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Disease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium


Although next-generation sequencing has revolutionized the ability to associate variants with human diseases, diagnostic rates and development of new therapies are still limited by a lack of knowledge of the functions and pathobiological mechanisms of most genes. To address this challenge, the International Mouse Phenotyping Consortium is creating a genome- and phenome-wide catalog of gene function by characterizing new knockout-mouse strains across diverse biological systems through a broad set of standardized phenotyping tests. All mice will be readily available to the biomedical community. Analyzing the first 3,328 genes identified models for 360 diseases, including the first models, to our knowledge, for type C Bernard–Soulier, Bardet–Biedl-5 and Gordon Holmes syndromes. 90% of our phenotype annotations were novel, providing functional evidence for 1,092 genes and candidates in genetically uncharacterized diseases including arrhythmogenic right ventricular dysplasia 3. Finally, we describe our role in variant functional validation with The 100,000 Genomes Project and others.

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Figure 1: IMPC mutant models of human disease and gene function.
Figure 2: Mouse models for mendelian disease.
Figure 3: Mouse model of phosphoserine phosphatase deficiency.
Figure 4: Novel mouse models of disease.

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  1. Bello, S.M., Smith, C.L. & Eppig, J.T. Allele, phenotype and disease data at Mouse Genome Informatics: improving access and analysis. Mamm. Genome 26, 285–294 (2015).

    Article  CAS  Google Scholar 

  2. Begley, C.G. & Ellis, L.M. Drug development: raise standards for preclinical cancer research. Nature 483, 531–533 (2012).

    Article  CAS  Google Scholar 

  3. Fonio, E., Golani, I. & Benjamini, Y. Measuring behavior of animal models: faults and remedies. Nat. Methods 9, 1167–1170 (2012).

    Article  CAS  Google Scholar 

  4. Kilkenny, C., Browne, W.J., Cuthill, I.C., Emerson, M. & Altman, D.G. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoS Biol. 8, e1000412 (2010).

    Article  Google Scholar 

  5. Brown, S.D.M. & Moore, M.W. The International Mouse Phenotyping Consortium: past and future perspectives on mouse phenotyping. Mamm. Genome 23, 632–640 (2012).

    Article  CAS  Google Scholar 

  6. Hrabě de Angelis, M. et al. Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics. Nat. Genet. 47, 969–978 (2015).

    Article  Google Scholar 

  7. Skarnes, W.C. et al. A conditional knockout resource for the genome-wide study of mouse gene function. Nature 474, 337–342 (2011).

    Article  CAS  Google Scholar 

  8. Bradley, A. et al. The mammalian gene function resource: the International Knockout Mouse Consortium. Mamm. Genome 23, 580–586 (2012).

    Article  Google Scholar 

  9. Rosen, B., Schick, J. & Wurst, W. Beyond knockouts: the International Knockout Mouse Consortium delivers modular and evolving tools for investigating mammalian genes. Mamm. Genome 26, 456–466 (2015).

    Article  CAS  Google Scholar 

  10. Dickinson, M.E. et al. High-throughput discovery of novel developmental phenotypes. Nature 537, 508–514 (2016).

    Article  CAS  Google Scholar 

  11. Adams, D. et al. Bloomsbury report on mouse embryo phenotyping: recommendations from the IMPC workshop on embryonic lethal screening. Dis. Model. Mech. 6, 571–579 (2013).

    Article  Google Scholar 

  12. Kurbatova, N., Mason, J.C., Morgan, H., Meehan, T.F. & Karp, N.A. PhenStat: a tool kit for standardized analysis of high throughput phenotypic data. PLoS One 10, e0131274 (2015).

    Article  Google Scholar 

  13. West, D.B. et al. A lacZ reporter gene expression atlas for 313 adult KOMP mutant mouse lines. Genome Res. 25, 598–607 (2015).

    Article  CAS  Google Scholar 

  14. Adissu, H.A. et al. Histopathology reveals correlative and unique phenotypes in a high-throughput mouse phenotyping screen. Dis. Model. Mech. 7, 515–524 (2014).

    Article  CAS  Google Scholar 

  15. Koscielny, G. et al. The International Mouse Phenotyping Consortium Web Portal, a unified point of access for knockout mice and related phenotyping data. Nucleic Acids Res. 42, D802–D809 (2014).

    Article  CAS  Google Scholar 

  16. Freedman, L.P., Cockburn, I.M. & Simcoe, T.S. The economics of reproducibility in preclinical research. PLoS Biol. 13, e1002165 (2015).

    Article  Google Scholar 

  17. Amberger, J.S., Bocchini, C.A., Schiettecatte, F., Scott, A.F. & Hamosh, A. Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 43, D789–D798 (2015).

    Article  Google Scholar 

  18. Rath, A. et al. Representation of rare diseases in health information systems: the Orphanet approach to serve a wide range of end users. Hum. Mutat. 33, 803–808 (2012).

