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Understanding genetic variability: exploring large-scale copy number variants through non-invasive prenatal testing in European populations.

Authors :
Holesova, Zuzana
Pös, Ondrej
Gazdarica, Juraj
Kucharik, Marcel
Budis, Jaroslav
Hyblova, Michaela
Minarik, Gabriel
Szemes, Tomas
Source :
BMC Genomics. 4/15/2024, Vol. 25 Issue 1, p1-9. 9p.
Publication Year :
2024

Abstract

Large-scale copy number variants (CNVs) are structural alterations in the genome that involve the duplication or deletion of DNA segments, contributing to genetic diversity and playing a crucial role in the evolution and development of various diseases and disorders, as they can lead to the dosage imbalance of one or more genes. Massively parallel sequencing (MPS) has revolutionized the field of genetic analysis and contributed significantly to routine clinical diagnosis and screening. It offers a precise method for detecting CNVs with exceptional accuracy. In this context, a non-invasive prenatal test (NIPT) based on the sequencing of cell-free DNA (cfDNA) from pregnant women's plasma using a low-coverage whole genome MPS (WGS) approach represents a valuable source for population studies. Here, we analyzed genomic data of 12,732 pregnant women from the Slovak (9,230), Czech (1,583), and Hungarian (1,919) populations. We identified 5,062 CNVs ranging from 200 kbp and described their basic characteristics and differences between the subject populations. Our results suggest that re-analysis of sequencing data from routine WGS assays has the potential to obtain large-scale CNV population frequencies, which are not well known and may provide valuable information to support the classification and interpretation of this type of genetic variation. Furthermore, this could contribute to expanding knowledge about the central European genome without investing in additional laboratory work, as NIPTs are a relatively widely used screening method. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14712164
Volume :
25
Issue :
1
Database :
Academic Search Index
Journal :
BMC Genomics
Publication Type :
Academic Journal
Accession number :
176610046
Full Text :
https://doi.org/10.1186/s12864-024-10267-5