Genetic architecture of subcortical brain structures in 38,851 individuals

Satizabal, Claudia L.; Adams, Hieab H.H.; Hibar, Derrek P.; White, Charles C.; Knol, Maria J.; Stein, Jason L.; Scholz, Markus; Sargurupremraj, Muralidharan; Jahanshad, Neda; Roshchupkin, Gennady V.; Smith, Albert V.; Bis, Joshua C.; Jian, Xueqiu; Luciano, Michelle; Hofer, Edith; Teumer, Alexander; Lee, Sven J. van der; Yang, Jingyun; Yanek, Lisa R.; Lee, Tom V.; Li, Shuo; Hu, Yanhui; Koh, Jia Yu; Eicher, John D.; Desrivières, Sylvane; Arias-Vásquez, Alejandro; Chauhan, Ganesh; Athanasiu, Lavinia; Rentería, Miguel E.; Kim, Sungeun; Hoehn, David; Armstrong, Nicola J.; Chen, Qiang; Holmes, Avram J.; Braber, Anouk den; Kłoszewska, Iwona; Andersson, Micael; Espeseth, Thomas; Grimm, O.; Abramovic, Lucija; Alhusaini, Saud; Milaneschi, Yuri; Papmeyer, Martina; Axelsson, Tomas; Ehrlich, Stefan; Roiz-Santiáñez, Roberto; Kraemer, Bernd; Håberg, Asta; Jones, Hannah; Pike, G. Bruce; Stein, Dan J.; Stevens, Allison; Bralten, Janita; Vernooij, Meike W.; Harris, Tamara B.; Filippi, Irina; Witte, A. Veronica; Guadalupe, Tulio; Wittfeld, Katharina; Mosley, Thomas H.; Becker, James T.; Doan, Nhat Trung; Hagenaars, Saskia P.; Saba, Yasaman; Cuéllar-Partida, Gabriel; Amin, Najaf; Hilal, Saima; Nho, Kwangsik; Mirza‐Schreiber, Nazanin; Arfanakis, Konstantinos; Becker, Diane M.; Ames, David; Goldman, Aaron L.; Lee, Phil H.; Boomsma, Dorret I.; Lovestone, Simon; Giddaluru, Sudheer; Hellard, Stéphanie Le; Mattheisen, Manuel; Bohlken, Marc M.; Kasperavičiūtė, Dalia; Schmaal, Lianne; Lawrie, Stephen M.; Agartz, Ingrid; Walton, Esther; Tordesillas‐Gutiérrez, Diana; Davies, Gareth E.; Shin, Jean; Ipser, Jonathan; Vinke, Louis; Hoogman, Martine; Jia, Tianye; Burkhardt, Ralph; Klein, Marieke; Crivello, Fabrice; Janowitz, Deborah; Carmichael, Owen; Haukvik, Unn K.; Aribisala, Benjamin S.; Schmidt, Helena; Strike, Lachlan T.; Cheng, Ching‐Yu; Risacher, Shannon L.; Pütz, Benno; Fleischman, Debra; Assareh, Amelia A.; Mattay, Venkata S.; Buckner, Randy L.; Mecocci, Patrizia; Dale, Anders M.; Cichon, Sven; Boks, Marco P.; Matarı́n, Mar; Penninx, Brenda W.J.H.; Calhoun, Vince D.; Chakravarty, M. Mallar; Marquand, André F.; Macare, Christine; Masouleh, Shahrzad Kharabian; Oosterlaan, Jaap; Amouyel, Philippe; Hegenscheid, Katrin; Rotter, Jerome I.; Schork, Andrew J.; Liewald, David C.; Zubicaray, Greig I. de; Wong, Tien Yin; Shen, Li; Sämann, Philipp G.; Brodaty, Henry; Roffman, Joshua L.; Geus, Eco J. C. de; Tsolaki, Magda; Erk, Susanne; Eijk, Kristel R. van; Cavalleri, Gianpiero L.; Wee, Nic J.A. van der; McIntosh, Andrew M.; Gollub, Randy L.; Bulayeva, Kazima; Bernard, Manon; Richards, Jennifer S.; Himali, Jayandra