Article | Published:

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

Nature Genetics volume 49, pages 12311238 (2017) | Download Citation


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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    , & Allele, phenotype and disease data at Mouse Genome Informatics: improving access and analysis. Mamm. Genome 26, 285–294 (2015).

  2. 2.

    & Drug development: raise standards for preclinical cancer research. Nature 483, 531–533 (2012).

  3. 3.

    , & Measuring behavior of animal models: faults and remedies. Nat. Methods 9, 1167–1170 (2012).

  4. 4.

    , , , & Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoS Biol. 8, e1000412 (2010).

  5. 5.

    & The International Mouse Phenotyping Consortium: past and future perspectives on mouse phenotyping. Mamm. Genome 23, 632–640 (2012).

  6. 6.

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

  7. 7.

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

  8. 8.

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

  9. 9.

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

  10. 10.

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

  11. 11.

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

  12. 12.

    , , , & PhenStat: a tool kit for standardized analysis of high throughput phenotypic data. PLoS One 10, e0131274 (2015).

  13. 13.

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

  14. 14.

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

  15. 15.

    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).

  16. 16.

    , & The economics of reproducibility in preclinical research. PLoS Biol. 13, e1002165 (2015).

  17. 17.

    , , , & Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 43, D789–D798 (2015).

  18. 18.

    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).

  19. 19.

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

  20. 20.

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

  21. 21.

    & 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).

  22. 22.

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

  23. 23.

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

  24. 24.

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

  25. 25.

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

  26. 26.

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

  27. 27.

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

  28. 28.

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

  29. 29.

    , , , & Functionally enigmatic genes: a case study of the brain ignorome. PLoS One 9, e88889 (2014).

  30. 30.

    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).

  31. 31.

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

  32. 32.

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

  33. 33.

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

  34. 34.

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

  35. 35.

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

  36. 36.

    , , & Trace amine-associated receptor 1 regulation of methamphetamine intake and related traits. Neuropsychopharmacology 40, 2175–2184 (2015).

  37. 37.

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

  38. 38.

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

  39. 39.

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

  40. 40.

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

  41. 41.

    , , & Collaborative cross mice in a genetic association study reveal new candidate genes for bone microarchitecture. BMC Genomics 16, 1013 (2015).

  42. 42.

    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).

  43. 43.

    , , & Robust and sensitive analysis of mouse knockout phenotypes. PLoS One 7, e52410 (2012).

  44. 44.

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

  45. 45.

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

Download references


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.).

Author information

Author notes

    • Terrence F Meehan
    • , Nathalie Conte
    •  & David B West

    These authors contributed equally to this work.


  1. European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.

    • Terrence F Meehan
    • , Nathalie Conte
    • , Jeremy Mason
    • , Jonathan Warren
    • , Chao-Kung Chen
    • , Ilinca Tudose
    • , Mike Relac
    • , Peter Matthews
    • , Paul Flicek
    •  & Helen Parkinson
  2. Children's Hospital Oakland Research Institute, Oakland, California, USA.

    • David B West
  3. William Harvey Research Institute, Queen Mary University of London, London, UK.

    • Julius O Jacobsen
    •  & Damian Smedley
  4. The Wellcome Trust Sanger Institute, Hinxton, UK.

    • Natasha Karp
    • , Jacqueline K White
    • , Allan Bradley
    • , William C Skarnes
    •  & David J Adams
  5. Medical Research Council Harwell, Mammalian Genetics Unit and Mary Lyon Centre, Harwell, UK.

    • Luis Santos
    • , Tanja Fiegel
    • , Natalie Ring
    • , Henrik Westerberg
    • , Simon Greenaway
    • , Duncan Sneddon
    • , Hugh Morgan
    • , Gemma F Codner
    • , Michelle E Stewart
    • , James Brown
    • , Neil Horner
    • , Sara Wells
    • , Ann-Marie Mallon
    •  & Steve D M Brown
  6. Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University, Portland, Oregon, USA.

    • Melissa Haendel
  7. Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, USA.

    • Nicole Washington
    •  & Christopher J Mungall
  8. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.

    • Corey L Reynolds
    • , Juan Gallegos
    • , John Seavitt
    • , Arthur L Beaudet
    •  & Mary E Dickinson
  9. Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Experimental Genetics, Neuherberg, Germany.

    • Valerie Gailus-Durner
    • , Wolfgang Wurst
    •  & Martin Hrabe de Angelis
  10. CELPHEDIA, PHENOMIN, Institut Clinique de la Souris (ICS), Illkirch-Graffenstaden, France.

    • Tania Sorg
    • , Guillaume Pavlovic
    •  & Yann Herault
  11. Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Université de Strasbourg, Illkirch, France.

    • Tania Sorg
    • , Guillaume Pavlovic
    •  & Yann Herault
  12. Centre National de la Recherche Scientifique, UMR7104, Illkirch, France.

    • Tania Sorg
    • , Guillaume Pavlovic
    •  & Yann Herault
  13. Institut National de la Santé et de la Recherche Médicale, U964, Illkirch, France.

    • Tania Sorg
    • , Guillaume Pavlovic
    •  & Yann Herault
  14. Mouse Biology Program, University of California, Davis, Davis, California, USA.

