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ParseCNV2: efficient sequencing tool for copy number variation genome-wide association studies

European Journal of Human Genetics volume 31, pages 304–312 (2023)Cite this article

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A Comment to this article was published on 11 January 2023

Abstract

Improved copy number variation (CNV) detection remains an area of heavy emphasis for algorithm development; however, both CNV curation and disease association approaches remain in its infancy. The current practice of focusing on candidate CNVs, where researchers study specific CNVs they believe to be pathological while discarding others, refrains from considering the full spectrum of CNVs in a hypothesis-free GWAS. To address this, we present a next-generation approach to CNV association by natively supporting the popular VCF specification for sequencing-derived variants as well as SNP array calls using a PennCNV format. The code is fast and efficient, allowing for the analysis of large (>100,000 sample) cohorts without dividing up the data on a compute cluster. The scripts are condensed into a single tool to promote simplicity and best practices. CNV curation pre and post-association is rigorously supported and emphasized to yield reliable results of highest quality. We benchmarked two large datasets, including the UK Biobank (n > 450,000) and CAG Biobank (n > 350,000) both of which are genotyped at >0.5 M probes, for our input files. ParseCNV has been actively supported and developed since 2008. ParseCNV2 presents a critical addition to formalizing CNV association for inclusion with SNP associations in GWAS Catalog. Clinical CNV prioritization, interactive quality control (QC), and adjustment for covariates are revolutionary new features of ParseCNV2 vs. ParseCNV. The software is freely available at: https://github.com/CAG-CNV/ParseCNV2.

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Fig. 1: ParseCNV2 process flow.
Fig. 2: ParseCNV2 process flow graphical representation.
Fig. 3: Upset Plot Comparing Nominally Significant CNVR Loci.
Fig. 4: ParseCNV2 Input and Output Formats.

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Data availability

• Project name: ParseCNV2

• Project (source code) home page: https://github.com/CAG-CNV/ParseCNV2

• Operating systems: Linux (32/64-bit), OS X (64-bit Intel), Windows (32/64-bit)

• Programming language: Perl, R, Bash

• Other requirements (when recompiling): none

• License: GNU General Public License version 3.0 (GPLv3)

• Any restrictions to use by non-academics: none

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Acknowledgements

We thank the study participants who allowed for the use of genotyping, sequencing, and disease phenotype data for this study, and to testers of the codes used in this study. Funding This work was supported in part by CHOP’s Endowed Chair in Genomic Research (Hakonarson), by 5U01HG011175-03 and U01-HG006830 (NHGRI-sponsored eMERGE Network), by a sponsored research agreement from Aevi Genomic Medicine Inc. (HH), Intellectual and Developmental Disabilities Research Center (IDDRC), Kids First Gabriella Miller Pediatric Research Program, and by an Institutional Development Award from Children’s Hospital of Philadelphia (HH).

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Authors and Affiliations

  1. Department of Pediatrics, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA

    Joseph T. Glessner, Yichuan Liu, Munir Khan, Xiao Chang, Patrick M. A. Sleiman & Hakon Hakonarson

  2. Department of Pediatrics, Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA, 19104, USA

    Joseph T. Glessner, Yichuan Liu, Munir Khan, Xiao Chang, Patrick M. A. Sleiman & Hakon Hakonarson

  3. Department of Cell Biology, the Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China

    Jin Li

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  1. Joseph T. Glessner
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Contributions

JTG conceived, designed, and implemented the code and wrote the paper. JL provided strategic guidance and ran other CNV association tools in benchmarking. YL provided and ran WES and WGS data CNV calls for validation of the ParseCNV2 algorithm and wrote those sections. MK compared ParseCNV2 with ParseCNV original version outputs to delineate reproducibility vs. new associations based on feature improvement. XC designed experiments and helped write the manuscript. PMAS contributed to data extraction. HH provided feedback on the report.

Corresponding author

Correspondence to Joseph T. Glessner.

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Ethical approval

All subjects were recruited through IRB-approved protocols. Participants enrolled in various studies and completed a broad informed consent, including consent for prospective analyses of EHRs. Confidentiality is guarded to address issues of privacy and insurability. Each subject is assigned a study number upon recruitment, using complex algorithms to remove personal identification. Encrypted patient data is integrated into the lab’s custom phenotype browser, where it can be coupled with genotyping and sequencing data.

Competing interests

The authors declare no competing interests. Unrelated to this manuscript, we disclose that HH and CHOP own stock in Aevi Genomic Medicine.

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Glessner, J.T., Li, J., Liu, Y. et al. ParseCNV2: efficient sequencing tool for copy number variation genome-wide association studies. Eur J Hum Genet 31, 304–312 (2023). https://doi.org/10.1038/s41431-022-01222-7

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