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NEK1 variants confer susceptibility to amyotrophic lateral sclerosis

Abstract

To identify genetic factors contributing to amyotrophic lateral sclerosis (ALS), we conducted whole-exome analyses of 1,022 index familial ALS (FALS) cases and 7,315 controls. In a new screening strategy, we performed gene-burden analyses trained with established ALS genes and identified a significant association between loss-of-function (LOF) NEK1 variants and FALS risk. Independently, autozygosity mapping for an isolated community in the Netherlands identified a NEK1 p.Arg261His variant as a candidate risk factor. Replication analyses of sporadic ALS (SALS) cases and independent control cohorts confirmed significant disease association for both p.Arg261His (10,589 samples analyzed) and NEK1 LOF variants (3,362 samples analyzed). In total, we observed NEK1 risk variants in nearly 3% of ALS cases. NEK1 has been linked to several cellular functions, including cilia formation, DNA-damage response, microtubule stability, neuronal morphology and axonal polarity. Our results provide new and important insights into ALS etiopathogenesis and genetic etiology.

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Figure 1: RVB analysis of FALS exomes.
Figure 2: Replication analysis of NEK1 p. Arg261His. NEK1 p.Arg261His genotypes were ascertained for 1,022 FALS samples, 6,172 SALS samples and 11,732 controls.

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References

  1. Gilissen, C., Hoischen, A., Brunner, H.G. & Veltman, J.A. Unlocking Mendelian disease using exome sequencing. Genome Biol. 12, 228 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Ng, S.B. et al. Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nat. Genet. 42, 790–793 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 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 

  4. Jian, X., Boerwinkle, E. & Liu, X. In silico prediction of splice-altering single nucleotide variants in the human genome. Nucleic Acids Res. 42, 13534–13544 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Shihab, H.A. et al. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum. Mutat. 34, 57–65 (2013).

    Article  CAS  PubMed  Google Scholar 

  6. Reid, E. et al. A kinesin heavy chain (KIF5A) mutation in hereditary spastic paraplegia (SPG10). Am. J. Hum. Genet. 71, 1189–1194 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 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 

  8. Brenner, D. et al. NEK1 mutations in familial amyotrophic lateral sclerosis. Brain 139, e28 (2016).

    Article  PubMed  Google Scholar 

  9. Renton, A.E., Chiò, A. & Traynor, B.J. State of play in amyotrophic lateral sclerosis genetics. Nat. Neurosci. 17, 17–23 (2014).

    Article  CAS  PubMed  Google Scholar 

  10. Kenna, K.P. et al. Delineating the genetic heterogeneity of ALS using targeted high-throughput sequencing. J. Med. Genet. 50, 776–783 (2013).

    Article  CAS  PubMed  Google Scholar 

  11. Lattante, S. et al. Contribution of major amyotrophic lateral sclerosis genes to the etiology of sporadic disease. Neurology 79, 66–72 (2012).

    Article  CAS  PubMed  Google Scholar 

  12. Chiò, A. et al. Extensive genetics of ALS: a population-based study in Italy. Neurology 79, 1983–1989 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Thiel, C. et al. NEK1 mutations cause short-rib polydactyly syndrome type majewski. Am. J. Hum. Genet. 88, 106–114 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Shalom, O., Shalva, N., Altschuler, Y. & Motro, B. The mammalian Nek1 kinase is involved in primary cilium formation. FEBS Lett. 582, 1465–1470 (2008).

    Article  CAS  PubMed  Google Scholar 

  15. White, M.C. & Quarmby, L.M. The NIMA-family kinase, Nek1 affects the stability of centrosomes and ciliogenesis. BMC Cell Biol. 9, 29 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Lee, J.H. & Gleeson, J.G. The role of primary cilia in neuronal function. Neurobiol. Dis. 38, 167–172 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lee, L. Riding the wave of ependymal cilia: genetic susceptibility to hydrocephalus in primary ciliary dyskinesia. J. Neurosci. Res. 91, 1117–1132 (2013).

