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Common genetic variants influence human subcortical brain structures

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doi:10.1038/nature14101 Common genetic variants influence human subcortical brain structures

A list of authors and their affiliations appears at the end of the paper

The highly complex structure of the human brain is strongly shaped by genetic influences1.Subcortical brain regions form circuits with cortical areas to coordinate movement2,learning,memory3and motivation4,and altered circuits can lead to abnormal behaviour and disease2.To investigate how common genetic variants affect the structure of these brain regions,here we conduct genome-wide asso-ciation studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717individuals from50cohorts.We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously estab-lished influences on hippocampal volume5and intracranial volume6. These variants show specific volumetric effects on brain structures rather than global effects across structures.The strongest effects were found for the putamen,where a novel intergenic locus with replicable influence on volume(rs945270;P51.08310233;0.52%variance explained)showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue.Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport.Identification of these genetic variants provides insight into the causes of variability in human brain development,and may help to determine mechanisms of neuropsy-chiatric dysfunction.

At the individual level,genetic variations exert lasting influences on brain structures and functions associated with behaviour and predispo-sition to disease.Within the context of the Enhancing Neuro Imaging Genetics through Meta-Analysis(ENIGMA)consortium,we conducted a collaborative large-scale genetic analysis of magnetic resonance imag-ing(MRI)scans to identify genetic variants that influence brain structure. Here,we focus on volumetric measures derived from a measure of head size(intracranial volume,ICV)and seven subcortical brain structures corrected for the ICV(nucleus accumbens,caudate,putamen,pallidum, amygdala,hippocampus and thalamus).To ensure data homogeneity within the ENIGMA consortium,we designed and implemented stan-dardized protocols for image analysis,quality assessment,genetic impu-tation(to1000Genomes references,version3)and association(Extended Data Fig.1and Methods).

After establishing that the volumes extracted using our protocols were substantially heritable in a large sample of twins(P,131024; see Methods and Extended Data Fig.11a),with similar distributions to previous studies1,we sought to identify common genetic variants con-tributing to volume differences by meta-analysing site-level genome-wide association study(GWAS)data in a discovery sample of13,171 subjects of European ancestry(Extended Data Fig.2).Population strat-ification was controlled for by including,as covariates,four population components derived from standardized multidimensional scaling ana-lyses of genome-wide genotype data conducted at each site(see Methods). Site-level GWAS results and distributions were visually inspected to check for statistical inflation and patterns indicating technical artefacts (see Methods).

Meta-analysis of the discovery sample identified six genome-wide sig-nificant loci after correcting for the number of variants and traits ana-lysed(P,7.131029;see Methods):one associated with the ICV,two associated with hippocampal volume,and three with putamen volume. Another four loci showed suggestive associations(P,131027)with putamen volume(one locus),amygdala volume(two loci),and caudate volume(one locus;Table1,Fig.1and Supplementary Table5).Quantile–quantile plots showed no evidence of population stratification or cryp-tic relatedness(Extended Data Fig.4a).We subsequently attempted to replicate the variants with independent data from17,546

individuals.

rs77956314

rs61921502

rs143679590

rs1318862

rs945270

rs150031419

rs17689882 10

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Accumbens Amygdala

Hippocampus

Caudate

Pallidum Putamen

Thalamus ICV

rs117253277

rs945270

rs683250

rs62097986

rs6087771

Figure1|Common genetic variants associated with subcortical volumes and the ICV.Manhattan plots coloured with a scheme that matches the corresponding structure(middle)are shown for each subcortical volume studied.Genome-wide significance is shown for the common threshold of

P5531028(grey dotted line)and also for the multiple comparisons-corrected threshold of P57.131029(red dotted line).The most significant SNP within an associated locus is labelled.

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All subcortical genome-wide significant variants identified in the dis-covery sample were replicated(Table1).The variant associated with the ICV did not replicate in a smaller independent sample,but was genome-wide significant in a previously published independent study6, providing strong evidence for its association with the ICV.Moreover, two suggestive variants associated with putamen and caudate volumes exceeded genome-wide significance after meta-analysis across the dis-covery and replication data sets(Table1).Effect sizes were similar across cohorts(P.0.1,Cochran’s Q test;Extended Data Fig.4b).Effect sizes remained consistent after excluding patients diagnosed with anxiety, Alzheimer’s disease,attention-deficit/hyperactivity disorder,bipolar disorder,epilepsy,major depressive disorder or schizophrenia(21%of the discovery participants).Correlation in effect size with and without patients was very high(r.0.99)for loci with P,131025,indicating that these effects were unlikely to be driven by disease(Extended Data Fig.5a).The participants’age range covered most of the lifespan(9–97years),but only one of the eight significant loci showed an effect related to the mean age of each cohort(P50.002;rs6087771affecting puta-men volume;Extended Data Fig.5b),suggesting that nearly all effects are stable across the lifespan.In addition,none of these loci showed evidence of sex effects(Extended Data Fig.5c).

In our cohorts,significant loci were associated with0.51–1.40%dif-ferences in volume per risk allele,explaining0.17–0.52%of the pheno-typic variance(Table1);such effect sizes are similar to those of common variants influencing other complex quantitative traits such as height7 and body mass index8.The full genome-wide association results explained 7–15%of phenotypic variance after controlling for the effects of cov-ariates(Extended Data Fig.11).Notably,the genome-wide significant variants identified here showed specific effects on single brain struc-tures rather than pleiotropic effects across multiple structures,despite similar developmental origins as in the case of caudate and putamen (Extended Data Fig.6a).Nevertheless,when we subjected the subcor-tical meta-analysis results to hierarchical clustering,genetic determinants of the subcortical structures were mostly grouped into larger circuits according to their developmental and functional subdivisions(Extended Data Fig.6b).Genetic variants may therefore have coherent effects on functionally associated subcortical networks.Multivariate cross-structure9 analyses confirmed the univariate results,but no additional loci reached genome-wide significance(Extended Data Fig.6c).The clustering of results into known brain circuits in the absence of individually signi-ficant genetic variants found in the cross-structure analysis suggests variants of small effect may have similar influences across structures. Most variants previously reported to be associated with brain structure and/or function showed little evidence of large-scale volumetric effects (Supplementary Table8).We detected an intriguing association with hippocampal volume at a single nucleotide polymorphism(SNP)with a genome-wide significant association with schizophrenia10(rs2909457; P52.1231026;where the A allele is associated with decreased risk for schizophrenia and decreased hippocampal volume).In general,how-ever,we detected no genome-wide significant association with brain structure for genome-wide significant loci that contribute risk for neu-ropsychiatric illnesses(Supplementary Table9).

Of the four loci influencing putamen volume,we identified an inter-genic locus50kilobases(kb)downstream of the KTN1gene(rs945270; 14q22.3;n528,275;P51.08310233),which encodes the protein kinec-tin,a receptor that allows vesicle binding to kinesin and is involved in organelle transport11.Second,we identified an intronic locus within DCC (rs62097986;18q21.2;n528,036;P51.01310213),which encodes a netrin receptor involved in axon guidance and migration,including in the developing striatum12(Extended Data Fig.3b).Expression of DCC throughout the brain is highest in the first two trimesters of prenatal development13(Extended Data Fig.8b),suggesting that this variant may influence brain volumes early in neurodevelopment.Third,we iden-tified an intronic locus within BCL2L1(rs6087771;20q11.21;n525,540; P51.28310212),which encodes an anti-apoptotic factor that inhibits programmed cell death of immature neurons throughout the brain14 (Extended Data Fig.3c).Consistent with this,expression of BCL2L1in the striatum strongly decreases at the end of neurogenesis(24–38post-conception weeks(PCW);Extended Data Fig.8c),a period marked by increased apoptosis in the putamen13,15.Fourth,we identified an intronic locus within DLG2(rs683250;11q14.1;n526,258;P53.94310211), which encodes the postsynaptic density93(PSD-93)protein(Extended Data Fig.3d).PSD-93is a membrane-associated guanylate kinase involved in organizing channels in the postsynaptic density16.DLG2expression increases during early mid-fetal development in the striatum13(Extended Data Fig.8d).Genetic variants in DLG2affect learning and cognitive flexibility17and are associated with schizophrenia18.Notably,SNPs asso-ciated with variation in putamen volume showed enrichment of genes involved in apoptosis and axon guidance pathways(Extended Data Fig.7and Supplementary Table7).

