Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Exome sequencing in amyotrophic lateral sclerosis implicates a novel gene, DNAJC7, encoding a heat-shock protein

Nature Neuroscience volume 22, pages 1966–1974 (2019)Cite this article

Subjects

A Publisher Correction to this article was published on 19 December 2019

This article has been updated

Abstract

To discover novel genes underlying amyotrophic lateral sclerosis (ALS), we aggregated exomes from 3,864 cases and 7,839 ancestry-matched controls. We observed a significant excess of rare protein-truncating variants among ALS cases, and these variants were concentrated in constrained genes. Through gene level analyses, we replicated known ALS genes including SOD1, NEK1 and FUS. We also observed multiple distinct protein-truncating variants in a highly constrained gene, DNAJC7. The signal in DNAJC7 exceeded genome-wide significance, and immunoblotting assays showed depletion of DNAJC7 protein in fibroblasts in a patient with ALS carrying the p.Arg156Ter variant. DNAJC7 encodes a member of the heat-shock protein family, HSP40, which, along with HSP70 proteins, facilitates protein homeostasis, including folding of newly synthesized polypeptides and clearance of degraded proteins. When these processes are not regulated, misfolding and accumulation of aberrant proteins can occur and lead to protein aggregation, which is a pathological hallmark of neurodegeneration. Our results highlight DNAJC7 as a novel gene for ALS.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Exome-wide enrichment of PTVs in ALS cases.
Fig. 2: Enrichment of PTVs in constrained genes in ALS cases.
Fig. 3: No enrichment of variants in known ALS genes, other related neurodegenerative disease genes or brain-specific genes.
Fig. 4: Quantile–quantile plot of discovery results for rare variants.
Fig. 5: Effects of DNAJC7 PTV p.Arg156Ter on transcript and protein levels.

Similar content being viewed by others

Data availability

The sequencing data discussed in this publication were obtained through dbGaP and are available under the following accession codes: MIGen Exome Sequencing: Ottawa Heart (phs000806.v1.p1); MIGen Exome Sequencing: Leicester UK Heart Study (phs001000.v1.p1); Swedish Schizophrenia Population-Based Case-control Exome Sequencing (phs000473.v2.p2); Genome-Wide Association Study of Amyotrophic Lateral Sclerosis (phs000101.v5.p1).

Code availability

Code used to conduct the analysis is provided online (Supplementary Software).

Change history

  • 19 December 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. Strong, M. J. et al. Amyotrophic lateral sclerosis–frontotemporal spectrum disorder (ALS-FTSD): revised diagnostic criteria. Amyotroph. Lateral Scler. Frontotemporal Degener. 18, 153–174 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Al-Chalabi, A., van den Berg, L. H. & Veldink, J. Gene discovery in amyotrophic lateral sclerosis: implications for clinical management. Nat. Rev. Neurol. 13, 96–104 (2017).

    Article  CAS  PubMed  Google Scholar 

  3. Al-Chalabi, A. Perspective: don’t keep it in the family. Nature 550, S112 (2017).

    Article  CAS  PubMed  Google Scholar 

  4. Singh, T. et al. Rare loss-of-function variants in SETD1A are associated with schizophrenia and developmental disorders. Nat. Neurosci. 19, 571–577 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Mohanan, V. et al. C1orf106 is a colitis risk gene that regulates stability of epithelial adherens junctions. Science 359, 1161–1166 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Manning, A. et al. A low-frequency inactivating AKT2 variant enriched in the Finnish population is associated with fasting insulin levels and type 2 diabetes risk. Diabetes 66, 2019–2032 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Hamburg, M. A. & Collins, F. S. The path to personalized medicine. N. Engl. J. Med. 363, 301–304 (2010).

