TRANSCRIPTOMIC PROFILING OF POSTMORTEM PREFRONTAL CORTEX AND NUCLEUS ACCUMBENS FROM CHRONIC ALCOHOL ABUSERS.

Alcohol use disorder (AUD) is a debilitating psychiatric illness that develops from a combination of genetic and environmental factors. While it is well documented that AUD is heritable, the shift from recreational alcohol use to abuse/dependence is poorly understood. In this dissertation, using postmortem brain tissue from individuals with alcohol dependence (AD), we profiled the genome-wide expression of circular RNA (circRNA), microRNA (miRNA), and messenger RNA (mRNA) to better understand the impact of gene expression on the development of AUD. To achieve this, we performed two independent studies that explore transcriptome differences between AD cases and controls. The first of which examines differentially expressed gene (DEG) networks associated with AD that show either high or low levels of network preservation between two key areas of the mesocorticolimbic system (MCL), the prefrontal cortex (PFC) and nucleus accumbens (NAc). The second is a pilot study that interrogates the function of circRNA as miRNA sponges to impact the expression of mRNA. Overall, our findings corroborate results from recent studies while also providing novel evidence for biological processes that are differentially expressed between the PFC and NAc. Additionally, the second study is the first to explore circRNA:miRNA:mRNA interactions in the brains of chronic alcohol abusers and the role of circRNA as potential regulators of known AUD risk genes. Finally, we integrate genetic information in the form of eQTL analyses to determine the clinical relevance of these findings within the context of recent GWAS of AUD and other addiction phenotypes

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

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.</p

Neurobiological mechanisms of heterogeneous nuclear ribonucleoprotein H1 in methamphetamine stimulant and addictive behaviors

Addiction to psychostimulants such as methamphetamine (MA) is a significant public health issue in the United States with no FDA-approved pharmacological interventions. MA addiction is a heritable neuropsychiatric disorder, however, its genetic basis is almost entirely unknown. Available human genome-wide association studies (GWAS) lack sufficient power to detect the influence of common genetic variation on the risk of addiction. Mammalian model organisms offer an attractive alternative to more rapidly uncover novel genetic factors that contribute to addiction-relevant neurobehavioral traits. Using quantitative trait locus (QTL) mapping in mice, we identified a locus on chromosome 11 that contributed to a decrease in sensitivity to the locomotor stimulant properties of MA. To fine map this QTL, we generated interval-specific congenic lines and deduced a 206 kb critical interval on chromosome 11 that contained only two protein coding genes (Rufy1 and Hnrnph1). Replicate mouse lines heterozygous for Transcription Activator-like Effector Nucleases (TALENs)-induced frameshift deletions in Hnrnph1 (Hnrnph1+/-), but not in Rufy1 (Rufy1+/-), recapitulated the decrease in MA sensitivity observed in congenic mice; thus, identifying Hnrnph1 as a novel quantitative trait gene for MA sensitivity. Hnrnph1, an RNA-binding protein, has not previously been identified in human GWAS of neuropsychiatric disorders but has been implicated in mu-opioid receptor splicing associated with heroin dependence. The primary objectives of this dissertation is to (1) detail the forward genetic and reverse genetic approaches taken to identify Hnrnph1 as a quantitative trait gene for MA sensitivity; (2) assess the MA addiction-relevant behaviors presented by Hnrnph1+/- mice through conditioned place preference (CPP) and oral self-administration procedures; and (3) identify the neurobiological mechanisms through which Hnrnph1 affects behavior via transcriptome, immunohistochemical and neurochemical assessments of the mesocorticolimbic dopamine circuit. Overall, Hnrnph1+/- mice display increased dopaminergic innervation and MA dose-dependent dopamine release in nucleus accumbens, which could underlie reduced drug sensitivity, reward, and reinforcement. The results of this thesis provide substantial evidence to implicate Hnrnph1 in MA addiction

Multi-trait genome-wide association study of opioid addiction: OPRM1 and beyond

Opioid addiction (OA) is moderately heritable, yet only rs1799971, the A118G variant in OPRM1, has been identified as a genome-wide significant association with OA and independently replicated. We applied genomic structural equation modeling to conduct a GWAS of the new Genetics of Opioid Addiction Consortium (GENOA) data together with published studies (Psychiatric Genomics Consortium, Million Veteran Program, and Partners Health), comprising 23,367 cases and effective sample size of 88,114 individuals of European ancestry. Genetic correlations among the various OA phenotypes were uniformly high (

Interpretation of psychiatric genome-wide association studies with multispecies heterogeneous functional genomic data integration.

