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CADD v1.7: Using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions

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Schubach, Max, Maass, Thorben, Nazaretyan, Lusiné, Röner, Sebastian, Kircher, Martin·2024·Nucleic Acids Research
DOI 10.1093/nar/gkad989PMC PMC10767851
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Abstract

Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of the first methods for the genome-wide prioritization of variants across different molecular functions and has been continuously developed and improved since its original publication. Here, we present our most recent release, CADD v1.7. We explored and integrated new annotation features, among them state-of-the-art protein language model scores (Meta ESM-1v), regulatory variant effect predictions (from sequence-based convolutional neural networks) and sequence conservation scores (Zoonomia). We evaluated the new version on data sets derived from ClinVar, ExAC/gnomAD and 1000 Genomes variants. For coding effects, we tested CADD on 31 Deep Mutational Scanning (DMS) data sets from ProteinGym and, for regulatory effect prediction, we used saturation mutagenesis reporter assay data of promoter and enhancer sequences. The inclusion of new features further improved the overall performance of CADD. As with previous releases, all data sets, genome-wide CADD v1.7 scores, scripts for on-site scoring and an easy-to-use webserver are readily provided via https://cadd.bihealth.org/ or https://cadd.gs.washington.edu/ to the community.

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  • The pathogenicity of APOE variants was analyzed based on ACMG/AMP2015 guidelines, combined with CADD and comprehensive literature data analysis.

    “Therefore, we analyzed the pathogenicity of APOE variants based on the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP2015) guidelines [43], combined with the modern predictive metric [44] and comprehensive literature data analysis.”

    From Spectrum and Prevalence of Rare APOE Variants and Their Association with Familial Dysbetalipoproteinemia (2024) · cites this paper as [44]

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Authors
Schubach, M., Maass, T., Nazaretyan, L., Röner, S., & Kircher, M.
Affiliations
  • Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
  • University Hospital Schleswig-Holstein (DE)
Corresponding author
Kircher, Martin
ORCID
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2024-01-05
Volume
52
Issue
D1
Pages
D1143-D1154
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkad989
PMC
PMC10767851
OpenAlex
W4390616142
Cited by
455
References
8
Citations by year
2026: 191 · 2025: 183 · 2024: 77 · 2023: 4
FWCI
76.11
Type
journal article
Language
en
Open access
Licence
Locations
  • Nucleic Acids Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Berlin Institute of Health at Charité – Universitätsmedizin Berlin
  • Deutsche Forschungsgemeinschaft (464313370)
  • NHGRI (1UM1HG011966-01)
  • Helmholtz Einstein International Berlin Research School in Data Science
  • University of Lübeck
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, variant prioritization, protein language models, convolutional neural networks, Zoonomia, deep mutational scanning, saturation mutagenesis, ClinVar, gnomAD, 1000 Genomes Project, variant annotation
MeSH terms
Machine Learning, Humans, Nucleotides, Software, Genetic Variation, Genome, Human

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