Transcriptome-Wide Structural Equation Modeling of 13 Major Psychiatric Disorders for Cross-Disorder Risk and Drug Repurposing.

Importance Psychiatric disorders display high levels of comorbidity and genetic overlap, necessitating multivariate approaches for parsing convergent and divergent psychiatric risk pathways. Identifying gene expression patterns underlying cross-disorder risk also stands to propel drug discovery and repurposing in the face of rising levels of polypharmacy. Objective To identify gene expression patterns underlying genetic convergence and divergence across psychiatric disorders along with existing pharmacological interventions that target these genes. Design, Setting, and Participants This genomic study applied a multivariate transcriptomic method, transcriptome-wide structural equation modeling (T-SEM), to investigate gene expression patterns associated with 5 genomic factors indexing shared risk across 13 major psychiatric disorders. Follow-up tests, including overlap with gene sets for other outcomes and phenome-wide association studies, were conducted to better characterize T-SEM results. The Broad Institute Connectivity Map Drug Repurposing Database and Drug-Gene Interaction Database public databases of drug-gene pairs were used to identify drugs that could be repurposed to target genes found to be associated with cross-disorder risk. Data were collected from database inception up to February 20, 2023. Main Outcomes and Measures Gene expression patterns associated with genomic factors or disorder-specific risk and existing drugs that target these genes. Results In total, T-SEM identified 466 genes whose expression was significantly associated (z ≥ 5.02) with genomic factors and 36 genes with disorder-specific effects. Most associated genes were found for a thought disorders factor, defined by bipolar disorder and schizophrenia. Several existing pharmacological interventions were identified that could be repurposed to target genes whose expression was associated with the thought disorders factor or a transdiagnostic p factor defined by all 13 disorders. Conclusions and Relevance The findings from this study shed light on patterns of gene expression associated with genetic overlap and uniqueness across psychiatric disorders. Future versions of the multivariate drug repurposing framework outlined here have the potential to identify novel pharmacological interventions for increasingly common, comorbid psychiatric presentations.

[1]  G. Davies,et al.  Transcriptome-wide and stratified genomic structural equation modeling identify neurobiological pathways shared across diverse cognitive traits , 2022, Nature Communications.

[2]  Javier de la Fuente,et al.  Pervasive Downward Bias in Estimates of Liability-Scale Heritability in Genome-wide Association Study Meta-analysis: A Simple Solution , 2022, Biological Psychiatry.

[3]  Michael F. Green,et al.  Mapping genomic loci implicates genes and synaptic biology in schizophrenia , 2022, Nature.

[4]  T. Werge,et al.  Cross-trait assortative mating is widespread and inflates genetic correlation estimates , 2022, bioRxiv.

[5]  M. Whalen,et al.  Combining Human Genetics of Multiple Sclerosis with Oxidative Stress Phenotype for Drug Repositioning , 2021, Pharmaceutics.

[6]  M. Gerstein,et al.  Illuminating links between cis-regulators and trans-acting variants in the human prefrontal cortex , 2021, bioRxiv.

[7]  M. Cairns,et al.  Advancing the use of genome-wide association studies for drug repurposing , 2021, Nature Reviews Genetics.

[8]  C. Lewis,et al.  Transcriptome-wide association study of treatment-resistant depression and depression subtypes for drug repurposing , 2021, European Neuropsychopharmacology.

[9]  Dan J Stein,et al.  Genome-wide association study of over 40,000 bipolar disorder cases provides new insights into the underlying biology , 2021, Nature Genetics.

[10]  Joshua F. McMichael,et al.  Integration of the Drug–Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts , 2020, bioRxiv.

[11]  C. Zink,et al.  Nimodipine improves cortical efficiency during working memory in healthy subjects , 2020, Translational Psychiatry.

[12]  Jingyun Yang,et al.  Mendelian randomization integrating GWAS and eQTL data revealed genes pleiotropically associated with major depressive disorder , 2020, Translational Psychiatry.

