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Am J Hum Genet. 2016 Oct 6;99(4):877-885. doi: 10.1016/j.ajhg.2016.08.016. Epub 2016 Sep 22.

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants.

Author information

1
Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Health Research and Policy, Stanford University, Stanford, CA 94305, USA.
2
Department of Health Research and Policy, Stanford University, Stanford, CA 94305, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
3
Department of Computer Science and Informatics, Indiana University, Bloomington, IN 47405, USA.
4
Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
5
Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.
6
Computational and Statistical Genomics Branch, National Human Genome Research Institute, Baltimore, MD 21224, USA.
7
Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, Bethesda, MD 20892, USA.
8
Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT 84108, USA.
9
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
10
Brady Urological Institute, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
11
NorthShore University HealthSystem Research Institute, Evanston, IL 60201, USA.
12
Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT 84108, USA; Departments of Internal Medicine and Urology, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
13
Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
14
Department of Medical Biochemistry and Genetics, University of Turku, Turku 20014, Finland; Department of Medical Genetics, Tyks Microbiology and Genetics, Turku University Hospital, Turku 20520, Finland.
15
Integrated Cancer Genomics Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA.
16
Department of Urology, Wayne State University, Detroit, MI 48201, USA.
17
Centre de Recherche sur les Pathologies Prostatiques et Urologiques, Universite Paris, Paris, 75013, France.
18
Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, VIC 3004, Australia; Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, VIC 3010, Australia.
19
Institute of Human Genetics, University Hospital of Ulm, Ulm 89075, Germany; Department of Urology, University Hospital of Ulm, Ulm 89075, Germany.
20
Department of Urology, University of Southern California, Los Angeles, CA 90033, USA.
21
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 77, Sweden.
22
Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
23
Departments of Oncology and Human Genetics, Montreal General Hospital, Montreal, QC H3G 1A4, Canada.
24
Department of Genetics, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA.
25
Division of Genetics and Epidemiology, Institute of Cancer Research, London SM2 5NG, UK.
26
Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
27
Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Department of Statistics, Stanford University, Stanford, CA 94305, USA.
28
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA.
29
Department of Health Research and Policy, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
30
Department of Health Research and Policy, Stanford University, Stanford, CA 94305, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. Electronic address: weiva.sieh@mssm.edu.

Abstract

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 < 10-12) 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 other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale.

PMID:
27666373
PMCID:
PMC5065685
DOI:
10.1016/j.ajhg.2016.08.016
[Indexed for MEDLINE]
Free PMC Article

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