Einzeltreffer — DigiBib

Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.

Titel:	Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults.
Autor/in / Beteiligte Person:	Hofer, E ; Roshchupkin, GV ; Adams, HHH ; Knol, MJ ; Lin, H ; Li, S ; Zare, H ; Ahmad, S ; Armstrong, NJ ; Satizabal, CL ; Bernard, M ; Bis, JC ; Gillespie, NA ; Luciano, M ; Mishra, A ; Scholz, M ; Teumer, A ; Xia, R ; Jian, X ; Mosley, TH ; Saba, Y ; Pirpamer, L ; Seiler, S ; Becker, JT ; Carmichael, O ; Rotter, JI ; Psaty, BM ; Lopez, OL ; Amin, N ; van der Lee SJ ; Yang, Q ; Himali, JJ ; Maillard, P ; Beiser, AS ; DeCarli, C ; Karama, S ; Lewis, L ; Harris, M ; Bastin, ME ; Deary, IJ ; Veronica Witte, A ; Beyer, F ; Loeffler, M ; Mather, KA ; Schofield, PR ; Thalamuthu, A ; Kwok, JB ; Wright, MJ ; Ames, D ; Trollor, J ; Jiang, J ; Brodaty, H ; Wen, W ; Vernooij, MW ; Hofman, A ; Uitterlinden, AG ; Niessen, WJ ; Wittfeld, K ; Bülow, R ; Völker, U ; Pausova, Z ; Bruce Pike, G ; Maingault, S ; Crivello, F ; Tzourio, C ; Amouyel, P ; Mazoyer, B ; Neale, MC ; Franz, CE ; Lyons, MJ ; Panizzon, MS ; Andreassen, OA ; Dale, AM ; Logue, M ; Grasby, KL ; Jahanshad, N ; Painter, JN ; Colodro-Conde, L ; Bralten, J ; Hibar, DP ; Lind, PA ; Pizzagalli, F ; Stein, JL ; Thompson, PM ; Medland, SE ; Sachdev, PS ; Kremen, WS ; Wardlaw, JM ; Villringer, A ; van Duijn CM ; Grabe, HJ ; Longstreth WT Jr ; Fornage, M ; Paus, T ; Debette, S ; Ikram, MA ; Schmidt, H ; Schmidt, R ; Seshadri, S
Link:	Zum Volltext
Zeitschrift:	Nature communications, Jg. 11 (2020-09-22), Heft 1, S. 4796
Veröffentlichung:	[London] : Nature Pub. Group, 2020
Medientyp:	academicJournal
ISSN:	2041-1723 (electronic)
DOI:	10.1038/s41467-020-18367-y
Schlagwort:	Adult Aged Aged, 80 and over Chromosome Structures Cognition Female Genomics Humans Male Middle Aged Phenotype Polymorphism, Single Nucleotide Aging genetics Brain Genome-Wide Association Study Mental Disorders genetics Neurodegenerative Diseases genetics
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Corporate Authors: ENIGMA consortium Publication Type: Journal Article; Meta-Analysis; Research Support, American Recovery and Reinvestment Act; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S. Language: English [Nat Commun] 2020 Sep 22; Vol. 11 (1), pp. 4796. <i>Date of Electronic Publication: </i>2020 Sep 22. MeSH Terms: Brain* ; Genome-Wide Association Study* ; Aging / genetics ; Mental Disorders / genetics ; Neurodegenerative Diseases / *genetics ; Adult ; Aged ; Aged, 80 and over ; Chromosome Structures ; Cognition ; Female ; Genomics ; Humans ; Male ; Middle Aged ; Phenotype ; Polymorphism, Single Nucleotide References: Storsve, A. B. et al. Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: regions of accelerating and decelerating change. J. Neurosci. 