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CADD: predicting the deleteriousness of variants throughout the human genome

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Rentzsch, Philipp, Witten, Daniela, Cooper, Gregory M, Shendure, Jay, Kircher, Martin·2019·Nucleic Acids Research
DOI 10.1093/nar/gky1016PMC PMC6323892
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Abstract

Combined Annotation-Dependent Depletion (CADD) is a widely used measure of variant deleteriousness that can effectively prioritize causal variants in genetic analyses, particularly highly penetrant contributors to severe Mendelian disorders. CADD is an integrative annotation built from more than 60 genomic features, and can score human single nucleotide variants and short insertion and deletions anywhere in the reference assembly. CADD uses a machine learning model trained on a binary distinction between simulated de novo variants and variants that have arisen and become fixed in human populations since the split between humans and chimpanzees; the former are free of selective pressure and may thus include both neutral and deleterious alleles, while the latter are overwhelmingly neutral (or, at most, weakly deleterious) by virtue of having survived millions of years of purifying selection. Here we review the latest updates to CADD, including the most recent version, 1.4, which supports the human genome build GRCh38. We also present updates to our website that include simplified variant lookup, extended documentation, an Application Program Interface and improved mechanisms for integrating CADD scores into other tools or applications. CADD scores, software and documentation are available at https://cadd.gs.washington.edu.

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  • New VEPs, such as AlphaMissense, now utilising deep learning techniques offer significant improvements over earlier heuristic-based approaches.

    “New VEPs continue rapidly to be developed, with many, like AlphaMissense, now utilising deep learning techniques that offer significant improvements over earlier heuristic-based – that is, involving computational ‘trial and error’ (27, 28) – approaches.”

    From Massively parallel functional genomic assays in endocrinology: from promise to delivery (2026) · cites this paper as [27]

    In “The Impact of CADD Scores in Human Genetics” (page unavailable), the paper says that CADD’s feature set has been used to train deep neural networks, supporting the point that deep-learning variant predictors were being developed. It does not mention AlphaMissense or establish significant improvements over earlier heuristic-based approaches; it says it knows of no competing tool that consistently outperforms CADD across diverse tests. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Authors
Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J., & Kircher, M.
Affiliations
  • Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
  • University of Washington (US)
  • HudsonAlpha Institute for Biotechnology (US)
Corresponding author
  • Shendure, Jay
  • Kircher, Martin
ORCID
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2018-10-29
Volume
47
Issue
D1
Pages
D886-D894
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gky1016
PMC
PMC6323892
OpenAlex
W2905452503
Cited by
3,534
References
8
Citations by year
2026: 456 · 2025: 470 · 2024: 482 · 2023: 441 · 2022: 540 · 2021: 561 · 2020: 418 · 2019: 159 · 2018: 4 · 2017: 1
FWCI
174.52
Type
journal article
Language
en
Open access
Licence
Locations
  • Nucleic Acids Research (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Refubium (Universitätsbibliothek der Freien Universität Berlin) (submittedVersion, cc-by)
  • Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver (cc-by)
Funders
  • National Cancer Institute (1R01CA197139)
  • National Human Genome Research Institute (1U54HG006493)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
CADD, single nucleotide variants, insertion-deletion variants, de novo mutations, purifying selection, monogenic disorders, variant annotation
MeSH terms
Machine Learning, Humans, Genetic Variation, Genome, Human, Databases, Nucleic Acid, Molecular Sequence Annotation

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