Technical Report

A general framework for estimating the relative pathogenicity of human genetic variants

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Abstract

Current methods for annotating and interpreting human genetic variation tend to exploit a single information type (for example, conservation) and/or are restricted in scope (for example, to missense changes). Here we describe Combined Annotation–Dependent Depletion (CADD), a method for objectively integrating many diverse annotations into a single measure (C score) for each variant. We implement CADD as a support vector machine trained to differentiate 14.7 million high-frequency human-derived alleles from 14.7 million simulated variants. We precompute C scores for all 8.6 billion possible human single-nucleotide variants and enable scoring of short insertions-deletions. C scores correlate with allelic diversity, annotations of functionality, pathogenicity, disease severity, experimentally measured regulatory effects and complex trait associations, and they highly rank known pathogenic variants within individual genomes. The ability of CADD to prioritize functional, deleterious and pathogenic variants across many functional categories, effect sizes and genetic architectures is unmatched by any current single-annotation method.

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Acknowledgements

We thank P. Green and members of the Shendure laboratory for helpful discussions and suggestions. Our work was supported by US NIH grants U54HG006493 (to J.S. and G.M.C.), DP5OD009145 (to D.M.W.) and DP1HG007811 (to J.S.).

Author information

Author notes

    • Preti Jain
    •  & Brian J O'Roak

    Present address: Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, USA.

    • Martin Kircher
    •  & Daniela M Witten

    These authors contributed equally to this work.

Affiliations

  1. Department of Genome Sciences, University of Washington, Seattle, Washington, USA.

    • Martin Kircher
    • , Brian J O'Roak
    •  & Jay Shendure
  2. Department of Biostatistics, University of Washington, Seattle, Washington, USA.

    • Daniela M Witten
  3. HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA.

    • Preti Jain
    •  & Gregory M Cooper

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Contributions

G.M.C. and J.S. designed the study. M.K. processed the annotation data and scores and developed and implemented the simulator and scripts required for scoring. P.J. and B.J.O. prepared and provided data sets and annotations. D.M.W. and M.K. developed the model and performed model training. D.M.W. performed the analysis of individual features and interactions. M.K., D.M.W., G.M.C. and J.S. analyzed the model's performance on different data sets. G.M.C. analyzed the GWAS data. J.S., G.M.C., M.K. and D.M.W. wrote the manuscript with input from all authors.

Competing interests

The authors (M.K., D.M.W., G.M.C. and J.S.) have filed a provisional patent application with the US Patent and Trademark Office on the basis of CADD.

Corresponding authors

Correspondence to Gregory M Cooper or Jay Shendure.

Supplementary information

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    Supplementary Text and Figures

    Supplementary Figures 1–18, Supplementary Tables 1–12 and Supplementary Note