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.

  • Original Article
  • Published:

Rare deleterious mutations are associated with disease in bipolar disorder families

Molecular Psychiatry volume 22, pages 1009–1014 (2017)Cite this article

Subjects

Abstract

Bipolar disorder (BD) is a common, complex and heritable psychiatric disorder characterized by episodes of severe mood swings. The identification of rare, damaging genomic mutations in families with BD could inform about disease mechanisms and lead to new therapeutic interventions. To determine whether rare, damaging mutations shared identity-by-descent in families with BD could be associated with disease, exome sequencing was performed in multigenerational families of the NIMH BD Family Study followed by in silico functional prediction. Disease association and disease specificity was determined using 5090 exomes from the Sweden-Schizophrenia (SZ) Population-Based Case-Control Exome Sequencing study. We identified 14 rare and likely deleterious mutations in 14 genes that were shared identity-by-descent among affected family members. The variants were associated with BD (P<0.05 after Bonferroni’s correction) and disease specificity was supported by the absence of the mutations in patients with SZ. In addition, we found rare, functional mutations in known causal genes for neuropsychiatric disorders including holoprosencephaly and epilepsy. Our results demonstrate that exome sequencing in multigenerational families with BD is effective in identifying rare genomic variants of potential clinical relevance and also disease modifiers related to coexisting medical conditions. Replication of our results and experimental validation are required before disease causation could be assumed.

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

Figure 1

Similar content being viewed by others

References

  1. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®), Fifth Edition. American Psychiatric Association: Arlington, VA, 2013.

  2. Craddock N, Jones I . Genetics of bipolar disorder. J Med Genet 1999; 36: 585–594.

    Article  CAS  Google Scholar 

  3. Song J, Bergen SE, Kuja-Halkola R, Larsson H, Landén M, Lichtenstein P . Bipolar disorder and its relation to major psychiatric disorders: a family-based study in the Swedish population. Bipolar Disord 2015; 17: 184–193.

    Article  Google Scholar 

  4. Kieseppä T, Partonen T, Haukka J, Kaprio J, Lönnqvist J . High concordance of bipolar I disorder in a nationwide sample of twins. Am J Psychiatry 2004; 161: 1814–1821.

    Article  Google Scholar 

  5. Craddock N, Sklar P . Genetics of bipolar disorder. Lancet 2013; 381: 1654–1662.

    Article  CAS  Google Scholar 

  6. Kerner B . Genetics of bipolar disorder. Appl Clin Genet 2014; 7: 33–42.

    Article  CAS  Google Scholar 

  7. Kessler RC, Chiu WT, Demler O, Walters EE . Prevalence, severity, and comorbidity of twelve-month DSM-IV disorders in the National Comorbidity Survey Replication (NCS-R). Arch Gen Psychiatry 2005; 62: 617–627.

    Article  Google Scholar 

  8. Kessler RC, Berglund PA, Demler O, Jin R, Walters EE . Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication (NCS-R). Arch Gen Psychiatry 2005; 62: 593–602.

    Article  Google Scholar 

  9. Nurnberger JI Jr, DePaulo JR, Gershon ES, Reich T, Blehar MC, Edenberg HJ et al. Genomic survey of bipolar illness in the NIMH genetics initiative pedigrees: a preliminary report. Am J Med Genet 1997; 74: 227–237.

    Article  Google Scholar 

  10. Crow TJ, DeLisi LE . The chromosome workshops at the 5th International Congress of Psychiatric Genetics—the weight of the evidence from genome scans. Psychiatr Genet 1998; 8: 59–126.

    Article  Google Scholar 

  11. DeLisi LE, Crow TJ . Chromosome Workshops 1998: current state of psychiatric linkage. Am J Med Genet 1999; 88: 215–218.

    Article  CAS  Google Scholar 

  12. O'Rourke DH, McGuffin P, Reich T . Genetic analysis of manic-depressive illness. Am J Phys Anthropol 1983; 62: 51–59.

    Article  CAS  Google Scholar 

  13. Craddock N, Khodel V, Van Eerdewegh P, Reich T . Mathematical limits of multilocus models: the genetic transmission of bipolar disorder. Am J Hum Genet 1995; 57: 690–702.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. NIMH Genetics—Bipolar Disorder. Available from https://www.nimhgenetics.org/available_data/bipolar_disorder/index.php (accessed on 3 March 2015).

  15. Nurnberger JI Jr, Blehar MC, Kaufmann CA, York-Cooler C, Simpson SG, Harkavy-Friedman J et al. Diagnostic interview for genetic studies. Rationale, unique features, and training. NIMH Genetics Initiative. Arch Gen Psychiatry 1994; 51: 849–859.