34, 8488–8498 (2014). (PMID: 24948804660821710.1523/JNEUROSCI.0391-14.2014) ; Hogstrom, L. J., Westlye, L. T., Walhovd, K. B. & Fjell, A. M. The structure of the cerebral cortex across adult life: age-related patterns of surface area, thickness, and gyrification. Cereb. Cortex 23, 2521–2530 (2013). (PMID: 2289242310.1093/cercor/bhs231) ; Fjell, A. M. et al. Development and aging of cortical thickness correspond to genetic organization patterns. Proc. Natl Acad. Sci. USA 112, 15462–15467 (2015). (PMID: 2657562510.1073/pnas.15088311124687601) ; Vuoksimaa, E. et al. The genetic association between neocortical volume and general cognitive ability is driven by global surface area rather than thickness. Cereb. Cortex 25, 2127–2137 (2015). (PMID: 2455472510.1093/cercor/bhu018) ; Vuoksimaa, E. et al. Is bigger always better? The importance of cortical configuration with respect to cognitive ability. NeuroImage 129, 356–366 (2016). (PMID: 2682781010.1016/j.neuroimage.2016.01.049) ; Lerch, J. P. et al. Focal decline of cortical thickness in Alzheimer’s disease identified by computational neuroanatomy. Cereb. Cortex 15, 995–1001 (2005). (PMID: 1553767310.1093/cercor/bhh200) ; Uribe, C. et al. Patterns of cortical thinning in nondemented Parkinson’s disease patients. Mov. Disord. 31, 699–708 (2016). (PMID: 27094093506109910.1002/mds.26590) ; Steenwijk, M. D. et al. Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant. Brain 139, 115–126 (2016). (PMID: 2663748810.1093/brain/awv337) ; Rimol, L. M. et al. Cortical volume, surface area, and thickness in schizophrenia and bipolar disorder. Biol. Psychiatry 71, 552–560 (2012). (PMID: 2228112110.1016/j.biopsych.2011.11.026) ; Schmaal, L. et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol. Psychiatry 22, 900–909 (2017). (PMID: 2713774510.1038/mp.2016.60) ; van Rooij, D. et al. Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: results from the ENIGMA ASD Working Group. Am. J. Psychiatry 175, 359–369 (2018). (PMID: 2914575410.1176/appi.ajp.2017.17010100) ; Mountcastle, V. B. The columnar organization of the neocortex. Brain 120(Pt 4), 701–722 (1997). (PMID: 915313110.1093/brain/120.4.701) ; Rakic, P. A small step for the cell, a giant leap for mankind: a hypothesis of neocortical expansion during evolution. Trends Neurosci. 18, 383–388 (1995). (PMID: 748280310.1016/0166-2236(95)93934-P) ; Rakic, P. Evolution of the neocortex: a perspective from developmental biology. Nat. Rev. Neurosci. 10, 724–735 (2009). (PMID: 19763105291357710.1038/nrn2719) ; Panizzon, M. S. et al. Distinct genetic influences on cortical surface area and cortical thickness. Cereb. Cortex 19, 2728–2735 (2009). (PMID: 19299253275868410.1093/cercor/bhp026) ; Winkler, A. M. et al. Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. NeuroImage 53, 1135–1146 (2010). (PMID: 2000671510.1016/j.neuroimage.2009.12.028) ; Rimol, L. M. et al. Cortical thickness is influenced by regionally specific genetic factors. Biol. psychiatry 67, 493–499 (2010). (PMID: 1996320810.1016/j.biopsych.2009.09.032) ; Eyler, L. T. et al. Genetic and environmental contributions to regional cortical surface area in humans: a magnetic resonance imaging twin study. Cereb. Cortex 21, 2313–2321 (2011). (PMID: 21378112316966010.1093/cercor/bhr013) ; Kremen, W. S. et al. Genetic and environmental influences on the size of specific brain regions in midlife: the VETSA MRI study. Neuroimage 49, 1213–1223 (2010). (PMID: 1978610510.1016/j.neuroimage.2009.09.043) ; Joshi, A. A. et al. The contribution of genes to cortical thickness and volume. Neuroreport 22, 101–105 (2011). (PMID: 21233781307938410.1097/WNR.0b013e3283424c84) ; Wen, W. et al. Distinct genetic influences on cortical and subcortical brain structures. Sci. Rep. 6, 32760 (2016). (PMID: 27595976501170310.1038/srep32760) ; Grasby, K. L. et al. The genetic architecture of the human cerebral cortex. Science. https://doi.org/10.1126/science.aay6690 (2020). ; Elliott, L. T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210–216 (2018). (PMID: 30305740678697410.1038/s41586-018-0571-7) ; Zhao, B. et al. Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nat. Genet. 51, 1637–1644 (2019). (PMID: 31676860685858010.1038/s41588-019-0516-6) ; Hibar, D. P. et al. Novel genetic loci associated with hippocampal volume. Nat. Commun. 8, 13624 (2017). (PMID: 28098162525363210.1038/ncomms13624) ; Irvin, M. R. et al. Genome-wide meta-analysis of SNP-by9-ACEI/ARB and SNP-by-thiazide diuretic and effect on serum potassium in cohorts of European and African ancestry. Pharmacogenomics J. https://doi.org/10.1038/s41397-018-0021-9 (2018). ; Lu, L. et al. Common and rare variants of the THBS1 gene associated with the risk for autism. Psychiatr. Genet. 24, 235–240 (2014). (PMID: 2530422510.1097/YPG.0000000000000054) ; Park, H. J., Kim, S. K., Kim, J. W., Kang, W. S. & Chung, J. H. Association of thrombospondin 1 gene with schizophrenia in Korean population. Mol. Biol. Rep. 39, 6875–6880 (2012). (PMID: 2231102410.1007/s11033-012-1513-3) ; Christopherson, K. S. et al. Thrombospondins are astrocyte-secreted proteins that promote CNS synaptogenesis. Cell 120, 421–433 (2005). (PMID: 1570789910.1016/j.cell.2004.12.020) ; Blake, S. M. et al. Thrombospondin-1 binds to ApoER2 and VLDL receptor and functions in postnatal neuronal migration. EMBO J. 27, 3069–3080 (2008). (PMID: 18946489258517210.1038/emboj.2008.223) ; van der Lee, S. J. et al. A genome-wide association study identifies genetic loci associated with specific lobar brain volumes. Commun. Biol. 2, 285 (2019). (PMID: 31396565667773510.1038/s42003-019-0537-9) ; Ikram, M. A. et al. Common variants at 6q22 and 17q21 are associated with intracranial volume. Nat. Genet. 44, 539–544 (2012). (PMID: 22504418361829010.1038/ng0612-732c) ; Adams, H. H. et al. Novel genetic loci underlying human intracranial volume identified through genome-wide association. Nat. Neurosci. 19, 1569–1582 (2016). (PMID: 27694991522711210.1038/nn.4398) ; Davies, G. et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat. Commun. 9, 2098 (2018). (PMID: 29844566597408310.1038/s41467-018-04362-x) ; Luciano, M. et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nat. Genet. 50, 6–11 (2018). (PMID: 2925526110.1038/s41588-017-0013-8) ; Chang, D. et al. A meta-analysis of genome-wide association studies identifies 17 new Parkinson’s disease risk loci. Nat. Genet. 