CADD
CADD predicts the deleteriousness of human genetic variants by integrating diverse genomic annotations into a unified C score to prioritize functional, deleterious, and pathogenic SNVs, insertions, and deletions.
Key Features:
- Integration of annotations: Combines over 60 genomic features including conservation metrics, functional annotations, pathogenicity signals, regulatory effects, and associations with complex traits into a single score.
- Machine learning classifier: Uses a support vector machine (SVM) trained to distinguish high-frequency human-derived alleles from simulated variants to prioritize functional and deleterious variants.
- Precomputed SNV coverage: Provides precomputed scores for all possible ~8.6 billion human single-nucleotide variants and supports scoring of short insertions and deletions.
- Biological correlation: C scores correlate with allelic diversity, functional annotations, pathogenicity, disease severity, experimentally measured regulatory effects, and GWAS associations.
- Splicing prediction (cadd_phred): The cadd_phred extension incorporates deep neural network (DNN) splicing scores to predict splicing effects beyond canonical donor and acceptor dinucleotides.
- Genome build support: Version 1.4 includes support for the human genome build GRCh38.
Scientific Applications:
- Mendelian disorder variant prioritization: Prioritizes candidate causal variants in severe Mendelian disease analyses.
- GWAS interpretation: Ranks and prioritizes variants underlying genome-wide association study signals using integrated annotations.
- Exome and sequencing analyses: Supports variant effect prediction and prioritization in exome and genome sequencing workflows, including short indels.
- Splicing impact detection: Identifies variants likely to affect splicing, including those outside canonical splice-site dinucleotides.
Methodology:
Integrates >60 genomic annotations, uses an SVM trained on high-frequency human-derived alleles versus simulated variants, provides precomputed scores for ~8.6 billion SNVs and scores for short indels, and incorporates DNN-derived splicing scores in the cadd_phred extension; version 1.4 adds GRCh38 support.
Topics
Collections
Details
- License:
- Unlicense
- Maturity:
- Mature
- Cost:
- Free of charge
- Tool Type:
- web application
- Operating Systems:
- Linux, Windows, Mac
- Added:
- 7/4/2017
- Last Updated:
- 11/24/2024
Operations
Publications
Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nature Genetics. 2014;46(3):310-315. doi:10.1038/ng.2892. PMID:24487276. PMCID:PMC3992975.
Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Research. 2018;47(D1):D886-D894. doi:10.1093/nar/gky1016. PMID:30371827. PMCID:PMC6323892.
Rentzsch P, Schubach M, Shendure J, Kircher M. CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Medicine. 2021;13(1). doi:10.1186/s13073-021-00835-9. PMID:33618777. PMCID:PMC7901104.
Documentation
Downloads
- Downloads pagehttps://cadd.gs.washington.edu/download