    Article  CAS  PubMed  Google Scholar 

  8. Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

    Article  CAS  PubMed  Google Scholar 

  9. Cirulli, E. T. et al. Exome sequencing in amyotrophic lateral sclerosis identifies risk genes and pathways. Science 347, 1436–1441 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Smith, B. N. et al. Exome-wide rare variant analysis identifies TUBA4A mutations associated with familial ALS. Neuron 84, 324–331 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Smith, B. N. et al. Mutations in the vesicular trafficking protein annexin A11 are associated with amyotrophic lateral sclerosis. Sci. Transl Med. 9, eaad9157 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kenna, K. P. et al. NEK1 variants confer susceptibility to amyotrophic lateral sclerosis. Nat. Genet. 48, 1037–1042 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Nicolas, A. et al. Genome-wide analyses identify KIF5A as a novel ALS gene. Neuron 97, 1268–1283.e6 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. van Rheenen, W. et al. Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis. Nat. Genet. 48, 1043–1048 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Lackie, R. E. et al. The Hsp70/Hsp90 chaperone machinery in neurodegenerative diseases. Front. Neurosci. 11, 254 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Swinnen, B. & Robberecht, W. The phenotypic variability of amyotrophic lateral sclerosis. Nat. Rev. Neurol. 10, 661–670 (2014).

    Article  PubMed  Google Scholar 

  17. Farhan, S. M. et al. The Ontario Neurodegenerative Disease Research Initiative (ONDRI). Can. J. Neurol. Sci. 44, 196–202 (2017).

    Article  PubMed  Google Scholar 

  18. Farhan, S. M. K., Gendron, T. F., Petrucelli, L., Hegele, R. A. & Strong, M. J. OPTN p.Met468Arg and ATXN2 intermediate length polyQ extension in families with C9orf72 mediated amyotrophic lateral sclerosis and frontotemporal dementia. Am. J. Med. Genet. B Neuropsychiatr. Genet. 177, 75–85 (2018).

    Article  CAS  PubMed  Google Scholar 

  19. Aarsland, D., Zaccai, J. & Brayne, C. A systematic review of prevalence studies of dementia in Parkinson’s disease. Mov. Disord. 20, 1255–1263 (2005).

    Article  PubMed  Google Scholar 

  20. Hely, M. A., Reid, W. G., Adena, M. A., Halliday, G. M. & Morris, J. G. The Sydney multicenter study of Parkinson’s disease: the inevitability of dementia at 20 years. Mov. Disord. 23, 837–844 (2008).

    Article  PubMed  Google Scholar 

  21. Fagerberg, L. et al. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol. Cell. Proteomics 13, 397–406 (2014).

    Article  CAS  PubMed  Google Scholar 

  22. Consortium, G. T. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    Article  CAS  Google Scholar 

  23. Jiang, J. et al. Structural basis of J cochaperone binding and regulation of Hsp70. Mol. Cell 28, 422–433 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kampinga, H. H. & Craig, E. A. The HSP70 chaperone machinery: J proteins as drivers of functional specificity. Nat. Rev. Mol. Cell Biol. 11, 579–592 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Mayer, M. P. & Bukau, B. Hsp70 chaperones: cellular functions and molecular mechanism. Cell. Mol. Life Sci. 62, 670–684 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Clerico, E. M., Tilitsky, J. M., Meng, W. & Gierasch, L. M. How Hsp70 molecular machines interact with their substrates to mediate diverse physiological functions. J. Mol. Biol. 427, 1575–1588 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Uddin, M. S. et al. Autophagy and Alzheimer’s disease: from molecular mechanisms to therapeutic implications. Front. Aging Neurosci. 10, 04 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Irwin, D. J., Lee, V. M. & Trojanowski, J. Q. Parkinson’s disease dementia: convergence of alpha-synuclein, tau and amyloid-beta pathologies. Nat. Rev. Neurosci. 14, 626–636 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Imarisio, S. et al. Huntington’s disease: from pathology and genetics to potential therapies. Biochem. J. 412, 191–209 (2008).

    Article  CAS  PubMed  Google Scholar 

  30. Brundin, P., Melki, R. & Kopito, R. Prion-like transmission of protein aggregates in neurodegenerative diseases. Nat. Rev. Mol. Cell Biol. 11, 301–307 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ross, C. A. & Poirier, M. A. Protein aggregation and neurodegenerative disease. Nat. Med. 10 (Suppl.), S10–S17 (2004).

