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GATK-gCNV enables the discovery of rare copy number variants from exome sequencing data

Nature Genetics volume 55, pages 1589–1597 (2023)Cite this article

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An Author Correction to this article was published on 23 January 2024

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Abstract

Copy number variants (CNVs) are major contributors to genetic diversity and disease. While standardized methods, such as the genome analysis toolkit (GATK), exist for detecting short variants, technical challenges have confounded uniform large-scale CNV analyses from whole-exome sequencing (WES) data. Given the profound impact of rare and de novo coding CNVs on genome organization and human disease, we developed GATK-gCNV, a flexible algorithm to discover rare CNVs from sequencing read-depth information, complete with open-source distribution via GATK. We benchmarked GATK-gCNV in 7,962 exomes from individuals in quartet families with matched genome sequencing and microarray data, finding up to 95% recall of rare coding CNVs at a resolution of more than two exons. We used GATK-gCNV to generate a reference catalog of rare coding CNVs in WES data from 197,306 individuals in the UK Biobank, and observed strong correlations between per-gene CNV rates and measures of mutational constraint, as well as rare CNV associations with multiple traits. In summary, GATK-gCNV is a tunable approach for sensitive and specific CNV discovery in WES data, with broad applications.

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Fig. 1: GATK-gCNV pipeline steps.
Fig. 2: Calling and benchmarking of GATK-gCNV callset in a cohort of more than 7,000 samples with matching deep WGS sequencing.
Fig. 3: A high-quality rare CNV callset was generated on 200,624 exomes from the UKBB using GATK-gCNV.

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Data availability

The SSC benchmarking raw sequencing data can be accessed through NHGRI AnVIL; accession ID: phs000298; databank URL: https://anvilproject.org/data. SSC CNVs can be accessed through SFARIBase (base.sfari.org), accession IDs: SFARI_DS340921 (CNVs). Approval by the Simons Foundation for Autism Research Initiative (SFARI) is required.

Access to the UKBB raw sequencing data and the CNV data generated here will be provided by the UKBB (https://www.ukbiobank.ac.uk).

GENCODE V33 annotation can be found at https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_33/gencode.v33.annotation.gtf.gz

Code availability

GATK-gCNV is distributed as part of the GATK jar release. For an example workspace on Terra, with recommended parameters, please see https://app.terra.bio/#workspaces/help-gatk/Germline-CNVs-GATK4.

GATK-gCNV evaluation and benchmarking code is available at

https://github.com/broadinstitute/GATK-gCNV-publication/tree/master/evaluation_code.

CMA-CNV Validation code consists of

https://github.com/talkowski-lab/cnv-validation.

GenomeSTRiP version 2.00.1982

http://software.broadinstitute.org/software/genomestrip/.

MoChA version 2022-01-14 WDL https://software.broadinstitute.org/software/mocha/mocha.20220114.wdl.

Change history

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Acknowledgements

We thank L. Lichtenstein, Y. Farjoun, B. Neale and N. Lennon for insightful discussions at various stages of this project, and S. Zaheri for carefully reviewing and providing feedback on the manuscript. This work was supported by grants from the Simons Foundation for Autism Research Initiative (573206), the SPARK project and SPARK analysis projects (606362 and 608540) and the National Institutes of Health (MH115957, HD081256, HG008895 and HG011450). J.M.F. was supported by an Autism Speaks Postdoctoral Fellowship and R.L.C. was supported by NSF GRFP 2017240332.

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Author notes

  1. These authors contributed equally: Mehrtash Babadi, Jack M. Fu, Samuel K. Lee, Andrey N. Smirnov.

Authors and Affiliations

  1. Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Mehrtash Babadi, Samuel K. Lee, Andrey N. Smirnov, Laura D. Gauthier, Mark Walker, David I. Benjamin & Eric Banks

  2. Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Jack M. Fu, Xuefang Zhao, Konrad J. Karczewski, Isaac Wong, Ryan L. Collins, Alba Sanchis-Juan, Harrison Brand & Michael E. Talkowski

  3. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Jack M. Fu, Mark Walker, Xuefang Zhao, Isaac Wong, Ryan L. Collins, Alba Sanchis-Juan, Harrison Brand & Michael E. Talkowski

  4. Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Jack M. Fu, Xuefang Zhao, Alba Sanchis-Juan, Harrison Brand & Michael E. Talkowski

  5. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Konrad J. Karczewski & Michael E. Talkowski

  6. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Konrad J. Karczewski & Michael E. Talkowski

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Contributions

M.B., E.B. and M.E.T. designed these studies and analyses. M.B., D.I.B. and S.K.L. developed and implemented the GATK-gCNV model and the inference algorithm. A.N.S. contributed model enhancements and developed sample-clustering and batch-processing workflows. X.Z., A.N.S. and J.M.F. conducted benchmarking studies of GATK-gCNV performance. A.N.S., M.B. and S.K.L. developed WDL workflows for Terra integration and scalable analysis. M.E.T., J.M.F., E.B., H.B., S.K.L., M.W. and L.D.G. supervised aspects of this project at various stages of development. J.M.F., R.L.C., H.B. and K.J.K. contributed to association analyses. I.W. and J.M.F. generated the CNV callsets. J.M.F., I.W., R.L.C., A.S.J. and H.B. conducted quality control on generated callsets. M.B., J.M.F., R.L.C., H.B. and M.E.T. wrote the manuscript, which was edited by all authors.

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Correspondence to Mehrtash Babadi or Michael E. Talkowski.

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Nature Genetics thanks Birte Kehr, Christian Marshall and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary information

Supplementary Information

Supplementary Note, Supplementary Figs. 1–10 and Supplementary Tables 2 and 3.

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Supplementary Table 1

CNV-phenotype association analysis in UK Biobank.

Supplementary Data 1

Seven thousand nine hundred eighty-one target regions for PCA batching.

Supplementary Data 2

Standardized set of intervals based on Gencode V33 annotation.

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Babadi, M., Fu, J.M., Lee, S.K. et al. GATK-gCNV enables the discovery of rare copy number variants from exome sequencing data. Nat Genet 55, 1589–1597 (2023). https://doi.org/10.1038/s41588-023-01449-0

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