49, 1511–1516 (2017). (PMID: 28892059581247710.1038/ng.3955) ; Hibar, D. P. et al. Common genetic variants influence human subcortical brain structures. Nature 520, 224–229 (2015). (PMID: 25607358439336610.1038/nature14101) ; Pountney, D. L., Raftery, M. J., Chegini, F., Blumbergs, P. C. & Gai, W. P. NSF, Unc-18-1, dynamin-1 and HSP90 are inclusion body components in neuronal intranuclear inclusion disease identified by anti-SUMO-1-immunocapture. Acta Neuropathologica 116, 603–614 (2008). (PMID: 1883673410.1007/s00401-008-0437-4) ; Muller, M. B. & Wurst, W. Getting closer to affective disorders: the role of CRH receptor systems. Trends Mol. Med. 10, 409–415 (2004). (PMID: 1531046210.1016/j.molmed.2004.06.007) ; Desikan, R. S. et al. Genetic overlap between Alzheimer’s disease and Parkinson’s disease at the MAPT locus. Mol. Psychiatry 20, 1588–1595 (2015). (PMID: 25687773453930410.1038/mp.2015.6) ; Spillantini, M. G. & Goedert, M. Tau pathology and neurodegeneration. Lancet Neurol. 12, 609–622 (2013). (PMID: 2368408510.1016/S1474-4422(13)70090-5) ; Horng, S. et al. Differential gene expression in the developing lateral geniculate nucleus and medial geniculate nucleus reveals novel roles for Zic4 and Foxp2 in visual and auditory pathway development. The. J. Neurosci. 29, 13672–13683 (2009). (PMID: 19864579339507210.1523/JNEUROSCI.2127-09.2009) ; Grinberg, I. et al. Heterozygous deletion of the linked genes ZIC1 and ZIC4 is involved in Dandy-Walker malformation. Nat. Genet. 36, 1053–1055 (2004). (PMID: 1533800810.1038/ng1420) ; Umemura, K. et al. Autotaxin expression is enhanced in frontal cortex of Alzheimer-type dementia patients. Neurosci. Lett. 400, 97–100 (2006). (PMID: 1652986110.1016/j.neulet.2006.02.008) ; Wray, N. R. et al. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14, 507–515 (2013). (PMID: 23774735409680110.1038/nrg3457) ; Chen, C. H. et al. Genetic influences on cortical regionalization in the human brain. Neuron 72, 537–544 (2011). (PMID: 22099457322285710.1016/j.neuron.2011.08.021) ; Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006). (PMID: 165304301653043010.1016/j.neuroimage.2006.01.021) ; Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014). (PMID: 24762786408321710.1038/nprot.2014.071) ; Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010). (PMID: 20616382292288710.1093/bioinformatics/btq340) ; 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). (PMID: 17701901195083810.1086/519795) ; Nichols, T. E. Multiple testing corrections, nonparametric methods, and random field theory. NeuroImage 62, 811–815 (2012). (PMID: 2252125610.1016/j.neuroimage.2012.04.014) ; Uppu, S., Krishna, A. & Gopalan, R. P. A review on methods for detecting SNP interactions in high-dimensional genomic. Data. IEEE/ACM Trans. Comput Biol. Bioinform. 15, 599–612 (2018). (PMID: 2806071010.1109/TCBB.2016.2635125) ; Alberton, B. A. V., Nichols, T. E., Gamba, H. R. & Winkler, A. M. Multiple testing correction over contrasts for brain imaging. NeuroImage. https://doi.org/10.1016/j.neuroimage.2020.116760 (2020). ; Churchill, G. A. & Doerge, R. W. Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971 (1994). (PMID: 7851788120624110.1093/genetics/138.3.963) ; Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015). (PMID: 25722852434219310.1186/s13742-015-0047-8) ; Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015). (PMID: 25642630449576910.1038/ng.3211) ; Choi, S. W. & O’Reilly, P. F. PRSice-2: Polygenic Risk Score software for biobank-scale data. Gigascience https://doi.org/10.1093/gigascience/giz082 (2019). ; MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017). (PMID: 