DeepPVP: phenotype-based prioritization of causative variants using deep learning
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Abstract
BACKGROUND: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient's phenotype. RESULTS: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp . CONCLUSIONS: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.
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There are limited approaches for phenotype-based diagnosis, and none automatically learn phenotypic and/or genotypic features over patient phenotype terms.
“While deep learning approaches exist for image-based diagnosis10,11, there are limited approaches for phenotype-based diagnosis, and none automatically learn phenotypic and/or genotypic features over patient phenotype terms23–28,44.”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [44]
The Background (page unavailable) contrasts a large number of variant-prioritization methods with several that use patient phenotypes, supporting the relative scarcity of phenotype-based approaches. The Results describe DeepPVP as a neural network trained on computed similarity scores and other features, rather than directly on phenotype terms. The paper does not establish the broader claim that no approach automatically learns such features over patient phenotype terms. (LLM, conf=0.82)
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
(2014) · Cited by 33,419 · see citing works
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879 · see citing works
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247 · see citing works
The variant call format and VCFtools
Danecek, P., Auton, A., Abecasis, G. R., Albers, C. A., Banks, E., DePristo, M. A., Handsaker, R. E., Lunter, G. A., Marth, G., Sherry, S. T., et al. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr330 · Cited by 16,008 · see citing works
(2010) · International Conference on Machine Learning · Cited by 13,995 · see citing works
A general framework for estimating the relative pathogenicity of human genetic variants
Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., & Shendure, J. (2014) · Nature Genetics · doi:10.1038/ng.2892 · Cited by 6,183 · see citing works
(2011) · Cited by 3,939 · see citing works
ClinVar: public archive of relationships among sequence variation and human phenotype
Landrum, M. J., Lee, J. M., Riley, G. R., Jang, W., Rubinstein, W. S., Church, D. M., & Maglott, D. R. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1113 · Cited by 3,516 · see citing works
Proceedings of the 24th International Conference on Neural Information Processing Systems
Shawe-Taylor, J. S., Zemel, R. S., Bartlett, P. L., Pereira, F. B., & Weinberger, K. Q. (2011) · Cited by 1,184 · see citing works
DANN: a deep learning approach for annotating the pathogenicity of genetic variants
Quang, D. X., Chen, Y., & Xie, X. S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu703 · Cited by 1,073 · see citing works
The human phenotype ontology: a tool for annotating and analyzing human hereditary disease
Robinson, P. N., Köhler, S., Bauer, S., Seelow, D., Horn, D., & Mundlos, S. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.09.017 · Cited by 1,034 · see citing works
The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data
Köhler, S., Doelken, S., Mungall, C. J., Bauer, S., Firth, H., Bailleul-Forestier, I., Black, G., Brown, D. L., Brudno, M., Campbell, J., et al. (2013) · Nucleic Acids Res. · doi:10.1093/nar/gkt1026 · Cited by 840 · see citing works
Self-citationTabix: fast retrieval of sequence features from generic TAB-delimited files
Li, H. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btq671 · Cited by 575 · see citing works
Functional annotation of noncoding sequence variants
Ritchie, G. R. S., Dunham, I., Zeggini, E., & Flicek, P. (2014) · Nature Methods · doi:10.1038/nmeth.2832 · Cited by 505 · see citing works
Smith, C. L., Goldsmith, C.-A. W., & Eppig, J. T. (2004) · Genome Biology · doi:10.1186/gb-2004-6-1-r7 · Cited by 417 · see citing works
Improved exome prioritization of disease genes through cross-species phenotype comparison
Robinson, P. N., Köhler, S., Oellrich, A., Sanger Mouse Genetics Project, Wang, K., Mungall, C. J., Lewis, S. E., Washington, N., Bauer, S., Seelow, D., et al. (2014) · Genome Research · doi:10.1101/gr.160325.113 · Cited by 375 · see citing works
Grimm, D. G., Azencott, C., Aicheler, F., Gieraths, U., MacArthur, D. G., Samocha, K. E., Cooper, D. N., Stenson, P. D., Daly, M. J., Smoller, J. W., et al. (2015) · Human Mutation · doi:10.1002/humu.22768 · Cited by 356 · see citing works
A new face and new challenges for Online Mendelian Inheritance in Man (OMIM®)
Amberger, J. S., Bocchini, C. A., & Hamosh, A. (2011) · Human Mutation · doi:10.1002/humu.21466 · Cited by 332 · see citing works
Huang, Y.-F., Gulko, B., & Siepel, A. C. (2017) · Nature Genetics · doi:10.1038/ng.3810 · Cited by 329 · see citing works
Gorgolewski, K. J., Alfaro-Almagro, F., Auer, T., Bellec, P., Capotă, M., Chakravarty, M. M., Churchill, N. W., Cohen, A. L., Craddock, R. C., Devenyi, G. A., et al. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005209 · Cited by 311 · see citing works
Smedley, D., Schubach, M., Jacobsen, J. O. B., Köhler, S., Zemojtel, T., Spielmann, M., Jäger, M., Hochheiser, H., Washington, N. L., McMurry, J. A., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.07.005 · Cited by 283 · see citing works
Using ontologies to describe mouse phenotypes
