Dataset Information

Diagnostic implications of genetic copy number variation in epilepsy plus.

ABSTRACT: OBJECTIVE:Copy number variations (CNVs) represent a significant genetic risk for several neurodevelopmental disorders including epilepsy. As knowledge increases, reanalysis of existing data is essential. Reliable estimates of the contribution of CNVs to epilepsies from sizeable populations are not available. METHODS:We assembled a cohort of 1255 patients with preexisting array comparative genomic hybridization or single nucleotide polymorphism array based CNV data. All patients had "epilepsy plus," defined as epilepsy with comorbid features, including intellectual disability, psychiatric symptoms, and other neurological and nonneurological features. CNV classification was conducted using a systematic filtering workflow adapted to epilepsy. RESULTS:Of 1097 patients remaining after genetic data quality control, 120 individuals (10.9%) carried at least one autosomal CNV classified as pathogenic; 19 individuals (1.7%) carried at least one autosomal CNV classified as possibly pathogenic. Eleven patients (1%) carried more than one (possibly) pathogenic CNV. We identified CNVs covering recently reported (HNRNPU) or emerging (RORB) epilepsy genes, and further delineated the phenotype associated with mutations of these genes. Additional novel epilepsy candidate genes emerge from our study. Comparing phenotypic features of pathogenic CNV carriers to those of noncarriers of pathogenic CNVs, we show that patients with nonneurological comorbidities, especially dysmorphism, were more likely to carry pathogenic CNVs (odds ratio = 4.09, confidence interval = 2.51-6.68; P = 2.34 × 10-9 ). Meta-analysis including data from published control groups showed that the presence or absence of epilepsy did not affect the detected frequency of CNVs. SIGNIFICANCE:The use of a specifically adapted workflow enabled identification of pathogenic autosomal CNVs in 10.9% of patients with epilepsy plus, which rose to 12.7% when we also considered possibly pathogenic CNVs. Our data indicate that epilepsy with comorbid features should be considered an indication for patients to be selected for a diagnostic algorithm including CNV detection. Collaborative large-scale CNV reanalysis leads to novel declaration of pathogenicity in unexplained cases and can promote discovery of promising candidate epilepsy genes.

SUBMITTER: Coppola A

PROVIDER: S-EPMC6488157 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Diagnostic implications of genetic copy number variation in epilepsy plus.

Coppola Antonietta A Cellini Elena E Stamberger Hannah H Saarentaus Elmo E Cetica Valentina V Lal Dennis D Djémié Tania T Bartnik-Glaska Magdalena M Ceulemans Berten B Helen Cross J J Deconinck Tine T Masi Salvatore De S Dorn Thomas T Guerrini Renzo R Hoffman-Zacharska Dorotha D Kooy Frank F Lagae Lieven L Lench Nicholas N Lemke Johannes R JR Lucenteforte Ersilia E Madia Francesca F Mefford Heather C HC Morrogh Deborah D Nuernberg Peter P Palotie Aarno A Schoonjans An-Sofie AS Striano Pasquale P Szczepanik Elzbieta E Tostevin Anna A Vermeesch Joris R JR Van Esch Hilde H Van Paesschen Wim W Waters Jonathan J JJ Weckhuysen Sarah S Zara Federico F De Jonghe Peter P Sisodiya Sanjay M SM Marini Carla C

Epilepsia 20190313 4

<h4>Objective</h4>Copy number variations (CNVs) represent a significant genetic risk for several neurodevelopmental disorders including epilepsy. As knowledge increases, reanalysis of existing data is essential. Reliable estimates of the contribution of CNVs to epilepsies from sizeable populations are not available.<h4>Methods</h4>We assembled a cohort of 1255 patients with preexisting array comparative genomic hybridization or single nucleotide polymorphism array based CNV data. All patients ha ...[more]

