Application of array comparative genomic hybridization in 102 patients with epilepsy and additional neurodevelopmental disorders

Department of Medical Genetics, Institute of Mother and Child, Warsaw, Poland.
American Journal of Medical Genetics Part B Neuropsychiatric Genetics (Impact Factor: 3.42). 10/2012; 159B(7):760-71. DOI: 10.1002/ajmg.b.32081
Source: PubMed


Copy-number variants (CNVs) collectively represent an important cause of neurodevelopmental disorders such as developmental delay (DD)/intellectual disability (ID), autism, and epilepsy. In contrast to DD/ID, for which the application of microarray techniques enables detection of pathogenic CNVs in ∼10-20% of patients, there are only few studies of the role of CNVs in epilepsy and genetic etiology in the vast majority of cases remains unknown. We have applied whole-genome exon-targeted oligonucleotide array comparative genomic hybridization (array CGH) to a cohort of 102 patients with various types of epilepsy with or without additional neurodevelopmental abnormalities. Chromosomal microarray analysis revealed 24 non-polymorphic CNVs in 23 patients, among which 10 CNVs are known to be clinically relevant. Two rare deletions in 2q24.1q24.3, including KCNJ3 and 9q21.13 are novel pathogenic genetic loci and 12 CNVs are of unknown clinical significance. Our results further support the notion that rare CNVs can cause different types of epilepsy, emphasize the efficiency of detecting novel candidate genes by whole-genome array CGH, and suggest that the clinical application of array CGH should be extended to patients with unexplained epilepsies. © 2012 Wiley Periodicals, Inc.

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Available from: Hanna Mierzewska, Oct 20, 2014
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    • "We performed array CGH studies in 517 patients with various types of epilepsy (primarily generalized); ~5 % of patients carried a non-recurrent CNV that affected at least one gene and was not seen in controls [33]. In a study of 102 patients with epilepsy with or without other neurodevelopmental abnormalities, 23/102 individuals had at least one non-polymorphic CNV [34]. Investigation of patients with epileptic encephalopathy syndromes also confirms the role of non-recurrent CNVs in severe epilepsies [35•]. "
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    ABSTRACT: Copy number variants (CNVs) are deletions or duplications of DNA. CNVs have been increasingly recognized as an important source of both normal genetic variation and pathogenic mutation. Technologies for genome-wide discovery of CNVs facilitate studies of large cohorts of patients and controls to identify CNVs that cause increased risk for disease. Over the past 5 years, studies of patients with epilepsy confirm that both recurrent and non-recurrent CNVs are an important source of mutation for patients with various forms of epilepsy. Here, we will review the latest findings and explore the clinical implications.
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    • "Rare copy number variants (CNV) have been implicated in the pathogenesis of many neuropsychiatric diseases despite the appreciation of the abundance of common CNVs in normal individuals [9,10]. Several studies have elucidated the causative role of CNV in DD/ID, ASD [11], congenital heart diseases [12], epilepsy [13], and congenital kidney malformation [14]. However, these studies also illustrated the phenotypic heterogeneity associated with a particular CNV. "
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    ABSTRACT: Background Chromosomal microarray (CMA) is currently the first-tier genetic test for patients with idiopathic neuropsychiatric diseases in many countries. Its improved diagnostic yield over karyotyping and other molecular testing facilitates the identification of the underlying causes of neuropsychiatric diseases. In this study, we applied oligonucleotide array comparative genomic hybridization as the molecular genetic test in a Chinese cohort of children with DD/ID, autism or MCA. Results CMA identified 7 clinically significant microduplications and 17 microdeletions in 19.0% (20/105) patients, with size of aberrant regions ranging from 11 kb to 10.7 Mb. Fourteen of the pathogenic copy number variant (CNV) detected corresponded to well known microdeletion or microduplication syndromes. Four overlapped with critical regions of recently identified genomic syndromes. We also identified a rare de novo 2.3 Mb deletion at 8p21.3-21.2 as a pathogenic submicroscopic CNV. We also identified two novel CNVs, one at Xq28 and the other at 12q21.31-q21.33, in two patients (1.9%) with unclear clinical significance. Overall, the detection rate of CMA is comparable to figures previously reported for accurately detect submicroscopic chromosomal imbalances and pathogenic CNVs except mosaicism, balanced translocation and inversion. Conclusions This study provided further evidence of an increased diagnostic yield of CMA and supported its use as a first line diagnostic tool for Chinese individuals with DD/ID, ASD, and MCA.
    Full-text · Article · May 2014 · Molecular Cytogenetics
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    • "This purposes to filter out known polymorphisms and, by interrogation of all known syndrome regions, to try to narrow down the segment set to only those clinically relevant. This step is followed by FISH or PCR confirmation of the CNVs existence in patient’s DNA [30,31]. "
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    ABSTRACT: Background DNA copy number variations (CNV) constitute an important source of genetic variability. The standard method used for CNV detection is array comparative genomic hybridization (aCGH). Results We propose a novel multiple sample aCGH analysis methodology aiming in rare CNVs detection. In contrast to the majority of previous approaches, which deal with cancer datasets, we focus on constitutional genomic abnormalities identified in a diverse spectrum of diseases in human. Our method is tested on exon targeted aCGH array of 366 patients affected with developmental delay/intellectual disability, epilepsy, or autism. The proposed algorithms can be applied as a post–processing filtering to any given segmentation method. Conclusions Thanks to the additional information obtained from multiple samples, we could efficiently detect significant segments corresponding to rare CNVs responsible for pathogenic changes. The robust statistical framework applied in our method enables to eliminate the influence of widespread technical artifact termed ‘waves’.
    Full-text · Article · Jun 2013 · Journal of Clinical Bioinformatics
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