Pre-capture multiplexing provides additional power to detect copy number variation in exome sequencing

Area of Science:

• Genomics
• Genetic diagnostics

Background:

• Exome sequencing (ES) is increasingly used clinically, but detecting copy-number variants (CNVs), especially small ones, remains challenging.
• Existing CNV detection methods for ES often lack power for small variants and require large sample sizes or prior knowledge, which are not always available in clinical settings.

Purpose of the Study:

• To demonstrate the benefits of multiplexed exome capture for reducing data variance.
• To introduce mcCNV, a novel algorithm for detecting CNVs from multiplexed ES data, specifically designed to improve the detection of small CNVs.

Main Methods:

• Multiplexed exome capture was employed by pooling samples before the capture process.
• A novel depth-based algorithm, mcCNV, was developed for CNV detection using multiplexed capture data.
• The performance of mcCNV was evaluated using simulation studies and compared against matched whole-genome sequencing data and the ExomeDepth algorithm.

Main Results:

• Multiplexed capture significantly reduces inter-sample variance.
• The mcCNV algorithm demonstrates improved detection of small CNVs.
• mcCNV shows a favorable false discovery rate (FDR) in simulations and performs comparably to ExomeDepth when validated against genome sequencing data.

Conclusions:

• Multiplexed exome capture enhances the power to detect single-exon CNVs.
• The mcCNV algorithm offers a potentially more favorable FDR compared to ExomeDepth.
• Key advantages of the mcCNV approach include independence from reference sample databases and prior variant information.