    Article  PubMed  CAS  Google Scholar 

  32. Winklhofer, K. F., Tatzelt, J. & Haass, C. The two faces of protein misfolding: gain- and loss-of-function in neurodegenerative diseases. EMBO J. 27, 336–349 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Gidalevitz, T., Ben-Zvi, A., Ho, K. H., Brignull, H. R. & Morimoto, R. I. Progressive disruption of cellular protein folding in models of polyglutamine diseases. Science 311, 1471–1474 (2006).

    Article  CAS  PubMed  Google Scholar 

  34. Voisine, C., Pedersen, J. S. & Morimoto, R. I. Chaperone networks: tipping the balance in protein folding diseases. Neurobiol. Dis. 40, 12–20 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Brehme, M. et al. A chaperome subnetwork safeguards proteostasis in aging and neurodegenerative disease. Cell Rep. 9, 1135–1150 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Roodveldt, C. et al. Chaperone proteostasis in Parkinson’s disease: stabilization of the Hsp70/alpha-synuclein complex by Hip. EMBO J. 28, 3758–3770 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Auluck, P. K., Chan, H. Y., Trojanowski, J. Q., Lee, V. M. & Bonini, N. M. Chaperone suppression of alpha-synuclein toxicity in a Drosophila model for Parkinson’s disease. Science 295, 865–868 (2002).

    Article  CAS  PubMed  Google Scholar 

  38. Wacker, J. L. et al. Loss of Hsp70 exacerbates pathogenesis but not levels of fibrillar aggregates in a mouse model of Huntington’s disease. J. Neurosci. 29, 9104–9114 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Kovacs, G. G. et al. Prominent stress response of Purkinje cells in Creutzfeldt–Jakob disease. Neurobiol. Dis. 8, 881–889 (2001).

    Article  CAS  PubMed  Google Scholar 

  40. Jones, G., Song, Y., Chung, S. & Masison, D. C. Propagation of Saccharomyces cerevisiae [PSI+] prion is impaired by factors that regulate Hsp70 substrate binding. Mol. Cell. Biol 24, 3928–3937 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Chen, H. J. et al. The heat shock response plays an important role in TDP-43 clearance: evidence for dysfunction in amyotrophic lateral sclerosis. Brain 139, 1417–1432 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Udan-Johns, M. et al. Prion-like nuclear aggregation of TDP-43 during heat shock is regulated by HSP40/70 chaperones. Hum. Mol. Genet. 23, 157–170 (2014).

    Article  CAS  PubMed  Google Scholar 

  43. Zhang, Y. J. et al. Phosphorylation regulates proteasomal-mediated degradation and solubility of TAR DNA binding protein-43 C-terminal fragments. Mol. Neurodegener. 5, 33 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Benatar, M. et al. Randomized, double-blind, placebo-controlled trial of arimoclomol in rapidly progressive SOD1 ALS. Neurology 90, e565–e574 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Hageman, J. et al. A DNAJB chaperone subfamily with HDAC-dependent activities suppresses toxic protein aggregation. Mol. Cell 37, 355–369 (2010).

    Article  CAS  PubMed  Google Scholar 

  46. Outeiro, T. F. et al. Formation of toxic oligomeric alpha-synuclein species in living cells. PLoS One 3, e1867 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Crippa, V. et al. The small heat shock protein B8 (HspB8) promotes autophagic removal of misfolded proteins involved in amyotrophic lateral sclerosis (ALS). Hum. Mol. Genet. 19, 3440–3456 (2010).

    Article  CAS  PubMed  Google Scholar 

  48. Sharp, P. S. et al. Protective effects of heat shock protein 27 in a model of ALS occur in the early stages of disease progression. Neurobiol. Dis. 30, 42–55 (2008).

    Article  CAS  PubMed  Google Scholar 

  49. Novoselov, S. S. et al. Molecular chaperone mediated late-stage neuroprotection in the SOD1G93A mouse model of amyotrophic lateral sclerosis. PLoS One 8, e73944 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ganna, A. et al. Ultra-rare disruptive and damaging mutations influence educational attainment in the general population. Nat. Neurosci. 19, 1563–1565 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ganna, A. et al. Quantifying the impact of rare and ultra-rare coding variation across the phenotypic spectrum. Am. J. Hum. Genet. 102, 1204–1211 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. 17, 122 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Wang, X. Firth logistic regression for rare variant association tests. Front. Genet. 5, 187 (2014).

