The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease
Open accessAbstract
The International Mouse Phenotyping Consortium (IMPC; https://www.mousephenotype.org/) web portal makes available curated, integrated and analysed knockout mouse phenotyping data generated by the IMPC project consisting of 85M data points and over 95,000 statistically significant phenotype hits mapped to human diseases. The IMPC portal delivers a substantial reference dataset that supports the enrichment of various domain-specific projects and databases, as well as the wider research and clinical community, where the IMPC genotype–phenotype knowledge contributes to the molecular diagnosis of patients affected by rare disorders. Data from 9,000 mouse lines and 750 000 images provides vital resources enabling the interpretation of the ignorome, and advancing our knowledge on mammalian gene function and the mechanisms underlying phenotypes associated with human diseases. The resource is widely integrated and the lines have been used in over 4,600 publications indicating the value of the data and the materials.
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There is a growing body of evidence supporting the hypothesis that brain infection may be the primary cause of AD.
“There is a growing body of evidence supporting the hypothesis that brain infection may be the primary cause of AD (69).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [69]
The Introduction (page unavailable) describes the consortium’s mouse-phenotyping resource, while the Use Cases section discusses genetic models of neurodevelopmental disorders. Searches for brain infection, Alzheimer’s disease, and pathogens found no passage in our copy addressing the proposed cause of AD. (LLM, conf=0.77)
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Cites
High-throughput discovery of novel developmental phenotypes
The International Mouse Phenotyping Consortium, Dickinson, M. E., Flenniken, A. M., Ji, X., Teboul, L., Wong, M. D., White, J. K., Meehan, T. F., Weninger, W. J., Westerberg, H., et al. (2016) · Nature · doi:10.1038/nature19356 · Cited by 1,196 · see citing works
Self-citationOMIM.org: leveraging knowledge across phenotype–gene relationships
Amberger, J., Bocchini, C. A., Scott, A., & Hamosh, A. (2018) · Nucleic Acids Res. · doi:10.1093/nar/gky1151 · Cited by 977 · see citing works
100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care — Preliminary Report
The 100, 0. G. P. P. I. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2035790 · Cited by 716 · see citing works
Mouse Genome Database (MGD) 2019
Bult, C. J., Blake, J. A., Smith, C. L., Kadin, J. A., Richardson, J. E., the Mouse Genome Database Group, Anagnostopoulos, A., Asabor, R., Baldarelli, R. M., Beal, J. S., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1056 · Cited by 686 · see citing works
Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources
Köhler, S., Carmody, L., Vasilevsky, N., Jacobsen, J. O. B., Danis, D., Gourdine, J.-P., Gargano, M., Harris, N. L., Matentzoglu, N., McMurry, J. A., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1105 · Cited by 677 · see citing works
Self-citationGenome-wide generation and systematic phenotyping of knockout mice reveals new roles for many genes.
White, J. K., Gerdin, A.-K., Karp, N. A., Ryder, E., Buljan, M., Bussell, J. N., Salisbury, J., Clare, S., Ingham, N. J., Podrini, C., et al. (2013) · Cell · doi:10.1016/j.cell.2013.06.022 · Cited by 509 · see citing works
Self-citationOpen Targets Platform: supporting systematic drug–target identification and prioritisation
Ochoa, D., Hercules, A., Carmona, M., Süveges, D., Gonzalez‐Uriarte, A., Malangone, C., Miranda, A., Fumis, L., Carvalho‐Silva, D., Spitzer, M., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1027 · Cited by 499 · see citing works
Mungall, C. J., McMurry, J. A., Köhler, S., Balhoff, J. P., Borromeo, C., Brush, M., Carbon, S., Conlin, T., Dunn, N., Engelstad, M., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1128 · Cited by 441 · see citing works
Self-citationThe mammalian phenotype ontology: enabling robust annotation and comparative analysis
Smith, C. L., & Eppig, J. T. (2009) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.44 · Cited by 353 · see citing works
100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care - Preliminary Report.