    Article  Google Scholar 

  16. Leckman JF, Sholomskas D, Thompson WD, Belanger A, Weissman MM . Best estimate of lifetime psychiatric diagnosis: a methodological study. Arch Gen Psychiatry 1982; 39: 879–883.

    Article  CAS  Google Scholar 

  17. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. DSM-IV-TR® American Psychiatric Association: Arlingon, VA, 2000.

  18. Fromer M, Moran JL, Chambert K, Banks E, Bergen SE, Ruderfer DM et al. Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. Am J Hum Genet 2012; 91: 597–607.

    Article  CAS  Google Scholar 

  19. Bozeman MT. SNP & Variation Suite: Golden Helix, Inc. http://www.goldenhelix.com.

  20. Ng PC, Henikoff S . Predicting deleterious amino acid substitutions. Genome Res 2001; 11: 863–741.

    Article  CAS  Google Scholar 

  21. Ramensky V, Bork P, Sunyaev S . Human non-synonymous SNPs: server and survey. Nucleic Acid Res 2002; 30: 3894–3900.

    Article  CAS  Google Scholar 

  22. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P et al. A method and server for predicting damaging missense mutations. Nat Meth 2010; 7: 248–249.

    Article  CAS  Google Scholar 

  23. Schwarz JM, Rödelsperger C, Schuelke M, Seelow D . MutationTaster evaluates disease-causing potential of sequence alterations. Nat Meth 2010; 7: 575–576.

    Article  CAS  Google Scholar 

  24. Reva B, Antipin Y, Sander C . Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acid Res 2011; 39: e118.

    Article  CAS  Google Scholar 

  25. Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A . Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res 2010; 20: 110–211.

    Article  CAS  Google Scholar 

  26. Chun S, Fay JC . Identification of deleterious mutations within three human genomes. Genome Res 2009; 19: 1553–1561.

    Article  CAS  Google Scholar 

  27. St Pierre J, Cadieux M, Guérault A, Quevillon M . Statistical tables to detect significance between frequencies in two small samples, with particular reference to biological assays. Rev Can Biol 1976; 35: 17–23.

    CAS  PubMed  Google Scholar 

  28. Simes RJ . An improved Bonferroni procedure for multiple tests of significance. Biometrika 1986; 73: 751–754.

    Article  Google Scholar 

  29. Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 2003; 4: P3.

    Article  Google Scholar 

  30. Huang DW, Sherman BT, Lempicki RA . Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat Protoc 2009; 4: 44–57.

    Article  CAS  Google Scholar 

  31. Huang DW, Sherman BT, Lempicki RA . Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acid Res 2009; 37: 1–13.

    Article  Google Scholar 

  32. Benjamini Y, Hochberg Y . Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc B 1995; 57: 289–300.

    Google Scholar 

  33. Neville MJ, Johnstone EC, Walton RT . Identification and characterization of ANKK1: a novel kinase gene closely linked to DRD2 on chromosome band 11q23.1. Hum Mutat 2004; 23: 540–545.

    Article  CAS  Google Scholar 

  34. Ridge JP, Dodd PR . Cortical NMDA receptor expression in human chronic alcoholism: influence of the TaqIA allele of ANKK1. Neurochem Res 2009; 34: 1775–1782.

    Article  CAS  Google Scholar 

  35. Ghosh J, Pradhan S, Mittal B . Identification of a novel ANKK1 and other dopaminergic (DRD2 and DBH) gene variants in migraine susceptibility. Neuromolecular Med 2013; 15: 61–73.

    Article  CAS  Google Scholar 

  36. Neuman RJ, Rice JP . Two-locus models of disease. Genet Epidemiol 1992; 9: 347–365.

    Article  CAS  Google Scholar 

  37. Gershon ES . Bipolar illness and schizophrenia as oligogenic diseases: implications for the future. Biol Psychiatry 2000; 47: 240–244.

    Article  CAS  Google Scholar 

  38. Binder EB . The genetic basis of mood and anxiety disorders—changing paradigms. Biol Mood Anxiety Disord 2012; 2: 17.

    Article  Google Scholar 

  39. Dick DM, Foroud T, Flury L, Bowman ES, Miller MJ, Rau NL et al. Genomewide linkage analyses of bipolar disorder: a new sample of 250 pedigrees from the National Institute of Mental Health Genetics Initiative. Am J Hum Genet 2003; 73: 107–114.

    Article  CAS  Google Scholar 

  40. Cheng R, Juo SH, Loth JE, Nee J, Iossifov I, Blumenthal R et al. Genome-wide linkage scan in a large bipolar disorder sample from the National Institute of Mental Health genetics initiative suggests putative loci for bipolar disorder, psychosis, suicide, and panic disorder. Mol Psychiatry 2006; 11: 252–260.

