Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap

Genes overlap across psychiatric disease Many genome-wide studies have examined genes associated with a range of neuropsychiatric disorders. However, the degree to which the genetic underpinnings of these diseases differ or overlap is unknown. Gandal et al. performed meta-analyses of transcriptomic studies covering five major psychiatric disorders and compared cases and controls to identify coexpressed gene modules. From this, they found that some psychiatric disorders share global gene expression patterns. This overlap in polygenic traits in neuropsychiatric disorders may allow for better diagnosis and treatment. Science, this issue p. 693 Neuropsychiatric disease patients have both shared and distinct gene expression pattern changes in the brain compared with controls. The predisposition to neuropsychiatric disease involves a complex, polygenic, and pleiotropic genetic architecture. However, little is known about how genetic variants impart brain dysfunction or pathology. We used transcriptomic profiling as a quantitative readout of molecular brain-based phenotypes across five major psychiatric disorders—autism, schizophrenia, bipolar disorder, depression, and alcoholism—compared with matched controls. We identified patterns of shared and distinct gene-expression perturbations across these conditions. The degree of sharing of transcriptional dysregulation is related to polygenic (single-nucleotide polymorphism–based) overlap across disorders, suggesting a substantial causal genetic component. This comprehensive systems-level view of the neurobiological architecture of major neuropsychiatric illness demonstrates pathways of molecular convergence and specificity.

[1]  Beth Stevens,et al.  Microglia emerge as central players in brain disease , 2017, Nature Medicine.

[2]  Jeffrey T Leek,et al.  qSVA framework for RNA quality correction in differential expression analysis , 2017, Proceedings of the National Academy of Sciences of the United States of America.

[3]  M. Daly,et al.  The iPSYCH2012 case–cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders , 2017, Molecular Psychiatry.

[4]  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.

[5]  Hyejung Won,et al.  The road to precision psychiatry: translating genetics into disease mechanisms , 2016, Nature Neuroscience.

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

[7]  Jonathan P. Beauchamp,et al.  Genetic variants associated with subjective well-being, depressive symptoms and neuroticism identified through genome-wide analyses , 2016, Nature Genetics.

[8]  Jonathan P. Beauchamp,et al.  Genome-wide association study identifies 74 loci associated with educational attainment , 2016, Nature.

[9]  C. Spencer,et al.  A contribution of novel CNVs to schizophrenia from a genome-wide study of 41,321 subjects: CNV Analysis Group and the Schizophrenia Working Group of the Psychiatric Genomics Consortium , 2016, bioRxiv.

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

[11]  E. Chang,et al.  Purification and Characterization of Progenitor and Mature Human Astrocytes Reveals Transcriptional and Functional Differences with Mouse , 2016, Neuron.

[12]  Michael J. Purcaro,et al.  The PsychENCODE project , 2015, Nature Neuroscience.

[13]  Daniel H. Geschwind,et al.  Genetics and genomics of psychiatric disease , 2015, Science.

[14]  Christopher S. Poultney,et al.  Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci , 2015, Neuron.

[15]  Yakir A Reshef,et al.  Partitioning heritability by functional annotation using genome-wide association summary statistics , 2015, Nature Genetics.

[16]  A. McAllister,et al.  Immune mediators in the brain and peripheral tissues in autism spectrum disorder , 2015, Nature Reviews Neuroscience.

[17]  Judy H. Cho,et al.  Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations , 2015, Nature Genetics.

[18]  Irina Voineagu,et al.  Coexpression networks identify brain region–specific enhancer RNAs in the human brain , 2015, Nature Neuroscience.

[19]  Daniel H. Geschwind,et al.  Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders , 2015, Nature Reviews Genetics.

[20]  Warren W. Kretzschmar,et al.  Sparse whole genome sequencing identifies two loci for major depressive disorder , 2015, Nature.

[21]  A. Hofman,et al.  Polygenic risk scores for schizophrenia and bipolar disorder predict creativity , 2015, Nature Neuroscience.

[22]  M. Vawter,et al.  Olanzapine Reversed Brain Gene Expression Changes Induced by Phencyclidine Treatment in Non-Human Primates , 2015, Molecular Neuropsychiatry.

[23]  Jun S. Liu,et al.  The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans , 2015, Science.

[24]  S. Bove,et al.  Evaluation of TrkB and BDNF transcripts in prefrontal cortex, hippocampus, and striatum from subjects with schizophrenia, bipolar disorder, and major depressive disorder , 2015, Neurobiology of Disease.

[25]  Joris M. Mooij,et al.  MAGMA: Generalized Gene-Set Analysis of GWAS Data , 2015, PLoS Comput. Biol..