2789967010.1093/nar/gkw1133) ; Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010). (PMID: 20634204293540110.1093/bioinformatics/btq419) ; Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010). (PMID: 20601685293820110.1093/nar/gkq603) ; Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017). (PMID: 29184056570569810.1038/s41467-017-01261-5) ; de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 11, e1004219 (2015). (PMID: 25885710440165710.1371/journal.pcbi.1004219) ; Bennett, D. A., Schneider, J. A., Arvanitakis, Z. & Wilson, R. S. Overview and findings from the religious orders study. Curr. Alzheimer Res. 9, 628–645 (2012). (PMID: 22471860340929110.2174/156720512801322573) ; Bennett, D. A. et al. Overview and findings from the rush Memory and Aging Project. Curr. Alzheimer Res. 9, 646–663 (2012). (PMID: 22471867343919810.2174/156720512801322663) ; Breuer, K. et al. InnateDB: systems biology of innate immunity and beyond-recent updates and continuing curation. Nucleic acids Res. 41, D1228–D1233 (2013). (PMID: 2318078110.1093/nar/gks1147) ; Szklarczyk, D. et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic acids Res. 45, D362–D368 (2017). (PMID: 2792401410.1093/nar/gkw937) ; Almasy, L. & Blangero, J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am. J. Hum. Genet. 62, 1198–1211 (1998). (PMID: 9545414137710110.1086/301844) ; 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). (PMID: 21167468301436310.1016/j.ajhg.2010.11.011) ; Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015). (PMID: 26414678462628510.1038/ng.3404) ; Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015). (PMID: 26414676479732910.1038/ng.3406) Grant Information: HHSN268201100008C United States HL NHLBI NIH HHS; 75N92019D00031 United States HL NHLBI NIH HHS; G0700704 United Kingdom MRC_ Medical Research Council; N01HC25195 United States HL NHLBI NIH HHS; HHSN268201100009C United States HL NHLBI NIH HHS; R01 HL087652 United States HL NHLBI NIH HHS; N01HC85081 United States HL NHLBI NIH HHS; P41 EB015922 United States EB NIBIB NIH HHS; MC_PC_17228 United Kingdom MRC_ Medical Research Council; N01HC85080 United States HL NHLBI NIH HHS; P30 AG010129 United States AG NIA NIH HHS; MR/K026992/1 United Kingdom MRC_ Medical Research Council; MR/M01311/1 United Kingdom MRC_ Medical Research Council; G1001401 United Kingdom MRC_ Medical Research Council; TMH109788 Canada CIHR; BB/F019394/1 United Kingdom BB_ Biotechnology and Biological Sciences Research Council; R01 AG023629 United States AG NIA NIH HHS; HHSN268201800001C United States HL NHLBI NIH HHS; U01 HL080295 United States HL NHLBI NIH HHS; HHSN268201100007C United States HL NHLBI NIH HHS; R01 HL120393 United States HL NHLBI NIH HHS; R01 EB015611 United States EB NIBIB NIH HHS; HHSN268201200036C United States HL NHLBI NIH HHS; RC2 HL102419 United States HL NHLBI NIH HHS; R01 NS087541 United States NS NINDS NIH HHS; MR/R024065/1 United Kingdom MRC_ Medical Research Council; R01 HL103612 United States HL NHLBI NIH HHS; R01 AG049607 United States AG NIA NIH HHS; HHSN268201100011C United States HL NHLBI NIH HHS; U01 HL130114 United States HL NHLBI NIH HHS; MR/M013111/1 United Kingdom MRC_ Medical Research Council; N01HC85082 