Gkoutos, G. V., Green, E. C. J., Mallon, A., Hancock, J. M., & Davidson, D. R. (2004) · Genome biology · doi:10.1186/gb-2004-6-1-r8 · Cited by 241 · see citing works
Self-citationSettling the score: variant prioritization and Mendelian disease
Eilbeck, K. L., Quinlan, A. R., & Yandell, M. D. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.52 · Cited by 221 · see citing works
Loss-of-function variants in the genomes of healthy humans
MacArthur, D. G., & Tyler‐Smith, C. (2010) · Human Molecular Genetics · doi:10.1093/hmg/ddq365 · Cited by 195 · see citing works
Singleton, M., Guthery, S. L., Voelkerding, K. V., Chen, K., Kennedy, B., Margraf, R. L., Durtschi, J., Eilbeck, K. L., Reese, M. G., Jorde, L. B., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.03.010 · Cited by 186 · see citing works
eXtasy: variant prioritization by genomic data fusion
Sifrim, A., Popovic, D. D., Tranchevent, L.-C., Ardeshirdavani, A., Sakai, R., Könings, P., Vermeesch, J. R., Aerts, J. A., Moor, B. D., & Moreau, Y. (2013) · Nature Methods · doi:10.1038/nmeth.2656 · Cited by 163 · see citing works
de Angelis, M. H., Nicholson, G., Selloum, M., White, J. K., Morgan, H., Ramírez‐Solis, R., Sorg, T., Wells, S., Fuchs, H., Fray, M., et al. (2015) · Nature Genetics · doi:10.1038/ng.3360 · Cited by 146 · see citing works
Self-citationPhenotype-driven strategies for exome prioritization of human Mendelian disease genes
Smedley, D. P., & Robinson, P. N. (2015) · Genome Medicine · doi:10.1186/s13073-015-0199-2 · Cited by 112 · see citing works
Harvard Personal Genome Project: lessons from participatory public research
Ball, M. P., Bobe, J. R., Chou, M. F., Clegg, T., Estep, P. W., Lunshof, J. E., Vandewege, W., Zaranek, A. W., & Church, G. M. (2014) · Genome Medicine · doi:10.1186/gm527 · Cited by 103 · see citing works
Köhler, S., Doelken, S. C., Ruef, B. J., Bauer, S., Washington, N. L., Westerfield, M., Gkoutos, G. V., Schofield, P. N., Smedley, D. P., Lewis, S., et al. (2013) · F1000Research · doi:10.12688/f1000research.2-30.v2 · Cited by 83 · see citing works
Self-citationIntegrating phenotype ontologies with PhenomeNET
Rodríguez‐García, M. Á., Gkoutos, G. V., Schofield, P. N., & Hoehndorf, R. (2017) · Journal of Biomedical Semantics · doi:10.1186/s13326-017-0167-4 · Cited by 33 · see citing works
Self-citationPhenoRank: reducing study bias in gene prioritization through simulation
Cornish, A. J., David, A., & Sternberg, M. J. E. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty028 · Cited by 33 · see citing works
de Bono, B., Hoehndorf, R., Wimalaratne, S., Gkoutos, G. V., & Grenon, P. (2011) · BMC Research Notes · doi:10.1186/1756-0500-4-313 · Cited by 32 · see citing works
Self-citationSemantic prioritization of novel causative genomic variants
Boudellioua, I., Razali, R., Kulmanov, M., Hashish, Y., Bajić, V. B., Serra, E. G., Schoenmakers, N. A., Gkoutos, G. V., Schofield, P. N., & Hoehndorf, R. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005500 · Cited by 30 · see citing works
Self-citation
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- [25]Quang D, Chen Y, Xie X. Dann: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 2015; 31(5):761–3. 10.1093/bioinformatics/btu703. http://bioinformatics.oxfordjournals.org/content/31/5/761.full.pdf+html. PMC4341060 25338716 · DOI
Cited by
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Artificial intelligence (AI) in rare diseases: is the future brighter?
Brasil, S., Pascoal, C., Francisco, R., dos Reis Ferreira, V., A. Videira, P., & Valadão, G. (2019) · Genes · doi:10.3390/genes10120978 · Cited by 160 · see citing works
Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases
Alsentzer, E., Li, M. M., Kobren, S. N., Noori, A., Undiagnosed Diseases Network, Kohane, I. S., & Zitnik, M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01749-1 · Cited by 54 · see citing works
Simulation of undiagnosed patients with novel genetic conditions
Alsentzer, E., Finlayson, S. G., Li, M. M., Undiagnosed Diseases Network, Kobren, S. N., & Kohane, I. S. (2023) · Nature Communications · doi:10.1038/s41467-023-41980-6 · Cited by 10 · see citing works
- Authors
- Boudellioua, I., Kulmanov, M., Schofield, P. N., Gkoutos, G. V., & Hoehndorf, R.
- Affiliations
- King Abdullah University of Science and Technology (SA)
- University of Cambridge (GB)
- University Hospitals Birmingham NHS Foundation Trust (GB)
- Corresponding author
- Hoehndorf, Robert
- Venue
- BMC Bioinformatics
- Published
- 2019-02-06
- Volume
- 20
- Issue
- 1
- Pages
- 65-65
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC6364462
- OpenAlex
- W2952128461
- Cited by
- 79
- References
- 36
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 15 · 2023: 5 · 2022: 18 · 2021: 8 · 2020: 10 · 2019: 7 · 2018: 1
- FWCI
- 6.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, cc-by)
- King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo
- Apollo (cc-by)
- Funders
- King Abdullah University of Science and Technology (URF/1/3454-01-01, FCC/1/1976-08-01, FCS/1/3657-02-01)
- Horizon 2020 (731075)
- National Science Foundation (IOS:1340112)
- 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 › Bioinformatics and Genomic Networks
- Keywords
- variant prioritization, pathogenicity prediction, semantic similarity, deep neural network, exome sequencing, whole genome sequencing
- MeSH terms
- Exome Sequencing, Deep Learning, Gene Frequency, Humans, Phenotype, Software, Genetic Variation, Neural Networks, Computer, Exome
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