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Background <p>Copy number variation (CNV) is a class of genomic structural variation (SV) that contributes to genomic disorders and can significantly impact health. Short-read genome sequencing (sr-GS) enables genome-wide SV calling which has been shown to increase diagnosis in unsolved rare disease families. The growing number of large sequencing cohort projects with sr-GS data available requires open free analytical tools that provide visualization of CNV and SV integrated calls associated with gene annotation, proband-parent trio analysis to enable prioritization of <i>de novo</i> variants, B-allele frequency (BAF) plots to support CNV calls, parent of origin assessment and mosaicism detection.</p> Methods <p>To support those needs, we developed VizCNV, an open-source platform that incorporates read depth and BAF to enable haplotype-aware CNV analysis. The tool incorporates multiple interactive view modes for SV concurrent calls and annotation tracks for analyzing chromosomal abnormalities [e.g., aneuploidy, segmental aneusomy, and chromosome translocations], gene exonic rearrangements and non-coding gene regulatory regions. In addition, VizCNV includes a built-in filter schema for trio genomes, prioritizing the detection of <i>de novo</i> CNVs. We optimized VizCNV using 1000 Genomes Project data and benchmarked its performance against a cohort containing CNVs validated by multiple technologies. Finally, we applied VizCNV to a molecularly unsolved primary immunodeficiency disease cohort (PIDD, <i>n</i> = 39) previously analyzed by exome sequencing.</p> Results <p>Upon computational optimization, VizCNV achieved approximately 82.3% recall and 76.3% precision for deletions &gt; 10&#xa0;kb. VizCNV accurately detected all 71 validated copy number gains and correctly indicated potential underlying genomic complexities. Haplotype-aware CNV analysis identified a meiosis I non-disjunction event (trisomy 21), three <i>de novo</i> CNVs at two unique loci and 48 inherited candidate CNVs in the PIDD cohort of which 42% (20/48) were validated by integrated CNV/BAF analysis. Moreover, genotype–phenotype analyses revealed that a compound heterozygous combination of a paternal 12.8&#xa0;kb deletion of exon 5 and a maternal missense variant allele of <i>DOCK8</i> are the molecular cause of one proband diagnosed with Hyper-IgE syndrome.</p> Conclusions <p>VizCNV provides a robust and flexible platform for identification of aneuploidies, CNV, SV discovery and visualization of CNV and BAF data. It is also a useful tool to investigate features of genomic rearrangements such as parental origin which has implications for genetic counseling and mechanistic studies. The tool is freely available through <a href="https://doi.org/10.6084/m9.figshare.25869523">https://doi.org/10.6084/m9.figshare.25869523</a>.</p>

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An integrated platform for concurrent structural and single-nucleotide variants improves copy-number detection and reveals pathogenic alleles in undiagnosed Mendelian families

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月31日

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  • Haowei Du,
  • Ming Yin Lun,
  • Lidiia Gagarina,
  • Jesse D. Bengtsson,
  • Christopher M. Grochowski,
  • Michele G. Mehaffey,
  • James Paul Hwang,
  • Shalini N. Jhangiani,
  • Sravya V. Bhamidipati,
  • Donna M. Muzny,
  • M. Cecilia Poli,
  • Sebastian Ochoa,
  • Ivan K. Chinn,
  • Anna Lindstrand,
  • Jennifer E. Posey,
  • Richard A. Gibbs,
  • Pengfei Liu,
  • James R. Lupski,
  • Claudia M. B. Carvalho

摘要

Background

Copy number variation (CNV) is a class of genomic structural variation (SV) that contributes to genomic disorders and can significantly impact health. Short-read genome sequencing (sr-GS) enables genome-wide SV calling which has been shown to increase diagnosis in unsolved rare disease families. The growing number of large sequencing cohort projects with sr-GS data available requires open free analytical tools that provide visualization of CNV and SV integrated calls associated with gene annotation, proband-parent trio analysis to enable prioritization of de novo variants, B-allele frequency (BAF) plots to support CNV calls, parent of origin assessment and mosaicism detection.

Methods

To support those needs, we developed VizCNV, an open-source platform that incorporates read depth and BAF to enable haplotype-aware CNV analysis. The tool incorporates multiple interactive view modes for SV concurrent calls and annotation tracks for analyzing chromosomal abnormalities [e.g., aneuploidy, segmental aneusomy, and chromosome translocations], gene exonic rearrangements and non-coding gene regulatory regions. In addition, VizCNV includes a built-in filter schema for trio genomes, prioritizing the detection of de novo CNVs. We optimized VizCNV using 1000 Genomes Project data and benchmarked its performance against a cohort containing CNVs validated by multiple technologies. Finally, we applied VizCNV to a molecularly unsolved primary immunodeficiency disease cohort (PIDD, n = 39) previously analyzed by exome sequencing.

Results

Upon computational optimization, VizCNV achieved approximately 82.3% recall and 76.3% precision for deletions > 10 kb. VizCNV accurately detected all 71 validated copy number gains and correctly indicated potential underlying genomic complexities. Haplotype-aware CNV analysis identified a meiosis I non-disjunction event (trisomy 21), three de novo CNVs at two unique loci and 48 inherited candidate CNVs in the PIDD cohort of which 42% (20/48) were validated by integrated CNV/BAF analysis. Moreover, genotype–phenotype analyses revealed that a compound heterozygous combination of a paternal 12.8 kb deletion of exon 5 and a maternal missense variant allele of DOCK8 are the molecular cause of one proband diagnosed with Hyper-IgE syndrome.

Conclusions

VizCNV provides a robust and flexible platform for identification of aneuploidies, CNV, SV discovery and visualization of CNV and BAF data. It is also a useful tool to investigate features of genomic rearrangements such as parental origin which has implications for genetic counseling and mechanistic studies. The tool is freely available through https://doi.org/10.6084/m9.figshare.25869523.