Copy number variation in a hospital-based cohort of children with epilepsy.
Ontology highlight
ABSTRACT:
SUBMITTER: Vlaskamp DRM
PROVIDER: S-EPMC5719854 | biostudies-literature | 2017 Jun
REPOSITORIES: biostudies-literature
Publications
Copy number variation in a hospital-based cohort of children with epilepsy.
Vlaskamp Danique R M DRM Callenbach Petra M C PMC Rump Patrick P Giannini Lucia A A LAA Dijkhuizen Trijnie T Brouwer Oebele F OF van Ravenswaaij-Arts Conny M A CMA
Epilepsia open 20170508 2
<h4>Objective</h4>To evaluate the diagnostic yield of microarray analysis in a hospital-based cohort of children with seizures and to identify novel candidate genes and susceptibility loci for epilepsy.<h4>Methods</h4>Of all children who presented with their first seizure in the University Medical Center Groningen (January 2000 through May 2013) (n = 1,368), we included 226 (17%) children who underwent microarray analysis before June 2014. All 226 children had a definite diagnosis of epilepsy. A ...[more]
Similar Datasets
Project description:To evaluate the role of copy number abnormalities detectable using chromosomal microarray (CMA) testing in patients with epilepsy at a tertiary care center.We identified patients with International Classification of Diseases, ninth revision (ICD-9) codes for epilepsy or seizures and clinical CMA testing performed between October 2006 and February 2011 at Boston Children's Hospital. We reviewed medical records and included patients who met criteria for epilepsy. We phenotypically characterized patients with epilepsy-associated abnormalities on CMA.Of 973 patients who had CMA and ICD-9 codes for epilepsy or seizures, 805 patients satisfied criteria for epilepsy. We observed 437 copy number variants (CNVs) in 323 patients (1-4 per patient), including 185 (42%) deletions and 252 (58%) duplications. Forty (9%) were confirmed de novo, 186 (43%) were inherited, and parental data were unavailable for 211 (48%). Excluding full chromosome trisomies, CNV size ranged from 18kb to 142Mb, and 34% were >500kb. In at least 40 cases (5%), the epilepsy phenotype was explained by a CNV, including 29 patients with epilepsy-associated syndromes and 11 with likely disease-associated CNVs involving epilepsy genes or "hotspots." We observed numerous recurrent CNVs including 10 involving loss or gain of Xp22.31, a region described in patients with and without epilepsy.Copy number abnormalities play an important role in patients with epilepsy. Because the diagnostic yield of CMA for epilepsy patients is similar to the yield in autism spectrum disorders and in prenatal diagnosis, for which published guidelines recommend testing with CMA, we recommend the implementation of CMA in the evaluation of unexplained epilepsy.
| S-EPMC4487364 | biostudies-literature
Project description:ObjectiveCopy 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.MethodsWe 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.ResultsOf 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.SignificanceThe 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.
