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CNV Detection from Exome Sequencing Data in Routine Diagnostics of Rare Genetic Disorders: Opportunities and Limitations
To assess the potential of detecting copy number variations (CNVs) directly from exome sequencing (ES) data in diagnostic settings, we developed a CNV-detection pipeline based on ExomeDepth software and applied it to ES data of 450 individuals. Initially, only CNVs affecting genes in the requested d...
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Published in: | Genes 2021-09, Vol.12 (9), p.1427 |
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description | To assess the potential of detecting copy number variations (CNVs) directly from exome sequencing (ES) data in diagnostic settings, we developed a CNV-detection pipeline based on ExomeDepth software and applied it to ES data of 450 individuals. Initially, only CNVs affecting genes in the requested diagnostic gene panels were scored and tested against arrayCGH results. Pathogenic CNVs were detected in 18 individuals. Most detected CNVs were larger than 400 kb (11/18), but three individuals had small CNVs impacting one or a few exons only and were thus not detectable by arrayCGH. Conversely, two pathogenic CNVs were initially missed, as they impacted genes not included in the original gene panel analysed, and a third one was missed as it was in a poorly covered region. The overall combined diagnostic rate (SNVs + CNVs) in our cohort was 36%, with wide differences between clinical domains. We conclude that (1) the ES-based CNV pipeline detects efficiently large and small pathogenic CNVs, (2) the detection of CNV relies on uniformity of sequencing and good coverage, and (3) in patients who remain unsolved by the gene panel analysis, CNV analysis should be extended to all captured genes, as diagnostically relevant CNVs may occur everywhere in the genome. |
doi_str_mv | 10.3390/genes12091427 |
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Initially, only CNVs affecting genes in the requested diagnostic gene panels were scored and tested against arrayCGH results. Pathogenic CNVs were detected in 18 individuals. Most detected CNVs were larger than 400 kb (11/18), but three individuals had small CNVs impacting one or a few exons only and were thus not detectable by arrayCGH. Conversely, two pathogenic CNVs were initially missed, as they impacted genes not included in the original gene panel analysed, and a third one was missed as it was in a poorly covered region. The overall combined diagnostic rate (SNVs + CNVs) in our cohort was 36%, with wide differences between clinical domains. We conclude that (1) the ES-based CNV pipeline detects efficiently large and small pathogenic CNVs, (2) the detection of CNV relies on uniformity of sequencing and good coverage, and (3) in patients who remain unsolved by the gene panel analysis, CNV analysis should be extended to all captured genes, as diagnostically relevant CNVs may occur everywhere in the genome.</description><identifier>ISSN: 2073-4425</identifier><identifier>EISSN: 2073-4425</identifier><identifier>DOI: 10.3390/genes12091427</identifier><identifier>PMID: 34573409</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Adolescent ; Adult ; Aged ; Aged, 80 and over ; Annotations ; Bioinformatics ; Child ; Child, Preschool ; Chromosomes ; Cohort Studies ; Copy number ; Diagnostic Tests, Routine ; DNA Copy Number Variations ; Exons ; Female ; Genes ; Genetic disorders ; Genetic Testing - methods ; Genomes ; Genomics ; High-Throughput Nucleotide Sequencing - methods ; Humans ; Infant ; Informed consent ; Laboratories ; Male ; Middle Aged ; Patients ; Rare Diseases - diagnosis ; Rare Diseases - epidemiology ; Rare Diseases - genetics ; Sequence Analysis, DNA - methods ; Software ; Switzerland - epidemiology ; Whole Exome Sequencing - methods ; Young Adult</subject><ispartof>Genes, 2021-09, Vol.12 (9), p.1427</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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Initially, only CNVs affecting genes in the requested diagnostic gene panels were scored and tested against arrayCGH results. Pathogenic CNVs were detected in 18 individuals. Most detected CNVs were larger than 400 kb (11/18), but three individuals had small CNVs impacting one or a few exons only and were thus not detectable by arrayCGH. Conversely, two pathogenic CNVs were initially missed, as they impacted genes not included in the original gene panel analysed, and a third one was missed as it was in a poorly covered region. The overall combined diagnostic rate (SNVs + CNVs) in our cohort was 36%, with wide differences between clinical domains. We conclude that (1) the ES-based CNV pipeline detects efficiently large and small pathogenic CNVs, (2) the detection of CNV relies on uniformity of sequencing and good coverage, and (3) in patients who remain unsolved by the gene panel analysis, CNV analysis should be extended to all captured genes, as diagnostically relevant CNVs may occur everywhere in the genome.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>34573409</pmid><doi>10.3390/genes12091427</doi><orcidid>https://orcid.org/0000-0002-3543-7531</orcidid><orcidid>https://orcid.org/0000-0003-2711-0480</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adolescent Adult Aged Aged, 80 and over Annotations Bioinformatics Child Child, Preschool Chromosomes Cohort Studies Copy number Diagnostic Tests, Routine DNA Copy Number Variations Exons Female Genes Genetic disorders Genetic Testing - methods Genomes Genomics High-Throughput Nucleotide Sequencing - methods Humans Infant Informed consent Laboratories Male Middle Aged Patients Rare Diseases - diagnosis Rare Diseases - epidemiology Rare Diseases - genetics Sequence Analysis, DNA - methods Software Switzerland - epidemiology Whole Exome Sequencing - methods Young Adult |
title | CNV Detection from Exome Sequencing Data in Routine Diagnostics of Rare Genetic Disorders: Opportunities and Limitations |
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