Hippocampal volume showed an intergenic association near the HRK gene(rs77956314;12q24.22;n517,190;P52.82310215;Extended Data Fig.3g)and with an intronic locus in the MSRB3gene(rs61921502; 12q14.3;n516,209;P56.87310211;Extended Data Fig.3h),support-ing our previous analyses5,19of smaller samples imputed to HapMap3 references.Caudate volume was associated with an intergenic locus80kb from FAT3(rs1318862;11q14.3;n515,031;P56.1731029;Extended Data Fig.3e).This gene encodes a cadherin specifically expressed in the

Table1|Genetic variants at eight loci were significantly associated with putamen,hippocampus,caudate nucleus and ICV

Discovery cohort Replication cohort Discovery1replication cohorts

Trait Marker A1A2Frq Effect(se)P value Sample

size Effect(se)P value Sample

size

Effect(se)P value Total

sample

size

Variance

explained

(%)

Diff./

allele

(%)

Putamen rs945270C G0.5860.64

(6.00)5.4331022413,14539.15

(5.46)

7.8131021315,13048.89

(4.04)

1.0831023328,2750.520.94

Putamen rs62097986A C0.4439.53

(6.01)4.8631021113,14522.46

(5.53)

4.893102514,89130.28

(4.07)

1.0131021328,0360.200.58

Putamen rs6087771T C0.7140.72

(6.82)2.423102911,86526.97

(6.57)

4.023102513,67533.58

(4.73)

1.2831021225,5400.200.64

Putamen rs683250A G0.63233.97

(6.08)2.333102813,145222.30

(5.89)

1.503102413,113227.95

(4.23)

3.9431021126,2580.170.51

Caudate rs1318862T C0.5826.27

(4.89)7.543102813,17131.82

(14.23)

0.0251,86026.86

(4.62)

6.173102915,0310.220.74

Hip.rs77956314T C0.91254.21

(8.37)9.3331021113,163257.43

(12.69)

6.04310264,027255.18

(6.99)

2.8231021517,1900.36 1.40

Hip.rs61921502T G0.8443.40

(6.89)2.9231021013,16326.81

(13.32)

0.0443,04639.90

(6.12)

6.8731021116,2090.26 1.01

ICV rs17689882A G0.22215,335.88

(2,582.20)2.873102910,94425,202.15

(5,428.60)

0.3371,878213,460.47

(2,331.05)

7.723102912,8220.260.96

The allele frequency(frq)and effect size are given with reference to allele1(A1).Effect sizes are given in units of mm3per effect allele.Results are provided for the discovery samples and the combined meta-analysis of the discovery and replication cohorts(all European ancestry).Additional validation was attempted in non-European ancestry generalization samples(shown in Supplementary Table6).The variance explained gives the percentage variance explained by a given SNP after correcting for covariates(see Methods for additional details).The percentage difference in volume per effect allele(Diff./allele)is based on the absolute value of the final combined effect divided by a weighted average of the brain volume of interest across all sites in the discovery sample and then multiplied by100.Hip,hippocampus. RESEARCH LETTER

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nervous system during embryonic development that influences neu-ronal morphology through cell–cell interactions 20.The ICV was asso-ciated with an intronic locus within CRHR1that tags the chromosome 17q21inversion 21,which has been previously found to influence ICV 6(rs17689882;17q21.31;n 512,822;P 57.7231029;Extended Data Fig.3f).Another previously identified variant with association to ICV (rs10784502)5,19did not survive genome-wide significance in this analysis but did show a nominal effect in the same direction (P 52.0531023;n 511,373).None of the genome-wide significant loci in this study were in linkage disequilibrium with known functional coding variants,splice sites,or 39/59untranslated regions,although several of the loci had epigenetic markings suggesting a regulatory role (Extended Data Fig.3).

Given the strong association with putamen volume,we further exam-ined the rs945270locus.Epigenetic markers suggest insulator function-ality near the locus as this is the lone chromatin mark in the intergenic region 22(Extended Data Fig.3a).Chromatin immunoprecipitation fol-lowed by sequencing (ChIP-seq)indicate that a variant (rs8017172)in complete linkage disequilibrium with rs945270(r 251.0)lies within a binding site of the CTCF (CCCTC-binding factor)transcription regulator 23(Extended Data Fig.9)in embryonic stem cells.To assess potential functionality in brain tissue,we tested for association with gene expression 1megabase (Mb)up/downstream.We identified and rep-licated an effect of rs945270on the expression of the KTN1gene.The C allele,associated with larger putamen volume,also increased expres-sion of KTN1in the frontal cortex (discovery sample:304neuropatho-logically normal controls 24(P 54.1310211);replication sample:134neuropathologically normal controls (P 50.025)),and putamen (sample:134neuropathologically normal controls 25(P 50.049);Fig.2a,b).In blood,rs945270was also strongly associated with KTN1expression 26(P 55.94310231;n 55,311).After late fetal development,KTN1is expressed in the human thalamus,striatum and hippocampus;it is more highly expressed in the striatum than the cortex 13(Extended Data Fig.8a).KTN1encodes the kinectin receptor facilitating vesicle binding to kinesin,and is heavily involved in organelle transport 11.Kinectin is only found in the dendrites and soma of neurons,not their axons;neurons with

more kinectin have larger cell bodies 27,and kinectin knockdown strongly influences cell shape 28.The volumetric effects identified here may there-fore reflect genetic control of neuronal cell size and/or dendritic https://www.wendangku.net/doc/1d16011281.html,ing three-dimensional surface models of putamen segmentations in MRI scans of 1,541healthy adolescent subjects,we further localized the allelic effects of rs945270to regions along the superior and lateral putamen bilaterally,independent of chosen segmentation protocol (Fig.2c and Extended Data Fig.10).Each copy of the C allele was asso-ciated with an increase in volume along anterior superior regions receiv-ing dense cortical projections from dorsolateral prefrontal cortex and supplementary motor areas 29,30.

In summary,we discovered several common genetic variants underly-ing variation in different structures within the human brain.Many seem to exert their effects through known developmental pathways includ-ing apoptosis,axon guidance and vesicle transport.All structure volumes showed high heritability,but individual genetic variants had diverse effects.The strongest effects were found for putamen and hippocampal volumes,whereas other structures delineated with similar reliability such as the thalamus showed no association with these or other loci (Sup-plementary Table 4).Discovery of common variants affecting the human brain is now feasible using collaborative analysis of MRI data,and may determine genetic mechanisms driving development and disease.

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Frontal cortex

n = 304 ; P = 4.1×10–11

Genotype at rs945270G/G

C/G

C/C

4.5

5.0

5.5

6.0

n = 134 ; P = 0.025

Genotype at rs945270

G/G

C/G

C/C

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Genotype at rs945270

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K T N 1 l o g 2 e x p r e s s i o n

Left

L a t e r a l

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Right

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mm per effect allele

S I P

S I

P S

A 0.040.060.080.100.12S I

A P r s 945270 e f f e c t o n p u t a m e n s h a p e

Figure 2|Effect of rs945270on KTN1

expression and putamen shape.a ,b ,Expression quantitative trait loci study in brain tissue

demonstrates the effect of rs945270on KTN1gene expression in frontal cortex tissue from 304subjects from the North American Brain Expression Cohort (NABEC 25)(a )and in an independent sample of 134subjects from the UK Brain Expression Cohort (UKBEC)(b ),sampled from both frontal cortex and putamen.Boxplot dashed bars mark the twenty-fifth and seventy-fifth percentiles.c ,Surface-based analysis demonstrates that rs945270has strong effects on the shape of superior and lateral portions of the putamen in 1,541subjects.Each copy of the

rs945270-C allele was significantly associated with increased width in coloured areas (false discovery rate corrected at q 50.05),and the degree of deformation is labelled by colour,with red indicating greater deformation.Orientation is indicated by arrows.A,anterior;I,inferior;P,posterior,S,superior.

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Supplementary Information is available in the online version of the paper. Acknowledgements Funding sources for contributing sites and acknowledgments of contributing consortia authors can be found in Supplementary Note3.

Author Contributions Individual author contributions are listed in Supplementary Note4.

Author Information Summary statistics from GWAS results are available online using the ENIGMA-Vis web tool:https://www.wendangku.net/doc/1d16011281.html,/enigma-vis/.Reprints and permissions information is available at https://www.wendangku.net/doc/1d16011281.html,/reprints.The authors declare no competing financial interests.Readers are welcome to comment on the online version of the paper.Correspondence and requests for materials should be addressed to P.M.T.(pthomp@https://www.wendangku.net/doc/1d16011281.html,)or S.E.M.(Sarah.Medland@https://www.wendangku.net/doc/1d16011281.html,.au). Derrek P.Hibar1*,Jason L.Stein1,2*,Miguel E.Renteria3*,Alejandro

Arias-Vasquez4,5,6,7*,Sylvane Desrivie`res8*,Neda Jahanshad1,Roberto Toro9,10,11, Katharina Wittfeld12,13,Lucija Abramovic14,Micael Andersson15,Benjamin

S.Aribisala16,17,18,Nicola J.Armstrong19,20,Manon Bernard21,Marc M.Bohlken14, Marco P.Boks14,Janita Bralten4,6,7,Andrew A.Brown22,23,M.Mallar Chakravarty24,25, Qiang Chen26,Christopher R.K.Ching1,27,Gabriel Cuellar-Partida3,Anouk den Braber28,Sudheer Giddaluru29,30,Aaron L.Goldman26,Oliver Grimm31,Tulio Guadalupe32,33,Johanna Hass34,Girma Woldehawariat35,Avram J.Holmes36,37, Martine Hoogman4,7,Deborah Janowitz13,Tianye Jia8,Sungeun Kim38,39,40,Marieke Klein4,7,Bernd Kraemer41,Phil H.Lee37,42,43,44,Loes M.Olde Loohuis45, Michelle Luciano46,Christine Macare8,Karen A.Mather19,Manuel Mattheisen47,48,49, Yuri Milaneschi50,Kwangsik Nho38,39,40,Martina Papmeyer51,Adaikalavan Ramasamy52,53,Shannon L.Risacher38,40,Roberto Roiz-Santian?ez54,55,

Emma J.Rose56,57,Alireza Salami15,58,Philipp G.Sa¨mann59,Lianne Schmaal50, Andrew J.Schork60,61,Jean Shin21,Lachlan T.Strike3,62,63,Alexander Teumer64, Marjolein M.J.van Donkelaar4,7,Kristel R.van Eijk14,Raymond K.Walters65,66,

