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Chromosome microarrays in diagnostic testing: interpreting the genomic data.

Greg B Peters1, Mark D Pertile

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Chromosome MicroArrays (CMAs) are essential for detecting copy number variants (CNVs) linked to developmental disorders. Interpreting CMA data involves challenges like distinguishing benign from pathogenic CNVs and managing technical noise for accurate genomic diagnostics.

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Area of Science:

  • Genomic Medicine
  • Clinical Genetics
  • Molecular Diagnostics

Background:

  • Chromosome MicroArrays (CMAs) are established diagnostic tools for detecting copy number variants (CNVs).
  • CMA testing is a first-tier recommendation for identifying CNVs associated with intellectual disability, autism spectrum disorders, and congenital anomalies.
  • CNVs are pathogenic in 14-18% of patients with these disorders.

Purpose of the Study:

  • To provide an overview of microarray diagnostics, from data inspection to report generation.
  • To discuss challenges in interpreting CMA data, specifically differentiating clinically relevant from irrelevant CNVs.
  • To address technical noise in CMA data and its impact on CNV detection.

Main Methods:

  • Review of clinical examples and various microarray platforms.
  • Analysis of CMA data interpretation strategies.
  • Discussion of technical noise sources and mitigation approaches in genomic data.

Main Results:

  • A significant challenge in CMA interpretation is distinguishing benign from pathogenic CNVs.
  • CNV length and gene content are imperfect indicators of pathogenicity.
  • Technical noise is inherent in CMA data, impacting resolution and potentially causing miscalls.

Conclusions:

  • Effective CMA data interpretation requires understanding the limitations and challenges, including differentiating CNVs and managing technical noise.
  • Strategies for handling noise are crucial for accurate genomic diagnostics.
  • Insights into CNV discovery are vital for clinicians and scientists in genome diagnostics.