Schizophrenia has a heritability of 60–80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.
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Summary statistics for the ‘extended’, ‘core’, ancestry-specific and sex-stratified analyses are available at https://www.med.unc.edu/pgc/download-results/scz/. Genotype data are available for a subset of cohorts, including dbGAP accession numbers and/or restrictions, as described in the ‘Case–control sample descriptions’ section of the Supplementary Information.
Core analysis code for RICOPILI can be found at https://sites.google.com/a/broadinstitute.org/ricopili/. This wraps PLINK (https://www.cog-genomics.org/plink2/), EIGENSOFT (https://www.hsph.harvard.edu/alkes-price/software/), Eagle2 (https://alkesgroup.broadinstitute.org/Eagle/), Minimac3 (https://genome.sph.umich.edu/wiki/Minimac3), SHAPEIT3 (https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html), METAL (https://genome.sph.umich.edu/wiki/METAL_Documentation) and LDSR (https://github.com/bulik/ldsc). For downstream analyses, FINEMAP can be found at http://christianbenner.com/, and our utility for meta-analysing cohort-specific LD matrices can be found at https://github.com/Pintaius/LDmergeFM. MAGMA can be found at https://ctg.cncr.nl/software/magma and the GO gene sets and automated curation pipeline are provided in https://github.com/janetcharwood/pgc3-scz_wg-genesets. SMR is available at https://cnsgenomics.com/software/smr/ and SbayesS at https://cnsgenomics.com/software/gctb/.
Owen, M. J., Sawa, A. & Mortensen, P. B. Schizophrenia. Lancet 388, 86–97 (2016).
Plana-Ripoll, O. et al. A comprehensive analysis of mortality-related health metrics associated with mental disorders: a nationwide, register-based cohort study. Lancet 394, 1827–1835 (2019).
Momen, N. C. et al. Association between mental disorders and subsequent medical conditions. N. Engl. J. Med. 382, 1721–1731 (2020).
Jääskeläinen, E. et al. A systematic review and meta-analysis of recovery in schizophrenia. Schizophr. Bull. 39, 1296–1306 (2013).
International Schizophrenia Consortium. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).
Pocklington, A. J. et al. Novel findings from CNVs implicate inhibitory and excitatory signaling complexes in schizophrenia. Neuron 86, 1203–1214 (2015).
Singh, T. et al. The contribution of rare variants to risk of schizophrenia in individuals with and without intellectual disability. Nat. Genet. 49, 1167–1173 (2017).
Rees, E. et al. De novo mutations identified by exome sequencing implicate rare missense variants in SLC6A1 in schizophrenia. Nat. Neurosci 23, 179–184 (2020).
Lam, M. et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat. Genet. 51, 1670–1678 (2019).
Bigdeli, T. B. et al. Contributions of common genetic variants to risk of schizophrenia among individuals of African and Latino ancestry. Mol. Psychiatry 25, 2455–2467 (2020).
Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
Räsänen, S., Pakaslahti, A., Syvälahti, E., Jones, P. B. & Isohanni, M. Sex differences in schizophrenia: a review. Nord. J. Psychiatry 54, 37–45 (2000).
Zeng, J. et al. Widespread signatures of natural selection across human complex traits and functional genomic categories. Nat. Commun. 12, 1164 (2021).
Aguet, F. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
Genome-wide association study identifies five new schizophrenia loci. Nat. Genet. 43, 969–978 (2011).
Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).
Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).
Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018).
Koopmans, F. et al. SynGO: an evidence-based, expert-curated knowledge base for the synapse. Neuron 103, 217–234 (2019).
Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).
Sakuntabhai, A. et al. Mutations in ATP2A2, encoding a Ca2+ pump, cause Darier disease. Nat. Genet. 21, 271–277 (1999).
Cederlöf, M. et al. The association between Darier disease, bipolar disorder, and schizophrenia revisited: a population-based family study. Bipolar Disord. 17, 340–344 (2015).
Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).
O’Brien, H. E. et al. Expression quantitative trait loci in the developing human brain and their enrichment in neuropsychiatric disorders. Genome Biol. 19, 194 (2018).