[26]  M J Wright,et al.  Common polygenic risk for autism spectrum disorder (ASD) is associated with cognitive ability in the general population , 2015, Molecular Psychiatry.

[27]  V. Feigin,et al.  The Global Burden of Mental, Neurological and Substance Use Disorders: An Analysis from the Global Burden of Disease Study 2010 , 2015, PloS one.

[28]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[29]  Laura J. Scott,et al.  Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways , 2015, Nature Neuroscience.

[30]  G. Kirov,et al.  Copy number variation in bipolar disorder , 2015, Molecular Psychiatry.

[31]  Shannon E. Ellis,et al.  Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism , 2014, Nature Communications.

[32]  P. Gold,et al.  The organization of the stress system and its dysregulation in depressive illness , 2014, Molecular Psychiatry.

[33]  Christopher S. Poultney,et al.  Synaptic, transcriptional, and chromatin genes disrupted in autism , 2014, Nature.

[34]  Boris Yamrom,et al.  The contribution of de novo coding mutations to autism spectrum disorder , 2014, Nature.

[35]  Kali T. Witherspoon,et al.  Recurrent de novo mutations implicate novel genes underlying simplex autism risk , 2014, Nature Communications.

[36]  Carson C Chow,et al.  Second-generation PLINK: rising to the challenge of larger and richer datasets , 2014, GigaScience.

[37]  C. Spencer,et al.  Biological Insights From 108 Schizophrenia-Associated Genetic Loci , 2014, Nature.

[38]  Kathryn Roeder,et al.  Most genetic risk for autism resides with common variation , 2014, Nature Genetics.

[39]  S. Beggs,et al.  Sublime Microglia: Expanding Roles for the Guardians of the CNS , 2014, Cell.

[40]  Yuan Tian,et al.  A Quantitative Framework to Evaluate Modeling of Cortical Development by Neural Stem Cells , 2014, Neuron.

[41]  Peter M Visscher,et al.  Large-scale genomics unveils the genetic architecture of psychiatric disorders , 2014, Nature Neuroscience.

[42]  A. Bertelsen,et al.  A comprehensive nationwide study of the incidence rate and lifetime risk for treated mental disorders. , 2014, JAMA psychiatry.

[43]  D. Rujescu,et al.  A Conserved BDNF, Glutamate- and GABA-Enriched Gene Module Related to Human Depression Identified by Coexpression Meta-Analysis and DNA Variant Genome-Wide Association Studies , 2014, PloS one.

[44]  M. Daly,et al.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies , 2014, Nature Genetics.

[45]  I. Kohane,et al.  iPSC-derived neurons as a higher-throughput readout for autism: promises and pitfalls. , 2014, Trends in molecular medicine.

[46]  E. Banks,et al.  De novo mutations in schizophrenia implicate synaptic networks , 2014, Nature.

[47]  David R. O'Brien,et al.  Cell Type-Specific Expression Analysis to Identify Putative Cellular Mechanisms for Neurogenetic Disorders , 2014, The Journal of Neuroscience.

[48]  Adam J. Schwarz,et al.  CNVs conferring risk of autism or schizophrenia affect cognition in controls , 2013, Nature.

[49]  S. Horvath,et al.  Integrative Functional Genomic Analyses Implicate Specific Molecular Pathways and Circuits in Autism , 2013, Cell.

[50]  S. Djurovic,et al.  Polygenic dissection of diagnosis and clinical dimensions of bipolar disorder and schizophrenia , 2013, Molecular Psychiatry.

[51]  Jianxin Shi,et al.  Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs , 2013, Nature Genetics.

[52]  L. Siever,et al.  Spatial and Temporal Mapping of De Novo Mutations in Schizophrenia to a Fetal Prefrontal Cortical Network , 2013, Cell.

[53]  S. Horvath,et al.  Genes and pathways underlying regional and cell type changes in Alzheimer's disease , 2013, Genome Medicine.

[54]  L. Tran,et al.  Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease , 2013, Cell.

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

[56]  N. Wray,et al.  A mega-analysis of genome-wide association studies for major depressive disorder , 2013, Molecular Psychiatry.

[57]  Arne K. Sandvik,et al.  Whole Genome Gene Expression Meta-Analysis of Inflammatory Bowel Disease Colon Mucosa Demonstrates Lack of Major Differences between Crohn's Disease and Ulcerative Colitis , 2013, PloS one.

[58]  C. Jack,et al.  Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity , 2013, Proceedings of the National Academy of Sciences.

[59]  Bradley P. Coe,et al.  Refinement and discovery of new hotspots of copy-number variation associated with autism spectrum disorder. , 2013, American journal of human genetics.