Smedley, D. P., Smedley, D. P., Smith, K. R., Martin, A., Thomas, E. A., McDonagh, E. M., Cipriani, V., Ellingford, J. M., Arno, G., Tucci, A., et al. (2021) · UCL Discovery (University College London) · doi:10.17863/cam.108059 · Cited by 324 · see citing works
Self-citationPrevalence of sexual dimorphism in mammalian phenotypic traits
Karp, N. A., Mason, J., Beaudet, A. L., Benjamini, Y., Bower, L., Braun, R. E., Brown, S. D. M., Chesler, E. J., Dickinson, M. E., Flenniken, A. M., et al. (2017) · Nature Communications · doi:10.1038/ncomms15475 · Cited by 257 · see citing works
Self-citationDisease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium
Meehan, T. F., Conte, N., West, D. B., Jacobsen, J. O. B., Mason, J., Warren, J., Chen, C.-K., Tudose, I., Relac, M., Matthews, P., et al. (2017) · Nature Genetics · doi:10.1038/ng.3901 · Cited by 245 · see citing works
Self-citationShefchek, K., Harris, N. L., Gargano, M. A., Matentzoglu, N., Unni, D. R., Brush, M., Keith, D., Conlin, T., Vasilevsky, N., Zhang, X., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz997 · Cited by 235 · see citing works
Self-citationClinical Practice Guidelines for Rare Diseases: The Orphanet Database
Pavan, S., Rommel, K., Marquina, M. E. M., Höhn, S., Serrière-Lanneau, V., & Rath, A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170365 · Cited by 191 · 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-citationBowl, M. R., Simon, M. M., Ingham, N. J., Greenaway, S., Santos, L., Cater, H. L., Taylor, S., Mason, J., Kurbatova, N., Pearson, S. A., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00595-4 · Cited by 145 · see citing works
Self-citationPhenoDigm: analyzing curated annotations to associate animal models with human diseases
Smedley, D. P., Oellrich, A., Köhler, S., et al. (2013) · Database · doi:10.1093/database/bat025 · Cited by 137 · see citing works
Self-citationMouse screen reveals multiple new genes underlying mouse and human hearing loss
Ingham, N. J., Pearson, S. A., Vancollie, V. E., et al. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000194 · Cited by 117 · see citing works
Muñoz‐Fuentes, V., Cacheiro, P., Meehan, T. F., Aguilar‐Pimentel, J. A., Brown, S. D. M., Flenniken, A. M., Flicek, P., Galli, A., Mashhadi, H. H., de Angelis, M. H., et al. (2018) · Conservation Genetics · doi:10.1007/s10592-018-1072-9 · Cited by 114 · see citing works
Self-citationA resource of targeted mutant mouse lines for 5,061 genes
Birling, M., Yoshiki, A., Adams, D. J., Ayabe, S., Beaudet, A. L., BOTTOMLEY, J. R., Bradley, A., Brown, S. D. M., Bürger, A., Bushell, W., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00825-y · Cited by 110 · see citing works
Self-citationHuman and mouse essentiality screens as a resource for disease gene discovery
Cacheiro, P., Muñoz‐Fuentes, V., Murray, S. A., Dickinson, M. E., Bućan, M., Nutter, L. M. J., Peterson, K. A., Haselimashhadi, H., Flenniken, A. M., Morgan, H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14284-2 · Cited by 105 · see citing works
Self-citationBrommage, R., Liu, J., Hansen, G. M., Kirkpatrick, L., Potter, D., Sands, A., Zambrowicz, B., Powell, D. R., & Vogel, P. (2014) · Bone Research · doi:10.1038/boneres.2014.34 · Cited by 100 · see citing works
High-throughput mouse phenomics for characterizing mammalian gene function
Brown, S. D. M., Holmes, C. C., Mallon, A., Meehan, T. F., Smedley, D. P., & Wells, S. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0005-2 · Cited by 78 · see citing works
Self-citationIdentification of genetic elements in metabolism by high-throughput mouse phenotyping
