High Detection Rate of Copy Number Variations Using Capture Sequencing Data: A Retrospective Study

Area of Science:

• Genomics
• Clinical Diagnostics
• Genetic Variation Analysis

Background:

• Capture sequencing (CS) is standard for detecting small genetic variations like SNVs and indels.
• Existing algorithms for copy number variation (CNV) detection from CS data require large-scale validation for clinical utility.
• A systematic evaluation of CS-based CNV detection in clinical diagnosis using a large sample size was lacking.

Purpose of the Study:

• To evaluate the efficacy and diagnostic yield of a capture sequencing-based copy number variation (CS-CNV) detection pipeline.
• To assess the clinical utility of CS for identifying copy number variations in undiagnosed samples.
• To determine the diagnostic yield and spectrum of genetic disorders identified by CS-based CNV analysis.

Main Methods:

• Retrospective analysis of 3010 samples undergoing CS testing.
• Development of a robust CS-CNV pipeline using 68 chromosomal microarray-positive samples (true set) and 1520 reference samples.
• Detection and confirmation of candidate clinically relevant CNVs in 1422 undiagnosed samples using an alternative method.

Main Results:

• The CS-CNV pipeline achieved 98.7% analytical sensitivity and 49.4% positive predictive value in the true set.
• Clinically relevant CNVs were identified in 106 of 1422 undiagnosed samples, with 90.6% confirmed.
• The overall diagnostic yield was 6.8%, identifying aneuploidies, microdeletion/microduplication syndromes, and Mendelian disorders.

Conclusions:

• Capture sequencing-based CNV detection demonstrates high diagnostic yield and potential clinical utility.
• The developed CS-CNV pipeline can simultaneously evaluate CNVs and small genetic variations.
• Further refinement of the CS-CNV pipeline may enhance its application in pre- and postnatal genetic diagnostics.