    Article  CAS  Google Scholar 

  41. Kalkman HO . Potential opposite roles of the extracellular signal-regulated kinase (ERK) pathway in autism spectrum and bipolar disorders. Neurosci Biobehav Rev 2012; 36: 2206–2213.

    Article  CAS  Google Scholar 

  42. Akula N, Barb J, Jiang X, Wendland JR, Choi KH, Sen SK et al. RNA-sequencing of the brain transcriptome implicates dysregulation of neuroplasticity, circadian rhythms and GTPase binding in bipolar disorder. Mol Psychiatry 2014; 19: 1179–1185.

    Article  CAS  Google Scholar 

  43. Farhy Tselnicker I, Tsemakhovich V, Rishal I, Kahanovitch U, Dessauer CW, Dascal N . Dual regulation of G proteins and the G-protein-activated K+ channels by lithium. Proc Natl Acad Sci USA 2014; 111: 5018–5023.

    Article  Google Scholar 

  44. Srinivasan C, Toon J, Amari L, Abukhdeir AM, Hamm H, Geraldes CF et al. Competition between lithium and magnesium ions for the G-protein transducin in the guanosine 5'-diphosphate bound conformation. J Inorg Biochem 2004; 98: 691–701.

    Article  CAS  Google Scholar 

  45. Drummond AH . Lithium affects G-protein receptor coupling. Nature 1988; 331: 388.

    Article  CAS  Google Scholar 

  46. Schreiber G, Avissar S . Lithium sensitive G protein hyperfunction: a dynamic model for the pathogenesis of bipolar affective disorder. Med Hypotheses 1991; 35: 237–243.

    Article  CAS  Google Scholar 

  47. Corena-McLeod M, Walss-Bass C, Oliveros A, Gordillo Villegas A, Ceballos C, Charlesworth CM et al. New model of action for mood stabilizers: phosphoproteome from rat pre-frontal cortex synaptoneurosomal preparations. PLoS One 2013; 8: e52147.

    Article  CAS  Google Scholar 

  48. Gonzalez R . The relationship between bipolar disorder and biological rhythms. J Clin Psychiatry 2014; 75: e323–e331.

    Article  Google Scholar 

  49. Lee HJ, Son GH, Geum D . Circadian rhythm hypotheses of mixed features, antidepressant treatment resistance, and manic switching in bipolar disorder. Psychiatry Investig 2013; 10: 225–232.

    Article  Google Scholar 

  50. Carpenter JS, Robillard R, Lee RS, Hermens DF, Naismith SL, White D et al. The relationship between sleep-wake cycle and cognitive functioning in young people with affective disorders. PLoS One 2015; 10: e0124710.

    Article  Google Scholar 

  51. Naismith SL, Lagopoulos J, Hermens DF, White D, Duffy SL, Robillard R et al. Delayed circadian phase is linked to glutamatergic functions in young people with affective disorders: a proton magnetic resonance spectroscopy study. BMC Psychiatry 2014; 14: 345.

    Article  Google Scholar 

  52. Lee HJ, Rex KM, Nievergelt CM, Kelsoe JR, Kripke DF . Delayed sleep phase syndrome is related to seasonal affective disorder. J Affect Disord 2011; 133: 573–579.

    Article  Google Scholar 

  53. Roybal K, Theobold D, Graham A, DiNieri JA, Russo SJ, Krishnan V et al. Mania-like behavior induced by disruption of CLOCK. Proc Natl Acad Sci USA 2007; 104: 6406–6411.

    Article  CAS  Google Scholar 

  54. Bellivier F, Geoffroy PA, Etain B, Scott J . Sleep- and circadian rhythm-associated pathways as therapeutic targets in bipolar disorder. Expert Opin Ther Targets 2015; 19: 747–763.

    Article  CAS  Google Scholar 

  55. Soreca I . Circadian rhythms and sleep in bipolar disorder: implications for pathophysiology and treatment. Curr Opin Psychiatry 2014; 27: 467–471.

    Article  Google Scholar 

  56. Kõks S, Luuk H, Nelovkov A, Areda T, Vasar E . A screen for genes induced in the amygdaloid area during cat odor exposure. Genes Brain Behav 2004; 3: 80–89.

    Article  Google Scholar 

  57. Kõks S, Soomets U, Plaas M, Terasmaa A, Noormets K, Tillmann V et al. Hypothalamic gene expression profile indicates a reduction in G protein signaling in the Wfs1 mutant mice. Physiol Genomics 2011; 43: 1351–1358.

    Article  Google Scholar 

  58. Must A, Kõks S, Vasar E, Tasa G, Lang A, Maron E et al. Common variations in 4p locus are related to male completed suicide. Neuromolecular Med 2009; 11: 13–19.