Lars T.Westlye23,67,Christopher D.Whelan1,Anderson M.Winkler68,69,

Marcel P.Zwiers7,Saud Alhusaini70,71,Lavinia Athanasiu22,23,Stefan Ehrlich34,37,72, Marina M.H.Hakobjan4,7,Cecilie B.Hartberg22,73,Unn K.Haukvik22,Angelien J.G.A. M.Heister4,7,David Hoehn59,Dalia Kasperaviciute74,75,David C.M.Liewald46, Lorna M.Lopez46,Remco R.R.Makkinje4,7,Mar Matarin76,Marlies A.M.Naber4,7, D.Reese McKay69,77,Margaret Needham56,Allison C.Nugent35,Benno Pu¨tz59, Natalie A.Royle16,46,18,Li Shen38,39,40,Emma Sprooten51,69,77,Daniah Trabzuni53,78, Saskia S.L.van der Marel4,7,Kimm J.E.van Hulzen4,7,Esther Walton34,

Christiane Wolf59,Laura Almasy79,80,David Ames81,82,Sampath Arepalli83,

Amelia A.Assareh19,Mark E.Bastin16,18,46,84,Henry Brodaty19,Kazima B.Bulayeva85, Melanie A.Carless79,Sven Cichon86,87,88,89,Aiden Corvin56,Joanne E.Curran79, Michael Czisch59,Greig I.de Zubicaray62,Allissa Dillman83,Ravi Duggirala79, Thomas D.Dyer79,80,Susanne Erk90,Iryna O.Fedko28,Luigi Ferrucci91,

Tatiana M.Foroud40,92,Peter T.Fox80,93,Masaki Fukunaga94,J.Raphael Gibbs53,82, Harald H.H.Go¨ring79,Robert C.Green95,96,Sebastian Guelfi53,Narelle K.Hansell3, Catharina A.Hartman97,Katrin Hegenscheid98,Andreas Heinz89,Dena

G.Hernandez53,82,Dirk J.Heslenfeld99,Pieter J.Hoekstra97,Florian Holsboer59, Georg Homuth100,Jouke-Jan Hottenga28,Masashi Ikeda101,Clifford R.Jack Jr102, Mark Jenkinson103,Robert Johnson104,Ryota Kanai105,106,Maria Keil41,Jack W.Kent Jr79,Peter Kochunov107,John B.Kwok108,109,Stephen https://www.wendangku.net/doc/1d16011281.html,wrie51,Xinmin Liu35,110, Dan L.Longo111,Katie L.McMahon63,Eva Meisenzahl112,Ingrid Melle22,23, Sebastian Mohnke90,Grant W.Montgomery3,Jeanette C.Mostert4,7,

Thomas W.Mu¨hleisen87,88,89,Michael A.Nalls83,Thomas E.Nichols103,113,

Lars G.Nilsson15,Markus M.No¨then87,89,Kazutaka Ohi114,Rene L.Olvera92,

Rocio Perez-Iglesias55,115,G.Bruce Pike116,117,Steven G.Potkin118,Ivar Reinvang67, Simone Reppermund19,Marcella Rietschel31,Nina Romanczuk-Seiferth90,

Glenn D.Rosen119,120,Dan Rujescu112,Knut Schnell121,Peter R.Schofield108,109, Colin Smith122,Vidar M.Steen29,30,Jessika E.Sussmann51,Anbupalam Thalamuthu19,Arthur W.Toga123,Bryan J.Traynor83,Juan Troncoso124,

Jessica A.Turner125,Maria C.Valde′s Herna′ndez84,Dennis van’t Ent28,Marcel van der Brug126,Nic J.A.van der Wee127,Marie-Jose van Tol128,Dick J.Veltman50, Thomas H.Wassink129,Eric Westman130,Ronald H.Zielke104,Alan B.Zonderman131, David G.Ashbrook132,Reinmar Hager132,Lu Lu133,134,135,Francis J.McMahon35, Derek W.Morris56,136,Robert W.Williams133,134,Han G.Brunner4,7,137,

Randy L.Buckner37,138,Jan K.Buitelaar6,7,139,Wiepke Cahn14,Vince

D.Calhoun140,141,Gianpiero L.Cavalleri71,Benedicto Crespo-Facorro54,55,

Anders M.Dale142,143,Gareth E.Davies144,Norman Delanty71,145,Chantal Depondt146,Srdjan Djurovic22,147,Wayne C.Drevets35,148,Thomas Espeseth23,67, Randy L.Gollub37,72,96,Beng-Choon Ho149,Wolfgang Hoffmann12,64,Norbert Hosten98,Rene′S.Kahn14,Stephanie Le Hellard29,30,Andreas Meyer-Lindenberg31, Bertram Mu¨ller-Myhsok59,150,151,Matthias Nauck152,Lars Nyberg15,Massimo Pandolfo146,Brenda W.J.H.Penninx50,Joshua L.Roffman37,Sanjay M.Sisodiya74, Jordan W.Smoller37,42,43,96,Hans van Bokhoven4,7,Neeltje E.M.van Haren14, Henry Vo¨lzke64,Henrik Walter90,Michael W.Weiner153,Wei Wen19,Tonya

White154,155,Ingrid Agartz22,73,156,Ole A.Andreassen22,23,John Blangero79,80, Dorret I.Boomsma28,Rachel M.Brouwer14,Dara M.Cannon35,157,Mark R.Cookson83, Eco J.C.de Geus28,Ian J.Deary46,Gary Donohoe56,136,Guille′n Ferna′ndez6,7, Simon E.Fisher7,32,Clyde Francks7,32,David C.Glahn69,77,Hans J.Grabe13,158, Oliver Gruber41,59,John Hardy53,Ryota Hashimoto159,Hilleke E.Hulshoff Pol14, Erik G.Jo¨nsson22,156,Iwona Kloszewska160,Simon Lovestone161,162,

Venkata S.Mattay26,163,Patrizia Mecocci164,Colm McDonald157,Andrew

M.McIntosh46,51,Roel A.Ophoff14,45,Tomas Paus165,166,Zdenka Pausova21,167, Mina Ryten53,52,Perminder S.Sachdev19,168,Andrew J.Saykin38,40,90,

Andy Simmons169,170,171,Andrew Singleton83,Hilkka Soininen172,173,

Joanna M.Wardlaw16,18,46,84,Michael E.Weale52,Daniel R.Weinberger26,174,

Hieab H.H.Adams155,175,Lenore https://www.wendangku.net/doc/1d16011281.html,uner176,Stephan Seiler177,Reinhold Schmidt177, Ganesh Chauhan178,Claudia L.Satizabal179,180,James T.Becker181,182,183,

Lisa Yanek184,Sven J.van der Lee175,Maritza Ebling72,185,Bruce Fischl72,185,186, W.T.Longstreth Jr187,Douglas Greve72,185,Helena Schmidt188,Paul Nyquist189, Louis N.Vinke72,185,Cornelia M.van Duijn175,Luting Xue190,Bernard Mazoyer191, Joshua C.Bis192,Vilmundur Gudnason193,Sudha Seshadri179,181,M.Arfan

Ikram155,175,The Alzheimer’s Disease Neuroimaging Initiative{,The CHARGE Consortium{,EPIGEN{,IMAGEN{,SYS{,Nicholas G.Martin31,Margaret J.Wright3,621, Gunter Schumann81,Barbara Franke4,5,71,Paul M.Thompson11&Sarah E.Medland31

1Imaging Genetics Center,Institute for Neuroimaging&Informatics,Keck School of Medicine of the University of Southern California,Los Angeles,California90292,USA.

2Neurogenetics Program,Department of Neurology,UCLA School of Medicine,Los Angeles,California90095,USA.3QIMR Berghofer Medical Research Institute,Brisbane 4006,Australia.4Department of Human Genetics,Radboud university medical center, Nijmegen6500HB,The Netherlands.5Department of Psychiatry,Radboud university medical center,Nijmegen6500HB,The Netherlands.6Department of Cognitive Neuroscience,Radboud university medical center,Nijmegen6500HB,The Netherlands. 7Donders Institute for Brain,Cognition and Behaviour,Radboud University,Nijmegen 6500GL,The Netherlands.8MRC-SGDP Centre,Institute of Psychiatry,Psychology and Neuroscience,King’s College London,London SE58AF,UK.9Laboratory of Human Genetics and Cognitive Functions,Institut Pasteur,Paris75015,France.10Centre Nationale de Recherche Scientifique(CNRS)Unite′de Recherche Associe′e(URA)2182 Genes,Synapses and Cognition,Institut Pasteur,Paris75015,France.11Universite′Paris

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?2015

Diderot,Sorbonne Paris Cite′,Paris75015,France.12German Center for

Neurodegenerative Diseases(DZNE)Rostock/Greifswald,Greifswald17487,Germany. 13Department of Psychiatry,University Medicine Greifswald,Greifswald17489,Germany.