    Article  CAS  PubMed  Google Scholar 

  18. Cohen, S., Aizer, A., Shav-Tal, Y., Yanai, A. & Motro, B. Nek7 kinase accelerates microtubule dynamic instability. Biochim. Biophys. Acta 1833, 1104–1113 (2013).

    Article  CAS  PubMed  Google Scholar 

  19. Chang, J., Baloh, R.H. & Milbrandt, J. The NIMA-family kinase Nek3 regulates microtubule acetylation in neurons. J. Cell Sci. 122, 2274–2282 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Puls, I. et al. Mutant dynactin in motor neuron disease. Nat. Genet. 33, 455–456 (2003).

    Article  CAS  PubMed  Google Scholar 

  21. Ma, X., Peterson, R. & Turnbull, J. Adenylyl cyclase type 3, a marker of primary cilia, is reduced in primary cell culture and in lumbar spinal cord in situ in G93A SOD1 mice. BMC Neurosci. 12, 71 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Chen, Y., Craigen, W.J. & Riley, D.J. Nek1 regulates cell death and mitochondrial membrane permeability through phosphorylation of VDAC1. Cell Cycle 8, 257–267 (2009).

    Article  CAS  PubMed  Google Scholar 

  23. Pelegrini, A.L. et al. Nek1 silencing slows down DNA repair and blocks DNA damage-induced cell cycle arrest. Mutagenesis 25, 447–454 (2010).

    Article  CAS  PubMed  Google Scholar 

  24. Sama, R.R., Ward, C.L. & Bosco, D.A. Functions of FUS/TLS from DNA repair to stress response: implications for ALS. ASN Neuro 6, 1759091414544472 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Tafuri, F., Ronchi, D., Magri, F., Comi, G.P. & Corti, S. SOD1 misplacing and mitochondrial dysfunction in amyotrophic lateral sclerosis pathogenesis. Front. Cell. Neurosci. 9, 336 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Madabhushi, R., Pan, L. & Tsai, L.H. DNA damage and its links to neurodegeneration. Neuron 83, 266–282 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Coppedè, F. & Migliore, L. DNA damage in neurodegenerative diseases. Mutat. Res. 776, 84–97 (2015).

    Article  PubMed  Google Scholar 

  28. Genetic Modifiers of Huntington's Disease (GeM-HD) Consortium. Identification of genetic factors that modify clinical onset of Huntington's disease. Cell 162, 516–526 (2015).

  29. Surpili, M.J., Delben, T.M. & Kobarg, J. Identification of proteins that interact with the central coiled-coil region of the human protein kinase NEK1. Biochemistry 42, 15369–15376 (2003).

    Article  CAS  PubMed  Google Scholar 

  30. Tryka, K.A. et al. NCBI's Database of Genotypes and Phenotypes: dbGaP. Nucleic Acids Res. 42, D975–D979 (2014).

    Article  CAS  PubMed  Google Scholar 

  31. DePristo, M.A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Fang, H. et al. Reducing INDEL calling errors in whole genome and exome sequencing data. Genome Med. 6, 89 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Raczy, C. et al. Isaac: ultra-fast whole-genome secondary analysis on Illumina sequencing platforms. Bioinformatics 29, 2041–2043 (2013).

    Article  CAS  PubMed  Google Scholar 

  34. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Yang, J., Lee, S.H., Goddard, M.E. & Visscher, P.M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Heinze, G. & Schemper, M. A solution to the problem of separation in logistic regression. Stat. Med. 21, 2409–2419 (2002).

    Article  PubMed  Google Scholar 

  38. 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

  39. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012).

    Article  CAS  Google Scholar 

  40. Adzhubei, I., Jordan, D.M. & Sunyaev, S.R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. Chapter 7, Unit 7.20 (2013).