[13]  John P. Rice,et al.  A large-scale genome-wide association study meta-analysis of cannabis use disorder , 2020, The lancet. Psychiatry.

[14]  G. Breen,et al.  Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic, and molecular genetic levels of analysis , 2020, Nature Genetics.

[15]  C. Lewis,et al.  Delineating the Genetic Component of Gene Expression in Major Depression , 2020, Biological Psychiatry.

[16]  P. Visscher,et al.  Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders , 2020, Molecular Psychiatry.

[17]  Christopher D. Brown,et al.  The GTEx Consortium atlas of genetic regulatory effects across human tissues , 2019, Science.

[18]  Tyrone D. Cannon,et al.  M41 IDENTIFYING NOOTROPIC DRUG TARGETS VIA LARGE-SCALE COGNITIVE GWAS AND TRANSCRIPTOMICS , 2019, European Neuropsychopharmacology.

[19]  Sarah M. Hartz,et al.  Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium , 2019, bioRxiv.

[20]  J. Kennedy,et al.  The complement system in schizophrenia: where are we now and what’s next? , 2019, Molecular Psychiatry.

[21]  M. Owen,et al.  Novel Insight Into the Etiology of Autism Spectrum Disorder Gained by Integrating Expression Data With Genome-wide Association Statistics , 2018, Biological Psychiatry.

[22]  T. Amelsvoort,et al.  Efficacy and tolerability of riluzole in psychiatric disorders: A systematic review and preliminary meta-analysis , 2019, Psychiatry Research.

[23]  Hunna J. Watson,et al.  Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa , 2019, Nature Genetics.

[24]  Jing Wang,et al.  WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs , 2019, Nucleic Acids Res..

[25]  R. Joober,et al.  Transcriptome-wide association study of attention deficit hyperactivity disorder identifies associated genes and phenotypes , 2019, Nature Communications.

[26]  T. Werge,et al.  The Common Genetic Architecture of Anxiety Disorders , 2017, bioRxiv.

[27]  H. Stefánsson,et al.  Interrogating the Genetic Determinants of Tourette's Syndrome and Other Tic Disorders Through Genome-Wide Association Studies. , 2019, The American journal of psychiatry.

[28]  Hunna J. Watson,et al.  Genome wide meta-analysis identifies genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders , 2019, bioRxiv.

[29]  G. Lewis,et al.  Association of Hydroxylmethyl Glutaryl Coenzyme A Reductase Inhibitors, L-Type Calcium Channel Antagonists, and Biguanides With Rates of Psychiatric Hospitalization and Self-Harm in Individuals With Serious Mental Illness , 2019, JAMA psychiatry.

[30]  Annie W Shieh,et al.  Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder , 2018, Science.

[31]  Xihong Lin,et al.  ACAT: A Fast and Powerful P-value Combination Method for Rare-variant Analysis in Sequencing Studies , 2018, bioRxiv.

[32]  R. Marioni,et al.  Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions , 2018, Nature Neuroscience.

[33]  Alicia R. Martin,et al.  Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder , 2018, Nature Genetics.

[34]  J. Perry,et al.  Elucidating the genetic basis of social interaction and isolation , 2018, Nature Communications.

[35]  O. Andreassen,et al.  Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model , 2017, bioRxiv.

[36]  Dan J Stein,et al.  Revealing the complex genetic architecture of obsessive–compulsive disorder using meta-analysis , 2018, Molecular Psychiatry.

[37]  Stuart J. Ritchie,et al.  Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits , 2019, Nature Human Behaviour.

[38]  Avshalom Caspi,et al.  All for One and One for All: Mental Disorders in One Dimension. , 2018, The American journal of psychiatry.

[39]  Milton Pividori,et al.  Integrating predicted transcriptome from multiple tissues improves association detection , 2018, bioRxiv.

[40]  Sarah M. Hartz,et al.  Trans-ancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders , 2018, Nature Neuroscience.

[41]  S. Djurovic,et al.  Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation , 2017, bioRxiv.

