A Comprehensive Workflow for Read Depth-Based Identification of Copy-Number Variation from Whole-Genome Sequence Data

Area of Science:

• Genetics
• Genomic Medicine

Background:

• Accurate detection of copy-number variations (CNVs) from whole-genome sequencing (WGS) data remains a challenge for its use as a first-tier genetic test.
• Existing methods may not fully capture the spectrum of genetic variations.

Purpose of the Study:

• To develop and validate a comprehensive workflow for identifying germline CNVs greater than 1 kilobase (kb) from short-read WGS data.
• To evaluate the performance of this workflow in detecting rare, genic CNVs in individuals with autism spectrum disorder (ASD).

Main Methods:

• Development of a detailed workflow encompassing DNA library preparation, sequencing, quality control, reference mapping, and computational CNV identification using read depth-based algorithms.
• Application of the workflow to identify germline CNVs in individuals with ASD.
• Orthogonal validation of identified CNVs and comparison with microarray data.

Main Results:

• The workflow successfully confirmed 100% of rare, genic CNVs detected in individuals with ASD using orthogonal methods.
• Identified 71 putative genic de novo CNVs in the ASD cohort with a 70% confirmation rate.
• The WGS workflow detected all clinically relevant CNVs identified by microarrays and additional potentially pathogenic CNVs under 20 kb in individuals with ASD.

Conclusions:

• The developed WGS workflow provides accurate and comprehensive detection of germline CNVs.
• WGS with this workflow offers a higher detection rate for clinically relevant CNVs compared to microarrays.
• WGS is positioned as a single, powerful assay for detecting diverse genetic variations, including CNVs.