14Brain Center Rudolf Magnus,Department of Psychiatry,University Medical Center Utrecht,Utrecht,3584CX,The Netherlands.15Umea?Centre for Functional Brain Imaging (UFBI),Umea?University,Umea?90187,Sweden.16Brain Research Imaging Centre, University of Edinburgh,Edinburgh EH42XU,UK.17Department of Computer Science, Lagos State University,Lagos,Nigeria.18Scottish Imaging Network,A Platform for Scientific Excellence(SINAPSE)Collaboration,Department of Neuroimaging Sciences, University of Edinburgh,Edinburgh EH42XU,UK.19Centre for Healthy Brain Ageing, School of Psychiatry,University of New South Wales,Sydney2052,Australia.20School of Mathematics and Statistics,University of Sydney,Sydney2006,Australia.21The Hospital for Sick Children,University of Toronto,Toronto M5G1X8,Canada.22NORMENT-KG Jebsen Centre,Institute of Clinical Medicine,University of Oslo,Oslo N-0316,Norway. 23NORMENT-KG Jebsen Centre,Division of Mental Health and Addiction,Oslo University Hospital,Oslo0424,Norway.24Cerebral Imaging Centre,Douglas Mental Health University Institute,Montreal H4H1R3,Canada.25Department of Psychiatry and Biomedical Engineering,McGill University,Montreal H3A2B4,Canada.26Lieber Institute for Brain Development,Baltimore,Maryland21205,USA.27Interdepartmental Neuroscience Graduate Program,UCLA School of Medicine,Los Angeles,California 90095,USA.28Biological Psychology,Neuroscience Campus Amsterdam&EMGO Institute for Health and Care Research,VU University&VU Medical Center,Amsterdam 1081BT,The Netherlands.29NORMENT-KG Jebsen Centre for Psychosis Research, Department of Clinical Science,University of Bergen,5021Bergen,Norway.30Dr.Einar Martens Research Group for Biological Psychiatry,Center for Medical Genetics and Molecular Medicine,Haukeland University Hospital,Bergen5021,Norway.31Central Institute of Mental Health,Medical Faculty Mannheim,University Heidelberg,Mannheim 68159,Germany.32Language and Genetics Department,Max Planck Institute for Psycholinguistics,Nijmegen6525XD,The Netherlands.33International Max Planck Research School for Language Sciences,Nijmegen6525XD,The Netherlands.

34Department of Child and Adolescent Psychiatry,Faculty of Medicine of the TU Dresden, Dresden01307Germany.35Human Genetics Branch and Experimental Therapeutics and Pathophysiology Branch,National Institute of Mental Health Intramural Research Program,Bethesda,Maryland20892,USA.36Department of Psychology,Yale University, New Haven,Connecticut06511,USA.37Department of Psychiatry,Massachusetts General Hospital,Boston,Massachusetts02115,USA.38Center for Neuroimaging, Radiology and Imaging Sciences,Indiana University School of Medicine,Indianapolis, Indiana46202,USA.39Center for Computational Biology and Bioinformatics,Indiana University School of Medicine,Indianapolis,Indiana46202,USA.40Indiana Alzheimer Disease Center,Indiana University School of Medicine,Indianapolis,Indiana46202,USA. 41Center for Translational Research in Systems Neuroscience and Psychiatry, Department of Psychiatry and Psychotherapy,University Medical Center,Goettingen 37075,Germany.42Psychiatric and Neurodevelopmental Genetics Unit,Center for Human Genetic Research,Massachusetts General Hospital,Boston,Massachusetts 02115,USA.43Stanley Center for Psychiatric Research,Broad Institute of MIT and Harvard,Boston,Massachusetts02141,USA.44Department of Psychiatry,Harvard Medical School,Boston,Massachusetts02115,USA.45Center for Neurobehavioral Genetics,University of California,Los Angeles,California90095,USA.46Centre for Cognitive Ageing and Cognitive Epidemiology,Psychology,University of Edinburgh, Edinburgh EH89JZ,UK.47Department of Biomedicine,Aarhus University,Aarhus

DK-8000,Denmark.48The Lundbeck Foundation Initiative for Integrative Psychiatric

Research,iPSYCH,Aarhus and Copenhagen DK-8000,Denmark.49Center for integrated

Sequencing,iSEQ,Aarhus University,Aarhus DK-8000,Denmark.50Department of

Psychiatry,Neuroscience Campus Amsterdam,VU University Medical Center/GGZ

inGeest,Amsterdam1081HL,The Netherlands.51Division of Psychiatry,Royal

Edinburgh Hospital,University of Edinburgh,Edinburgh EH105HF,UK.52Department of

Medical and Molecular Genetics,King’s College London,London SE19RT,UK.53Reta Lila

Weston Institute and Department of Molecular Neuroscience,UCL Institute of Neurology,

London WC1N3BG,UK.54Department of Psychiatry,University Hospital Marque′s de

Valdecilla,School of Medicine,University of Cantabria-IDIVAL,Santander39008,Spain. 55Cibersam(Centro Investigacio′n Biome′dica en Red Salud Mental),Madrid28029, Spain.56Neuropsychiatric Genetics Research Group and Department of Psychiatry, Trinity College Institute of Psychiatry,Trinity College Dublin,Dublin2,Ireland.57Center for Translational Research on Adversity,Neurodevelopment and Substance Abuse

(C-TRANS),Department of Psychiatry,University of Maryland School of Medicine,

Baltimore,Maryland21045,USA.58Aging Research Center,Karolinska Institutet and

Stockholm University,11330Stockholm,Sweden.59Max Planck Institute of Psychiatry,

Munich80804,Germany.60Multimodal Imaging Laboratory,Department of

Neurosciences,University of California,San Diego,California92093,USA.61Department

of Cognitive Sciences,University of California,San Diego,California92161,USA.62School

of Psychology,University of Queensland,Brisbane4072,Australia.63Centre for Advanced

Imaging,University of Queensland,Brisbane4072,Australia.64Institute for Community

Medicine,University Medicine Greifswald,Greifswald D-17475,Germany.65Analytic and

Translational Genetics Unit,Massachusetts General Hospital,Boston,Massachusetts

02114,USA.66Medical and Population Genetics Program,Broad Institute of Harvard and

MIT,Boston,Massachusetts02142,USA.67Department of Psychology,University of Oslo,

Oslo0373,Norway.68The Oxford Centre for Functional MRI of the Brain,Nuffield

Department of Clinical Neurosciences,Oxford University,Oxford OX39DU,UK.

69Department of Psychiatry,Yale School of Medicine,New Haven,Connecticut06511, USA.70Department of Neurology and Neurosurgery,Montreal Neurological Institute, McGill University,Montreal H3A2B4,Canada.71Molecular and Cellular Therapeutics,The Royal College of Surgeons,Dublin2,Ireland.72The Athinoula A.Martinos Center for Biomedical Imaging,Massachusetts General Hospital,Charlestown,Massachusetts 02129,USA.73Department of Psychiatric Research and Development,Diakonhjemmet Hospital,Oslo0319,Norway.74UCL Institute of Neurology,London,United Kingdom and Epilepsy Society,London WC1N3BG,UK.75Department of Medicine,Imperial College London,London W120NN,UK.76Department of Clinical and Experimental Epilepsy,UCL Institute of Neurology,London WC1N3BG,UK.77Olin Neuropsychiatric Research Center, Institute of Living,Hartford Hospital,Hartford,Connecticut06106,USA.78Department of Genetics,King Faisal Specialist Hospital and Research Centre,Riyadh11211,Saudi Arabia.79Texas Biomedical Research Institute,San Antonio,Texas78245,USA.

80University of Texas Health Science Center,San Antonio,Texas78229,USA.81National Ageing Research Institute,Royal Melbourne Hospital,Melbourne3052,Australia.

82Academic Unit for Psychiatry of Old Age,University of Melbourne,Melbourne3101, Australia.83Laboratory of Neurogenetics,National Institute on Aging,National Institutes of Health,Bethesda,Maryland20892,USA.84Centre for Clinical Brain Sciences, University of Edinburgh,Edinburgh EH42XU,UK.85N.I.Vavilov Institute of General Genetics,Russian Academy of Sciences,Moscow119333,Russia.86Division of Medical Genetics,Department of Biomedicine,University of Basel,Basel4055,Switzerland.

87Institute of Human Genetics,University of Bonn,Bonn,D-53127,Germany.88Institute of Neuroscience and Medicine(INM-1),Research Centre Ju¨lich,Ju¨lich,D-52425, Germany.89Department of Genomics,Life&Brain Center,University of Bonn,Bonn

D-53127,Germany.90Department of Psychiatry and Psychotherapy,Charite′Universita¨tsmedizin Berlin,CCM,Berlin10117,Germany.91Clinical Research Branch, National Institute on Aging,Baltimore,Maryland20892,USA.92Department of Medical and Molecular Genetics,Indiana University School of Medicine,Indianapolis,Indiana 46202,USA.93South Texas Veterans Health Care System,San Antonio,Texas78229, USA.94Biofunctional Imaging,Immunology Frontier Research Center,Osaka University, Osaka565-0871,Japan.95Division of Genetics,Department of Medicine,Brigham and Women’s Hospital,Boston,Massachusetts02115,USA.96Harvard Medical School, Boston,Massachusetts02115,USA.97Department of Psychiatry,University of Groningen, University Medical Center Groningen,9713GZ Groningen,The Netherlands.98Institute of Diagnostic Radiology and Neuroradiology,University Medicine Greifswald,Greifswald 17475,Germany.99Departments of Cognitive and Clinical Neuropsychology,VU University Amsterdam,1081BT Amsterdam,The Netherlands100Interfaculty Institute for Genetics and Functional Genomics,University Medicine Greifswald,Greifswald17489, Germany.101Department of Psychiatry,Fujita Health University School of Medicine, Toyoake470-1192,Japan.102Radiology,Mayo Clinic,Rochester,Minnesota55905,USA. 103FMRIB Centre,University of Oxford,Oxford OX39DU,UK.104NICHD Brain and Tissue Bank for Developmental Disorders,University of Maryland Medical School,Baltimore, Maryland21201,USA.105School of Psychology,University of Sussex,Brighton BN19QH, UK.106Institute of Cognitive Neuroscience,University College London,London WC1N

3AR,UK.107Department of Psychiatry,Maryland Psychiatric Research Center,University of Maryland,Baltimore,Maryland21201,USA.108Neuroscience Research Australia, Sydney2031,Australia.109School of Medical Sciences,UNSW,Sydney2052,Australia. 110Department of Pathology and Cell Biology,Columbia University Medical Center,New York10032,USA.111Lymphocyte Cell Biology Unit,Laboratory of Genetics,National Institute on Aging,National Institutes of Health,Baltimore,Maryland21224,USA.