    PubMed  PubMed Central  Google Scholar 

  55. Lam, H. Y. et al. Performance comparison of whole-genome sequencing platforms. Nat. Biotechnol. 30, 78–82 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. O’Rawe, J. et al. Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing. Genome Med. 5, 28 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank and acknowledge the consent and cooperation of all study participants. Many thanks are given to F. Cerrato for helping assemble the dataset and providing general project management, and to T. Poterba, J. Bloom, D. King and C. Seed for their assistance in Hail. Data used in this research were in part obtained from the UK MND Collections for MND Research, funded by the MND Association and the Wellcome Trust. The authors thank people from MND and their families for their participation in this project. The project is supported through the following funding organizations under the aegis of the JPND (www.jpnd.eu (United Kingdom, Medical Research Council (MR/L501529/1; MR/R024804/1)), the Economic and Social Research Council (ES/L008238/1)) and through the Motor Neurone Disease Association. This study represents independent research part funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. Samples used in this research were in part obtained from the UK National DNA Bank for MND Research, funded by the MND Association and the Wellcome Trust. We acknowledge sample management undertaken by Biobanking Solutions funded by the Medical Research Council at the Centre for Integrated Genomic Medical Research, University of Manchester. The CReATe consortium (U54NS092091) is part of the Rare Diseases Clinical Research Network (RDCRN), an initiative of the Office of Rare Diseases Research (ORDR), NCATS. This consortium is funded through collaboration between NCATS and the NINDS. Additional support is provided by the ALS Association (17-LGCA-331). S.M.K.F. was supported by the ALS Canada Tim E. Noël Postdoctoral Fellowship. J.R.K. was supported by the Project ALS Tom Kirchhoff Family Postdoctoral Fellowship and acknowledges K. Mamia and L. T. Kane for their work banking fibroblasts.

Author information

Author notes

  1. A list of members and affiliations appears in the Supplementary Information.

Authors and Affiliations

  1. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Sali M. K. Farhan, Daniel P. Howrigan, Liam E. Abbott, Andrea E. Byrnes, Claire Churchhouse, Mark J. Daly & Benjamin M. Neale

  2. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Sali M. K. Farhan, Daniel P. Howrigan, Liam E. Abbott, Andrea E. Byrnes, Claire Churchhouse, Mark J. Daly & Benjamin M. Neale

  3. Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Sali M. K. Farhan, Daniel P. Howrigan, Liam E. Abbott, Andrea E. Byrnes, Claire Churchhouse, Sulagna Ghosh, Kevin Eggan, Mark J. Daly & Benjamin M. Neale

  4. Department of Stem Cell and Regenerative Biology, Harvard Stem Cell Institute, Harvard University, Cambridge, MA, USA

    Joseph R. Klim, Daniel A. Mordes, Sulagna Ghosh & Kevin Eggan

  5. United Kingdom Dementia Research Institute Centre, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Simon D. Topp, Bradley N. Smith & Christopher E. Shaw

  6. Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA

    Hemali Phatnani

  7. Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Evadnie Rampersaud & Gang Wu

  8. Department of Neurology, University of Miami, Miami, FL, USA

    Joanne Wuu & Michael Benatar

  9. Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London, UK

    Aleksey Shatunov, Alfredo Iacoangeli, Ahmad Al Khleifat & Ammar Al-Chalabi

  10. Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Alfredo Iacoangeli

  11. Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA

    Rosa Rademakers

  12. John P. Hussman Institute for Human Genomics, University of Miami, Miller School of Medicine, Miami, FL, USA

    Jacob L. McCauley & Stephan Züchner

  13. The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL, USA

    Jacob L. McCauley & Stephan Züchner

  14. Center for Neurology and Hertie Institute für Clinical Brain Research, University of Tübingen, German Center for Neurodegenerative Diseases, Tübingen, Germany