  41. Kumar, P., Henikoff, S. & Ng, P.C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009).

    Article  CAS  PubMed  Google Scholar 

  42. Chun, S. & Fay, J.C. Identification of deleterious mutations within three human genomes. Genome Res. 19, 1553–1561 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Schwarz, J.M., Rödelsperger, C., Schuelke, M. & Seelow, D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat. Methods 7, 575–576 (2010).

    Article  CAS  PubMed  Google Scholar 

  44. Reva, B., Antipin, Y. & Sander, C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39, e118 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Choi, Y., Sims, G.E., Murphy, S., Miller, J.R. & Chan, A.P. Predicting the functional effect of amino acid substitutions and indels. PLoS One 7, e46688 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Davydov, E.V. et al. Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput. Biol. 6, e1001025 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Cooper, G.M. et al. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 15, 901–913 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Garber, M. et al. Identifying novel constrained elements by exploiting biased substitution patterns. Bioinformatics 25, i54–i62 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Liu, X., Wu, C., Li, C. & Boerwinkle, E. dbNSFP v3.0: a one-stop database of functional predictions and annotations for human non-synonymous and splice site SNVs. Hum. Mutat. 37, 235–241 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Seelow, D., Schuelke, M., Hildebrandt, F. & Nürnberg, P. HomozygosityMapper—an interactive approach to homozygosity mapping. Nucleic Acids Res. 37, W593–W599 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Willer, C.J., Li, Y. & Abecasis, G.R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We acknowledge all of the study participants and our collaborators for enabling this study by graciously providing samples for this study. Funding was provided by US National Institutes of Health (NIH)/National Institute of Neurological Disorders and Stroke (NINDS) (R01NS073873, J.E.L.), the American ALS Association (N.T., V.S., C.E.S., J.E.L. and R.H.B.Jr.), the Motor Neuron Disease (MND) Association (N.T., V.S., C.E.S. and J.E.L.), the Angel Fund (R.H.B.Jr.), Project ALS/P2ALS (R.H.B.Jr.), the ALS Therapy Alliance (R.H.B.Jr. and J.E.L.), The Netherlands ALS Foundation (Project MinE; J.H.V. and L.H.v.d.B.), ALS liga Belgium (P.V.D. and W.Ro.), Suna and Inan Kirac Foundation (N.A.B.). Computer resources for this study were provided by the Green High Performance Computing Center at the University of Massachusetts Medical School. L.H.v.d.B. received grants from the Netherlands Organization for Health Research and Development (Vici Scheme; the SOPHIA and STRENGTH projects through the EU Joint Programme – Neurodegenerative Disease Research, JPND). I.P.B. received grant funding from the National Health and Medical Research Council (NHMRC) of Australia (1095215, 1107644). P.C.S. was supported through the auspices of H. Robert Horvitz (Massachusetts Institute of Technology), an Investigator of the Howard Hughes Medical Institute. M.A.v.E. received a grant from the Netherlands Organization for Health Research and Development (Veni scheme) and travel grants from Baxter. This is an EU Joint Programme - Neurodegenerative Disease Research (JPND) project. The project is supported through the following funding organizations under the aegis of JPND (United Kingdom, Medical Research Council; Netherlands, ZonMW; Italy, Ministero dell'Istruzione, dell'Università e della Ricerca; Belgium, Fonds Wetenschappelijk Onderzoek; Germany, Bundesministerium für Bildung und Forschung). C.E.S. and A.A.