112Department of Psychiatry,Ludwig-Maximilians-Universita¨t,Munich80336,Germany. 113Department of Statistics&WMG,University of Warwick,Coventry CV47AL,UK.

114Department of Psychiatry,Osaka University Graduate School of Medicine,Osaka 565-0871,Japan.115Institute of Psychiatry,King’s College London,London SE58AF,UK. 116Department of Neurology,University of Calgary,Calgary T2N2T9,Canada.

117Department of Clinical Neuroscience,University of Calgary,Calgary T2N2T9,Canada. 118Psychiatry and Human Behavior,University of California,Irvine,California92617,USA. 119Department of Neurology,Beth Israel Deaconess Medical Center,Boston, Massachusetts02215,USA.120Harvard Medical School,Boston,Massachusetts02115, USA.121Department of General Psychiatry,Heidelberg University Hospital,Heidelberg 69115,Germany.122Department of Neuropathology,MRC Sudden Death Brain Bank Project,University of Edinburgh,Edinburgh EH89AG,UK.123Laboratory of Neuro Imaging,Institute for Neuroimaging and Informatics,Keck School of Medicine of the University of Southern California,Los Angeles,California90033,USA.124Department of Pathology,Johns Hopkins University,Baltimore,Maryland21287,USA.125Psychology Department and Neuroscience Institute,Georgia State University,Atlanta,Georgia30302, USA.126Genentech,South San Francisco,California94080,USA127Psychiatry and Leiden Institute for Brain and Cognition,Leiden University Medical Center,Leiden2333 ZA,The Netherlands.128Neuroimaging Centre,University of Groningen,University Medical Center Groningen,Groningen9713AW,The Netherlands.129Department of Psychiatry,Carver College of Medicine,University of Iowa,Iowa City,Iowa52242,USA. 130Department of Neurobiology,Care Sciences and Society,Karolinska Institutet, Stockholm SE-14183,Sweden.131Behavioral Epidemiology Section,National Institute on Aging Intramural Research Program,Baltimore,Maryland20892,USA.132Faculty of Life Sciences,University of Manchester,Manchester M139PT,UK.133Center for Integrative and Translational Genomics,University of Tennessee Health Science Center, Memphis,Tennessee38163,USA.134Department of Genetics,Genomics,and Informatics,University of Tennessee Health Science Center,Memphis,Tennessee38163, USA.135Jiangsu Province Key Laboratory for Inflammation and Molecular Drug Target, Medical College of Nantong University,Nantong226001,China.136Cognitive Genetics and Therapy Group,School of Psychology&Discipline of Biochemistry,National University of Ireland Galway,Galway,Ireland.137Department of Clinical Genetics, Maastricht University Medical Center,Maastricht6200MD,The Netherlands.

138Department of Psychology,Center for Brain Science,Harvard University,Boston, Massachusetts02138,USA.139Karakter Child and Adolescent Psychiatry,Radboud university medical center,Nijmegen6500HB,The Netherlands.140The Mind Research Network&LBERI,Albuquerque,New Mexico87106,USA.141Department of ECE, University of New Mexico,Albuquerque,New Mexico87131,USA.142Center for Translational Imaging and Personalized Medicine,University of California,San Diego, California92093,USA.143Departments of Neurosciences,Radiology,Psychiatry,and Cognitive Science,University of California,San Diego,California92093,USA.144Avera Institute for Human Genetics,Sioux Falls,South Dakota,57108,USA.145Neurology Division,Beaumont Hospital,Dublin9,Ireland.146Department of Neurology,Hopital Erasme,Universite Libre de Bruxelles,Brussels1070,Belgium.147Department of Medical Genetics,Oslo University Hospital,Oslo0450,Norway.148Janssen Research& Development,Johnson&Johnson,Titusville,New Jersey08560,USA.149Department of

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Psychiatry,University of Iowa,Iowa City,Iowa52242,USA.150Munich Cluster for Systems Neurology(SyNergy),Munich81377,Germany.151University of Liverpool,Institute of Translational Medicine,Liverpool L693BX,UK.152Institute of Clinical Chemistry and Laboratory Medicine,University Medicine Greifswald,Greifswald17475,Germany.

153Center for Imaging of Neurodegenerative Disease,San Francisco VA Medical Center, University of California,San Francisco,California94121,USA.154Department of Child and Adolescent Psychiatry,Erasmus University Medical Centre,Rotterdam3000CB,The Netherlands.155Department of Radiology,Erasmus University Medical Centre, Rotterdam3015CN,The Netherlands.156Department of Clinical Neuroscience, Psychiatry Section,Karolinska Institutet,Stockholm SE-17176,Sweden.157Clinical Neuroimaging Laboratory,College of Medicine,Nursing and Health Sciences,National University of Ireland Galway,Galway,Ireland.158Department of Psychiatry and Psychotherapy,HELIOS Hospital Stralsund18435,Germany.159Molecular Research Center for Children’s Mental Development,United Graduate School of Child Development,Osaka University,Osaka565-0871,Japan.160Medical University of Lodz, Lodz90-419,Poland.161Department of Psychiatry,University of Oxford,Oxford OX37JX, UK.162NIHR Dementia Biomedical Research Unit,King’s College London,London SE5 8AF,UK.163Department of Neurology,Johns Hopkins University School of Medicine, Baltimore,Maryland21205,USA.164Section of Gerontology and Geriatrics,Department of Medicine,University of Perugia,Perugia06156,Italy.165Rotman Research Institute, University of Toronto,Toronto M6A2E1,Canada.166Departments of Psychology and Psychiatry,University of Toronto,Toronto M5T1R8,Canada.167Departments of Physiology and Nutritional Sciences,University of Toronto,Toronto M5S3E2,Canada. 168Neuropsychiatric Institute,Prince of Wales Hospital,Sydney2031,Australia.

169Department of Neuroimaging,Institute of Psychiatry,King’s College London,London SE58AF,UK.170Biomedical Research Centre for Mental Health,King’s College London, London SE58AF,UK.171Biomedical Research Unit for Dementia,King’s College London, London SE58AF,UK.172Institute of Clinical Medicine,Neurology,University of Eastern Finland,Kuopio FI-70211,Finland.173Neurocentre Neurology,Kuopio University Hospital,Kuopio FI-70211,Finland.174Departments of Psychiatry,Neurology,Neuroscience and the Institute of Genetic Medicine,Johns Hopkins University School of

Medicine,Baltimore,Maryland21205,USA.175Department of Epidemiology,Erasmus

University Medical Centre,Rotterdam3015CN,The Netherlands.176Laboratory of

Epidemiology and Population Sciences,Intramural Research Program,National Institute

on Aging,Bethesda,Maryland20892,USA.177Department of Neurology,Clinical Division

of Neurogeriatrics,Medical University Graz,Graz8010,Austria.178INSERM U897,

University of Bordeaux,Bordeaux33076,France.179Department of Neurology,Boston

University School of Medicine,Boston,Massachusetts02118,USA.180Framingham

Heart Study,Framingham,Massachusetts01702,USA.181Department of Neurology,

School of Medicine,University of Pittsburgh,Pittsburgh,Pennsylvania15260,USA. 182Department of Psychiatry,School of Medicine,University of Pittsburgh,Pittsburgh, Pennsylvania15260,USA.183Department of Psychology,Dietrich School of Arts and

Sciences,University of Pittsburgh,Pittsburgh,Pennsylvania15260,USA.184General

Internal Medicine,Johns Hopkins School of Medicine,Baltimore,Maryland21205,USA. 185Department of Radiology,Massachusetts General Hospital,Harvard Medical School, Boston,Massachusetts02114,USA.186Computer Science and AI Lab,Massachusetts Institute of Technology,Boston,Massachusetts02141,USA.187Department of Neurology University of Washington,Seattle,Washington98195,USA.188Institute of Molecular Biology and Biochemistry,Medical University Graz,8010Graz,Austria.189Department of Neurology,Johns Hopkins University School of Medicine,Baltimore,Maryland21205, USA.190Department of Biostatistics,Boston University School of Public Health,Boston, Massachusetts02118,USA.191Groupe d’Imagerie Neurofonctionnelle,UMR5296CNRS, CEA and University of Bordeaux,Bordeaux33076,France.192Cardiovascular Health Research Unit,Department of Medicine,University of Washington,Seattle,Washington 98101,USA.193Icelandic Heart Association,University of Iceland,Faculty of Medicine, Reykjavik101,Iceland.