J.; Loeffler, Markus; Rommelse, Nanda; Hoffmann, Wolfgang; Westlye, Lars T.; Hernández, Maria C. Valdés; Hansell, Narelle K.; Erp, Theo G.M. van; Wolf, Christiane; Kwok, John B.; Vellas, Bruno; Heinz, Andreas; Loohuis, Loes Olde; Delanty, Norman; Ho, Beng-Choon; Ching, Christopher R. K.; Shumskaya, Elena; Singh, Baljeet; Hofman, Albert; Meer, Dennis van der; Homuth, Georg; Psaty, Bruce M.; Bastin, Mark E.; Montgomery, Grant W.; Foroud, Tatiana; Reppermund, Simone; Hottenga, Jouke‐Jan; Simmons, Andrew; Meyer‐Lindenberg, Andreas; Cahn, Wiepke; Whelan, Christopher D.; Donkelaar, Marjolein M. J. Van; Yang, Qiong; Hosten, Norbert; Green, Robert C.; Thalamuthu, Anbupalam; Mohnke, Sebastian; Pol, Hilleke E. Hulshoff; Lin, Honghuang; Jack, Clifford R.; Schofield, Peter R.; Mühleisen, Thomas W.; Maillard, Pauline; Potkin, Steven G.; Wang, Wen; Fletcher, Evan; Toga, Arthur W.; Gruber, Oliver; Huentelman, Matthew J.; Smith, George Davey; Launer, Lenore J.; Nyberg, Lars; Jönsson, Erik G.; Crespo‐Facorro, Benedicto; Koen, Nastassja; Greve, Douglas N.; Uitterlinden, André G.; Weinberger, Daniel R.; Steen, Vidar M.; Fedko, Iryna O.; Groenewold, Nynke A.; Niessen, Wiro J.; Toro, Roberto; Tzourio, Christophe; Longstreth, W. T.; Ikram, M. Arfan; Smoller, Jordan W.; Tol, Marie-José van; Sussmann, Jessika E.; Paus, Tomáš; Lemaître, Hervé; Schroeter, Matthias L.; Mazoyer, Bernard; Andreassen, Ole A.; Holsboer, Florian; Depondt, Chantal; Veltman, Dick J.; Turner, Jessica A.; Pausová, Zdenka; Schumann, Gunter; Rooij, Daan van; Djurovic, Srdjan; Deary, Ian J.; McMahon, Katie L.; Müller‐Myhsok, Bertram; Brouwer, Rachel M.; Soininen, Hilkka; Pandolfo, Massimo; Wassink, Thomas H.; Cheung, Joshua W.; Wolfers, Thomas; Martinot, Jean‐Luc; Zwiers, Marcel P.; Nauck, Matthias; Melle, Ingrid; Martin, Nicholas G.; Kanai, Ryota; Westman, Eric; Kahn, René S.; Sisodiya, Sanjay M.; White, Tonya; Saremi, Arvin; Bokhoven, Hans van; Brunner, Han G.; Völzke, Henry; Wright, Margaret J.; Ent, Dennis van ‘t; Nöthen, Markus M.; Ophoff, Roel A.; Buitelaar, Jan K.; Fernández, Guillén; Sachdev, Perminder S.; Rietschel, Marcella; Haren, Neeltje E.M. van; Fisher, Simon E.; Beiser, Alexa S.; Francks, Clyde; Saykin, Andrew J.; Mather, Karen A.; Romanczuk-Seiferth, Nina; Hartman, Catharina A.; DeStefano, Anita L.; Heslenfeld, Dirk J.; Weiner, Michael W.; Walter, Henrik; Hoekstra, Pieter J.; Nyquist, Paul; Franke, Barbara; Bennett, David A.; Grabe, Hans J.; Johnson, Andrew D.; Chen, Christopher; Duijn, Cornelia M. van; López, Oscar L.; Fornage, Myriam; Wardlaw, Joanna M.; Schmidt, Reinhold; DeCarli, Charles; Jager, Philip L. De; Villringer, Arno; Debette, Stéphanie; Gudnason, Vilmundur; Medland, Sarah E.; Liu, Zhandong; Thompson, Paul M.; Seshadri, Sudha