Võsa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 1300–1310 (2021).
Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).
Galvan, L. et al. The striatal kinase DCLK3 produces neuroprotection against mutant huntingtin. Brain 141, 1434–1454 (2018).
Singh, T. et al. Rare coding variants in 10 genes confer substantial risk for schizophrenia. Nature https://doi.org/10.1038/s41586-022-04556-w (2022).
Rees, E. et al. Analysis of intellectual disability copy number variants for association with schizophrenia. JAMA Psychiatry 73, 963–969 (2016).
Fromer, M. et al. De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184 (2014).
Kaplanis, J. et al. Evidence for 28 genetic disorders discovered by combining healthcare and research data. Nature 586, 757–762 (2020).
Satterstrom, F. K. et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 180, 568–584 (2020).
Luo, Y. et al. Exploring the genetic architecture of inflammatory bowel disease by whole-genome sequencing identifies association at ADCY7. Nat. Genet. 49, 186–192 (2017).
Cheng, Y. et al. Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism. Nat. Commun. 12, 964 (2021).
Singh, T., Neale, B. M. & Daly, M. J. Exome sequencing identifies rare coding variants in 10 genes which confer substantial risk for schizophrenia. Preprint at https://doi.org/10.1101/2020.09.18.20192815 (2020).
Priya, A., Johar, K. & Wong-Riley, M. T. T. Specificity protein 4 functionally regulates the transcription of NMDA receptor subunits GluN1, GluN2A, and GluN2B. Biochim. Biophys. Acta 1833, 2745–2756 (2013).
Ripke, S. et al. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat. Genet. 45, 1150–1159 (2013).
Kirov, G. et al. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol. Psychiatry 17, 142–153 (2012).
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
Fagerberg, L. et al. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol. Cell. Proteomics 13, 397–406 (2014).
Stephens, R. et al. Gene organisation, sequence variation and isochore structure at the centromeric boundary of the human MHC. J. Mol. Biol. 291, 789–799 (1999).
Lam, M. et al. RICOPILI: Rapid Imputation for COnsortias PIpeLIne. Bioinformatics 36, 930–933 (2019).
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L. & Ferreira, M. A. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am. J. Hum. Genet. 81, 559–575 (2007).
Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).
Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
The Haplotype Reference Consortium. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
O’Connell, J. et al. Haplotype estimation for biobank-scale data sets. Nat. Genet. 48, 817–820 (2016).
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).
Vittinghoff, E. & McCulloch, C. E. Relaxing the rule of ten events per variable in logistic and cox regression. Am. J. Epidemiol. 165, 710–718 (2007).
Heinze, G. & Ploner, M. A SAS macro, S-PLUS library and R package to perform logistic regression without convergence problems. Technical report 2/2004 https://cemsiis.meduniwien.ac.at/fileadmin/user_upload/_imported/fileadmin/msi_akim/CeMSIIS/KB/programme/tr2_2004.pdf (Medical University of Vienna, 2004).
Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).
Lee, S. H., Goddard, M. E., Wray, N. R. & Visscher, P. M. A better coefficient of determination for genetic profile analysis. Genet. Epidemiol. 36, 214–224 (2012).
Martínez-Camblor, P. Fully non-parametric receiver operating characteristic curve estimation for random-effects meta-analysis. Stat. Methods Med. Res. 26, 5–20 (2017).
Bryois, J. et al. Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson’s disease. Nat. Genet. 52, 482–493 (2020).
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).
Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).
Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/ Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).
Maston, G. A., Evans, S. K. & Green, M. R. Transcriptional regulatory elements in the human genome. Annu. Rev. Genomics Hum. Genet. 7, 29–59 (2006).
A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
Genovese, G. et al. Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia. Nat. Neurosci. 19, 1433–1441 (2016).
Merico, D., Isserlin, R., Stueker, O., Emili, A. & Bader, G. D. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 5, e13984 (2010).
Benner, C. et al. Prospects of fine-mapping trait-associated genomic regions by using summary statistics from genome-wide association studies. Am. J. Hum. Genet 101, 539–551 (2017).
