Enhancing the Reliability of PMP22 Copy Number Variation Detection with an Inherited Peripheral Neuropathy Panel

Area of Science:

• Genetics
• Neurology
• Molecular Diagnostics

Background:

• Hereditary peripheral neuropathies are often caused by PMP22 gene abnormalities.
• Next-generation sequencing (NGS) panels offer a broad approach to genetic testing.
• Accurate detection of PMP22 copy number variation (CNV) is crucial for diagnosing these conditions.

Purpose of the Study:

• To evaluate the analytical performance of a novel algorithm for detecting PMP22 CNV using NGS panel data.
• To assess the utility of including adjacent genes within the 17p11.2 region in PMP22 CNV detection.
• To determine if NGS-based CNV calling can simplify diagnostic strategies for peripheral polyneuropathies.

Main Methods:

• Development and application of a laboratory-developed algorithm for CNV calling from NGS data.
• Targeted NGS panel analysis of 141 genes, including PMP22 and neighboring genes in the 17p11.2 region.
• Validation of NGS results against multiplex ligation-dependent probe amplification (MLPA) and a blind dataset.

Main Results:

• The NGS algorithm demonstrated 100% concordance with MLPA for PMP22 CNV detection in 58 cases (23 duplications, 2 deletions).
• Analysis of adjacent genes within the 1.5-megabase duplicated region at 17p11.2 improved the reliability of PMP22 CNV detection.
• Validation on a blind dataset confirmed the reliable performance of the NGS pipeline for PMP22 CNV calling.

Conclusions:

• NGS-based detection of PMP22 CNV is highly accurate and reliable.
• Analyzing adjacent genes alongside PMP22 enhances the diagnostic yield of NGS panels for hereditary neuropathies.
• This NGS strategy can simplify the diagnostic workflow for peripheral polyneuropathies, potentially replacing MLPA.