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

一个集成的平台可以同时检测结构变异和单核苷酸变异,从而提高拷贝数检测的准确性,并揭示未确诊孟德尔遗传病家族中的致病等位基因。

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Abstract

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 .

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