doi:10.1038/s41588-019-0511-y

Cited by 205 publications

(219 citation statements)

References 121 publications

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“…The imaging samples from UK Biobank and ENIGMA were totally independent. More intriguingly, in a latest meta-analysis of imaging data from CHARGE, ENIGMA and UK Biobank (N = 38,851), both SNPs showed genome-wide significant associations with putamen volume (rs7227069, p = 4.64 × 10 −10 ; rs1367635, p = 8.72 × 10 −10 ) 46 .…”

Section: The Depression Risk Alleles Indicated Higher DCC Mrna Levelmentioning

confidence: 99%

“…According to the previous study 44 , there was a strong correlation between the UK Biobank and ENIGMA imaging samples, suggesting that methodologies applied in the measurement and statistical analyses of these phenotypes were relatively consistent. In addition, a more recent GWAS has been conducted to meta-analyze imaging data from CHARGE, ENIGMA and UK Biobank, resulting in a total of 38,851 subjects 46 .…”

Section: Gwas Of Brain Imaging Phenotypesmentioning

confidence: 99%

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Further confirmation of netrin 1 receptor (DCC) as a depression risk gene via integrations of multi-omics data

et al. 2020

Transl Psychiatry

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Genome-wide association studies (GWAS) of major depression and its relevant biological phenotypes have been extensively conducted in large samples, and transcriptome-wide analyses in the tissues of brain regions relevant to pathogenesis of depression, e.g., dorsolateral prefrontal cortex (DLPFC), have also been widely performed recently. Integrating these multi-omics data will enable unveiling of depression risk genes and even underlying pathological mechanisms. Here, we employ summary data-based Mendelian randomization (SMR) and integrative risk gene selector (iRIGS) approaches to integrate multi-omics data from GWAS, DLPFC expression quantitative trait loci (eQTL) analyses and enhancer-promoter physical link studies to prioritize high-confidence risk genes for depression, followed by independent replications across distinct populations. These integrative analyses identify multiple high-confidence depression risk genes, and numerous lines of evidence supporting pivotal roles of the netrin 1 receptor (DCC) gene in this illness across different populations. Our subsequent explorative analyses further suggest that DCC significantly predicts neuroticism, well-being spectrum, cognitive function and putamen structure in general populations. Gene expression correlation and pathway analyses in DLPFC further show that DCC potentially participates in the biological processes and pathways underlying synaptic plasticity, axon guidance, circadian entrainment, as well as learning and long-term potentiation. These results are in agreement with the recent findings of this gene in neurodevelopment and psychiatric disorders, and we thus further confirm that DCC is an important susceptibility gene for depression, and might be a potential target for new antidepressants.

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Section: The Depression Risk Alleles Indicated Higher DCC Mrna Levelmentioning

confidence: 99%

Section: Gwas Of Brain Imaging Phenotypesmentioning

confidence: 99%

Further confirmation of netrin 1 receptor (DCC) as a depression risk gene via integrations of multi-omics data

et al. 2020

Transl Psychiatry

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“…Such a coordinated, two-stage meta-analysis approach has been pursued by most ENIGMA working groups (Hibar et al, 2015(Hibar et al, , 2016Schmaal et al, 2016;Stein et al, 2012;van Erp et al, 2016), particularly due to privacy concerns regarding genetic data. ENIGMA genome-wide association studies still use a meta-analysis approach (Hibar et al, 2017;Satizabal et al, 2019); sites analyze their own data with an agreed upon protocol, which avoids the need to transfer individual participant genomic data, and allows distributed analysis of computationally intense approaches.…”

Section: Meta-analysis Vs Mega-analysismentioning

confidence: 99%

Mega-Analysis Methods in ENIGMA: The Experience of the Generalized Anxiety Disorder Working Group

Zugman¹,

Harrewijn²,

Cardinale³

et al. 2020

Preprint

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The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses.

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“…For the genome-wide significant genes and genes associated with genomewide significant SNPs, we compared our findings with cross-sectional GWAS summary statistics when available. To this end datasets from (Elliott et al, 2018;Hibar et al, 2017;Satizabal et al, 2019;Grasby et al, 2020) were requested/downloaded from (http://enigma.ini.usc.edu/research/downloadenigma-gwas-results/; http://big.stats.ox.ac.uk/download_page). Gene-based association analyses for cross-sectional brain GWAS summary statistics were performed using MAGMA (as described above).…”

Section: Comparison With Cross-sectional Resultsmentioning

confidence: 99%

Age-dependent genetic variants associated with longitudinal changes in brain structure across the lifespan

Brouwer¹,

Haren²,

Forti³

et al. 2020

Preprint

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We identified common genetic variants associated with the rate of brain development and aging, in longitudinal MRI scans worldwide. AbstractHuman brain structure changes throughout our lives. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental, and neurodegenerative diseases. While heritable, specific loci in the genome that influence these rates are largely unknown. Here, we sought to find common genetic variants that affect rates of brain growth or atrophy, in the first genome-wide association analysis of longitudinal changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 10,163 individuals aged 4 to 99 years, on average 3.5 years apart, were used to compute rates of morphological change for 15 brain structures. We discovered 5 genome-wide significant loci and 15 genes associated with brain structural changes. Most individual variants exerted age-dependent effects. All identified genes are expressed in fetal and adult brain tissue, and some exhibit developmentally regulated expression across the lifespan. We demonstrate genetic overlap with depression, schizophrenia, cognitive functioning, height, body mass index and smoking. Several of the discovered loci are implicated in early brain development and point to involvement of metabolic processes. Gene-set findings also implicate immune processes in the rates of brain changes. Taken together, in the world's largest longitudinal imaging genetics dataset we identified genetic variants that alter agedependent brain growth and atrophy throughout our lives. a Position based on build hg19. Study-wide significant hits are displayed in bold. *This gene also showed a genome-wide significant quadratic age effect. The most parsimonious model is listed in this table.Genome-wide significant gene sets based on gene ontology. Study-wide significant gene sets are displayed in bold. a See Supplementary Table S9 for genes included in the gene set. Genes included in GO_INTERLEUKIN_1_RECEPTOR_ACTIVITY and GO_RESPONSE_TO_INTERLEUKIN_2 do not overlap.

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Genetic architecture of subcortical brain structures in 38,851 individuals

Cited by 205 publications

References 121 publications

Further confirmation of netrin 1 receptor (DCC) as a depression risk gene via integrations of multi-omics data

Further confirmation of netrin 1 receptor (DCC) as a depression risk gene via integrations of multi-omics data

Mega-Analysis Methods in ENIGMA: The Experience of the Generalized Anxiety Disorder Working Group

Age-dependent genetic variants associated with longitudinal changes in brain structure across the lifespan

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