{A list of authors and affiliations appears in the Supplementary Information.

*These authors contributed equally to this work.

1These authors jointly supervised this work.

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METHODS

Details of the GWAS meta-analysis are outlined in Extended Data Fig.1.All parti-cipants in all cohorts in this study gave written informed consent and sites involved obtained approval from local research ethics committees or Institutional Review Boards.The ENIGMA consortium follows a rolling meta-analysis framework for incorporating sites into the analysis.The discovery sample comprises studies of European ancestry(Extended Data Fig.2)that contributed GWAS summary sta-tistics for the purpose of this analysis on or before1October2013.The deadline for discovery samples to upload their data was made before inspecting the data and was not influenced by the results of the analyses.The meta-analysed results from discovery cohorts were carried forward for secondary analyses and functional vali-dation studies.Additional samples of European ancestry were gathered to provide in silico or single genotype replication of the strongest associations as part of the replication sample.A generalization sample of sites with non-European ancestry was used to examine the effects across ethnicities.In all,data were contributed from50cohorts,each of which is detailed in Supplementary Tables1–3.

The brain measures examined in this study were obtained from structural MRI data collected at participating sites around the world.Brain scans were processed and examined at each site locally,following a standardized protocol procedure to harmonize the analysis across sites.The standardized protocols for image analysis and quality assurance are openly available online(https://www.wendangku.net/doc/1d16011281.html,/protocols/ imaging-protocols/).The subcortical brain measures(nucleus accumbens,amyg-dala,caudate nucleus,hippocampus,pallidum,putamen and thalamus)were delin-eated in the brain using well-validated,freely available brain segmentation software packages:FIRST31,part of the FMRIB Software Library(FSL),or FreeSurfer32.The agreement between the two software packages has been well documented in the literature5,33and was further detailed here(Supplementary Table4).Participating sites used the software package most suitable for their data set(the software used at each site is given in Supplementary Table2)without selection based on genotype or the associations present in this study.In addition to the subcortical structures of the brain,we examined the genetic effects of a measure of global head size,the ICV. The ICV was calculated as:1/(determinant of a rotation-translation matrix obtained after affine registration to a common study template and multiplied by the tem-plate volume(1,948,105mm3)).After image processing,each image was inspected individually to identify poorly segmented structures.Each site contributed histo-grams of the distribution of volumes for the left and right hemisphere structures (and a measure of asymmetry)of each subcortical region used in the analysis.Scans marked as outliers(.3standard deviations from the mean)based on the histogram plots were re-checked at each site to locate any errors.If a scan had an outlier for a given structure,but was segmented properly,it was retained in the analysis.Site-specific phenotype histograms,Manhattan plots and quantile–quantile plots from each participating site are available on the ENIGMA website(https://www.wendangku.net/doc/1d16011281.html,c. edu/publications/enigma-2/).

Each study in the discovery sample was genotyped using commercially available platforms.Before imputation,genetic homogeneity was assessed in each sample using multi-dimensional scaling(MDS)analysis(Extended Data Fig.2).Ancestry outliers were excluded through visual inspection of the first two components.Quality control filtering was applied to remove genotyped SNPs with low minor allele frequency(,0.01),poor genotype call rate(,95%),and deviations from Hardy–Weinberg equilibrium(P,131026)before imputation.The imputation proto-cols used MaCH34for haplotype phasing and minimac35for imputation and are freely available online(https://www.wendangku.net/doc/1d16011281.html,/protocols/genetics-protocols/). Full details of quality control procedures and any deviations from the imputation protocol are given in Supplementary Table3.

Genome-wide association scans were conducted at each site for all eight traits of interest including the ICV and bilateral volumes of the nucleus accumbens,amyg-dala,caudate nucleus,hippocampus,pallidum,putamen and thalamus.For each SNP in the genome,the additive dosage value was regressed against the trait of interest separately using a multiple linear regression framework controlling for age,age2, sex,4MDS components,ICV(for non-ICV phenotypes)and diagnosis(when appli-cable).For studies with data collected from several centres or scanners,dummy-coded covariates were also included in the model.Sites with family data(NTR-Adults, BrainSCALE,QTIM,SYS,GOBS,ASPSFam,ERF,GeneSTAR,NeuroIMAGE and OATS)used mixed-effects models to control for familial relationships in addition to covariates stated previously.The primary analyses for this paper focused on the full set of subjects including data sets with patients to maximize the power to detect effects.We re-analysed the data excluding patients to verify that detected effects were not due to disease alone(Extended Data Fig.5a).The protocols used for test-ing association with mach2qtl(ref.34)for studies with unrelated subjects and merlin-offline36for family-based designs are freely available online(https://www.wendangku.net/doc/1d16011281.html,c. edu/protocols/genetics-protocols/).Full details for the software used at each site are given in Supplementary Table3.

The GWAS results from each site were uploaded to a centralized server for quality checking and processing.Results files from each cohort were free from genomic inflation in quantile–quantile plots and Manhattan plots(https://www.wendangku.net/doc/1d16011281.html,c. edu/publications/enigma-2/).Poorly imputed SNPs(with R2,0.5)and low minor allele count(,10)were removed from the GWAS result files from each site.The resulting files were combined meta-analytically using a fixed-effect,inverse-variance-weighted model as implemented in the software package METAL37.The discovery cohorts were meta-analysed first,controlling for genomic inflation.The combined discovery data set(comprised of all meta-analysed SNPs with data from at least 5,000subjects)was carried forward for the additional analyses detailed below. To account appropriately for multiple comparisons over the eight traits in our analysis,we first examined the degree of independence between each trait.We gen-erated an838correlation matrix based on the Pearson’s correlation between all pair-wise combinations of the mean volumes of each structure in the QTIM study. Using the matSpD software38we found that the effective number of independent traits in our analysis was7.We therefore set a significance criteria threshold of P,(531028/7)57.131029.

Heritability estimates for mean volumes of each of the eight structures in this study were calculated using structural equation modelling in OpenMx39.Twin mod-elling was performed controlling for age and sex differences on a large sample (n51,030)of healthy adolescent and young adult twins(148monozygotic and 202dizygotic pairs)and their siblings from the Queensland Twin Imaging(QTIM) study.Subsequently,a multivariate analysis showed that common environmental factors(C)could be dropped from the model without a significant reduction in the goodness-of-fit(D x236529.81;P50.76).Heritability(h2)was significantly dif-ferent from zero for all eight brain measures:putamen(h250.89;95%confidence interval0.85–0.92),thalamus(h250.88;0.85–0.92),ICV(h250.88;0.84–0.90), hippocampus(h250.79;0.74–0.83),caudate nucleus(h250.78;0.75–0.82),pal-lidum(h250.75;0.72–0.78),nucleus accumbens(h250.49;0.45–0.55),amygdala (h250.43;0.39,0.48)(Extended Data Fig.11a).

Percentage variance explained by each genome-wide significant SNP was deter-mined based on the final combined discovery data set(Extended Data Fig.6a)or the discovery combined with the replication samples(Table1)after correction for covariates using the following equation:

R2g j c=(1{R2c)~(t2=((n{k{1)z t2))?100

where the t-statistic is calculated as the beta coefficient for a given SNP from the regression model(controlling for covariates)divided by the standard error of the beta estimate,and where n is the total number of subjects and k is the total number of covariates included in the model(k510)(ref.40).R2g j c is the variance explained by the variant controlling for covariates and R2c is the variance explained by the covariates alone.R2g j c/(12R2c)gives the variance explained by the genetic variant after accounting for covariate effects.The total variance explained by the GWAS (Extended Data Fig.11b,c)was calculated by first linkage disequilibrium pruning the results without regard to significance(pruning parameters in PLINK:––indep-pairwise1000kb250.1).The t-statistics of the regression coefficients from the pruned results are then corrected for the effects of‘winner’s curse’and the variance explained by each SNP after accounting for covariate effects is summed across SNPs using freely available code(https://www.wendangku.net/doc/1d16011281.html,/site/honcheongso/software/ total-vg)40,41.As the correction for winners curse may be influenced by asymmetry in the distribution of t(arising from the choice of reference allele)we bootstrapped the choice of reference allele(5,000iterations)to derive the median value and95% confidence intervals of the estimates of variance explained(Extended Data Fig.11b,c). The correction for winner’s curse corrected for upward biases when estimating the percentage variance explained by each SNP across the genome via simulation40, but this correction could still allow some bias.Future large studies will be able to evaluate independently the percentage variance explained.

We performed multivariate GWAS using the Trait-based Association Test that uses Extended Simes procedure(TATES)9.For the TATES analysis we used GWAS summary statistics from the discovery data set and the correlation matrix created from the eight phenotypes using the QTIM data set(Extended Data Fig.6c). We examined the moderating effects of mean age and proportion of females on the effect sizes estimated for the top loci influencing brain volumes(Extended Data Fig.5b,c)using a mixed-effect meta-regression model such that:

effect~b0z b mod X mod z e z g

In this model,the effect and variance at each site are treated as random effects and the moderator X mod(either mean age or proportion of females)is treated as a fixed effect.Meta-regression tests were performed using the metafor package(version 1.9-1)in R.