    Article  Google Scholar 

  19. Köhler, S. et al. The Human Phenotype Ontology in 2017. Nucleic Acids Res. 45, D1, D865–D876 (2017).

    Article  Google Scholar 

  20. Mungall, C.J. et al. Use of model organism and disease databases to support matchmaking for human disease gene discovery. Hum. Mutat. 36, 979–984 (2015).

    Article  Google Scholar 

  21. Smith, C.L. & Eppig, J.T. Expanding the mammalian phenotype ontology to support automated exchange of high throughput mouse phenotyping data generated by large-scale mouse knockout screens. J. Biomed. Semantics 6, 11 (2015).

    Article  Google Scholar 

  22. Smedley, D. et al. PhenoDigm: analyzing curated annotations to associate animal models with human diseases. Database (Oxford) 2013, bat025 (2013).

    Article  Google Scholar 

  23. Savoia, A. et al. Spectrum of the mutations in Bernard-Soulier syndrome. Hum. Mutat. 35, 1033–1045 (2014).

    Article  CAS  Google Scholar 

  24. Khan, S.A. et al. Genetics of human Bardet-Biedl syndrome, an updates. Clin. Genet. 90, 3–15 (2016).

    Article  CAS  Google Scholar 

  25. Margolin, D.H. et al. Ataxia, dementia, and hypogonadotropism caused by disordered ubiquitination. N. Engl. J. Med. 368, 1992–2003 (2013).

    Article  CAS  Google Scholar 

  26. Santens, P. et al. RNF216 mutations as a novel cause of autosomal recessive Huntington-like disorder. Neurology 84, 1760–1766 (2015).

    Article  CAS  Google Scholar 

  27. White, J.K. et al. Genome-wide generation and systematic phenotyping of knockout mice reveals new roles for many genes. Cell 154, 452–464 (2013).

    Article  CAS  Google Scholar 

  28. Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049–D1056 (2015).

  29. Pandey, A.K., Lu, L., Wang, X., Homayouni, R. & Williams, R.W. Functionally enigmatic genes: a case study of the brain ignorome. PLoS One 9, e88889 (2014).

    Article  Google Scholar 

  30. Petryszak, R. et al. Expression Atlas update: an integrated database of gene and protein expression in humans, animals and plants. Nucleic Acids Res. 44, D746–D752 (2016).

    Article  CAS  Google Scholar 

  31. Kingsley, P.D. et al. Ontogeny of erythroid gene expression. Blood 121, e5–e13 (2013).

    Article  CAS  Google Scholar 

  32. Boria, I. et al. The ribosomal basis of Diamond-Blackfan anemia: mutation and database update. Hum. Mutat. 31, 1269–1279 (2010).

    Article  CAS  Google Scholar 

  33. Kizil, C. et al. Simplet/Fam53b is required for Wnt signal transduction by regulating β-catenin nuclear localization. Development 141, 3529–3539 (2014).

    Article  CAS  Google Scholar 

  34. Smedley, D. et al. Next-generation diagnostics and disease-gene discovery with the Exomiser. Nat. Protoc. 10, 2004–2015 (2015).

    Article  CAS  Google Scholar 

  35. Bone, W.P. et al. Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency. Genet. Med. 18, 608–617 (2016).

    Article  CAS  Google Scholar 

  36. Harkness, J.H., Shi, X., Janowsky, A. & Phillips, T.J. Trace amine-associated receptor 1 regulation of methamphetamine intake and related traits. Neuropsychopharmacology 40, 2175–2184 (2015).

    Article  CAS  Google Scholar 

  37. Cade, B.E. et al. Genetic associations with obstructive sleep apnea traits in Hispanic/Latino Americans. Am. J. Respir. Crit. Care Med. 194, 886–897 (2016).

    Article  Google Scholar 

  38. Knowles, J.W. et al. Identification and validation of N-acetyltransferase 2 as an insulin sensitivity gene. J. Clin. Invest. 126, 403 (2016).

    Article  Google Scholar 

  39. Lang, B. et al. Recurrent deletions of ULK4 in schizophrenia: a gene crucial for neuritogenesis and neuronal motility. J. Cell Sci. 127, 630–640 (2014).

    Article  CAS  Google Scholar 

  40. McIntyre, R.E. et al. A genome-wide association study for regulators of micronucleus formation in mice. G3 (Bethesda) 6, 2343–2354 (2016).

    Article  CAS  Google Scholar 

  41. Levy, R., Mott, R.F., Iraqi, F.A. & Gabet, Y. Collaborative cross mice in a genetic association study reveal new candidate genes for bone microarchitecture. BMC Genomics 16, 1013 (2015).

    Article  Google Scholar 

  42. Ringwald, M. et al. The IKMC web portal: a central point of entry to data and resources from the International Knockout Mouse Consortium. Nucleic Acids Res. 39, D849–D855 (2011).