| S-EPMC6488157 | biostudies-literature
Project description:Introduction: The concurrence of intellectual disability/global developmental delay and epilepsy (ID/GDD-EP) is very common in the pediatric population. The etiologies for both conditions are complex and largely unknown. The predictors of significant copy number variations (CNVs) are known for the cases with ID/GDD, but unknown for those with exclusive ID/GDD-EP. Importantly, the known predictors are largely from the same ethnic group; hence, they lack replication. Purpose: We aimed to determine and investigate the diagnostic yield of CNV tests, new causative CNVs, and the independent predictors of significant CNVs in Chinese children with unexplained ID/GDD-EP. Materials and methods: A total of 100 pediatric patients with unexplained ID/GDD-EP and 1,000 healthy controls were recruited. The American College of Medical Genetics guideline was used to classify the CNVs. Additionally, clinical information was collected and compared between those with significant and non-significant CNVs. Results: Twenty-eight percent of the patients had significant CNVs, 16% had variants of unknown significance, and 56% had non-significant CNVs. In total, 31 CNVs were identified in 28% (28/100) of cases: 25 pathogenic and 6 likely pathogenic. Eighteen known syndromes were diagnosed in 17 cases. Thirteen rare CNVs (8 novel and 5 reported in literature) were identified, of which three spanned dosage-sensitive genes: 19q13.2 deletion (ATP1A3), Xp11.4-p11.3 deletion (CASK), and 6q25.3-q25.3 deletion (ARID1B). By comparing clinical features in patients with significant CNVs against those with non-significant CNVs, a statistically significant association was found between the presence of significant CNVs and speech and language delay for those aged above 2 years and for those with facial malformations, microcephaly, congenital heart disease, fair skin, eye malformations, and mega cisterna magna. Multivariate logistic regression analysis allowed the identification of two independent significant CNV predictors, which are eye malformations and facial malformations. Conclusion: Our study supports the performance of CNV tests in pediatric patients with unexplained ID/GDD-EP, as there is high diagnostic yield, which informs genetic counseling. It adds 13 rare CNVs (8 novel), which can be accountable for both conditions. Moreover, congenital eye and facial malformations are clinical markers that can aid clinicians to understand which patients can benefit from the CNV testing and which will not, thus helping patients to avoid unnecessary and expensive tests.
| S-EPMC6252327 | biostudies-literature
Project description:The analysis of structural variants, in particular of copy-number variations (CNVs), has proven valuable in unraveling the genetic basis of human diseases. Hence, a large number of algorithms have been developed for the detection of CNVs in SNP array signal intensity data. Using the European and African HapMap trio data, we undertook a comparative evaluation of six commonly used CNV detection software tools, namely Affymetrix Power Tools (APT), QuantiSNP, PennCNV, GLAD, R-gada and VEGA, and assessed their level of pair-wise prediction concordance. The tool-specific CNV prediction accuracy was assessed in silico by way of intra-familial validation. Software tools differed greatly in terms of the number and length of the CNVs predicted as well as the number of markers included in a CNV. All software tools predicted substantially more deletions than duplications. Intra-familial validation revealed consistently low levels of prediction accuracy as measured by the proportion of validated CNVs (34-60%). Moreover, up to 20% of apparent family-based validations were found to be due to chance alone. Software using Hidden Markov models (HMM) showed a trend to predict fewer CNVs than segmentation-based algorithms albeit with greater validity. PennCNV yielded the highest prediction accuracy (60.9%). Finally, the pairwise concordance of CNV prediction was found to vary widely with the software tools involved. We recommend HMM-based software, in particular PennCNV, rather than segmentation-based algorithms when validity is the primary concern of CNV detection. QuantiSNP may be used as an additional tool to detect sets of CNVs not detectable by the other tools. Our study also reemphasizes the need for laboratory-based validation, such as qPCR, of CNVs predicted in silico.
| S-EPMC4510559 | biostudies-literature
Project description:BACKGROUND:Copy number variations (CNVs) are large segments of the genome that are duplicated or deleted. Structural variations in the genome have been linked to many complex diseases. Similar to how genome-wide association studies (GWAS) have helped discover single-nucleotide polymorphisms linked to disease phenotypes, the extension of GWAS to CNVs has aided the discovery of structural variants associated with human traits and diseases. RESULTS:We present CoNVaQ, an easy-to-use web-based tool for CNV-based association studies. The web service allows users to upload two sets of CNV segments and search for genomic regions where the occurrence of CNVs is significantly associated with the phenotype. CoNVaQ provides two models: a simple statistical model using Fisher's exact test and a novel query-based model matching regions to user-defined queries. For each region, the method computes a global q-value statistic by repeated permutation of samples among the populations. We demonstrate our platform by using it to analyze a data set of HPV-positive and HPV-negative penile cancer patients. CONCLUSIONS:CoNVaQ provides a simple workflow for performing CNV-based association studies. It is made available as a web platform in order to provide a user-friendly workflow for biologists and clinicians to carry out CNV data analysis without installing any software. Through the web interface, users are also able to analyze their results to find overrepresented GO terms and pathways. In addition, our method is also available as a package for the R programming language. CoNVaQ is available at https://convaq.compbio.sdu.dk .