Hierarchical clustering was performed on the GWAS t-statistics from the dis-covery data set results using independent SNPs clumped from the TATES results (clumping parameters:significance threshold for index SNP50.01,significance

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threshold for clumped SNPs50.01,r250.25,physical distance51Mb;Extended Data Fig.6b).Regions with the strongest genetic similarity were grouped together based on the strength of their pairwise correlations.The results were represented visually using hierarchical clustering with default settings from the gplots package (version2.12.1)in R.

Gene annotation,gene-based test statistics and pathway analysis were performed using the KGG2.5software package42(Supplementary Table7and Extended Data Fig.7).Linkage disequilibrium was calculated based on RSID numbers using the 1000Genomes Project European samples as a reference(https://www.wendangku.net/doc/1d16011281.html,/ protocols/genetics-protocols/).For the annotation,SNPs were considered‘within’a gene if they fell within5kb of the39/59untranslated regions based on human genome(hg19)coordinates.Gene-based tests were performed using the GATES test42without weighting P values by predicted functional relevance.Pathway ana-lysis was performed using the hybrid set-based test(HYST)of association43.For all gene-based tests and pathway analyses,results were considered significant if they exceeded a Bonferroni correction threshold accounting for the number of path-ways and traits tested such that P thresh50.05/(671pathways37independent traits)51.0631025.

Expression quantitative loci were examined in two independent data sets:the NABEC(GSE36192)24and UKBEC(GSE46706)44,45.Detailed processing and exclu-sion criteria for both data sets are described elsewhere24,45.In brief,the UKBEC consists of134neuropathologically normal donors from the MRC Sudden Death Brain Bank in Edinburgh and Sun Health Research Institute;expression was pro-filed on the Affymetrix Exon1.0ST array.The NABEC is comprised of304neu-rologically normal donors from the National Institute of Ageing and expression profiled on the Illumina HT12v3array.The expression values were corrected for gender and batch effects and probes that contained polymorphisms(seen.1% in European1000G)were excluded from analyses44.Blood expression quantita-tive trait loci(eQTL)data were queried using the Blood eQTL Browser(http:// genenetwork.nl/bloodeqtlbrowser/)26.Brain expression over the lifespan was mea-sured from a spatio-temporal atlas of human gene expression and graphed using custom R scripts(GSE25219;details given in13).

Fine-grained three-dimensional surface mappings of the putamen were generated using a medial surface modelling method46,47in1,541healthy subjects from the IMAGEN study48(Fig.2c and Extended Data Fig.10a,b).Putamen volume seg-mentations from either FSL(Fig.2c and Extended Data Fig.10a)or FreeSurfer (Extended Data Fig.10b)were first converted to three-dimensional meshes and then co-registered to an average template for statistical analysis.The medial core distance was used as a measure of shape and was calculated as the distance from each point on the surface to the centre of the putamen.At each point along the sur-face of the putamen,an association test was performed using multiple linear regres-sion in which the medial core distance at a given point on the surface was the outcome measure and the additive dosage value of the top SNP was the predictor of interest while including the same covariates that were used for volume including age,sex,age2,4MDS,ICV and site.

In Extended Data Fig.3,all tracks were taken from the UCSC Genome Browser Human hg19assembly.SNPs(top5%)shows the top5%associated SNPs within the locus and are coloured by their correlation to the top SNP.Genes shows the gene models from GENCODE version19.Conservation was defined at each base through the phyloP algorithm which assigns scores as2log10P values under a null hypothesis of neutral evolution calculated from pre-computed genomic alignment of100vertebrate species49.Conserved sites are assigned positive scores,while faster-than-neutral evolving sites are given negative scores.TFBS conserved shows com-putationally predicted transcription factor binding sites using the Transfac Matrix Database(v.7.0)found in human,mouse and rat.Brain histone(1.3year)and brain histone(68year)show mapsof histone trimethylation at histone H3Lys4(H3K4me3), an epigenetic mark for transcriptional activation,measured by ChIP-seq.These measurements were made in neuronal nuclei(NeuN1)collected from prefrontal cortex of post-mortem human brain50.CpG methylation was generated using meth-ylated DNA immunoprecipitation and sequencing from postmortem human frontal cortex of a57-year-old male51.DNaseI hypersens displays DNaseI hypersensitivity, evidence of open chromatin,which was evaluated in postmortem human frontal cerebrum from three donors(age22–35),through the ENCODE consortium52.Finally,hES Chrom State gives the predicted chromatin states based on computa-tional integration of ChIP-seq data for nine chromatin marks in H1human embry-onic stem cell lines derived in the ENCODE consortium53.

31.Patenaude,B.,Smith,S.M.,Kennedy,D.N.&Jenkinson,M.A Bayesian model of

shape and appearance for subcortical brain segmentation.Neuroimage56,

907–922(2011).

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structures in the human brain.Neuron33,341–355(2002).

33.Morey,R.A.et al.Scan-rescan reliability of subcortical brain volumes derived from

automated segmentation.Hum.Brain Mapp.31,1751–1762(2010).

34.Li,Y.,Willer,C.J.,Ding,J.,Scheet,P.&Abecasis,G.R.MaCH:using sequence and

genotype data to estimate haplotypes and unobserved genotypes.Genet.

Epidemiol.34,816–834(2010).

35.Howie,B.,Fuchsberger,C.,Stephens,M.,Marchini,J.&Abecasis,G.R.Fast and

accurate genotype imputation in genome-wide association studies through

pre-phasing.Nature Genet.44,955–959(2012).

36.Abecasis,G.R.,Cherny,S.S.,Cookson,W.O.&Cardon,L.R.Merlin-rapid analysis of

dense genetic maps using sparse gene flow trees.Nature Genet.30,97–101

(2002).

37.Willer,C.J.,Li,Y.&Abecasis,G.R.METAL:fast and efficient meta-analysis of

genomewide association scans.Bioinformatics26,2190–2191(2010).

38.Nyholt,D.R.A simple correction for multiple testing for single-nucleotide

polymorphisms in linkage disequilibrium with each other.Am.J.Hum.Genet.74, 765–769(2004).

39.Boker,S.et al.OpenMx:an open source extended structural equation modeling

framework.Psychometrika76,306–317(2011).

40.Walters,R.,Bartels,M.&Lubke,G.Estimating variance explained by all variants in

meta-analysis with heterogeneity.Behav.Genet.43,543(2013).

41.So,H.C.,Li,M.&Sham,P.C.Uncovering the total heritability explained by all true

susceptibility variants in a genome-wide association study.Genet.Epidemiol.35, 447–456(2011).

42.Li,M.X.,Gui,H.S.,Kwan,J.S.&Sham,P.C.GATES:a rapid and powerful

gene-based association test using extended Simes procedure.Am.J.Hum.Genet.

88,283–293(2011).

43.Li,M.X.,Kwan,J.S.&Sham,P.C.HYST:a hybrid set-based test for genome-wide

association studies,with application to protein-protein interaction-based

association analysis.Am.J.Hum.Genet.91,478–488(2012).

44.Ramasamy,A.et al.Resolving the polymorphism-in-probe problem is critical for

correct interpretation of expression QTL studies.Nucleic Acids Res.41,e88

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progression by LDA-based weighting of longitudinal ventricular surface features.

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matching with medial curves and1-d group-wise registration.In20129th IEEE International Symposium on Biomedical Imaging(ISBI),716–719(2012).

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normal brain function and psychopathology.Mol.Psychiatry15,1128–1139

(2010).

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substitution rates on mammalian phylogenies.Genome Res.20,110–121(2010).

50.Cheung,I.et al.Developmental regulation and individual differences of neuronal

H3K4me3epigenomes in the prefrontal cortex.Proc.Natl https://www.wendangku.net/doc/1d16011281.html,A107, 8824–8829(2010).

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alternative promoters.Nature466,253–257(2010).

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across the genome.Cell132,311–322(2008).

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Extended Data Figure1|Outline of the genome-wide association

meta-analysis.Structural T1-weighted brain MRI and biological specimens for DNA extraction were acquired from each individual at each site.Imaging protocols were distributed to and completed by each site for standardized automated segmentation of brain structures and calculation of the ICV. Volumetric phenotypes were calculated from the segmentations.Genome-wide genotyping was completed at each site using commercially available chips. Standard imputation protocols to the1000Genomes reference panel(phase1, version3)were also distributed and completed at each site.Each site completed genome-wide association for each of the eight volumetric brain phenotypes with the listed covariates.Statistical results from GWAS files were uploaded to a central site for quality checking and fixed effects meta-analysis.

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Extended Data Figure2|Ancestry inference via multi-dimensional scaling plots.Multi-dimensional scaling(MDS)plots of the discovery cohorts to HapMap III reference panels of known ancestry are displayed.Ancestry is generally homogeneous within each group.In all discovery samples any individuals with non-European ancestry were excluded before association.The axes have been flipped to the same orientation for each sample for ease of comparison.ASW,African ancestry in southwest USA;CEU,Utah residents with northern and western European ancestry from the CEPH collection; CHD,Chinese in metropolitan Denver,Colorado;GIH,Gujarati Indians in Houston,Texas;LWK,Luhya in Webuye,Kenya;MEX,Mexican ancestry

in Los Angeles,California;MKK,Maasai in Kinyawa,Kenya;TSI,Tuscans in Italy;YRI,Yoruba in Ibadan,Nigeria.