-C. receive salary support from the National Institute for Health Research (NIHR) Dementia Biomedical Research Unit at South London and Maudsley NHS Foundation Trust and King's College London. The work leading up to this publication was funded by the European Community's Health Seventh Framework Programme (FP7/2007–2013; grant agreement number 259867). 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. I.R.C.C.S. Istituto Auxologico Italiano; AriSLA - Fondazione Italiana di Ricerca per la SLA co-financed with support of “5x1000” - Healthcare Research of the Italian Ministry of Health (grants EXOMEFALS 2009 and NOVALS 2012 (N.T., C.T., C.G., V.S. and J.E.L.)), (grant RepeatALS 2013 (S.D.T. and L.C.)), Italian Ministry of Health (grant GR-2011-02347820 - IRisALS (N.T., C.T. and D.C.)). This work was supported by a grant from the Flemish agency for Innovation by Science and Technology (IWT, Project MinE), the Interuniversity Attraction Poles (IUAP) program P7/16 of the Belgian Federal Science Policy Office, by the FWO-Vlaanderen under the frame of E-RARE-2, the ERA-Net for Research on Rare Diseases (PYRAMID), by a EU JPND project (STRENGTH). P.V.D. is supported by FWO Vlaanderen and the Belgian ALS liga. In Australia, this work was supported by a Leadership Grant to I.P.B. from MND Australia and an NHMRC fellowship (1092023) to K.L.W. G.A.R. is funded by the Canadian Institute of Health Research (CIHR), Genome-wide exon capture for targeted resequencing in patients with FALS (#208973) the Muscular Dystrophy Association, and Whole exome sequencing in patients with FALS (#153959). We thank JMB Vianney de Jong for collection of clinical data. C.S.L. is recipient of Tim E. Noël fellowship from ALS society of Canada. W.Ro. is supported through the E. von Behring Chair for Neuromuscular and Neurodegenerative Disorders, the Laevers Fund for ALS Research, the ALS Liga België, the fund 'Een Hart voor ALS' and the fund 'Opening the Future'. The research leading to these results has received funding from the European Research Council und the European's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement 340429 and from the Geneeskundige Stichting Koningin Elisabeth (G.S.K.E.). M.S. and C.D. were supported by the Deutsche Forschungsgemeinschaft, SE697/4-1, BMBF EnergI and the Deutsche Gesellschaft für Muskelkranke, Project He 2/2. This work was supported in whole or in parts by a grant from the German Federal Ministry of Education and Research (JPND STRENGTH consortium; German network for ALS research MND-NET), the Charcot Foundation for ALS Research, the virtual Helmholtz Institute “RNA-Dysmetabolismus in ALS and FTD” and the DFG-funded Swabian ALS Registry. A.Ch. is funded in part by Italian Ministry of Health (Ricerca Sanitaria Finalizzata 2010, grant RF-2010-2309849, project EXPALS), the European Community's Health Seventh Framework Programme (FP7/2007-2013 under grant agreements 259867), the Joint Programme - Neurodegenerative Disease Research (Italian Ministry of Education and University) (Sophia, and Strength Projects), A.C. is funded in part by Italian Ministry of Health (Ricerca Sanitaria Finalizzata 2010, grant GR-2010-2320550, project EXTRALS) and Fondazione Vialli e Mauro per la Ricerca sulla SLA onlus (grant #4). FUNDELA – Spanish Foundation to the development of ALS research, ISCIII – Carlos III Institute / Fondo de Investigación Sanitaria of Spain (PI10/00092; PI14/00088), ADELA – ALS Spanish Association. Part