    • Lynette R Bower
    •  & K C Kent Lloyd
  15. IMPC, San Anselmo, California, USA.

    • Mark Moore
  16. Charles River Laboratories, Wilmington, Massachusetts, USA.

    • Iva Morse
  17. SKL of Pharmaceutical Biotechnology and Model Animal Research Center, Collaborative Innovation Center for Genetics and Development, Nanjing Biomedical Research Institute, Nanjing University, Nanjing, China.

    • Xiang Gao
  18. Monterotondo Mouse Clinic, Italian National Research Council (CNR), Institute of Cell Biology and Neurobiology, Monterotondo Scalo, Italy.

    • Glauco P Tocchini-Valentini
  19. RIKEN BioResource Center, Tsukuba, Japan.

    • Yuichi Obata
  20. Korea Mouse Phenotyping Center, Seoul, Republic of Korea.

    • Soo Young Cho
    •  & Je Kyung Seong
  21. National Cancer Center, Goyang, Republic of Korea.

    • Soo Young Cho
  22. Research Institute for Veterinary Science, Seoul National University, Seoul, Republic of Korea.

    • Je Kyung Seong
  23. Centre for Phenogenomics, Toronto, Ontario, Canada.

    • Ann M Flenniken
    • , Lauryl M J Nutter
    • , Susan Newbigging
    •  & Colin McKerlie
  24. Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada.

    • Monica J Justice
  25. Jackson Laboratory, Bar Harbor, Maine, USA.

    • Stephen A Murray
    • , Karen L Svenson
    •  & Robert E Braun


  1. The International Mouse Phenotyping Consortium

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


  1. Search for Terrence F Meehan in:

  2. Search for Nathalie Conte in:

  3. Search for David B West in:

  4. Search for Julius O Jacobsen in:

  5. Search for Jeremy Mason in:

  6. Search for Jonathan Warren in:

  7. Search for Chao-Kung Chen in:

  8. Search for Ilinca Tudose in:

  9. Search for Mike Relac in:

  10. Search for Peter Matthews in:

  11. Search for Natasha Karp in:

  12. Search for Luis Santos in:

  13. Search for Tanja Fiegel in:

  14. Search for Natalie Ring in:

  15. Search for Henrik Westerberg in:

  16. Search for Simon Greenaway in:

  17. Search for Duncan Sneddon in:

  18. Search for Hugh Morgan in:

  19. Search for Gemma F Codner in:

  20. Search for Michelle E Stewart in:

  21. Search for James Brown in:

  22. Search for Neil Horner in:

  23. Search for Melissa Haendel in:

  24. Search for Nicole Washington in:

  25. Search for Christopher J Mungall in:

  26. Search for Corey L Reynolds in:

  27. Search for Juan Gallegos in:

  28. Search for Valerie Gailus-Durner in:

  29. Search for Tania Sorg in:

  30. Search for Guillaume Pavlovic in:

  31. Search for Lynette R Bower in:

  32. Search for Mark Moore in:

  33. Search for Iva Morse in:

  34. Search for Xiang Gao in:

  35. Search for Glauco P Tocchini-Valentini in:

  36. Search for Yuichi Obata in:

  37. Search for Soo Young Cho in:

  38. Search for Je Kyung Seong in:

  39. Search for John Seavitt in:

  40. Search for Arthur L Beaudet in:

  41. Search for Mary E Dickinson in:

  42. Search for Yann Herault in:

  43. Search for Wolfgang Wurst in:

  44. Search for Martin Hrabe de Angelis in:

  45. Search for K C Kent Lloyd in:

  46. Search for Ann M Flenniken in:

  47. Search for Lauryl M J Nutter in:

  48. Search for Susan Newbigging in:

  49. Search for Colin McKerlie in:

  50. Search for Monica J Justice in:

  51. Search for Stephen A Murray in:

  52. Search for Karen L Svenson in:

  53. Search for Robert E Braun in:

  54. Search for Jacqueline K White in:

  55. Search for Allan Bradley in:

  56. Search for Paul Flicek in:

  57. Search for Sara Wells in:

  58. Search for William C Skarnes in:

  59. Search for David J Adams in:

  60. Search for Helen Parkinson in:

  61. Search for Ann-Marie Mallon in:

  62. Search for Steve D M Brown in:

  63. Search for Damian Smedley in:


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.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Damian Smedley.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figure 1 and Supplementary Note.

Excel files

  1. 1.

    Supplementary Table 1

    Reproducibility of 2547 MGI curated gene–phenotype associations that have also been assessed by the IMPC.

  2. 2.

    Supplementary Table 2

    Comparison of human mendelian disease caused by known gene mutations with targeted null mice.

  3. 3.

    Supplementary Table 3

    Summary of phenotypes for human mendelian disease mapping to mouse mutations with adult mutant phenotypes.

  4. 4.

    Supplementary Table 4

    Manual curation of human disease and mouse phenotypes for 100 genes.

  5. 5.

    Supplementary Table 5

    Mutant mouse gene IDs with phenotypes having no or minimal Gene Ontology annotations.

  6. 6.

    Supplementary Table 6

    Candidate genes for genetically mapped human mendelian disease.

  7. 7.

    Supplementary Table 7

    Contributing institute animal welfare approvals.

About this article

Publication history





Further reading