    Article  CAS  Google Scholar 

  43. Karp, N.A., Melvin, D., Sanger Mouse Genetics Project & Mott, R.F. Robust and sensitive analysis of mouse knockout phenotypes. PLoS One 7, e52410 (2012).

    Article  CAS  Google Scholar 

  44. Sayers, E.W. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 40, D13–D25 (2012).

    Article  CAS  Google Scholar 

  45. Binns, D. et al. QuickGO: a web-based tool for Gene Ontology searching. Bioinformatics 25, 3045–3046 (2009).

    Article  CAS  Google Scholar 

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This work was supported by NIH grants U54 HG006370 (T.F.M., P.F., A.-M.M., H.P., D.S. and S.D.M.B.), U42 OD011185 (S.A.M.), U54 HG006332 (R.E.B. and K.L.S.), U54 HG006348-S1 and OD011174 (A.L.B.), 1R24OD011883 (C.J.M., M.H., N.W. and D.S.), HG006364-03S1, U54H G006364 (K.C.K.L. and C.M.) and U42 OD011175 (C.M. and K.C.K.L.). Additional support was provided by the Wellcome Trust, Medical Research Council Strategic Award 53658 (S.W. and S.D.M.B.); the government of Canada through Genome Canada and Ontario Genomics (OGI-051) (C.M. and S.D.M.B.); the National Centre for Scientific Research (CNRS); the French National Institute of Health and Medical Research (INSERM); the University of Strasbourg (UDS); the Centre Européen de Recherche en Biologie et en Médecine; the Agence Nationale de la Recherche under the framework program Investissements d'Avenir labeled ANR-10-IDEX-0002-02, ANR-10-INBS-07 PHENOMIN (Y.H.); the German Federal Ministry of Education and Research through Infrafrontier grant 01KX1012 (S.A.M., V.G.-D. and M.H.d.A.); and the 'EUCOMM: Tools for Functional Annotation of the Mouse Genome' (EUCOMMTOOLS) project, grant agreement FP7-HEALTH-F4-2010-261492 (W.W.).

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Authors and Affiliations




T.F.M., D.B.W., N.C. and D. Smedley contributed to data analysis, writing the paper and the design and execution of the work. N.H., M.H., N.W., C.J.M., P.M., J.O.J., C.-K.C., I.T., H.M., M.R., N.K., J.W., H.W., J.M. and D. Sneddon contributed to development of the software, statistical analysis, database and APIs. L.S., T.F., N.R. and S.G. performed quality control of the phenotype data. J.B., J.K.W., S.Y.C., G.F.C., M.E.S., C.L.R., J.G., V.G.-D., T.S., G.P. and L.R.B. led the experimental work and data production. I.M., J.S., A.B., M.E.D., M.H.d.A., M.M., Y.H., G.P.T.-V., K.C.K.L., X.G., C.M., M.J.J., S.A.M., K.L.S., R.E.B., S.W., A.-M.M., P.F., H.P., J.W., A.L.B., W.C.S., D.J.A., S.D.M.B., W.W., S.N., A.M.F., L.M.J.N., Y.O. and J.K.S. were senior principal investigators of the key programs that contributed to the paper and were critical in the design, management and execution of the study, and the writing and reviewing of the manuscript. The additional IMPC consortium members all contributed to data acquisition and data handling.

Corresponding author

Correspondence to Damian Smedley.

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The authors declare no competing financial interests.

Additional information

A full list of members and affiliations appears in the Supplementary Note.

Integrated supplementary information

Supplementary Figure 1 Precision and recall of the automated PhenoDigm algorithm.

IMPC mouse strains were ranked by the PhenoDigm algorithm for diseases involved in 650 known gene associations from OMIM and Orphanet then the precision and recall of the lines involving the orthologue of the known disease gene were plotted. Performance was measured where models were excluded below a threshold of either 1.0, 1.25, 1.35, 1.5 or 1.75 for the geometric mean of the information content and Jaccard index of the best phenotype match.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1 and Supplementary Note. (PDF 259 kb)

Supplementary Table 1

Reproducibility of 2547 MGI curated gene–phenotype associations that have also been assessed by the IMPC. (XLSX 114 kb)

Supplementary Table 2

Comparison of human mendelian disease caused by known gene mutations with targeted null mice. (XLSX 82 kb)

Supplementary Table 3

Summary of phenotypes for human mendelian disease mapping to mouse mutations with adult mutant phenotypes. (XLSX 53 kb)

Supplementary Table 4

Manual curation of human disease and mouse phenotypes for 100 genes. (XLSX 66 kb)

Supplementary Table 5

Mutant mouse gene IDs with phenotypes having no or minimal Gene Ontology annotations. (XLSX 164 kb)

Supplementary Table 6

Candidate genes for genetically mapped human mendelian disease. (XLSX 51 kb)

Supplementary Table 7

Contributing institute animal welfare approvals. (XLSX 10 kb)

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Meehan, T., Conte, N., West, D. et al. Disease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium. Nat Genet 49, 1231–1238 (2017).

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