of this work was carried out on the Dutch national e-infrastructure with the support of SURF Foundation. The Alzheimer's Disease Sequencing Project (ADSP), phs000572.v7.p4, is comprised of two Alzheimer's disease (AD) genetics consortia and three National Human Genome Research Institute (NHGRI) funded Large Scale Sequencing Centers (LSSC). The two AD genetics consortia are the Alzheimer's Disease Genetics Consortium (ADGC) funded by the National Institute on Aging (NIA) (U01 AG032984), and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) funded by NIA (R01 AG033193), the National Heart, Lung, and Blood Institute (NHLBI), other NIH institutes and other foreign governmental and nongovernmental organizations. The Discovery Phase analysis of sequence data is supported through UF1AG047133 to G.D. Schellenberg, L.A. Farrer, M.A. Pericak-Vance, R. Mayeux and J.L. Haines; U01AG049505 to S. Seshadri; U01AG049506 to E. Boerwinkle; U01AG049507 to E. Wijsman; and U01AG049508 to A.M.Goate. The ADGC cohorts include: Adult Changes in Thought (ACT), the Alzheimer's Disease Centers (ADC), the Chicago Health and Aging Project (CHAP), the Memory and Aging Project (MAP), Mayo Clinic, Mayo Parkinson's Disease controls, University of Miami, the Multi-Institutional Research in Alzheimer's Genetic Epidemiology Study (MIRAGE), the National Cell Repository for Alzheimer's disease (NCRAD), the National Institute on Aging Late Onset Alzheimer's Disease Family Study (NIA-LOAD), the Religious Orders Study (ROS), the Texas Alzheimer's Research and Care Consortium (TARC), Vanderbilt University/Case Western Reserve University (VAN/CWRU), the Washington Heights-Inwood Columbia Aging Project (WHICAP) and the Washington University Sequencing Project (WUSP), the Columbia University Hispanic-Estudio Familiar de Influencia Genetica de Alzheimer (EFIGA), the University of Toronto, and Genetic Differences (GD). The CHARGE cohorts, with funding provided by 5RC2HL102419 and HL105756, include the following: Atherosclerosis Risk in Communities (ARIC) Study which is carried out as a collaborative study supported by NHLBI contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C and HHSN268201100012C), Austrian Stroke Prevention Study (ASPS), Cardiovascular Health Study (CHS), Erasmus Rucphen Family Study (ERF), Framingham Heart Study (FHS), and Rotterdam Study (RS). The three LSSC are: the Human Genome Sequencing Center at the Baylor College of Medicine (U54 HG003273), the Broad Institute Genome Center (U54HG003067), and the Washington University Genome Institute (U54HG003079). Biological samples and associated phenotypic data used in primary data analyses were stored at Study Investigators institutions, and at the National Cell Repository for Alzheimer's Disease (NCRAD, U24AG021886) at Indiana University funded by NIA. Associated phenotypic data used in primary and secondary data analyses were provided by Study Investigators, the NIA-funded Alzheimer's Disease Centers (ADCs), and the National Alzheimer's Coordinating Center (NACC, U01AG016976) and the National Institute on Aging Alzheimer's Disease Data Storage Site (NIAGADS, U24AG041689) at the University of Pennsylvania, funded by NIA, and at the Database for Genotypes and Phenotypes (dbGaP) funded by NIH. Contributors to the Genetic Analysis Data included Study Investigators on projects that were individually funded by NIA, and other NIH institutes, and by private US organizations, or foreign governmental or nongovernmental organizations. We thank people with MND, their families and control individuals for their participation in this project.