Rozman, J., Rathkolb, B., Oestereicher, M. A., Schütt, C., Ravindranath, A. C., Leuchtenberger, S., Sharma, S., Kistler, M., Willershäuser, M., Brommage, R., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-01995-2 · Cited by 70 · see citing works
New models for human disease from the International Mouse Phenotyping Consortium
Cacheiro, P., Haendel, M. A., & Smedley, D. P. (2019) · Mammalian Genome · doi:10.1007/s00335-019-09804-5 · Cited by 65 · see citing works
Self-citationSpielmann, N., Miller, G., Oprea, T. I., Hsu, C., Fobo, G., Frishman, G., Montrone, C., Mashhadi, H. H., Mason, J., Muñoz‐Fuentes, V., et al. (2022) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00018-8 · Cited by 60 · see citing works
Self-citationLarge-scale neuroanatomical study uncovers 198 gene associations in mouse brain morphogenesis
Collins, S. C., Mikhaleva, A., Vrcelj, K., Vancollie, V. E., Wagner, C., Demeure, N., Whitley, H., Kannan, M., Balz, R., Anthony, L. F. E., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11431-2 · Cited by 46 · see citing works
How to Illuminate the Druggable Genome Using Pharos
Sheils, T. K., Mathias, S. L., Siramshetty, V. B., Bocci, G., Bologa, C., Yang, J. J., Waller, A., Southall, N. T., Nguyễn, Ð.-T., & Oprea, T. I. (2020) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.92 · Cited by 44 · see citing works
Abeler‐Dörner, L., Laing, A. G., Lorenc, A., Ushakov, D. S., Clare, S., Speak, A. O., Correa, M. A. D., White, J. K., Ramírez‐Solis, R., Saran, N., et al. (2019) · Nature Immunology · doi:10.1038/s41590-019-0549-0 · Cited by 42 · see citing works
Identification of genes required for eye development by high-throughput screening of mouse knockouts
Moore, B. A., Leonard, B. C., Sebbag, L., Edwards, S., Cooper, A. E., Imai, D. M., Straiton, E., Santos, L., Reilly, C., Griffey, S. M., et al. (2018) · Communications Biology · doi:10.1038/s42003-018-0226-0 · Cited by 41 · see citing works
Self-citationMouse mutant phenotyping at scale reveals novel genes controlling bone mineral density
Swan, A., Schütt, C., Rozman, J., Moreno, M. D. M. M., Brandmaier, S., Simon, M., Leuchtenberger, S., Griffiths, M. N. D., Brommage, R., Keskivali-Bond, P., et al. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1009190 · Cited by 28 · see citing works
Self-citationA trans-eQTL network regulates osteoclast multinucleation and bone mass
Pereira, M., Ko, J., Logan, J. G., Protheroe, H., Kim, K., Tan, A. L. M., Croucher, P. I., Park, K.-S., Rotival, M., Petretto, E. G., et al. (2020) · eLife · doi:10.7554/elife.55549 · Cited by 27 · see citing works
Efficient gene targeting in mouse zygotes mediated by CRISPR/Cas9-protein
Jung, C., Zhang, J., Trenchard, E., Lloyd, K. C. K., West, D. B., Rosen, B. P., & de Jong, P. J. (2016) · Transgenic Research · doi:10.1007/s11248-016-9998-5 · Cited by 25 · see citing works
Whole genome analysis for 163 gRNAs in Cas9-edited mice reveals minimal off-target activity
Peterson, K. A., Khalouei, S., Hanafi, N., Wood, J. A., Lanza, D. G., Lintott, L. G., Willis, B., Seavitt, J. R., Braun, R. E., Dickinson, M. E., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04974-0 · Cited by 24 · see citing works
High-throughput discovery of genetic determinants of circadian misalignment
Zhang, T., Xie, P., Dong, Y., Liu, Z., Zhou, F., Pan, D., Huang, Z., Zhai, Q., Gu, Y., Wu, Q., et al. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008577 · Cited by 21 · see citing works
Self-citationMachine learning-based automated phenotyping of inflammatory nocifensive behavior in mice
Wotton, J. M., Peterson, E., Anderson, L. C., Murray, S. A., Braun, R. E., Chesler, E. J., White, J. K., & Kumar, V. (2020) · Molecular Pain · doi:10.1177/1744806920958596 · Cited by 21 · see citing works
