV studies of epilepsy patients used array-based technology and were restricted to the detection of large or exonic events. Whole-genome sequencing (WGS) has the potential to more comprehensively profile CNVs. However, existing analytical methods suffer from limited sensitivity and specificity, especially for small events. We show that this is in part due to the non-uniformity of read coverage, even after intra-sample normalization. Instead, we propose a new analytical method, PopSV, that uses multiple samples to control for technical variation and enables the robust detection of CNVs. Using WGS and this new method, we perform a comprehensive characterization of CNVs in 198 epilepsy patients and 301 controls. This genome-wide profile first reveals an enrichment of rare exonic CNVs in patients. We also observe that those rare exonic CNVs are more recurrent in patients. More interestingly, more patients harbor non-coding CNVs in proximity to known epilepsy genes compared to controls. We found that surveying the whole genome reveals a much higher yield of putative pathogenic CNVs than when comparing with exon regions. We also report on 21 potentially damaging events that could be associated with new candidate disease genes. Our results suggest that more comprehensive profiling of CNVs, and more generally of structural variants, will help explain a larger fraction of epilepsy cases.
🏆 Honorable Mentions for Oral Presentation.