Genome-wide association studies and other discovery genetics methods provide a means to identify previously unknown biological mechanisms underlying behavioral disorders that may point to new therapeutic avenues, augment diagnostic tools, and yield a deeper understanding of the biology of psychiatric conditions. Recent advances in psychiatric genetics have been made possible through large-scale collaborative efforts. These studies have begun to unearth many novel genetic variants associated with psychiatric disorders and behavioral traits in human populations. Significant challenges remain in characterizing the resulting disease-associated genetic variants and prioritizing functional follow-up to make them useful for mechanistic understanding and development of therapeutics. Model organism research has generated extensive genomic data that can provide insight into the neurobiological mechanisms of variant action, but a cohesive effort must be made to establish which aspects of the biological modulation of behavioral traits are evolutionarily conserved across species. Scalable computing, new data integration strategies, and advanced analysis methods outlined in this review provide a framework to efficiently harness model organism data in support of clinically relevant psychiatric phenotypes

ANNOTATING GENETIC RISK VARIANTS TO TARGET GENES USING Hi-C COUPLED MAGMA (H-MAGMA)

An outstanding goal in modern genomics is to systematically predict the functional outcome of non-coding variation associated with complex traits. To bridge the gap between non-coding variation and its functional impact, we developed Hi-C Coupled Multi-Marker Analysis of GenoMic Annotation (H-MAGMA), a framework that converts SNP associations into gene-level associations based on chromatin interaction profiles to assign variants to their target genes. Applying this approach, we identified key biological pathways implicated in a wide range of brain disorders and showed its utility in complementing other functional genomic resources such as expression quantitative trait loci (eQTL)-based variant annotation. We applied H-MAGMA to five psychiatric and four neurodegenerative disorders. We identified that H-MAGMA detects risk genes associated with brain disorders. Additionally, we identified excitatory neurons as the critical cell types underlying psychiatric disorders compared to neurodegenerative disorders. Furthermore, we identified that genes associated with psychiatric disorders are expressed during early brain development, while those associated with neurodegenerative disorders are expressed in later years. Next, we utilized H-MAGMA to pinpoint genes associated with cigarette smoking and alcohol use traits. We next characterized the underlying biological processes and critical cell types underlying substance use traits. We found that pathways including ethanol metabolic process and alcohol catabolic process to be associated with alcohol use traits, while response to nicotinic and acetylcholinergic pathways were identified for cigarette smoking traits. Moreover, we identified dopaminergic, GABAergic, and serotonergic neurons in the midbrain as relevant cell types that may contribute to substance use etiology. Lastly, we provide a detailed protocol for generating the H-MAGMA variant-gene annotation file and provide additional annotation files for 28 tissues and cell types, with the hope of contributing a resource for researchers.Doctor of Philosoph

The Pharmacoepigenomics Informatics Pipeline and H-GREEN Hi-C Compiler: Discovering Pharmacogenomic Variants and Pathways with the Epigenome and Spatial Genome

Over the last decade, biomedical science has been transformed by the epigenome and spatial genome, but the discipline of pharmacogenomics, the study of the genetic underpinnings of pharmacological phenotypes like drug response and adverse events, has not. Scientists have begun to use omics atlases of increasing depth, and inferences relating to the bidirectional causal relationship between the spatial epigenome and gene expression, as a foundational underpinning for genetics research. The epigenome and spatial genome are increasingly used to discover causative regulatory variants in the significance regions of genome-wide association studies, for the discovery of the biological mechanisms underlying these phenotypes and the design of genetic tests to predict them. Such variants often have more predictive power than coding variants, but in the area of pharmacogenomics, such advances have been radically underapplied. The majority of pharmacogenomics tests are designed manually on the basis of mechanistic work with coding variants in candidate genes, and where genome wide approaches are used, they are typically not interpreted with the epigenome. This work describes a series of analyses of pharmacogenomics association studies with the tools and datasets of the epigenome and spatial genome, undertaken with the intent of discovering causative regulatory variants to enable new genetic tests. It describes the potent regulatory variants discovered thereby to have a putative causative and predictive role in a number of medically important phenotypes, including analgesia and the treatment of depression, bipolar disorder, and traumatic brain injury with opiates, anxiolytics, antidepressants, lithium, and valproate, and in particular the tendency for such variants to cluster into spatially interacting, conceptually unified pathways which offer mechanistic insight into these phenotypes. It describes the Pharmacoepigenomics Informatics Pipeline (PIP), an integrative multiple omics variant discovery pipeline designed to make this kind of analysis easier and cheaper to perform, more reproducible, and amenable to the addition of advanced features. It described the successes of the PIP in rediscovering manually discovered gene networks for lithium response, as well as discovering a previously unknown genetic basis for warfarin response in anticoagulation therapy. It describes the H-GREEN Hi-C compiler, which was designed to analyze spatial genome data and discover the distant target genes of such regulatory variants, and its success in discovering spatial contacts not detectable by preceding methods and using them to build spatial contact networks that unite disparate TADs with phenotypic relationships. It describes a potential featureset of a future pipeline, using the latest epigenome research and the lessons of the previous pipeline. It describes my thinking about how to use the output of a multiple omics variant pipeline to design genetic tests that also incorporate clinical data. And it concludes by describing a long term vision for a comprehensive pharmacophenomic atlas, to be constructed by applying a variant pipeline and machine learning test design system, such as is described, to thousands of phenotypes in parallel. Scientists struggled to assay genotypes for the better part of a century, and in the last twenty years, succeeded. The struggle to predict phenotypes on the basis of the genotypes we assay remains ongoing. The use of multiple omics variant pipelines and machine learning models with omics atlases, genetic association, and medical records data will be an increasingly significant part of that struggle for the foreseeable future.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145835/1/ariallyn_1.pd

