Genome-wide mapping of brain phenotypes in extended pedigrees with strong genetic loading for bipolar disorder

Abstract

Bipolar disorder is a highly heritable illness, associated with alterations of brain structure. As such, identification of genes influencing inter-individual differences in brain morphology may help elucidate the underlying pathophysiology of bipolar disorder (BP). To identify quantitative trait loci (QTL) that contribute to phenotypic variance of brain structure, structural neuroimages were acquired from family members (n = 527) of extended pedigrees heavily loaded for bipolar disorder ascertained from genetically isolated populations in Latin America. Genome-wide linkage and association analysis were conducted on the subset of heritable brain traits that showed significant evidence of association with bipolar disorder (n = 24) to map QTL influencing regional measures of brain volume and cortical thickness. Two chromosomal regions showed significant evidence of linkage; a QTL on chromosome 1p influencing corpus callosum volume and a region on chromosome 7p linked to cortical volume. Association analysis within the two QTLs identified three SNPs correlated with the brain measures.

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Fig. 1: Top linkage results for each of the 24 BP-associated brain phenotypes.
Fig. 2: Results of association testing within the two significant linkage regions.

References

  1. 1.

    Vos T, Barber RM, Bell B, Bertozzi-Villa A, Biryukov S, Bolliger I, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;386:743–800. http://www.ncbi.nlm.nih.gov/pubmed/26063472.

    Google Scholar 

  2. 2.

    Ferrari AJ, Stockings E, Khoo J-P, Erskine HE, Degenhardt L, Vos T, et al. The prevalence and burden of bipolar disorder: findings from the Global Burden of Disease Study 2013. Bipolar Disord. 2016;18:440–50. http://www.ncbi.nlm.nih.gov/pubmed/27566286.

    PubMed  Google Scholar 

  3. 3.

    Hibar DP, Westlye LT, Doan NT, Jahanshad N, Cheung JW, Ching CRK, et al. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Mol Psychiatry. 2018;23:932–42. http://www.ncbi.nlm.nih.gov/pubmed/28461699.

    CAS  PubMed  Google Scholar 

  4. 4.

    Hibar DP, Westlye LT, Doan NT, Jahanshad N, Cheung JW, Ching CRK, et al. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Mol Psychiatry. 2018;23:932–42. http://www.ncbi.nlm.nih.gov/pubmed/28461699.

  5. 5.

    Hanford LC, Nazarov A, Hall GB, Sassi RB. Cortical thickness in bipolar disorder: a systematic review. Bipolar Disord. 2016;18:4–18. http://www.ncbi.nlm.nih.gov/pubmed/26851067.

    PubMed  Google Scholar 

  6. 6.

    Hibar DP, Westlye LT, van Erp TGM, Rasmussen J, Leonardo CD, Faskowitz J, et al. Subcortical volumetric abnormalities in bipolar disorder. Mol Psychiatry. 2016;21:1710–6. http://www.ncbi.nlm.nih.gov/pubmed/26857596.

  7. 7.

    Vuoksimaa E, Panizzon MS, Hagler DJ, Hatton SN, Fennema-Notestine C, Rinker D, et al. Heritability of white matter microstructure in late middle age: A twin study of tract-based fractional anisotropy and absolute diffusivity indices. Hum Brain Mapp. 2017;38:2026–36. http://doi.wiley.com/10.1002/hbm.23502.

    PubMed  Google Scholar 

  8. 8.

    Peper JS, Brouwer RM, Boomsma DI, Kahn RS, Hulshoff Pol HE. Genetic influences on human brain structure: A review of brain imaging studies in twins. Hum Brain Mapp. 2007;28:464–73. http://www.ncbi.nlm.nih.gov/pubmed/17415783.

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Flint J, Timpson N, Munafò M. Assessing the utility of intermediate phenotypes for genetic mapping of psychiatric disease. Trends Neurosci. 2014;37:733–41. http://www.ncbi.nlm.nih.gov/pubmed/25216981.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Glahn DC, Knowles EEM, McKay DR, Sprooten E, Raventós H, Blangero J, et al. Arguments for the sake of endophenotypes: Examining common misconceptions about the use of endophenotypes in psychiatric genetics. Am J Med Genet Part B Neuropsychiatr Genet. 2014;165:122–30. http://www.ncbi.nlm.nih.gov/pubmed/24464604.