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Extended Data Figure3|Genomic function is annotated near novel genome-wide significant loci.a–h,For each panel,zoomed-in Manhattan plots(6400kb from top SNP)are shown with gene models below(GENCODE version19).Plots below are zoomed to highlight the genomic region that probably contains the causal variant(s)(r2.0.8from the top SNP).Genomic annotations from the UCSC browser and ENCODE are displayed to indicate potential functionality(see Methods for detailed track information).SNP coverage is low in f owing to a common genetic inversion in the region.Each plot was made using the LocusTrack software(https://www.wendangku.net/doc/1d16011281.html,.au/ general/gabrieC/LocusTrack/).

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Extended Data Figure4|Quantile–quantile and forest plots from

meta-analysis of discovery cohorts.a,Quantile–quantile plots show that the observed P values only deviate from the expected null distribution at the most significant values,indicating that population stratification or cryptic relatedness are not unduly inflating the results.This is quantified through the genomic control parameter(l;which evaluates whether the median test statistic deviates from expected)54.l values near1indicate that the median test statistic is similar to those derived from a null distribution.Corresponding meta-analysis Manhattan plots can be found in Fig.1.b,Forest plots show the effect at each of the contributing sites to the meta-analysis.The size of the dot is proportional to the sample size,the effect is shown by the position on the x axis,and the standard error is shown by the line.Sites with an asterisk indicate the genotyping of a proxy SNP(in perfect linkage disequilibrium calculated from1000Genomes)for replication.

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Extended Data Figure5|Influence of patients with neuropsychiatric disease,age and gender on association results.a,Scatterplot of effect sizes including and excluding patients with neuropsychiatric disorders for nominally significant SNPs.For each of the eight volumetric phenotypes,SNPs with P,131025in the full discovery set meta-analysis were also evaluated excluding the patients.The beta values from regression,a measure of effect size, are plotted(blue dots)along with a line of equivalence between the two conditions(red line).The correlation between effect sizes with and without patients was very high(r.0.99),showing that the SNPs with significant effects on brain structure are unlikely to be driven by the diseased individuals.

b,Meta-regression comparison of effect size with mean age at each site.Each site has a corresponding number and coloured dot in each graph.The size of each dot is based on the standard error such that bigger sites with more definitive estimates have larger dots(and more influence on the meta-regression).The age range of participants covered most of the lifespan (9–97years),but only one of these eight loci showed a significant relationship with the mean age of each cohort(rs608771affecting putamen volume).

c,Meta-regression comparison of effect size with the proportion of females at each site.No loci showed evidence of moderation by the proportion of females in a given sample.However,the proportion of females at each site has a very restricted range,so results should be interpreted with caution.Plotted information follows the same convention as described in b.The sites are numbered in the following order:(1)AddNeuroMed,(2)ADNI,

(3)ADNI2GO,(4)BETULA,(5)BFS,(6)BIG,(7)BIG-Rep,(8)BrainSCALE, (9)BRCDECC,(10)CHARGE,(11)EPIGEN,(12)GIG,(13)GSP,

(14)HUBIN,(15)IMAGEN,(16)IMpACT,(17)LBC1936,(18)Lieber, (19)MAS,(20)MCIC,(21)MooDS,(22)MPIP,(23)NCNG,(24)NESDA, (25)neuroIMAGE,(26)neuroIMAGE-Rep,(27)NIMH,(28)NTR-Adults, (29)OATS,(30)PAFIP,(31)QTIM,(32)SHIP,(33)SHIP-TREND,(34)SYS, (35)TCD-NUIG,(36)TOP,(37)UCLA-BP-NL and(38)UMCU.

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Extended Data Figure6|Cross-structure analyses.a,Radial plots of effect sizes from the discovery sample for all genome-wide significant SNPs identified in this study.Plots indicate the effect of each genetic variant,quantified as percentage variance explained,on the eight volumetric phenotypes studied.As expected,the SNPs identified with influence on a phenotype show the highest effect size for that phenotype:putamen volume(rs945270,rs62097986,

rs608771and rs683250),hippocampal volume(rs77956314and rs61921502), caudate volume(rs1318862)and ICV(rs17689882).In general much smaller effects are observed on other structures.b,Correlation heat map of GWAS test statistics(t-values)and hierarchical clustering55.Independent SNPs were chosen within an linkage disequilibrium block based on the highest association in the multivariate cross-structure analysis described in Extended Data Fig.6c. Two heat maps are shown taking only independent SNPs with either

P,131024(left)or P,0.01(right)in the multivariate cross-structure analysis.Different structures are labelled in developmentally similar regions by the colour bar on the top and side of the heat map including basal ganglia (putamen,pallidum,caudate and accumbens;blue),amygdalo–hippocampal complex(hippocampus and amygdala;red),thalamus(turquoise)and ICV (black).Hierarchical clustering showed that developmentally similar regions have mostly similar genetic influences across the entire genome.The low correlation with the ICV is owing to it being used as a covariate in the subcortical structure GWAS associations.c,A multivariate cross-structure analysis of all volumetric brain traits.A Manhattan plot(left)and corresponding quantile–quantile plot(right)of multivariate GWAS analysis of all traits(volumes of the accumbens,amygdala,caudate,hippocampus, pallidum,putamen,thalamus,and ICV)in the discovery data set using the TATES method9is shown.Multivariate cross-structure analysis confirmed the univariate analyses(see Table1),but did not reveal any additional loci achieving cross-structure levels of significance.

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Extended Data Figure7|Pathway analysis of GWAS results for each brain structure.A pathway analysis was performed on each brain volume GWAS using KGG42to conduct gene-based tests and the Reactome database for pathway definition43.Pathway-wide significance was calculated using a Bonferroni correction threshold accounting for the number of pathways and traits tested such that P thresh50.05/(671pathways37independent traits)51.0631025and is shown here as a red line.The number of independent traits was calculated by accounting for the non-independence of each of the eight traits examined(described in the Methods).Variants that influence the putamen were clustered near genes known to be involved in DSCAM interactions,neuronal arborization and axon guidance56.Variants that influence intracranial volume are clustered near genes involved in EGFR and phosphatidylinositol-3-OH kinase(PI(3)K)/AKT signalling pathways, known to be involved in neuronal survival57.All of these represent potential mechanisms by which genetic variants influence brain structure.It is important to note that the hybrid set-based test(HYST)method for pathway analysis used here can be strongly influenced by a few highly significant genes,as was the case for putamen hits in which DCC and BCL2L1were driving the pathway results.

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Extended Data Figure8|Spatio-temporal maps showing expression of genes near the four significant putamen loci over time and throughout regions of the brain.Spatio-temporal gene expression13was plotted as normalized log2expression.Different areas of the neocortex(A1C,primary auditory cortex;DFC,dorsolateral prefrontal cortex;IPC,posterior inferior parietal cortex;ITC,inferior temporal cortex;MFC,medial prefrontal cortex; M1C,primary motor cortex;OFC,orbital prefrontal cortex;STC,superior temporal cortex;S1C,primary somatosensory cortex;VFC,ventrolateral prefrontal cortex;V1C,primary visual cortex)as well as subcortical areas (AMY,amygdala;CBC,cerebellar cortex;HIP,hippocampus;MD, mediodorsal nucleus of the thalamus;STR,striatum)are plotted from10post-conception weeks(PCW)to more than60years old.Genes that probably influence putamen volume are expressed in the striatum at some point during the lifespan.After late fetal development,KTN1is expressed in the human thalamus,striatum and hippocampus and is more highly expressed in the striatum than the cortex.Most genes seem to have strong gradients of expression across time,with DCC most highly expressed during early prenatal life,and DLG2most highly expressed at mid-fetal periods and throughout adulthood.BCL2L1,which inhibits programmed cell death,has decreased striatal expression at the end of neurogenesis(24–38PCW),a period marked by increased apoptosis in the putamen15.

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Extended Data Figure9|CTCF-binding sites in the vicinity of the putamen locus marked by rs945270.CTCF-binding sites from the ENCODE project are displayed from the database CTCFBSDB2.0(ref.23)from two different cell types:embryonic stem cells(track ENCODE_Broad_H1-hESC_99540)and a neuroblastoma cell line differentiated with retinoic acid(ENCODE_UW_SK-N-SH_RA_97826).A proxy SNP to the top hit within the locus,rs8017172(r251.0to rs945270),lies within a CTCF-binding site called based on ChIP-seq data in the embryonic stem cells and near the binding site in neural SK-N-SH cells.As this is the lone chromatin mark in the intergenic region (see Extended Data Fig.3),it suggests that the variant may disrupt a

CTCF-binding site and thereby influence transcription of surrounding genes.

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Extended Data Figure10|Shape analysis in1,541young healthy subjects shows consistent deformations of the putamen regardless of segmentation protocol.a,b,The distance from a medial core to surfaces derived from FSL FIRST(a;identical to Fig.2c)or FreeSurfer(b)segmentations was derived in the same1,541subjects.Each copy of the rs945270-C allele was significantly associated with an increased width in coloured areas(false discovery rate corrected at q50.05)and the degree of deformation is labelled by colour. The orientation is indicated by arrows.A,anterior;I,inferior;P,posterior; S,superior.Shape analysis in both software suites gives statistically significant associations in the same direction.Although the effects are more widespread in the FSL segmentations,FreeSurfer segmentations also show overlapping regions of effect,which appears strongest in anterior and superior sections.

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