United States HL NHLBI NIH HHS; U54 EB020403 United States EB NIBIB NIH HHS; NET54015 Canada CIHR; N02 HL64278 United States HL NHLBI NIH HHS; R01 HL105756 United States HL NHLBI NIH HHS; HHSN268201500001I United States HL NHLBI NIH HHS; G1001245 United Kingdom MRC_ Medical Research Council; MR/N027558/1 United Kingdom MRC_ Medical Research Council; N01HC55222 United States HL NHLBI NIH HHS; N01HC85079 United States HL NHLBI NIH HHS; N01HC85083 United States HL NHLBI NIH HHS; N01HC85086 United States HL NHLBI NIH HHS; R01 AG033040 United States AG NIA NIH HHS; HHSN268201100012C United States HL NHLBI NIH HHS; R01 AG016495 United States AG NIA NIH HHS; HHSN268201100005C United States HL NHLBI NIH HHS; R01 MH117601 United States MH NIMH NIH HHS; R01 AG033193 United States AG NIA NIH HHS; U01 AG049505 United States AG NIA NIH HHS; RF1 AG051710 United States AG NIA NIH HHS; R01 AG059874 United States AG NIA NIH HHS; HHSN268201100006C United States HL NHLBI NIH HHS; HHSN268200800007C United States HL NHLBI NIH HHS; S10 OD023696 United States OD NIH HHS; R01 NS017950 United States NS NINDS NIH HHS; G0700704/84698 United Kingdom MRC_ Medical Research Council; G0701120 United Kingdom MRC_ Medical Research Council; R01 AG054076 United States AG NIA NIH HHS; NRF86678 Canada CIHR; R01 MH116147 United States MH NIMH NIH HHS; HHSN268201100010C United States HL NHLBI NIH HHS; R01 AG008122 United States AG NIA NIH HHS; MC_QA137853 United Kingdom MRC_ Medical Research Council; RF1 AG041915 United States AG NIA NIH HHS; R01 AG022381 United States AG NIA NIH HHS; R01 AG050595 United States AG NIA NIH HHS; 8200 United Kingdom MRC_ Medical Research Council; P01 AG026572 United States AG NIA NIH HHS Contributed Indexing: Investigator: KL Grasby; N Jahanshad; JN Painter; L Colodro-Conde; J Bralten; DP Hibar; PA Lind; F Pizzagalli; CRK Ching; MAB McMahon; N Shatokhina; LCP Zsembik; I Agartz; S Alhusaini; MAA Almeida; D Alnæs; IK Amlien; M Andersson; T Ard; NJ Armstrong; A Ashley-Koch; M Bernard; RM Brouwer; EEL Buimer; R Bülow; C Bürger; DM Cannon; M Chakravarty; Q Chen; JW Cheung; B Couvy-Duchesne; AM Dale; S Dalvie; TK de Araujo; GI de Zubicaray; SMC de Zwarte; A den Braber; NT Doan; K Dohm; S Ehrlich; HR Engelbrecht; S Erk; CC Fan; IO Fedko; SF Foley; JM Ford; M Fukunaga; ME Garrett; T Ge; S Giddaluru; AL Goldman; NA Groenewold; D Grotegerd; TP Gurholt; BA Gutman; NK Hansell; MA Harris; MB Harrison; CC Haswell; M Hauser; S Herms; DJ Heslenfeld; NF Ho; D Hoehn; P Hoffmann; L Holleran; M Hoogman; JJ Hottenga; M Ikeda; D Janowitz; IE Jansen; T Jia; C Jockwitz; R Kanai; S Karama; D Kasperaviciute; T Kaufmann; S Kelly; M Kikuchi; M Klein; M Knapp; AR Knodt; B Krämer; M Lam; TM Lancaster; PH Lee; TA Lett; LB Lewis; I Lopes-Cendes; M Luciano; F Macciardi; AF Marquand; SR Mathias; TR Melzer; Y Milaneschi; N Mirza-Schreiber; JCV Moreira; TW Mühleisen; B Müller-Myhsok; P Najt; S Nakahara; K Nho; LM Olde Loohuis; DP Orfanos; JF Pearson; TL Pitcher; B Pütz; A Ragothaman; FM Rashid; R Redlich; CS Reinbold; J Repple; G Richard; BC Riedel; SL Risacher; CS Rocha; NR Mota; L Salminen; A Saremi; AJ Saykin; F Schlag; L Schmaal; PR Schofield; R Secolin; CY Shapland; L Shen; J Shin; E Shumskaya; IE