    CAS  Google Scholar 

  11. 11.

    Grasby KL, Jahanshad N, Painter JN, Colodro-Conde L, Bralten J, Hibar DP, et al. The genetic architecture of the human cerebral cortex. Science. 2020;367:eaay6690. https://www.biorxiv.org/content/early/2018/09/09/399402.

  12. 12.

    Hofer E, Roshchupkin GV, Adams H, Knol M, Lin H, Li S, et al. Genetic determinants of cortical structure (thickness, surface area and volumes) among disease free adults in the CHARGE Consortium. bioRxiv. 2018. https://www.biorxiv.org/content/early/2018/09/09/409649.

  13. 13.

    Hibar DP, Stein JL, Renteria ME, Arias-Vasquez A, Desrivières S, Jahanshad N, et al. Common genetic variants influence human subcortical brain structures. Nature. 2015;520:224–9. http://www.ncbi.nlm.nih.gov/pubmed/25607358.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Satizabal CL, Adams HHH, Hibar DP, White CC, Knol MJ, Stein JL, et al. Genetic Architecture of Subcortical Brain Structures in 38,851 Individuals. Nat Genet. 2019;51:1624–36. http://www.ncbi.nlm.nih.gov/pubmed/31636452.

  15. 15.

    Stein JL, Medland SE, Vasquez AA, Hibar DP, Senstad RE, Winkler AM, et al. Identification of common variants associated with human hippocampal and intracranial volumes. Nat Genet. 2012;44:552–61. http://www.ncbi.nlm.nih.gov/pubmed/22504417.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Bis JC, DeCarli C, Smith AV, van der Lijn F, Crivello F, Fornage M, et al. Common variants at 12q14 and 12q24 are associated with hippocampal volume. Nat Genet. 2012;44:545–51. http://www.ncbi.nlm.nih.gov/pubmed/22504421.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Ott J, Kamatani Y, Lathrop M. Family-based designs for genome-wide association studies. Nat Rev Genet. 2011;12:465–74. http://www.ncbi.nlm.nih.gov/pubmed/21629274.

    CAS  PubMed  Google Scholar 

  18. 18.

    Benyamin B, Visscher PM, McRae AF. Family-based genome-wide association studies. Pharmacogenomics. 2009;10:181–90. http://www.ncbi.nlm.nih.gov/pubmed/19207019.

    CAS  PubMed  Google Scholar 

  19. 19.

    Knowles EEM, McKay DR, Kent JW, Sprooten E, Carless MA, Curran JE, et al. Pleiotropic locus for emotion recognition and amygdala volume identified using univariate and bivariate linkage. Am J Psychiatry. 2015;172:190–9. http://www.ncbi.nlm.nih.gov/pubmed/25322361.

    PubMed  Google Scholar 

  20. 20.

    Dager AD, McKay DR, Kent JW, Curran JE, Knowles E, Sprooten E, et al. Shared genetic factors influence amygdala volumes and risk for alcoholism. Neuropsychopharmacol. 2015;40:412–20. http://www.ncbi.nlm.nih.gov/pubmed/25079289.

    CAS  Google Scholar 

  21. 21.

    Mathias SR, Knowles EEM, Kent JW, McKay DR, Curran JE, de Almeida MAA, et al. Recurrent major depression and right hippocampal volume: a bivariate linkage and association study. Hum Brain Mapp. 2016;37:191–202. http://www.ncbi.nlm.nih.gov/pubmed/26485182.

    PubMed  Google Scholar 

  22. 22.

    Seshadri S, DeStefano AL, Au R, Massaro JM, Beiser AS, Kelly-Hayes M, et al. Genetic correlates of brain aging on MRI and cognitive test measures: a genome-wide association and linkage analysis in the Framingham Study. BMC Med Genet. 2007;8:S15. http://www.ncbi.nlm.nih.gov/pubmed/17903297.

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Fears SC, Service SK, Kremeyer B, Araya C, Araya X, Bejarano J, et al. Multisystem component phenotypes of bipolar disorder for genetic investigations of extended pedigrees. JAMA Psychiatry. 2014;71:375–87. http://www.ncbi.nlm.nih.gov/pubmed/24522887.