    Rebecca Schüle

  15. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    J. Paul Taylor

  16. Department of Cell and Molecular Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    J. Paul Taylor

  17. Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA

    Michael Nalls & Bryan Traynor

  18. Data Tecnica International, Glen Echo, MD, USA

    Michael Nalls

  19. Department of Neurology, The Agnes Ginges Center for Human Neurogenetics, Hadassah-Hebrew University Medical Center, Jerusalem, Israel

    Marc Gotkine

  20. Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK

    Pamela J. Shaw

  21. Faculty of Medicine, University of Southampton and Department of Neurology, University Hospital Southampton, Southampton, UK

    Karen E. Morrison

  22. Department of Neurology, King’s College Hospital, London, UK

    Ammar Al-Chalabi

  23. Department of Neurology, Johns Hopkins University, Baltimore, MD, USA

    Bryan Traynor

  24. Centre for Brain Research, University of Auckland, Auckland, New Zealand

    Christopher E. Shaw

  25. Institute for Genomic Medicine, Columbia University, New York, NY, USA

    David B. Goldstein

  26. Department of Neurology, Columbia University, New York, NY, USA

    Matthew B. Harms

Authors
  1. Sali M. K. Farhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel P. Howrigan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liam E. Abbott
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joseph R. Klim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Simon D. Topp
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Andrea E. Byrnes
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Claire Churchhouse
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hemali Phatnani
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bradley N. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Evadnie Rampersaud
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Gang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Joanne Wuu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Aleksey Shatunov
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Alfredo Iacoangeli
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Ahmad Al Khleifat
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Daniel A. Mordes
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Sulagna Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Kevin Eggan
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Rosa Rademakers
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Jacob L. McCauley
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Rebecca Schüle
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Stephan Züchner
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Michael Benatar
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. J. Paul Taylor
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Michael Nalls
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Marc Gotkine
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Pamela J. Shaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Karen E. Morrison
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Ammar Al-Chalabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Bryan Traynor
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Christopher E. Shaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. David B. Goldstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Matthew B. Harms
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Mark J. Daly
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Benjamin M. Neale
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

ALSGENS Consortium

FALS Consortium

Project MinE Consortium

CReATe Consortium

Contributions

S.M.K.F., M.J.D. and B.M.N. conceived and designed the experiments. S.M.K.F., S.D.T., H.P., B.N.S., E.R., G.W., J.W., A.S., A.I., A.A.K., D.A.M., S.G., K.E., R.R., J.L.M., R.S., S.Z., M.B., J.P.T., M.N., M.G., P.J.S., K.E.M., A.A.-C., B.T., C.E.S., D.B.G., M.B.H. and B.M.N. collected samples, prepared samples for analysis or were involved in clinical evaluation. M.B. and J.P.T. were the lead contacts for the CReATe Consortium. S.D.T. and C.E.S. were the lead contacts for the FALS Consortium. D.B.G. and M.B.H. were the lead contacts for the ALSGENS Consortium. S.M.K.F. performed all experiments and executed data analyses. D.P.H., L.E.A., A.E.B. and S.D.T. provided analysis suggestions. J.R.K. completed the cell culture, RNA and protein analyses. S.M.K.F. performed the primary writing of the manuscript with input from D.P.H., C.C., M.J.D. and B.M.N. All authors approved the final manuscript. M.J.D. and B.M.N. supervised the research.

Corresponding authors

Correspondence to Sali M. K. Farhan or Benjamin M. Neale.

Ethics declarations

Competing interests

As a possible conflict of interest, M.N.’s participation is supported by a consulting contract between Data Tecnica International and the National Institute on Aging, NIH, Bethesda, MD, USA. M.N. also consults for Lysosomal Therapeutics Inc, the Michael J. Fox Foundation and Vivid Genomics, among others. The other other authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Figure 1 Initial sample quality control analysis.

(A) Sample call rate. (B) Sample mean depth. (C) Sample mean genotype quality. (D) Sample transition to transversion ratio. (E) Sample heterozygous to homozygous ratio. (F) Sample insertion to deletion ratio. (G) Number of SNPs in each sample. (H) Number of singletons in each sample. N=3,864 ALS cases; N=7,839 controls. The box and whisker plots display the mean, minimum, and maximum.

