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Genetic Disorders

Neurologic Features with Pathogenic Copy Number Variants

Author:

Jason Coryell

Departments of Pediatrics and Neurology, Oregon Health & Sciences University, Portland, OR, US
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Abstract

Investigators from Children’s Hospital at Westmead, University of Sydney, performed a retrospective review (2006-2012) of the diagnostic yield of array comparative genomic hybridization among 555 children with diverse neurologic phenotypes in whom a genetic etiology was suspected.
How to Cite: Coryell, J., 2020. Neurologic Features with Pathogenic Copy Number Variants. Pediatric Neurology Briefs, 34, p.20. DOI: http://doi.org/10.15844/pedneurbriefs-34-20
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  Published on 18 Dec 2020
 Accepted on 10 Dec 2020            Submitted on 03 Dec 2020

Investigators from Children’s Hospital at Westmead, University of Sydney, performed a retrospective review (2006-2012) of the diagnostic yield of array comparative genomic hybridization (aCGH) among 555 children with diverse neurologic phenotypes in whom a genetic etiology was suspected [1]. Pathogenicity of copy number variants (CNV) was classified according to previously published guidelines [2]. Forty-seven patients (8.6%) had pathogenic variants. The neurologic phenotype was divided into 17 broad categories. Those with significantly increased odds ratios of a pathogenic CNV included: global developmental delay (DD) [OR 3.69], dysmorphism [OR 2.75], cortical visual impairment [2.73], and microcephaly [OR 2.16]. Logistic regression analysis showed an additive effect of multiple phenotypic categories being more likely associated with a pathogenic CNV (OR 1.18). The combination of developmental delay/intellectual disability with dysmorphism and abnormal head circumference showed the greatest effect among combined categories (OR 2.86). Epilepsy, cerebral palsy, tone abnormality, ataxia, movement disorder, psychiatric comorbidity, and abnormal neuro-diagnostics (MRI brain or spine, EEG) were not independently predictive for pathogenic CNV. [1]

COMMENTARY. This study is in line with multiple prior studies showing increased frequency (~15%) of pathogenic CNVs in individuals with developmental delay (DD)/ intellectual disability (ID) [3]. Pathogenic CNVs have also been shown at higher rates in those with multiple congenital anomalies (17%) [4]. Additionally, >50% of individuals with pathogenic CNVs may have dysmorphic features when refined phenotyping is applied [5].

The authors suggest that the diagnostic yield of aCGH warrants this as a first-tier test in pediatric neurology patients; however, aCGH is perhaps best suited for a targeted population: including those with DD/ID, dysmorphic features, multiple congenital anomalies, or microcephaly. Other studies addressing specific neuro-phenotypes, such as epilepsy or weakness, show a higher diagnostic yield with whole-exome sequencing (WES) or targeted panels. For example, in pediatric epilepsy patients, a meta-analysis revealed a diagnostic yield of 45% for WES, 23% for a targeted panel (TP), and 8% for CGH. A cost-effectiveness analysis indicated that a tiered testing system was cheaper when the initial test was WES or TP, rather than aCGH [6]. Similarly, the diagnostic yield of WES within a pediatric neuromuscular clinic was 39% [7].

This chart review predates the increased use of next-generation sequencing panels or WES. As the authors indicate, the increasing use of WES as a first test will identify many CNVs previously detected on aCGH. If there is a high a priori suspicion that the phenotype is more consistent with a CNV than a single gene disorder, aCGH could be a more rapid and cost-effective approach for that subset of neurology patients.

This article contributes to pediatric neurogenetics literature by helping to narrow the spectrum of neuro-phenotypes for whom CGH may be the best initial test.

Disclosures

The author has declared that no competing interests exist.

References

  1. Misra, S Peters, G Barnes, E Ardern-Holmes, S Webster, R Troedson, C et al. (2019). Yield of comparative genomic hybridization microarray in pediatric neurology practice. Neurol Genet Oct 20195(6): e367. https://doi.org/10.1212/NXG.0000000000000367. [PubMed]  

  2. Beaudet, AL (2014). Reaching a CNV milestone. Nat Genet Oct 201446(10): 1046–8. https://doi.org/10.1038/ng.3106. [PubMed]  

  3. Miller, DT Adam, MP Aradhya, S Biesecker, LG Brothman, AR Carter, NP et al. (2010). Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Am J Hum Genet May 201086(5): 749–64. https://doi.org/10.1016/j.ajhg.2010.04.006. [PubMed]  

  4. Lu, XY Phung, MT Shaw, CA Pham, K Neil, SE Patel, A et al. (2008). Genomic imbalances in neonates with birth defects: high detection rates by using chromosomal microarray analysis. Pediatrics Dec 2008122(6): 1310–8. https://doi.org/10.1542/peds.2008-0297. [PubMed]  

  5. Qiao, Y Mercier, E Dastan, J Hurlburt, J McGillivray, B Chudley, AE et al. (2014). Copy number variants (CNVs) analysis in a deeply phenotyped cohort of individuals with intellectual disability (ID). BMC Med Genet Jul 201415(1): 82. https://doi.org/10.1186/1471-2350-15-82. [PubMed]  

  6. Sánchez Fernández, I, Loddenkemper, T, Gaínza-Lein, M, Sheidley, BR and Poduri, A (2019). Diagnostic yield of genetic tests in epilepsy: A meta-analysis and cost-effectiveness study. Neurology Jan 201992(5): e418–28. https://doi.org/10.1212/WNL.0000000000006850. [PubMed]  

  7. Waldrop, MA Pastore, M Schrader, R Sites, E Bartholomew, D Tsao, CY et al. (2019). Diagnostic Utility of Whole Exome Sequencing in the Neuromuscular Clinic. Neuropediatrics Apr 201950(2): 96–102. https://doi.org/10.1055/s-0039-1677734. [PubMed]  


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