[42]  Hon-Cheong So,et al.  Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry , 2017, Nature Neuroscience.

[43]  P. Visscher,et al.  10 Years of GWAS Discovery: Biology, Function, and Translation. , 2017, American journal of human genetics.

[44]  Christopher S. Poultney,et al.  Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia , 2017, Molecular Autism.

[45]  Z. D. Kabir,et al.  From Gene to Behavior: L-Type Calcium Channel Mechanisms Underlying Neuropsychiatric Symptoms , 2017, Neurotherapeutics.

[46]  Dan J Stein,et al.  Largest GWAS of PTSD (N=20 070) yields genetic overlap with schizophrenia and sex differences in heritability , 2017, Molecular Psychiatry.

[47]  Jacob K. Asiedu,et al.  The Drug Repurposing Hub: a next-generation drug library and information resource , 2017, Nature Medicine.

[48]  J. Goldman,et al.  Disorders of Astrocytes: Alexander Disease as a Model. , 2017, Annual review of pathology.

[49]  D. G. Osborne,et al.  Structural and mechanistic insights into regulation of the retromer coat by TBC1d5 , 2016, Nature Communications.

[50]  Benjamin A. Logsdon,et al.  Gene Expression Elucidates Functional Impact of Polygenic Risk for Schizophrenia , 2016, Nature Neuroscience.

[51]  Giulio Genovese,et al.  Schizophrenia risk from complex variation of complement component 4 , 2016, Nature.

[52]  Gerald W. Zamponi,et al.  Targeting voltage-gated calcium channels in neurological and psychiatric diseases , 2015, Nature Reviews Drug Discovery.

[53]  T. Lehtimäki,et al.  Integrative approaches for large-scale transcriptome-wide association studies , 2015, Nature Genetics.

[54]  D. Campion,et al.  De novo deleterious genetic variations target a biological network centered on Aβ peptide in early-onset Alzheimer disease , 2015, Molecular Psychiatry.

[55]  M. Daly,et al.  An Atlas of Genetic Correlations across Human Diseases and Traits , 2015, Nature Genetics.

[56]  L. Kruglyak,et al.  The role of regulatory variation in complex traits and disease , 2015, Nature Reviews Genetics.

[57]  S. Akhondzadeh,et al.  A double-blind, placebo controlled, randomized trial of riluzole as an adjunct to risperidone for treatment of negative symptoms in patients with chronic schizophrenia , 2014, Psychopharmacology.

[58]  M. Peters,et al.  Systematic identification of trans eQTLs as putative drivers of known disease associations , 2013, Nature Genetics.

[59]  M. Daly,et al.  Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis , 2013, The Lancet.

[60]  K. Jones,et al.  The role of the innate immune system in psychiatric disorders , 2013, Molecular and Cellular Neuroscience.

[61]  B. Ludviksson,et al.  Clinical Symptoms in Adults with Selective IgA Deficiency: A Case-Control Study , 2013, Journal of Clinical Immunology.

[62]  Joel Dudley,et al.  Exploiting drug-disease relationships for computational drug repositioning , 2011, Briefings Bioinform..

[63]  M. Olfson,et al.  National trends in child and adolescent psychotropic polypharmacy in office-based practice, 1996-2007. , 2010, Journal of the American Academy of Child and Adolescent Psychiatry.

[64]  D. Glahn,et al.  The Neurocognitive Signature of Psychotic Bipolar Disorder , 2007, Biological Psychiatry.

[65]  Olga V. Demler,et al.  Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. , 2005, Archives of general psychiatry.

[66]  OUP accepted manuscript , 2021, Human Molecular Genetics.

[67]  M. Olfson,et al.  National trends in psychotropic medication polypharmacy in office-based psychiatry. , 2010, Archives of general psychiatry.

[68]  R. Flavell,et al.  CD40 and CD154 in cell-mediated immunity. , 1998, Annual review of immunology.

[69]  Pharmacopsychiatry , 2022 .