Chromosomal Microarray Testing in 42 Korean Patients with Unexplained Developmental Delay, Intellectual Disability, Autism Spectrum Disorders, and Multiple Congenital Anomalies

Lee, Sun-Ho; Song, Wenling

doi:10.5808/gi.2017.15.3.82

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…MCAs are defined as multiple major structural malformations that cannot be explained by an underlying syndrome or sequence. These disorders might have a genetic etiology involving the gains and losses of CNVs and loss of heterozygosity (LOH), and the clinical consequences of these rearrangements are commonly associated with location, size, and the gene content (Figures 2 and 3) [32][33][34].…”

Section: Application In the Diagnosis Of Human Diseasesmentioning

confidence: 99%

“…Using the combining SNPs with customized exon-targeted oligonucleotide array in a cohort of 3240 patients, Wiszniewska et al [24] provided a comprehensive approach for the identification of clinically relevant copy number neutral changes in addition to CNVs in a single assay. A study using CMA for 42 Korean patients with unexplained DD, ID, ASD, and MCA identified clinically relevant CNVs in 66.6% of patients [33]. Therefore, microarray-based technologies have become a powerful tool for the identification of genomic rearrangements smaller than 5 Mb that are associated with neurodevelopmental disorders.…”

Section: Application In the Diagnosis Of Human Diseasesmentioning

confidence: 99%

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Cytogenomic Microarray Testing

Pinto¹,

Cruz²,

Costa³

et al. 2019

Cytogenetics - Past, Present and Further Perspectives

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Cytogenomic microarray testing allows the detection of submicroscopic genomic rearrangements, commonly denominated copy number variations (CNVs) that are implicated with many neurodevelopmental disorders, dysmorphic features, multiple congenital anomalies, hematological and solid tumors, and complex disorders and traits in both humans and animals. On the other hand, this approach is also widely used for the identification of structural variations that are applied as a biomarker in pharmacogenomics, agriculture, and animal selection and breeding. The chromosomal microarray analysis (CMA) has been applied for over a decade to screen for submicroscopic genomic gains and losses in DNA sample in both diagnostic and functional scenarios. Herein, we present an overview of the fundamental concepts of cytogenomics and its potential application in human genetic diagnosis, agrigenomics, mutagenesis, and pharmacogenomics.

show abstract

Section: Application In the Diagnosis Of Human Diseasesmentioning

confidence: 99%

Section: Application In the Diagnosis Of Human Diseasesmentioning

confidence: 99%

Cytogenomic Microarray Testing

Pinto¹,

Cruz²,

Costa³

et al. 2019

Cytogenetics - Past, Present and Further Perspectives

View full text Add to dashboard Cite

show abstract

“…The use of NGS for diagnosing DMD also had recently been in the spotlight [ 9 ]. Chromosomal microarray analysis (CMA) can also be used to identify the underlying congenital causes of unexplained developmental delay [ 10 ]. To the authors’ knowledge, the diagnostic application of NGS and CMA for infantile presymptomatic DMD has not been previously reported.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Duchenne Muscular Dystrophy in a Presymptomatic Infant Using Next-Generation Sequencing and Chromosomal Microarray Analysis: A Case Report

Park

Shim

et al. 2021

Children

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Duchenne muscular dystrophy is a progressive and lethal X-linked recessive neuromuscular disease caused by mutations in the dystrophin gene. It has a high rate of diagnostic delay; early diagnosis and treatment are often not possible due to delayed recognition of muscle weakness and lack of effective treatments. Current treatments based on genetic therapy can improve clinical results, but treatment must begin as early as possible before significant muscle damage. Therefore, early diagnosis and rehabilitation of Duchenne muscular dystrophy are needed before symptom aggravation. Creatine kinase is a diagnostic marker of neuromuscular disorders. Herein, the authors report a case of an infant patient with Duchenne muscular dystrophy with a highly elevated creatine kinase level but no obvious symptoms of muscle weakness. The patient was diagnosed with Duchenne muscular dystrophy via next-generation sequencing and chromosomal microarray analysis to identify possible inherited metabolic and neuromuscular diseases related to profound hyperCKemia. The patient is enrolled in a rehabilitation program and awaits the approval of the genetic treatment in Korea. This is the first report of an infantile presymptomatic Duchenne muscular dystrophy diagnosis using next-generation sequencing and chromosomal microarray analysis.

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Combining Structured Node Content and Topology Information for Networked Graph Clustering

Guo

Zhu

et al. 2017

ACM Trans. Knowl. Discov. Data

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Graphs are popularly used to represent objects with shared dependency relationships. To date, all existing graph clustering algorithms consider each node as a single attribute or a set of independent attributes, without realizing that content inside each node may also have complex structures. In this article, we formulate a new networked graph clustering task where a network contains a set of inter-connected (or networked) super-nodes, each of which is a single-attribute graph. The new super-node representation is applicable to many real-world applications, such as a citation network where each node denotes a paper whose content can be described as a graph, and citation relationships between papers form a networked graph (i.e., a super-graph). Networked graph clustering aims to find similar node groups, each of which contains nodes with similar content and structure information. The main challenge is to properly calculate the similarity between super-nodes for clustering. To solve the problem, we propose to characterize node similarity by integrating structure and content information of each super-node. To measure node content similarity, we use cosine distance by considering overlapped attributes between two super-nodes. To measure structure similarity, we propose an Attributed Random Walk Kernel (ARWK) to calculate the similarity between super-nodes. Detailed node content analysis is also included to build relationships between super-nodes with shared internal structure information, so the structure similarity can be calculated in a precise way. By integrating the structure similarity and content similarity as one matrix, the spectral clustering is used to achieve networked graph clustering. Our method enjoys sound theoretical properties, including bounded similarities and better structure similarity assessment than traditional graph clustering methods. Experiments on real-world applications demonstrate that our method significantly outperforms baseline approaches.

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Chromosomal Microarray Testing in 42 Korean Patients with Unexplained Developmental Delay, Intellectual Disability, Autism Spectrum Disorders, and Multiple Congenital Anomalies

Cited by 5 publications

References 10 publications

Cytogenomic Microarray Testing

Cytogenomic Microarray Testing

Diagnosis of Duchenne Muscular Dystrophy in a Presymptomatic Infant Using Next-Generation Sequencing and Chromosomal Microarray Analysis: A Case Report

Combining Structured Node Content and Topology Information for Networked Graph Clustering

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