Supplementary Figure 2 Principal component analysis of ALS dataset with 1000 Genomes.

(A) PC1 and PC2 of ALS dataset with 1000 Genomes. Cases, controls, and the European population is shown. N=3,864 ALS cases; N=7,839 controls. Each point represents one individual. (B) PC1 and PC3 of ALS dataset with 1000 Genomes. Cases, controls, and the European population is shown. (C) PC2 and PC3 of ALS dataset with 1000 Genomes. Cases, controls, and the European population is shown. (D) PC1 and PC2 of ALS dataset with 1000 Genomes. Cases, controls, and the European subpopulations are shown. (E) PC1 and PC3 of ALS dataset with 1000 Genomes. Cases, controls, and the European subpopulations are shown. (F) PC2 and PC3 of ALS dataset with 1000 Genomes. Cases, controls, and the European subpopulations are shown.

Supplementary Figure 3 All models together.

Model 1: Sample variation. The graph display the mean and standard deviation. P-values from firth logistic regression test are also displayed. Multiple test correction P-value: 0.0125. N=3,864 ALS cases; N=7,839 controls. Model 2: Sample variation, sample sex, PC1-PC10. Model 3: Sample variation, sample sex, PC1-PC10, and total exome count (summation of synonymous, benign missense, damaging missense, and PTV). Model 4: Sample variation, sample sex, PC1-PC10, and benign variation count (summation of synonymous and benign missense variation).

Supplementary Figure 4 Exome wide enrichment of SNV-based PTVs and indel-based PTVs in ALS cases.

Extension of Fig. 1: Evaluating the effects of SNV-based and indel-based PTVs within singletons (AC=1), doubletons (AC=2), ultra-rare singletons (AC=1, 0 in DiscovEHR), and rare variants (MAF<0.01 in our dataset, DiscovEHR and ExAC). Odds ratios and 95% confidence intervals for each class of variation are depicted by different colors. P-values are also displayed. Model 3 evaluates sample variation with the covariates, sample sex, PC1-10, and total exome count (summation of synonymous variation, benign missense variation, damaging missense variation, and PTV SNV or PTV indel). Model 4 evaluates sample variation with the covariates, sample sex, PC1-10, and benign variation (summation of synonymous and benign missense variation). The graph display the mean and standard deviation. P-values from firth logistic regression test are also displayed. Multiple test correction P-value: 0.0125. N=3,864 ALS cases; N=7,839 controls.

Supplementary Figure 5 Enrichment of variants in constrained genes in ALS cases.

Extension of Fig. 2a, b: (a) Evaluating the effects of constrained genes in synonymous variants, benign missense variants, damaging missense variants, and PTVs within singletons (AC=1), doubletons (AC=2), ultra-rare singletons (AC=1, 0 in DiscovEHR), and rare variants (MAF<0.01 in our dataset, DiscovEHR and ExAC). Odds ratios and 95% confidence intervals for each class of variation are depicted by different colors. P-values are also displayed. Model 3 evaluates sample variation with the covariates, sample sex, PC1-10, and total exome count (summation of synonymous variation, benign missense variation, damaging missense variation, and PTV). Model 4 evaluates sample variation with the covariates, sample sex, PC1-10, and benign variation (summation of synonymous and benign missense variation). (b) Evaluating the residual effects with constrained genes removed. The graph display the mean and standard deviation. P-values from firth logistic regression test are also displayed. Multiple test correction P-value: 0.0125. N=3,864 ALS cases; N=7,839 controls.

Supplementary Figure 6 Burden analysis of known ALS genes.