| S-EPMC5960125 | biostudies-other
Project description:Importance:Copy number variation (CNV) is an important cause of neuropsychiatric disorders. Little is known about the role of CNV in adults with epilepsy and intellectual disability. Objectives:To evaluate the prevalence of pathogenic CNVs and identify possible candidate CNVs and genes in patients with epilepsy and intellectual disability. Design, Setting, and Participants:In this cross-sectional study, genome-wide microarray was used to evaluate a cohort of 143 adults with unexplained childhood-onset epilepsy and intellectual disability who were recruited from the Toronto Western Hospital epilepsy outpatient clinic from January 1, 2012, through December 31, 2014. The inclusion criteria were (1) pediatric seizure onset with ongoing seizure activity in adulthood, (2) intellectual disability of any degree, and (3) no structural brain abnormalities or metabolic conditions that could explain the seizures. Main Outcomes and Measures:DNA screening was performed using genome-wide microarray platforms. Pathogenicity of CNVs was assessed based on the American College of Medical Genetics guidelines. The Residual Variation Intolerance Score was used to evaluate genes within the identified CNVs that could play a role in each patient's phenotype. Results:Of the 2335 patients, 143 probands were investigated (mean [SD] age, 24.6 [10.8] years; 69 male and 74 female). Twenty-three probands (16.1%) and 4 affected relatives (2.8%) (mean [SD] age, 24.1 [6.1] years; 11 male and 16 female) presented with pathogenic or likely pathogenic CNVs (0.08-18.9 Mb). Five of the 23 probands with positive results (21.7%) had more than 1 CNV reported. Parental testing revealed de novo CNVs in 11 (47.8%), with CNVs inherited from a parent in 4 probands (17.4%). Sixteen of 23 probands (69.6%) presented with previously cataloged human genetic disorders and/or defined CNV hot spots in epilepsy. Eight nonrecurrent rare CNVs that overlapped 1 or more genes associated with intellectual disability, autism, and/or epilepsy were identified: 2p16.1-p15 duplication, 6p25.3-p25.1 duplication, 8p23.3p23.1 deletion, 9p24.3-p23 deletion, 10q11.22-q11.23 duplication, 12p13.33-13.2 duplication, 13q34 deletion, and 16p13.2 duplication. Five genes are of particular interest given their potential pathogenicity in the corresponding phenotypes and least tolerability to variation: ABAT, KIAA2022, COL4A1, CACNA1C, and SMARCA2. ABAT duplication was associated with Lennox-Gastaut syndrome and KIAA2022 deletion with Jeavons syndrome. Conclusions and Relevance:The high prevalence of pathogenic CNVs in this study highlights the importance of microarray analysis in adults with unexplained childhood-onset epilepsy and intellectual disability. Additional studies and comparison with similar cases are required to evaluate the effects of deletions and duplications that overlap specific genes.
| S-EPMC5710585 | biostudies-literature
Project description:Next-generation sequencing (NGS) technologies offer new opportunities for precise and accurate identification of genomic aberrations, including copy number variations (CNVs). For high-throughput NGS data, using depth of coverage has become a major approach to identify CNVs, especially for whole exome sequencing (WES) data. Due to the high level of noise and biases of read-count data and complexity of the WES data, existing CNV detection tools identify many false CNV segments. Besides, NGS generates a huge amount of data, requiring to use effective and efficient methods. In this work, we propose a novel segmentation algorithm based on the total variation approach to detect CNVs more precisely and efficiently using WES data. The proposed method also filters out outlier read-counts and identifies significant change points to reduce false positives. We used real and simulated data to evaluate the performance of the proposed method and compare its performance with those of other commonly used CNV detection methods. Using simulated and real data, we show that the proposed method outperforms the existing CNV detection methods in terms of accuracy and false discovery rate and has a faster runtime compared to the circular binary segmentation method.