PMID: 30866059

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Project description:BackgroundDetection of copy number variation (CNV) in genes associated with disease is important in genetic diagnostics, and next generation sequencing (NGS) technology provides data that can be used for CNV detection. However, CNV detection based on NGS data is in general not often used in diagnostic labs as the data analysis is challenging, especially with data from targeted gene panels. Wet lab methods like MLPA (MRC Holland) are widely used, but are expensive, time consuming and have gene-specific limitations. Our aim has been to develop a bioinformatic tool for CNV detection from NGS data in medical genetic diagnostic samples.ResultsOur computational pipeline for detection of CNVs in NGS data from targeted gene panels utilizes coverage depth of the captured regions and calculates a copy number ratio score for each region. This is computed by comparing the mean coverage of the sample with the mean coverage of the same region in other samples, defined as a pool. The pipeline selects pools for comparison dynamically from previously sequenced samples, using the pool with an average coverage depth that is nearest to the one of the samples. A sliding window-based approach is used to analyze each region, where length of sliding window and sliding distance can be chosen dynamically to increase or decrease the resolution. This helps in detecting CNVs in small or partial exons. With this pipeline we have correctly identified the CNVs in 36 positive control samples, with sensitivity of 100% and specificity of 91%. We have detected whole gene level deletion/duplication, single/multi exonic level deletion/duplication, partial exonic deletion and mosaic deletion. Since its implementation in mid-2018 it has proven its diagnostic value with more than 45 CNV findings in routine tests.ConclusionsWith this pipeline as part of our diagnostic practices it is now possible to detect partial, single or multi-exonic, and intragenic CNVs in all genes in our target panel. This has helped our diagnostic lab to expand the portfolio of genes where we offer CNV detection, which previously was limited by the availability of MLPA kits.

Project description:The identification of numerous genetically based epilepsies has resulted in the widespread use of genetic testing to inform epilepsy etiology. Our study aims to investigate whether a difference exists in the diagnostic evaluation and healthcare-related cost expenditures of pediatric patients with epilepsy of unknown etiology who receive a genetic diagnosis through multigene epilepsy panel (MEP) testing and comparing those who underwent early (EGT) versus late genetic testing (LGT). Testing was defined as early (less than 1 year), or late (more than 1 year), following clinical epilepsy diagnosis. A retrospective chart review of pediatric individuals (1-17 years) with epilepsy of unknown etiology who underwent multigene epilepsy panel (MEP) testing identified 28 of 226 (12%) individuals with a pathogenic epilepsy variant [EGT n = 8 (29%); LGT n = 20 (71%)]. The average time from clinical epilepsy diagnosis to genetic diagnosis was 0.25 years (EGT), compared with 7.1 years (LGT). The EGT cohort underwent fewer metabolic tests [EGT n = 0 (0%); LGT n = 16 (80%) (P < 0.01)] and invasive procedures [EGT n = 0 (0%); LGT n = 5 (25%) (P = 0.06)]. Clinical management changes implemented due to genetic diagnosis occurred in 10 (36%) patients [EGT n = 2 (25%); LGT n = 8 (40%) (P = 0.76)]. Early genetic testing with a MEP in pediatric patients with epilepsy of unknown etiology who receive a genetic diagnosis is associated with fewer non-diagnostic tests and invasive procedures and reduced estimated overall healthcare-related costs. PLAIN LANGUAGE SUMMARY: This study aims to investigate whether a difference exists in the diagnostic evaluation and cost expenditures of pediatric patients (1-17 years) with epilepsy of unknown cause who are ultimately diagnosed with a genetic cause of epilepsy through multigene epilepsy panel testing and comparing those who underwent early testing (less than 1 year) versus late testing (more than 1 year) after clinical epilepsy diagnosis. Of the 28 of 226 individuals with a confirmed genetic cause of epilepsy on multigene epilepsy panel testing, performing early testing was associated with fewer non-diagnostic tests, fewer invasive procedures and reduced estimated overall healthcare-related costs.

Dataset Information

Diagnostic implications of genetic copy number variation in epilepsy plus.

Publications

Diagnostic implications of genetic copy number variation in epilepsy plus.

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