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Contributions

Sample collection, preparation and clinical evaluation: P.T.C.v.D., A.M.D., N.T., F.P.D., W.v.R., K.R.v.E., A.R.J., P.K., A.S., W.S., B.N.S., M.A.v.E., S.D.T., A. Kenna, J.W.M., C. Tiloca, R.L.M., C.V., C. Troakes, C. Colombrita, G.M., A. Calvo, F.V., S.A.-S., A. King, D.C., J.d.B., F.B., A.J.v.d.K., M.d.V., A.L.M.A.t.A., P.C.S., D.M.-Y., M.P., S.A., J.L.M.-B., T.M.S., T.M., K.E.M., S.D'A., L.M., G.P.C., R.D.B., M.C., S.G., G.Q., C.B., V.P., B.C., S.C., C. Cereda, L.C., G.S., G.L., K.L.W., P.N.L., G.A.N., I.P.B., C.S.L., P.A.D., G.A.R., H.P., P.J.S., M.R.T., K.T., F.T., K.B.B., M.V.B., R.R., J.E.-P., A.G.-R., P.V.D., W.R., A. Chio, C.G., C.D., M.S., A.R., J.D.G., J.S.M., N.A.B., O.H., A.C.L., P.M.A., J.H.W., R.H.B., A.A.-C., V.S., C.E.S., L.H.v.d.B., J.H.V. and J.E.L. Experiments and data analysis: K.P.K., P.T.C.v.D., A.M.D., N.T., B.J.K., F.P.D., W.v.R., K.R.v.E., A.R.J., P.K., A.S., W.S., B.N.S., M.A.v.E., S.D.T., A. Kenna, J.W.M., C.F., C.T., R.L.M., C.V., C. Troakes, C. Colombrita, G.M., A. Calvo, F.V., S.A.-S., A. King, D.C., P.C.S., D.M.-Y., K.L.W., C.S.L., P.A.D., M.v.B., R.R., J.E.-P., A.G.-R., P.v.D., W.R., A. Chio, C.G., C.D., M.S., A.R., J.D.G., J.S.M., N.A.B., O.H., A.C.L., P.M.A., J.H.W., R.H.B.Jr, A.A.-C., V.S., C.E.S., L.H.v.d.B., J.H.V. and J.E.L. Scientific planning and direction: K.P.K., P.T.C.v.D., A.M.D., N.T., B.J.K., C.F., I.P.B., C.S.L., P.A.D., G.A.R., H.P., P.J.S., M.R.T., K.T., F.T., K.B.B., M.v.B., R.R., J.E.-P., A.G.-R., P.v.D., W.R., A. Chio, C.G., C.D., M.S., A.R., J.D.G., J.S.M., N.A.B., O.H., A.C.L., P.M.A., J.H.W., R.H.B.Jr, A.A.-C., V.S., C.E.S., L.H.v.d.B., J.H.V. and J.E.L. Initial manuscript preparation: K.P.K., P.T.C.v.D., A.M.D., N.T., A.A.-C., V.S., C.E.S., L.H.v.d.B., J.H.V. and J.E.L.

Corresponding author

Correspondence to Jan H Veldink.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Quality control of FALS discovery cohort.

Exome sequences were obtained for 1,376 FALS cases and 13,883 controls. Samples were excluded in the event of exome-wide call rate <70%, outlying heterozygosity (F <–0.1 or F >0.1), SNP-predicted and reported gender discrepancy, detectable relatedness to another retained sample (kinship coefficient ≥0.0442; ≥3rd-degree relationship), outlying ancestry with respect to FALS samples in pairwise tests of population concordance (exhibits P < 1 × 10−4 in tests with ≥10% of FALS cases; Supplementary Fig. 2) or outlying ancestry with respect to FALS samples in subsequent principal-components analysis (eigenvector value >4 s.d. from FALS mean along any of principal components 1–4).

Supplementary Figure 2 Stratification analysis of FALS discovery cohort.

(a) Results from first round of population outlier filtering. The y axis denotes the proportion of FALS samples for which a given test sample exhibits significant population discordance (P < 1.0 × 10−4 in pairwise population concordance testing). The x axis displays corresponding geographical labels for FALS cases. Horizontal dotted line denotes 10% FALS discordance threshold; all cases and controls falling above this line were removed during the first round of stratification filtering. (b) Distribution of FALS samples along eigenvectors 1 and 2 following principal-components analysis of the quality-control-filtered FALS discovery cohort. (c) Distribution of cases and controls along eigenvectors 1 and 2 following principal-components analysis of the quality-control-filtered FALS discovery cohort. AUS, Australia; BEL, Belgium; CAN, Canada; ESP, Spain; GER, Germany; IRL, Ireland; ITA, Italy; NLD, Netherlands; TUR, Turkey; UK, United Kingdom; USA, United States; USA_AFR, African American; USA_AMR, admixed American.

Supplementary Figure 3 Distribution of NEK1 variants.

(a,b) Observed case–control distribution of NEK1 variants in FALS (a) and SALS (b) cohorts. LOF variants are highlighted in black; missense variants are labeled in gray. HGVS descriptions are followed by case/control carrier counts in parentheses. Predicted splice-altering variants are indicated with an asterisk.

Supplementary Figure 4 Control–control analyses.