    Article  CAS  Google Scholar 

  59. Song W, Li W, Noltner K, Yan J, Green E, Grozeva D et al. Identification of high risk DISC1 protein structural variants in patients with bipolar spectrum disorder. Neurosci Lett 2010; 486: 136–140.

    Article  CAS  Google Scholar 

  60. Green EK, Grozeva D, Sims R, Raybould R, Forty L, Gordon-Smith K et al. DISC1 exon 11 rare variants found more commonly in schizoaffective spectrum cases than controls. Am J Med Genet B Neuropsychiatr Genet 2011; 156B: 490–492.

    Article  CAS  Google Scholar 

  61. Goes FS, Pirooznia M, Parla JS, Kramer M, Ghiban E, Mavruk S et al. Exome sequencing of familial bipolar disorder. JAMA Psychiatry 2016; 73: 590–597.

    Article  Google Scholar 

  62. Kember RL, Georgi B, Bailey-Wilson JE, Stambolian D, Paul SM, Bućan M . Copy number variants encompassing Mendelian disease genes in a large multigenerational family segregating bipolar disorder. BMC Genet 2015; 16: 27.

    Article  Google Scholar 

  63. Mehta D, Iwamoto K, Ueda J, Bundo M, Adati N, Kojima T et al. Comprehensive survey of CNVs influencing gene expression in the human brain and its implications for pathophysiology. Neurosci Res 2014; 79: 22–33.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the grant 1R01MH085744 from the National Institutes of Mental Health (NIMH), National Center for Advancing Translational Sciences Institute (CTSI) at UCLA grant UL1TR000124, as well as a NARSAD Young Investigator Award to BK. ARR was supported by a NIH Predoctoral Training Grant (No. T32HG002536). We acknowledge the staff at the UCLA Broad Stem Cell Research Center (BSCRC), the UCLA Clinical Genomics Center and the Genome Sequencing (GenoSeq) Core facility at UCLA for DNA preparation, exome sequencing and variant confirmation, for which these centers have received compensation. Data and biomaterials were collected as part of four projects that participated in the National Institute of Mental Health (NIMH) Bipolar Disorder Genetics Initiative (supported by NIMH grants U01MH46282, U01MH46280, U01MH46274, R01MH59545, R01MH59534, R01MH59533, R01MH59553, R01MH60068, R01MH59548, R01MH59535, R01MH59567, R01MH059556, 1Z01MH002810-01, MH52618, MH058693, R01MH59602, R01. We are indebted to the investigators of the NIMH-Bipolar Genetics Initiative and the GAIN Initiative, as well as the families, who provided the genetic and phenotype data. Samples used for data analysis were also provided by the Swedish Cohort Collection supported by the NIMH grant R01MH077139, the Sylvan C. Herman Foundation, the Stanley Medical Research Institute and The Swedish Research Council (grants 2009-4959 and 2011-4659). Support for the exome sequencing was provided by the NIMH Grand Opportunity grant RCMH089905, the Sylvan C. Herman Foundation, a grant from the Stanley Medical Research Institute, and multiple gifts to the Stanley Center for Psychiatric Research at the Broad Institute of MIT and Harvard. In addition, our study utilized data generated by the DECIPHER Consortium. A full list of centers who contributed to the generation of these data is available from http://decipher.sanger.ac.uk and via E-mail from decipher@sanger.ac.uk. Funding for the DECIPHER project was provided by the Wellcome Trust.

Author contributions

Conceived and designed the study, and wrote the paper: BK. Contributed to the analysis and the writing of the paper: ARR. Contributed analysis tools: BC and MY. Provided expertise: BC and SN. BK, and SN were thesis advisor. ARR had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Author information

Authors and Affiliations

  1. Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA

    A R Rao & S F Nelson

  2. Department of Pediatrics, University of California, Los Angeles, Los Angeles, CA, USA

    M Yourshaw

  3. Golden Helix Inc., Bozeman, MT, USA

    B Christensen

  4. Department of Psychiatry and Biobehavioral Sciences at the David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    S F Nelson

  5. Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    S F Nelson

  6. Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA

    B Kerner

Authors
  1. A R Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M Yourshaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B Christensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S F Nelson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. B Kerner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B Kerner.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Disclaimer

The funders had no role in design and conduct of the study; collection, management, analysis and interpretation of the data; or preparation, review and approval of the manuscript.

Supplementary Information accompanies the paper on the Molecular Psychiatry website

Supplementary information

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rao, A., Yourshaw, M., Christensen, B. et al. Rare deleterious mutations are associated with disease in bipolar disorder families. Mol Psychiatry 22, 1009–1014 (2017). https://doi.org/10.1038/mp.2016.181

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/mp.2016.181

This article is cited by

Search

Advanced search

Quick links