Genetic Dissection of Quantitative Trait Loci for Substances of Abuse

It has been reported that an individual’s initial level of response to a drug might be predictive of his or her future risk of becoming dependent, thus basal gene expression profiles underlying those drug responses may be informative for both predicting addiction susceptibility and determining targets for intervention. This dissertation research aims to elucidate genetic risk factors underlying acute alcohol and nicotine dependence phenotypes using mouse genetic models of addiction. Phenotyping, brain region-specific mRNA expression profiling, and genetic mapping of a recombinant inbred panel of over 25 mouse strains were performed in order to identify quantitative trait loci (QTL) harboring candidate genes that may modulate these phenotypes. Previous BXD (B6 x D2) behavioral studies performed in our laboratory identified an ethanol-induced anxiolysis-like QTL (Etanq1) in the light dark box (LDB). We hypothesized that genetic variation within Nin (a gene within the Etanq1 support interval involved in microtubule-anchoring) may modulate anxiolytic-like responses to acute ethanol in the LDB as well as other preclinical models of anxiety, the elevated plus maze (EPM), and marble burying (MB) task. Molecular studies have allowed us to confirm cis regulation of Nin transcript levels in the NAc. To elucidate potential mechanisms mediating Etanq1, the pharmacological tools, diazepam and HZ166 (a benzodiazepine derivative) were utilized to interrogate whether GABAA receptor activation modulates ethanol’s anxiety-like behaviors in the LDB. We show that the LDB phenotype, percent time spent (PTS) in the light following a brief restraint stress, is not being modulated through direct activation of GABAA α2/α3 receptor subunits. To genetically dissect Etanq1 as well as parse the ethanol anxiolytic-like phenotype, we have assayed 8 inbred strains, selected based on genotypes at Nin, in various preclinical models of anxiety. Principal components analysis of these behavioral data suggests that the gene(s) modulating the ethanol anxiolytic-like component in the LDB do not overlap with similar phenotypes in the elevated plus maze (EPM), nor the MB phenotype. Furthermore, site-specific delivery of an sh-Nin lentivirus into the NAc of D2 mice revealed that Nin may modulate one LDB endophenotype, latency to enter the light side of the LDB, which loaded as a part of the “anxiolysis” principal component. These data strongly imply that basal neuronal Nin expression in the NAc is important for acute ethanol anxiolytic-like behavior, perhaps through a novel mechanism involving synaptic remodeling. In separate behavioral QTL mapping studies, we hypothesized that genetic variation regulating expression of Chrna7 modulates the reward-like phenotype, conditioned place preference (CPP), for nicotine. We provide evidence for genetic regulation of Chrna7 across the BXD panel of mice and through pharmacological and genetic behavioral studies, confirm Chrna7 as a quantitative trait gene modulating CPP for nicotine in mice. Microarrays, followed by network analyses, allowed us to identify a genetically co-regulated network within the nucleus accumbens (NAc), differentially expressed in mice null for Chrna7, which was similarly correlated in the BXD panel of mice. Our network and molecular analyses suggest a putative role for Chrna7 in regulating insulin signaling in the NAc, which together, may contribute to the enhanced sensitivity to nicotine observed in strains of mice that lack or have low mRNA levels of Chrna7 in the NAc. Overall, this research has elucidated and confirmed new genetic risk factors underlying alcohol and nicotine dependence phenotypes and has enabled a better understanding of the neurogenomic bases of alcohol and nicotine addiction. Future studies that further investigate the signaling pathways and/or gene interactions involving Nin and Chrna7 may lead the field to new candidates for pharmacotherapies that may be tailored for use in individuals with susceptible genotypes. Supported by NIAAA grants P20AA017828 and R01AA020634 to MFM, NIDA T32DA007027 to WLD, and NIDA R01DA032246 to MFM and MID