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Bedoya G, Montoya P, García J, Soto I, Bourgeois S, Carvajal L, et al. Admixture dynamics in Hispanics: a shift in the nuclear genetic ancestry of a South American population isolate. Proc Natl Acad Sci USA. 2006;103:7234–9. http://www.pnas.org/cgi/doi/10.1073/pnas.0508716103.

    CAS  PubMed  Google Scholar 

  25. 25.

    Carvajal-Carmona LG, Ophoff R, Service S, Hartiala J, Molina J, Leon P, et al. Genetic demography of Antioquia (Colombia) and the Central Valley of Costa Rica. Hum Genet. 2003;112:534–41. http://www.ncbi.nlm.nih.gov/pubmed/12601469.

    CAS  PubMed  Google Scholar 

  26. 26.

    Fears SC, Schür R, Sjouwerman R, Service SK, Araya C, Araya X, et al. Brain structure-function associations in multi-generational families genetically enriched for bipolar disorder. Brain. 2015;138:2087–102. http://www.ncbi.nlm.nih.gov/pubmed/25943422.

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998;59:22–33. http://www.ncbi.nlm.nih.gov/pubmed/9881538.

    Google Scholar 

  28. 28.

    Palacio CA, García J, Arbeláez MP, Sánchez R, Aguirre B, Garcés IC, et al. Validation of the diagnostic interview for genetic studies (DIGS) in Colombia. Biomedicine. 2004;24:56–62. http://www.ncbi.nlm.nih.gov/pubmed/15239602.

    Google Scholar 

  29. 29.

    Nurnberger JI, 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;51:849–59. http://www.ncbi.nlm.nih.gov/pubmed/7944874.

    PubMed  Google Scholar 

  30. 30.

    Almasy L, Blangero J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet. 1998;62:1198–211.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Pagani L, St Clair PA, Teshiba TM, Service SK, Fears SC, Araya C, et al. Genetic contributions to circadian activity rhythm and sleep pattern phenotypes in pedigrees segregating for severe bipolar disorder. Proc Natl Acad Sci USA. 2016;113:E754–61. http://www.ncbi.nlm.nih.gov/pubmed/26712028.

    CAS  PubMed  Google Scholar 

  32. 32.

    Heath SC, Snow GL, Thompson EA, Tseng C, Wijsman EM. MCMC segregation and linkage analysis. Genet Epidemiol. 1997;14:1011–6. http://www.ncbi.nlm.nih.gov/pubmed/9433616.

    CAS  PubMed  Google Scholar 

  33. 33.

    Peterson CB, Bogomolov M, Benjamini Y, Sabatti C. Many phenotypes without many false discoveries: error controlling strategies for multitrait association studies. Genet Epidemiol. 2016;40:45–56. http://www.ncbi.nlm.nih.gov/pubmed/26626037.

    PubMed  Google Scholar 

  34. 34.

    Locke AE, Steinberg KM, Chiang CWK, Service SK, Havulinna AS, Stell L, et al. Exome sequencing of Finnish isolates enhances rare-variant association power. Nature. 2019. https://doi.org/10.1038/s41586-019-1457-z.

  35. 35.

    Simes RJ. An improved Bonferroni procedure for multiple tests of significance. Biometrika. 1986;73:751–4. http://biomet.oxfordjournals.org/cgi/doi/10.1093/biomet/73.3.751.

    Google Scholar 

  36. 36.

    Benjamini Y, Hochberg Y. Multiple hypotheses testing with weights. Scand J Stat. 1997;24:407–18. http://doi.wiley.com/10.1111/1467-9469.00072.

    Google Scholar 

  37. 37.

    Benjamini Y, Bogomolov M. Selective inference on multiple families of hypotheses. J R Stat Soc Ser B. 2014;76:297–318. http://doi.wiley.com/10.1111/rssb.12028.

  38. 38.

    Kang HM, Sul JH, Service SK, Zaitlen NA, Kong S-Y, Freimer NB, et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 2010;42:348–54. http://www.ncbi.nlm.nih.gov/pubmed/20208533.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21:263–5. https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/bth457.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Ramasamy A, Trabzuni D, Guelfi S, Varghese V, Smith C, Walker R, et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat Neurosci. 2014;17:1418–28.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Flint J, Kendler KS. The genetics of major depression. Neuron. 2014;81:484–503. http://www.ncbi.nlm.nih.gov/pubmed/24507187.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Stat. 2001;29:1165–88. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.8492.

    Google Scholar 

  43. 43.

    Sabatti C, Service S, Freimer N. False discovery rate in linkage and association genome screens for complex disorders. Genetics. 2003;164:829–33. http://www.ncbi.nlm.nih.gov/pubmed/12807801.

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Gurung R, Prata DP. What is the impact of genome-wide supported risk variants for schizophrenia and bipolar disorder on brain structure and function? A systematic review. Psychol Med. 2015;45:2461–80. http://www.ncbi.nlm.nih.gov/pubmed/25858580.

    CAS  PubMed  Google Scholar 

  45. 45.

    Rose EJ, Donohoe G. Brain vs behavior: an effect size comparison of neuroimaging and cognitive studies of genetic risk for schizophrenia. Schizophr Bull. 2013;39:518–26. http://www.ncbi.nlm.nih.gov/pubmed/22499782.

    PubMed  Google Scholar 

  46. 46.

    Tort O, Tanco S, Rocha C, Bièche I, Seixas C, Bosc C, et al. The cytosolic carboxypeptidases CCP2 and CCP3 catalyze posttranslational removal of acidic amino acids. Mol Biol Cell. 2014;25:3017–27. http://www.ncbi.nlm.nih.gov/pubmed/25103237.

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Greenwood TA, Akiskal HS, Akiskal KK, Bipolar Genome Study, Kelsoe JR. Genome-wide association study of temperament in bipolar disorder reveals significant associations with three novel Loci. Biol Psychiatry. 2012;72:303–10. http://linkinghub.elsevier.com/retrieve/pii/S0006322312000583.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M, et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet. 2018;50:1112–21. http://www.nature.com/articles/s41588-018-0147-3.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature. 2016;533:539–42. http://www.nature.com/articles/nature17671.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Lam M, Trampush JW, Yu J, Knowles E, Davies G, Liewald DC, et al. Large-scale cognitive GWAS meta-analysis reveals tissue-specific neural expression and potential nootropic drug targets. Cell Rep. 2017;21:2597–613. http://linkinghub.elsevier.com/retrieve/pii/S2211124717316480.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Hill WD, Marioni RE, Maghzian O, Ritchie SJ, Hagenaars SP, McIntosh AM, et al. A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Mol Psychiatry. 2018;24:169–81. http://www.nature.com/articles/s41380-017-0001-5.

  52. 52.

    Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium. Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia. Mol Autism. 2017;8:21. http://molecularautism.biomedcentral.com/articles/10.1186/s13229-017-0137-9.

    Google Scholar 

  53. 53.

    Li Z, Chen J, Yu H, He L, Xu Y, Zhang D, et al. Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia. Nat Genet [Internet]. 2017;49:1576–83. http://www.nature.com/doifinder/10.1038/ng.3973.

    CAS  Google Scholar 

  54. 54.

    Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7. http://www.nature.com/articles/nature13595.

    PubMed Central  Google Scholar 

  55. 55.

    Elliott LT, Sharp K, Alfaro-almagro F, Shi S, Miller KL, Douaud G, et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature. 2018;562:210–6.

  56. 56.

    Paulus MP, Thompson WK. The challenges and opportunities of small effects: the new normal in academic psychiatry. JAMA Psychiatry. 2019;76:353–4.

    PubMed  Google Scholar 

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Acknowledgements

We wish to thank our late colleagues Lori Altshuler MD and George Bartzokis MD for their valuable input and guidance on this work.

Funding

This research was supported by National Institute of Health Grants R01MH075007, R01MH095454, P30NS062691; (NBF), K23MH074644-01; (CEB) R01HG006695; (CS), and K08MH086786 (SCF), the Joanne and George Miller Family Endowed Term Chair (CEB), and Colciencias and Codi-University of Antioquia (CL-J).

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Correspondence to Scott C. Fears.

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Fears, S.C., Service, S.K., Kremeyer, B. et al. Genome-wide mapping of brain phenotypes in extended pedigrees with strong genetic loading for bipolar disorder. Mol Psychiatry (2020). https://doi.org/10.1038/s41380-020-0805-6

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