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NeuroArray: A Customized aCGH for the Analysis of Copy Number Variations in Neurological Disorders
Valentina La Cognata1, Giovanna Morello1, Giulia Gentile1
11Institute of Neurological Sciences, National Research Council, Via Paolo Gaifami 18, 95125, Catania, Italy; 2Institute of Neurological Sciences, National Research Council, 87050, Mangone, Cosenza, Italy; 3Department of Biomedical and Biotechnological Sciences, Section of Human Anatomy and Histology, University of Catania, Catania, Italy; 4Unit of Rare Diseases of the Nervous System in Childhood, Department of Clinical and Experimental Medicine, Section of Pediatrics and Child Neuropsychiatry, University of Catania, AOU "Policlinico-Vittorio Emanuele", Catania, Italy.
Background:
Neurological disorders are a highly heterogeneous group of pathological conditions that affect both the peripheral and the central nervous system. These pathologies are characterized by a complex and multifactorial etiology involving numerous environmental agents and genetic susceptibility factors. For this reason, the investigation of their pathogenetic basis by means of traditional methodological approaches is rather arduous. High-throughput genotyping technologies, including the microarray-based comparative genomic hybridization (aCGH), are currently replacing classical detection methods, providing powerful molecular tools to identify genomic unbalanced structural rearrangements and explore their role in the pathogenesis of many complex human diseases.
Methods:
In this report, we comprehensively describe the design method, the procedures, validation, and implementation of an exon-centric customized aCGH (NeuroArray 1.0), tailored to detect both single and multi-exon deletions or duplications in a large set of multi- and monogenic neurological diseases. This focused platform enables a targeted measurement of structural imbalances across the human genome, targeting the clinically relevant genes at exon-level resolution.
Conclusion:
An increasing use of the NeuroArray platform may offer new insights in investigating potential overlapping gene signatures among neurological conditions and defining genotype-phenotype relationships.
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