Võsa, U. et al. Large-scale cis- and trans-eQTL analysis identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 1300–1310 (2021).
Sonnega, A. et al. Cohort profile: The Health and Retirement Study (HRS). Int. J. Epidemiol. 43, 576–585 (2014).
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).
Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).
Zhang, W. et al. Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits. Nat. Commun. 10, 3834 (2019).
The National Institute of Mental Health (USA) provides core funding for the PGC under award no. U01MH109514. The content is the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The work of the contributing groups was supported by numerous grants from governmental and charitable bodies as well as philanthropic donation (details in Supplementary Note). We acknowledge a substantial contribution from P. Sklar (deceased) as one of the PGC principal investigators, and E. Scolnick, whose support for this study was vital. We acknowledge the Wellcome Trust Case Control Consortium for the provision of control genotype information. Membership of the Psychosis Endophenotypes International Consortium, the SynGO consortium, the PsychENCODE Consortium, the eQTLGen consortium, the BIOS Consortium and the Indonesia Consortium are provided in the author list. We are grateful to C. Hopkins for illustrations. The work at Cardiff University was additionally supported by Medical Research Council Centre grant no. MR/L010305/1 and program grant no. G0800509. S. Xu also gratefully acknowledges the support of the National Natural Science Foundation of China (NSFC) grants (31525014, 91731303, 31771388, 31961130380 and 32041008), the UK Royal Society-Newton Advanced Fellowship (NAF\R1\191094), the Key Research Program of Frontier Sciences (QYZDJ-SSW-SYS009) and the Strategic Priority Research Program (XDB38000000) of the Chinese Academy of Sciences, and the Shanghai Municipal Science and Technology Major Project (2017SHZDZX01). O. A. Andreassen was supported by the Research Council of Norway (283798, 262656, 248980, 273291, 248828, 248778, 223273); KG Jebsen Stiftelsen, South-East Norway Health Authority, EU H2020 no. 847776. B. Melegh was supported in part by the National Scientific Research Program (NKFIH) K 138669. S. V. Faraone is supported by the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 602805, the European Union’s Horizon 2020 research and innovation programme under grant agreements 667302 and 728018 and NIMH grants 5R01MH101519 and U01 MH109536-01. S. I. Belangero was supported by FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo), grant numbers: 2010/08968-6; 2014/07280-1 2011/50740-5 (including R. A. Bressan). The Singapore team (J. Lee, J. Liu, K. Sim, S. A. Chong and M. Subramanian) acknowledges the National Medical Research Council Translational and Clinical Research Flagship Programme (grant no.: NMRC/TCR/003/2008). M. Macek was supported by LM2018132, CZ.02.1.01/0.0/0.0/18_046/0015515 and IP6003 –VZFNM00064203. C. Arango has been funded by the Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/024), co-financed by ERDF Funds from the European Commission, ‘A way of making Europe’, CIBERSAM, Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds, European Union Seventh Framework Program and European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking (grant agreement no 115916, project PRISM; and grant agreement no. 777394, project AIMS-2-TRIALS), Fundación Familia Alonso and Fundación Alicia Koplowitz. E. Bramon acknowledges support from the National Institute of Health Research UK (grant NIHR200756); Mental Health Research UK John Grace QC Scholarship 2018; an ESRC collaborative award 2020; BMA Margaret Temple Fellowship 2016; Medical Research Council New Investigator Award (G0901310); MRC Centenary Award (G1100583); MRC project grant G1100583; National Institute of Health Research UK post-doctoral fellowship (PDA/02/06/016); NARSAD Young Investigator awards 2005 and 2008; Wellcome Trust Research Training Fellowship; Wellcome Trust Case Control Consortium awards (085475/B/08/Z, 085475/Z/08/Z); European Commission Horizon 2020 (747429); NIHR Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust and King’s College London; and NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust and University College London (UCLH BRC - Mental Health Theme). D. Molto is funded by the European Regional Development Fund (ERDF)–Valencian Community 2014–2020, Spain. E. G. Atkinson was supported by the NIMH K01MH121659.