To identify loci potentially subject to confounding bias in FALS RVB analyses, RVB analyses were performed across all known potential sources of heterogeneity in the FALS control cohort. This involved dividing controls into 28 distinct pseudo case–control groups on the basis of sequencing center and associated project to identify loci showing association with non-ALS-related data, population or phenotypic stratifiers. The y axis denotes P values observed during ALS-gene-trained RVB testing in FALS versus controls. The x axis denotes minimum P value observed during ALS-gene-trained RVB testing in the 28 pseudo case–control cohorts. Genes shown in gray achieve P < 1 × 10−3 for possible confounder association. Known and candidate ALS genes show no confounder association.

Supplementary Figure 5 NEK1 discovery cohort coverage.

Plot of variant call rate across the NEK1 protein-coding region in cases versus controls.

Supplementary Figure 6 Inbreeding coefficients from Dutch whole-genome sequencing cohort.

Four ALS patients sampled from an isolated community in the Netherlands can be seen to exhibit elevated coefficients of inbreeding (shown in red) relative to a larger panel of Dutch genome sequences (n = 1,861). Box plots show cohort median, interquartile range, 2.5% quantile and 97.5% quantile.

Supplementary Figure 7 Autozygosity mapping identifies NEK1 p.Arg261His as a candidate ALS variant.

Whole-genome sequencing followed by autozygosity mapping with allowed genetic heterogeneity identified ten runs of homozygosity present in one or more of four SALS patients from an isolated Dutch community (top). These regions contained four variants where at least one of the four patients was homozygous and where MAF was less than 0.01 in the 1000 Genomes Project, the NHLBI Exome Sequencing Project and ExAC (bottom). NEK1 p.Arg261His is the only variant identifiable in all patients and the only variant for which multiple homozygous genotypes were observed.

Supplementary Figure 8 Quality control of NEK1 LOF and p.R261H SALS replication cohorts.

Full NEK1 sequencing was performed for 2,387 SALS cases and 1,093 matched controls. p.Arg261His genotypes were obtained for 8,173 SALS cases and 5,189 controls (inclusive of 2,387 SALS cases and 1,093 controls with full NEK1 sequencing). Samples were excluded in the event of outlying heterozygosity (F <–0.1 or F >0.1), SNP-predicted and reported gender discrepancy, detectable relatedness to a sample from the FALS cohort or retained sample from SALS replication cohort (kinship coefficient >0.0884; ≤2rd-degree relationship), outlying ancestry as assessed by identity-by-state distance to the fifth nearest neighbor (>3 s.d. from group mean) or outlying ancestry as assessed by principal-components analysis (eigenvector value >4 s.d. from group mean along any of principal components 1–4).

Supplementary Figure 9 Stratification analysis of SALS replication cohorts.

(a,b) Distribution of cases and controls along eigenvectors 1 and 2 following principal-components analysis of the quality-control-filtered NEK1 LOF replication cohort. (c,d) Distribution of cases and controls along eigenvectors 1 and 2 following principal-components analysis of the quality-control-filtered NEK1 p.Arg261His replication cohort. BEL, Belgium; ESP, Spain; GER, Germany; IRL, Ireland; ITA, Italy; NLD, Netherlands; UK, United Kingdom; USA, United States.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9. (PDF 1491 kb)

Supplementary Table 1

Training using known ALS genes. (XLSX 72 kb)

Supplementary Table 2

RVB analyses of NEK1. (XLSX 34 kb)

Supplementary Table 3

Shared haplotype for p.R261H in the isolated community. (XLSX 185 kb)

Supplementary Table 4

RVB analyses of NEK1 with LOF and p.R261H conditioning. (XLSX 22 kb)

Supplementary Table 5

RVB analyses of NEK gene family. (XLSX 30 kb)

Supplementary Table 6

RVB analyses of STX12. (XLSX 29 kb)

Supplementary Table 7

RVB analyses of KIF5A. (XLSX 31 kb)

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Kenna, K., van Doormaal, P., Dekker, A. et al. NEK1 variants confer susceptibility to amyotrophic lateral sclerosis. Nat Genet 48, 1037–1042 (2016). https://doi.org/10.1038/ng.3626

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