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Funding Information: 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 ). 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. Funding Information: 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. Funding Information: A. Palotie is a member of Astra Zeneca’s Genomics Advisory Board. V. Salomaa has consulted for Novo Nordisk and Sanofi and has ongoing research collaboration with Bayer (both unrelated to the present study). M. F. Green is a paid consultant for AiCure, Biogen, Lundbeck and Roche, is a member of the Scientific Board of Cadent, and has received research funds from Forum. G. A. Light has consulted to Astellas, Forum, and Neuroverse. K. Nuechterlein has research support from Janssen, Genentech and Brain Plasticity, and has also consulted for Astellas, MedinCell, Takeda, Teva, Genentech, Otsuka, Janssen and Brain Plasticity. D. Cohen has reported past consultation for or the receipt of honoraria from Otsuka, Shire, Lundbeck, Roche and Janssen. M. J. Daly is a founder of Maze Therapeutics and on the scientific advisory board of Neumora Therapeutics. A. K. Malhotra is a consultant to Genomind, InformedDNA and Concert Pharmaceuticals. R. A. Bressan has received research grants from Janssen, has been a forum consultant for Janssen, Sanof and Roche and is on the speakers’ bureau for Ache, Janssen, Sanofi and Torrent. C. Noto was on the speakers’ bureau and/or has acted as a consultant for Janssen and Daiichi-Sankyo in the last 12 months. C. Pantelis has, for the last three years, served on an advisory board for Lundbeck and received honoraria for talks presented at educational meetings organized by Lundbeck. D. A. Collier is a full-time employee and stockholder of Eli Lilly and Company. M. C. O’Donovan is supported by a collaborative research grant from Takeda Pharmaceuticals. M. J. Owen is supported by a collaborative research grant from Takeda Pharmaceuticals. J. T. R. Walters is supported by a collaborative research grant from Takeda Pharmaceuticals. A. J. Pocklington is supported by a collaborative research grant from Takeda Pharmaceuticals. S. R. Marder has consulted for the following companies: Roche, Sunovion, Lundbeck, Boeringer-Ingelheim, Acadia and Merck. S. Gopal is a full time employee and shareholder in Johnson & Johnson (AMEX: JNJ). A. Savitz is an employee of Janssen Research & Development and owns stock or stock options in the company. Q. S. Li is an employee of Janssen Research & Development and owns stock or stock options in the company. T. Kam-Thong is an employee of F. Hoffman-La Roche. A. Rautanen is an employee of F. Hoffman-La Roche. D. Malhotra is an employee of F. Hoffman-La Roche. S. A. Paciga is an employee of Pfizer. O. A. Andreassen is a consultant for HealthLytix, and received speaker’s honorarium from Lundbeck. S. V. Faraone has received income, potential income, travel expenses continuing education support and/or research support from Akili Interactive Labs, Arbor, Genomind, Ironshore, Ondosis, Otsuka, Rhodes, Shire/Takeda, Sunovion, Supernus, Tris and Vallon. With his institution, he has US patent US20130217707 A1 for the use of sodium-hydrogen exchange inhibitors in the treatment of attention deficit hyperactivity disorder. In previous years, he received support from Alcobra, Aveksham, CogCubed, Eli Lilly, Enzymotec, Impact, Janssen, KemPharm, Lundbeck/Takeda, McNeil, Neurolifesciences, Neurovance, Novartis, Pfizer and Vaya. He also receives royalties from books published by Guilford Press: Straight Talk about Your Child’s Mental Health; Oxford University Press: Schizophrenia: The Facts; and Elsevier: ADHD: Non-Pharmacologic Interventions. He is also Program Director of https://adhdinadults.com/ . C. Arango has been a consultant to or has received honoraria or grants from Acadia, Angelini, Gedeon Richter, Janssen Cilag, Lundbeck, Minerva, Otsuka, Roche, Sage, Servier, Shire, Schering Plough, Sumitomo Dainippon Pharma, Sunovion and Takeda. K. Alptekin has received grants and honoraria for consulting work, lecturing and research from Abdi İbrahim, Abdi İbrahim Otsuka, Janssen, Ali Raif and TUBITAK. Publisher Copyright: © 2022, The Author(s), under exclusive licence to Springer Nature Limited.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.Peer reviewe