Detection of copy number variations in epilepsy using exome data

Clin Genet. 2018 Mar;93(3):577-587. doi: 10.1111/cge.13144. Epub 2018 Jan 25.

Abstract

Epilepsies are common neurological disorders and genetic factors contribute to their pathogenesis. Copy number variations (CNVs) are increasingly recognized as an important etiology of many human diseases including epilepsy. Whole-exome sequencing (WES) is becoming a standard tool for detecting pathogenic mutations and has recently been applied to detecting CNVs. Here, we analyzed 294 families with epilepsy using WES, and focused on 168 families with no causative single nucleotide variants in known epilepsy-associated genes to further validate CNVs using 2 different CNV detection tools using WES data. We confirmed 18 pathogenic CNVs, and 2 deletions and 2 duplications at chr15q11.2 of clinically unknown significance. Of note, we were able to identify small CNVs less than 10 kb in size, which might be difficult to detect by conventional microarray. We revealed 2 cases with pathogenic CNVs that one of the 2 CNV detection tools failed to find, suggesting that using different CNV tools is recommended to increase diagnostic yield. Considering a relatively high discovery rate of CNVs (18 out of 168 families, 10.7%) and successful detection of CNV with <10 kb in size, CNV detection by WES may be able to surrogate, or at least complement, conventional microarray analysis.

Keywords: copy number variation; epilepsy; microdeletion; whole-exome sequencing.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Alleles
  • Child
  • Child, Preschool
  • Comparative Genomic Hybridization
  • Computational Biology / methods
  • DNA Copy Number Variations*
  • Epilepsy / diagnosis
  • Epilepsy / genetics*
  • Exome
  • Exome Sequencing
  • Female
  • Genetic Association Studies* / methods
  • Genetic Predisposition to Disease*
  • Genetic Testing* / methods
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Male
  • Middle Aged
  • Mutation
  • Young Adult