Sønderby; E Sprooten; LT Strike; KE Tansey; A Teumer; A Thalamuthu; SI Thomopoulos; D Tordesillas-Gutiérrez; JA Turner; A Uhlmann; CL Vallerga; D van der Meer; MMJ van Donkelaar; L van Eijk; TGM van Erp; NEM van Haren; D van Rooij; MJ van Tol; JH Veldink; E Verhoef; E Walton; M Wang; Y Wang; JM Wardlaw; W Wen; LT Westlye; CD Whelan; SH Witt; K Wittfeld; C Wolf; T Wolfers; CL Yasuda; D Zaremba; Z Zhang; AH Zhu; MP Zwiers; E Artiges; AA Assareh; R Ayesa-Arriola; A Belger; CL Brandt; GG Brown; S Cichon; JE Curran; GE Davies; F Degenhardt; B Dietsche; S Djurovic; CP Doherty; R Espiritu; D Garijo; Y Gil; PA Gowland; RC Green; AN Häusler; W Heindel; BC Ho; WU Hoffmann; F Holsboer; G Homuth; N Hosten; CR Jack; M Jang; A Jansen; K Kolskår; S Koops; A Krug; KO Lim; JJ Luykx; DH Mathalon; KA Mather; VS Mattay; S Matthews; JMV Son; SC McEwen; I Melle; DW Morris; BA Mueller; M Nauck; JE Nordvik; MM Nöthen; DS O'Leary; N Opel; M-P Martinot; GB Pike; A Preda; EB Quinlan; V Ratnakar; S Reppermund; VM Steen; FR Torres; DJ Veltman; JT Voyvodic; R Whelan; T White; H Yamamori; MKM Alvim; D Ames; TJ Anderson; OA Andreassen; A Arias-Vasquez; ME Bastin; BT Baune; J Blangero; DI Boomsma; H Brodaty; HG Brunner; RL Buckner; JK Buitelaar; JR Bustillo; W Cahn; V Calhoun; X Caseras; S Caspers; GL Cavalleri; F Cendes; A Corvin; B Crespo-Facorro; JC Dalrymple-Alford; U Dannlowski; EJC de Geus; IJ Deary; N Delanty; C Depondt; S Desrivières; G Donohoe; T Espeseth; G Fernández; SE Fisher; H Flor; AJ Forstner; C Francks; B Franke; DC Glahn; RL Gollub; HJ Grabe; O Gruber; AK Håberg; AR Hariri; CA Hartman; R Hashimoto; A Heinz; MHJ Hillegers; PJ Hoekstra; AJ Holmes; LE Hong; WD Hopkins; HE Hulshoff Pol; TL Jernigan; EG Jönsson; RS Kahn; MA Kennedy; TTJ Kircher; P Kochunov; JBJ Kwok; SL Hellard; NG Martin; J- Martinot; C McDonald; KL McMahon; A Meyer-Lindenberg; RA Morey; L Nyberg; J Oosterlaan; RA Ophoff; T Paus; Z Pausova; BWJH Penninx; TJC Polderman; D Posthuma; M Rietschel; JL Roffman; LM Rowland; PS Sachdev; PG Sämann; G Schumann; K Sim; SM Sisodiya; JW Smoller; IE Sommer; BS Pourcain; DJ Stein; AW Toga; JN Trollor; NJA Van der Wee; D van 't Ent; H Völzke; H Walter; B Weber; DR Weinberger; MJ Wright; J Zhou; JL Stein; PM Thompson; SE Medland Entry Date(s): Date Created: 20200923 Date Completed: 20201013 Latest Revision: 20221017 Update Code: 20231215 PubMed Central ID: PMC7508833

Klicken Sie ein Format an und speichern Sie dann die Daten oder geben Sie eine Empfänger-Adresse ein und lassen Sie sich per Email zusenden.

BibTeX Citavi, JabRef, u.a.
(Literaturverwaltung)

PDF kein Volltext!
(Merkzettel, Notizen)

RIS Endnote, Citavi u.a.
(Literaturverwaltung)

MODS
(XML zur Weiterverarbeitung)

oder

Wählen Sie das für Sie passende Zitationsformat und kopieren Sie es dann in die Zwischenablage, lassen es sich per Mail zusenden oder speichern es als PDF-Datei.

Gewünschter Zitations-Stil:

oder

Bitte prüfen Sie, ob die Zitation formal korrekt ist, bevor Sie sie in einer Arbeit verwenden. Benutzen Sie gegebenenfalls den "Exportieren"-Dialog, wenn Sie ein Literaturverwaltungsprogramm verwenden und die Zitat-Angaben selbst formatieren wollen.

Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults.