REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants
Open accessAbstract
The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p -12 ) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046-0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027-0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for…
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All variants were prioritized considering allele frequency in gnomAD v4.0.0 below 1%, impact on protein function via different in silico programs, and involvement of candidate genes in neuronal processes.
“All variants were prioritized considering allele frequency in gnomAD v4.0.0 (Karczewski et al. 2020) below 1%, impact on protein function via different in silico programs (CADD (Rentzsch et al. 2021), REVEL (Ioannidis et al. 2016), MutPred2 (Pejaver et al. 2020) and Metadome (Wiel et al. 2019)) and involvement of candidate genes in neuronal processes.”
From Heterozygous loss-of-function variants in DOCK4 cause neurodevelopmental delay and microcephaly (2024) · cites this paper as Ioannidis et al. 2016
The rare significant (P ≤ 1 × 10−8) and suggestive (1 × 10−8 < P ≤ 1 × 10−4) missense cis-CDS pQTLs were more likely to be predicted damaging by REVEL.
“Moreover, the rare significant (P ≤ 1 × 10−8) and suggestive (1 × 10−8 < P ≤ 1 × 10−4) missense cis-CDS pQTLs were more likely to be predicted damaging by the in silico predictor, REVEL16 (Extended Data Fig. 3b).”
From Rare variant associations with plasma protein levels in the UK Biobank (2023) · cites this paper as [16]
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- Authors
- Ioannidis, N. M., Rothstein, J. H., Pejaver, V., Middha, S., McDonnell, S. K., Baheti, S., Musolf, A., Li, Q., Holzinger, E., Karyadi, D., Cannon-Albright, L. A., Teerlink, C. C., Stanford, J. L., Isaacs, W. B., Xu, J., Cooney, K. A., Lange, E. M., Schleutker, J., Carpten, J. D., Powell, I. J., Cussenot, O., Cancel-Tassin, G., Giles, G. G., MacInnis, R. J., Maier, C., Hsieh, C.-L., Wiklund, F., Catalona, W. J., Foulkes, W. D., Mandal, D., Eeles, R. A., Kote-Jarai, Z., Bustamante, C. D., Schaid, D. J., Hastie, T., Ostrander, E. A., Bailey-Wilson, J. E., Radivojac, P., Thibodeau, S. N., Whittemore, A. S., & Sieh, W.
- Affiliations
- Stanford University (US)
- Indiana University Bloomington (US)
- Memorial Sloan Kettering Cancer Center (US)
- Mayo Clinic (US)
- National Institutes of Health (US)
- National Human Genome Research Institute (US)
- University of Utah (US)
- Fred Hutch Cancer Center (US)
- Johns Hopkins University (US)
- Endeavor Health (US)
- University of North Carolina at Chapel Hill (US)
- University of Turku (FI)
- Translational Genomics Research Institute (US)
- Wayne State University (US)
- Université Paris Cité (FR)
- The University of Melbourne (AU)
- Universität Ulm (DE)
- University of Southern California (US)
- Karolinska Institutet (SE)
- Montreal General Hospital (CA)
- Louisiana State University Health Sciences Center New Orleans (US)
- Institute of Cancer Research (GB)
- Corresponding author
- Sieh, Weiva
- ORCID
- Ioannidis, Nilah M.
- Rothstein, Joseph H.
- Pejaver, Vikas
- Middha, Sumit
- McDonnell, Shannon K.
- Baheti, Saurabh
- Musolf, Anthony
- Li, Qing
- Holzinger, Emily
- Karyadi, Danielle
- Teerlink, Craig C.
- Stanford, Janet L.
- Xu, Jianfeng
- Cooney, Kathleen A.
- Lange, Ethan M.
- Schleutker, Johanna
- Carpten, John D.
- Cussenot, Olivier
- Giles, Graham G.
- MacInnis, Robert J.
- Hsieh, Chih-Lin
- Wiklund, Fredrik
- Foulkes, William D.
- Mandal, Diptasri
- Eeles, Rosalind A.
- Bustamante, Carlos D.
- Schaid, Daniel J.
- Hastie, Trevor
- Ostrander, Elaine A.
- Thibodeau, Stephen N.
- Whittemore, Alice S.
- Sieh, Weiva
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2016-10
- Volume
- 99
- Issue
- 4
- Pages
- 877-885
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC5065685
- OpenAlex
- W2521967673
- Cited by
- 2,908
- References
- 10
- Citations by year
- 2026: 465 · 2025: 528 · 2024: 480 · 2023: 374 · 2022: 318 · 2021: 279 · 2020: 191 · 2019: 151 · 2018: 85 · 2017: 30 · 2016: 1 · 2014: 3
- FWCI
- 71.82
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- UNC Libraries
- Funders
- Emory University
- National Institute for Health and Care Research (NF-SI-0510-10096)
- National Institutes of Health
- National Institute of Mental Health (R01MH105524)
- National Human Genome Research Institute (F32HG008330)
- National Cancer Institute (R01CA094069, U01CA089600, K07CA143047, P50CA180995, P30CA060553)
- U.S. National Library of Medicine (R01LM009722)
- 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 › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- rare missense variants, pathogenicity prediction, ensemble learning, REVEL, pathogenic variants, ClinVar, exome sequencing, allele frequency
- MeSH terms
- Disease, DNA Mutational Analysis, Gene Frequency, Humans, ROC Curve, Software, Area Under Curve, Mutation, Missense, Exome
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