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ParseCNV2: a versatile and integrated tool for copy number variation association studies

European Journal of Human Genetics volume 31, pages 275–277 (2023)Cite this article

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The Original Article was published on 01 November 2022

Genomic structural variants (SVs), including copy number variations (CNVs) and copy neutral variation, are understudied classes of genomic variations [1, 2]. They can comprise large DNA segments and include multiple genes and regulatory element, and they present in variable structure or copies in comparison to the reference genome. In contrast, single nucleotide variants (SNV), common or rare, identified in family-based or case-control association studies, have been the object of the vast majority of human genetic studies, but alone cannot fully explain the genetic basis of complex diseases [3]. The role of CNVs in human disease predisposition is well established, but their full contribution to this “missing heritability” still remains largely unknown.

In this issue, Glessner et al, report on ParseCNV2 [4], a new update of a software that performs CNV association with extra functionalities to natively support genotyping array and sequencing data. The critical additions in ParseCNV2 include: 1) a code revamp to develop a unified CNV variant call format (VCF) parser, as current VCF file format lacks a standard convention for reporting CNVs; 2) new options to perform either linear or logistic regression tests for quantitative and binary traits while adjusting for covariates and an extra option to perform rare variant association tests using the statistical models implemented in RVTESTS in addition to the existing Fisher’s exact test; and 3) interactive quality control support for the interpretation of a wider range of CNV calling tools output from sequencing data. One of the key highlights of ParseCNV2 is in its strategic way of merging probe markers or exon boundaries into larger population-level CNVrs. The tool functions by dynamically combining individual-level CNV segments based on similarities in P-values, direction of effect, and distance from adjacent markers or exon regions, rather than relying on a static list of CNVrs. This feature makes ParseCNV2 suitable for analyzing CNVrs derived from different genotyping arrays and, potentially, CNVrs derived from both arrays and sequencing data. While this has not been formally tested in the current manuscript, the potential for seamlessly combining data from multiple sources and technologies without the need for an added step of reformatting represents a significant improvement for the tool. Another important new feature of this suite is that it has a plugin implementation of RVTESTS, which is a comprehensive statistical tests toolkit that allows rare CNV association analysis including logistic Firth regression, variable threshold test, SKAT, burden test, and meta-analysis test, while correcting for issues like cohort heterogeneity in large-cohort studies (Fig. 1B). The new code efficiency and scalability also allow association analyses to be performed for CNV studies with large sample sizes (N > 100,000) on a desktop computer without the need of computing clusters. Current limitations of ParseCNV2 include the lack of support for analysis of smaller retrotransposons SVA, ALU, LINE elements, inversions, small insertions, short tandem repeats (STRs), and mosaic CNVs (MOS).

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Fig. 1: Structural variant detection and analysis over time and applications and characteristics of ParseCNV2.

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Funding

SSC is supported by grants (P20 DK116191, RO1 DK103184, and R01 DK115574) from the National Institutes of Health/NIDDK.

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  1. Division of Nephrology, Department of Medicine, Columbia University, New York, NY, USA

    Tze Y. Lim, Miguel Verbitsky & Simone Sanna-Cherchi

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  1. Tze Y. Lim
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  2. Miguel Verbitsky
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  3. Simone Sanna-Cherchi
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TYL drafted the manuscript and prepared the figures; MV edited and helped conceptualizing the manuscript; SSC conceptualized the manuscript and conducted the final edits.

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Correspondence to Simone Sanna-Cherchi.

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Lim, T.Y., Verbitsky, M. & Sanna-Cherchi, S. ParseCNV2: a versatile and integrated tool for copy number variation association studies. Eur J Hum Genet 31, 275–277 (2023). https://doi.org/10.1038/s41431-022-01280-x

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