| S-EPMC7278034 | biostudies-literature
Project description:Copy number variants (CNVs) are intraspecies duplications/deletions of large DNA segments (>1 kb). A growing number of reports highlight the functional and evolutionary impact of CNV in plants, increasing the need for appropriate tools that enable locus-specific CNV genotyping on a population scale. Multiplex ligation-dependent probe amplification (MLPA) is considered a gold standard in genotyping CNV in humans. Consequently, numerous commercial MLPA assays for CNV-related human diseases have been created. We routinely genotype complex multiallelic CNVs in human and plant genomes using the modified MLPA procedure based on fully synthesized oligonucleotide probes (90-200 nt), which greatly simplifies the design process and allows for the development of custom assays. Here, we present a step-by-step protocol for gene-specific MLPA probe design, multiplexed assay setup and data analysis in a copy number genotyping experiment in plants. As a case study, we present the results of a custom assay designed to genotype the copy number status of 12 protein coding genes in a population of 80 Arabidopsis accessions. The genes were pre-selected based on whole genome sequencing data and are localized in the genomic regions that display different levels of population-scale variation (non-variable, biallelic, or multiallelic, as well as CNVs overlapping whole genes or their fragments). The presented approach is suitable for population-scale validation of the CNV regions inferred from whole genome sequencing data analysis and for focused analysis of selected genes of interest. It can also be very easily adopted for any plant species, following optimization of the template amount and design of the appropriate control probes, according to the general guidelines presented in this paper.
| S-EPMC5318451 | biostudies-literature
Project description:BackgroundCopy number variants (CNVs) include deletions or multiplications spanning genomic regions. These regions vary in size and may span genes known to play a role in human diseases. As examples, duplications and triplications of SNCA have been shown to cause forms of Parkinson's disease, while duplications of APP cause early onset Alzheimer's disease (AD).ResultsHere, we performed a systematic analysis of CNVs in a Turkish dementia cohort in order to further characterize the genetic causes of dementia in this population. One hundred twenty-four Turkish individuals, either at risk of dementia due to family history, diagnosed with mild cognitive impairment, AD, or frontotemporal dementia, were whole-genome genotyped and CNVs were detected. We integrated family analysis with a comprehensive assessment of potentially disease-associated CNVs in this Turkish dementia cohort. We also utilized both dementia and non-dementia individuals from the UK Biobank in order to further elucidate the potential role of the identified CNVs in neurodegenerative diseases. We report CNVs overlapping the previously implicated genes ZNF804A, SNORA70B, USP34, XPO1, and a locus on chromosome 9 which includes a cluster of olfactory receptors and ABCA1. Additionally, we also describe novel CNVs potentially associated with dementia, overlapping the genes AFG1L, SNX3, VWDE, and BC039545.ConclusionsGenotyping data from understudied populations can be utilized to identify copy number variation which may contribute to dementia.
| S-EPMC8317312 | biostudies-literature
Project description:In recent years there has been a growing interest in the role of copy number variations (CNV) in genetic diseases. Though there has been rapid development of technologies and statistical methods devoted to detection in CNVs from array data, the inherent challenges in data quality associated with most hybridization techniques remains a challenging problem in CNV association studies.To help address these data quality issues in the context of family-based association studies, we introduce a statistical framework for the intensity-based array data that takes into account the family information for copy-number assignment. The method is an adaptation of traditional methods for modeling SNP genotype data that assume Gaussian mixture model, whereby CNV calling is performed for all family members simultaneously and leveraging within family-data to reduce CNV calls that are incompatible with Mendelian inheritance while still allowing de-novo CNVs. Applying this method to simulation studies and a genome-wide association study in asthma, we find that our approach significantly improves CNV calls accuracy, and reduces the Mendelian inconsistency rates and false positive genotype calls. The results were validated using qPCR experiments.In conclusion, we have demonstrated that the use of family information can improve the quality of CNV calling and hopefully give more powerful association test of CNVs.
| S-EPMC3668900 | biostudies-other