Extension of Fig. 3a: Evaluating the effects of known ALS genes in synonymous variants, benign missense variants, damaging missense variants, and PTVs within singletons (AC=1), doubletons (AC=2), ultra-rare singletons (AC=1, 0 in DiscovEHR), and rare variants (MAF<0.01 in our dataset, DiscovEHR and ExAC). Odds ratios and 95% confidence intervals for each class of variation are depicted by different colors. P-values are also displayed. Model 3 evaluates sample variation with the covariates, sample sex, PC1-10, and total exome count (summation of synonymous variation, benign missense variation, damaging missense variation, and PTV). Model 4 evaluates sample variation with the covariates, sample sex, PC1-10, and benign variation (summation of synonymous and benign missense variation). The graph display the mean and standard deviation. P-values from firth logistic regression test are also displayed. Multiple test correction P-value: 0.0125. N=3,864 ALS cases; N=7,839 controls.

Supplementary Figure 7 Analysis of other neurodegenerative disease genes.

Extension of Fig. 3b: Evaluating the effects of genes associated with other neurodegenerative disease (motor neuron diseases: primary lateral sclerosis, progressive muscular atrophy, progressive bulbar palsy, and spinal muscular atrophy; diseases with overlapping phenotypes: frontotemporal dementia, Parkinson’s disease, Pick’s disease, and Alzheimer’s disease) in synonymous variants, benign missense variants, damaging missense variants, and PTVs within singletons (AC=1), doubletons (AC=2), ultra-rare singletons (AC=1, 0 in DiscovEHR), and rare variants (MAF<0.01 in our dataset, DiscovEHR and ExAC). Odds ratios and 95% confidence intervals for each class of variation are depicted by different colors. P-values are also displayed. Model 3 evaluates sample variation with the covariates, sample sex, PC1-10, and total exome count (summation of synonymous variation, benign missense variation, damaging missense variation, and PTV). Model 4 evaluates sample variation with the covariates, sample sex, PC1-10, and benign variation (summation of synonymous and benign missense variation). The graph display the mean and standard deviation. P-values from firth logistic regression test are also displayed. Multiple test correction P-value: 0.0125. N=3,864 ALS cases; N=7,839 controls.

Supplementary Figure 8 Analysis of brain specific genes.

Extension of Fig. 3c: Analysis of brain specific genes in synonymous variants, benign missense variants, damaging missense variants, and PTVs within singletons (AC=1), doubletons (AC=2), ultra-rare singletons (AC=1, 0 in DiscovEHR), and rare variants (MAF<0.01 in our dataset, DiscovEHR and ExAC). Odds ratios and 95% confidence intervals for each class of variation are depicted by different colors. P-values are also displayed. Model 3 evaluates sample variation with the covariates, sample sex, PC1-10, and total exome count (summation of synonymous variation, benign missense variation, damaging missense variation, and PTV). Model 4 evaluates sample variation with the covariates, sample sex, PC1-10, and benign variation (summation of synonymous and benign missense variation). The graph display the mean and standard deviation. P-values from firth logistic regression test are also displayed. Multiple test correction P-value: 0.0125. N=3,864 ALS cases; N=7,839 controls.

Supplementary Figure 9 Quantile–quantile plots of ultra-rare singletons.

(a) Ultra-rare singletons (AC = 1, 0 in DiscovEHR database) for PTV. PTVs in NEK1 and OPTN, which are known ALS genes, are enriched in ALS cases. NEK1 and OPTN P values are displayed. (b) Ultra-rare singleton (AC = 1, 0 in DiscovEHR database) for damaging missense variants. Damaging missense variants in SOD1 are enriched in ALS cases. SOD1 P value is displayed.

Supplementary Figure 10 DNAJC7 qPCR and immunoblot assays.

(A-B) Relative levels of DNAJC7 mRNA in human fibroblasts using either primers recognizing exons 4 and 6 (A) or exons 13 and 14 (B). Levels for each sample were normalized to GAPDH and displayed relative to the average normalized levels of the healthy controls. Data are displayed as the mean of technical replicates with SD. (C) Uncropped immunoblot of human fibroblast protein lysates probed for the N-terminus of DNAJC7. Similar results were obtained in n=3 independent blots.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Farhan, S.M.K., Howrigan, D.P., Abbott, L.E. et al. Exome sequencing in amyotrophic lateral sclerosis implicates a novel gene, DNAJC7, encoding a heat-shock protein. Nat Neurosci 22, 1966–1974 (2019). https://doi.org/10.1038/s41593-019-0530-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-019-0530-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing