Genome-wide mapping of brain phenotypes in extended pedigrees with strong genetic loading for bipolar disorder.
Journal
Molecular psychiatry
ISSN: 1476-5578
Titre abrégé: Mol Psychiatry
Pays: England
ID NLM: 9607835
Informations de publication
Date de publication:
09 2021
09 2021
Historique:
received:
18
03
2019
accepted:
29
05
2020
revised:
26
05
2020
pubmed:
2
7
2020
medline:
1
2
2022
entrez:
2
7
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32606377
doi: 10.1038/s41380-020-0805-6
pii: 10.1038/s41380-020-0805-6
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5229-5238Subventions
Organisme : NIMH NIH HHS
ID : K23 MH074644
Pays : United States
Organisme : NINDS NIH HHS
ID : P30 NS062691
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH075007
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH095454
Pays : United States
Organisme : NHGRI NIH HHS
ID : R01 HG006695
Pays : United States
Organisme : NIMH NIH HHS
ID : K08 MH086786
Pays : United States
Informations de copyright
© 2020. The Author(s), under exclusive licence to Springer Nature Limited.
Références
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 .
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: 27566286
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 .
pubmed: 28461699
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 .
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: 26851067
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 .
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: 28032374
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: 17415783
pmcid: 6871295
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 .
pubmed: 25216981
pmcid: 4961231
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 .
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 .
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 .
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 .
pubmed: 25607358
pmcid: 4393366
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 .
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 .
pubmed: 22504417
pmcid: 3635491
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 .
pubmed: 22504421
pmcid: 3427729
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 .
pubmed: 21629274
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 .
pubmed: 19207019
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: 25322361
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 .
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: 26485182
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: 17903297
pmcid: 1995608
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: 24522887
pmcid: 4045237
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 .
pubmed: 16648268
pmcid: 1464326
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 .
pubmed: 12601469
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: 25943422
pmcid: 4572484
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 .
pubmed: 9881538
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 .
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: 7944874
Almasy L, Blangero J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet. 1998;62:1198–211.
pubmed: 9545414
pmcid: 1377101
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 .
pubmed: 26712028
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 .
pubmed: 9433616
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: 26626037
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 .
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 .
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 .
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 .
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 .
pubmed: 20208533
pmcid: 3092069
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 .
pubmed: 15297300
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.
pubmed: 25174004
pmcid: 4208299
Flint J, Kendler KS. The genetics of major depression. Neuron. 2014;81:484–503. http://www.ncbi.nlm.nih.gov/pubmed/24507187 .
pubmed: 24507187
pmcid: 3919201
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 .
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: 12807801
pmcid: 1462572
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 .
pubmed: 25858580
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: 22499782
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: 25103237
pmcid: 4230590
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 .
pubmed: 22365631
pmcid: 3925336
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 .
pubmed: 30038396
pmcid: 6393768
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 .
pubmed: 27225129
pmcid: 4883595
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 .
pubmed: 29186694
pmcid: 5789458
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 .
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 .
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 .
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 .
pmcid: 4112379
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.
Paulus MP, Thompson WK. The challenges and opportunities of small effects: the new normal in academic psychiatry. JAMA Psychiatry. 2019;76:353–4.
pubmed: 30810720