Identification of Structural Variation from NGS-Based Non-Invasive Prenatal Testing

Area of Science:

• Genomics
• Human Genetics
• Population Studies

Background:

• Copy number variants (CNVs) are key human genome variations influencing evolution, diversity, and disease.
• Next-generation sequencing (NGS) offers sensitive and accurate CNV detection for clinical diagnostics.
• Previous work established a non-invasive prenatal test (NIPT) for detecting CNV aberrations ≥600 kbp using low-coverage whole-genome sequencing.

Purpose of the Study:

• To reanalyze existing NIPT genomic data from 5018 patients to characterize CNV aberrations within the Slovak population.
• To assess the overlap of identified CNVs with existing databases like ClinVar.
• To classify the identified CNVs based on their clinical significance using the AnnotSV method.

Main Methods:

• Reanalysis of low-coverage, massively parallel whole-genome sequencing data from 5018 NIPT samples.
• Identification and characterization of autosomal CNVs (deletions and duplications).
• Comparison of identified CNVs against the ClinVar database and classification using the AnnotSV tool.

Main Results:

• Identified 225 maternal CNVs (47 deletions, 178 duplications) ranging from 600 to 7820 kbp in the Slovak population.
• Found significant overlap with ClinVar: 60.89% fully overlapping, 29.33% partially overlapping, and 9.78% novel variants.
• Classified variants as 129 likely benign, 13 of uncertain significance, and 83 likely pathogenic.

Conclusions:

• NIPT data serves as a valuable resource for generating population-specific genomic variation data.
• The study highlights the utility of commercial CNV analysis test data for population studies.
• Genomic data from NIPT can provide insights into population diversity and the spectrum of genetic variants.