LAMA: automated image analysis for the developmental phenotyping of mouse embryos
Horner, N., Venkataraman, S., Armit, C. J., Casero, R., Brown, J. M., Wong, M. D., van Eede, M. C., Henkelman, R. M., Johnson, S., Teboul, L., et al. (2021) · Development · doi:10.1242/dev.192955 · Cited by 20 · see citing works
Self-citationWotton, J. M., Peterson, E., Flenniken, A. M., Bains, R. S., Veeraragavan, S., Bower, L. R., Bubier, J. A., Parisien, M., Bezginov, A., Haselimashhadi, H., et al. (2021) · Pain · doi:10.1097/j.pain.0000000000002481 · Cited by 13 · see citing works
Self-citationSoft windowing application to improve analysis of high-throughput phenotyping data
Haselimashhadi, H., Mason, J., Muñoz‐Fuentes, V., López, F., Babalola, K., Acar, E. F., Kumar, V., White, J. K., Flenniken, A. M., King, R., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz744 · Cited by 10 · see citing works
Self-citationMuñoz‐Fuentes, V., Haselimashhadi, H., Santos, L., Westerberg, H., Parkinson, H., & Mason, J. (2021) · Mammalian Genome · doi:10.1007/s00335-021-09917-w · Cited by 9 · see citing works
Self-citationvan der Weyden, L., Świątkowska, A., Iyer, V., Speak, A. O., & Adams, D. J. (2020) · G3 Genes Genomes Genetics · doi:10.1534/g3.120.401128 · Cited by 8 · see citing works
Post-synaptic scaffold protein TANC2 in psychiatric and somatic disease risk
Garrett, L., da Silva‐Buttkus, P., Rathkolb, B., Gerlini, R., Becker, L., Sanz‐Moreno, A., Seisenberger, C., Zimprich, A., Aguilar‐Pimentel, J. A., Amarie, O. V., et al. (2021) · Disease Models & Mechanisms · doi:10.1242/dmm.049205 · Cited by 7 · see citing works
Elrick, H., Peterson, K. A., Willis, B., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.06.463037 · Cited by 5 · see citing works
Rowe, D. W., Hong, S.-H., Zhang, C., Shin, D., Adams, D. J., Youngstrom, D. W., Chen, L., Wu, Z., Zhou, Y., & Maye, P. F. (2020) · Bone · doi:10.1016/j.bone.2020.115688 · Cited by 3 · see citing works
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Artificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236 · see citing works
“In this case, XGBoost, a form of extreme gradient boosting (67), was utilized to identify novel genes associated with Alzheimer's disease (68).”
Method
- Authors
- Groza, T., Gomez, F. L., Mashhadi, H. H., Muñoz-Fuentes, V., Gunes, O., Wilson, R., Cacheiro, P., Frost, A., Keskivali-Bond, P., Vardal, B., McCoy, A., Cheng, T. K., Santos, L., Wells, S., Smedley, D., Mallon, A.-M., & Parkinson, H.
- Affiliations
- European Bioinformatics Institute (GB)
- European Bioinformatics Institute, European Molecular Biology Laboratory , Welcome Genome Campus, Hinxton CB10 1SD, UK
- Queen Mary University of London (GB)
- Mary Lyon Centre at MRC Harwell (GB)
- Research Data Team, The Turing Institute , 96 Euston Rd, London NW1 2DB, UK
- The Alan Turing Institute (GB)
- Corresponding author
- Groza, Tudor
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-10-28
- Volume
- 51
- Issue
- D1
- Pages
- D1038-D1045
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC9825559
- OpenAlex
- W4307468105
- Cited by
- 413
- References
- 44
- Citations by year
- 2026: 75 · 2025: 135 · 2024: 121 · 2023: 80 · 2022: 1
- FWCI
- 52.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (2UM1HG006370-11)
- EMBL-EBI Core Funding
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- International Mouse Phenotyping Consortium, genotype-phenotype mapping, human disease, rare diseases, molecular diagnosis, gene function, mouse lines
- MeSH terms
- Animals, Disease Models, Animal, Humans, Phenotype, Databases, Factual, Mice, Knockout, Mice
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