Detecting copy number variation in next generation sequencing data from diagnostic gene panels

Area of Science:

• Genomics
• Bioinformatics
• Medical Genetics

Background:

• Copy number variation (CNV) detection is crucial for genetic diagnostics.
• Next-generation sequencing (NGS) offers potential for CNV detection but faces analytical challenges, especially with targeted gene panels.
• Current wet lab methods like MLPA are costly, time-consuming, and gene-specific.

Purpose of the Study:

• To develop a bioinformatics tool for CNV detection from NGS data in medical genetic diagnostic samples.
• To address the limitations of existing methods for CNV analysis in targeted gene panels.

Main Methods:

• A computational pipeline utilizing coverage depth and copy number ratio scores.
• Dynamic pool selection based on nearest average coverage depth.
• Sliding window approach for analyzing regions at varying resolutions to detect small or partial exon CNVs.

Main Results:

• The pipeline achieved 100% sensitivity and 91% specificity in identifying CNVs in 36 positive control samples.
• Successfully detected whole gene deletions/duplications, single/multi-exonic deletions/duplications, partial exonic deletions, and mosaic deletions.
• Demonstrated diagnostic value with over 45 CNV findings in routine tests since mid-2018.

Conclusions:

• The pipeline facilitates the detection of various intragenic CNVs, including partial, single, or multi-exonic events.
• Expanded the range of genes for CNV detection in the diagnostic lab, overcoming previous MLPA kit limitations.
• Integrated into diagnostic practices, enhancing the lab's ability to identify genetic variations.