[60]  Emily K. Lehrman,et al.  The “quad‐partite” synapse: Microglia‐synapse interactions in the developing and mature CNS , 2013, Glia.

[61]  B. V. van Bon,et al.  Diagnostic exome sequencing in persons with severe intellectual disability. , 2012, The New England journal of medicine.

[62]  Chunyu Liu,et al.  Two Gene Co-expression Modules Differentiate Psychotics and Controls , 2012, Molecular Psychiatry.

[63]  D. Horn,et al.  Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study , 2012, The Lancet.

[64]  D H Geschwind,et al.  Using large clinical data sets to infer pathogenicity for rare copy number variants in autism cohorts , 2012, Molecular Psychiatry.

[65]  S. Levy,et al.  De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia , 2012, Nature Genetics.

[66]  Sarah C. Emerson,et al.  Length Bias Correction in Gene Ontology Enrichment Analysis Using Logistic Regression , 2012, PloS one.

[67]  D. Bentley,et al.  Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4 , 2012, Nature Genetics.

[68]  Philippe Hantraye,et al.  Reactive Astrocytes Overexpress TSPO and Are Detected by TSPO Positron Emission Tomography Imaging , 2012, The Journal of Neuroscience.

[69]  Peter Langfelder,et al.  Network methods for describing sample relationships in genomic datasets: application to Huntington’s disease , 2012, BMC Systems Biology.

[70]  Kenny Q. Ye,et al.  De Novo Gene Disruptions in Children on the Autistic Spectrum , 2012, Neuron.

[71]  M. Krams,et al.  Impaired mitochondrial function in psychiatric disorders , 2012, Nature Reviews Neuroscience.

[72]  Michael F. Walker,et al.  De novo mutations revealed by whole-exome sequencing are strongly associated with autism , 2012, Nature.

[73]  Evan T. Geller,et al.  Patterns and rates of exonic de novo mutations in autism spectrum disorders , 2012, Nature.

[74]  J. Sebat,et al.  CNVs: Harbingers of a Rare Variant Revolution in Psychiatric Genetics , 2012, Cell.

[75]  Nicholas J. Schork,et al.  Age-Dependent Brain Gene Expression and Copy Number Anomalies in Autism Suggest Distinct Pathological Processes at Young Versus Mature Ages , 2012, PLoS genetics.

[76]  Bradley P. Coe,et al.  Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations , 2012, Nature.

[77]  E. Deneris,et al.  Serotonergic transcriptional networks and potential importance to mental health , 2012, Nature Neuroscience.

[78]  R. Harris,et al.  Gene Coexpression Networks in Human Brain Identify Epigenetic Modifications in Alcohol Dependence , 2012, The Journal of Neuroscience.

[79]  K. Hansen,et al.  Removing technical variability in RNA-seq data using conditional quantile normalization , 2012, Biostatistics.

[80]  J. Marchini,et al.  Genotype Imputation with Thousands of Genomes , 2011, G3: Genes | Genomes | Genetics.

[81]  Manuel A. R. Ferreira,et al.  Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4 , 2011, Nature Genetics.

[82]  S. Lok,et al.  Increased exonic de novo mutation rate in individuals with schizophrenia , 2011, Nature Genetics.

[83]  Daniel R. Salomon,et al.  Strategies for aggregating gene expression data: The collapseRows R function , 2011, BMC Bioinformatics.

[84]  Kathryn Roeder,et al.  Multiple Recurrent De Novo CNVs, Including Duplications of the 7q11.23 Williams Syndrome Region, Are Strongly Associated with Autism , 2011, Neuron.

[85]  Jaak Vilo,et al.  g:Profiler—a web server for functional interpretation of gene lists (2011 update) , 2011, Nucleic Acids Res..

[86]  S. Horvath,et al.  Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology , 2011, Nature.

[87]  P. O’Reilly,et al.  Genome-wide association and genetic functional studies identify autism susceptibility candidate 2 gene (AUTS2) in the regulation of alcohol consumption , 2011, Proceedings of the National Academy of Sciences.

[88]  Jianxin Shi,et al.  Copy number variants in schizophrenia: confirmation of five previous findings and new evidence for 3q29 microdeletions and VIPR2 duplications. , 2011, The American journal of psychiatry.

[89]  J. Sebat,et al.  Duplications of the Neuropeptide Receptor VIPR2 Confer Significant Risk for Schizophrenia , 2011, Nature.

[90]  Struan F. A. Grant,et al.  Duplication of the SLIT3 Locus on 5q35.1 Predisposes to Major Depressive Disorder , 2010, PloS one.

[91]  Urvashi Surti,et al.  Deletion 17q12 is a recurrent copy number variant that confers high risk of autism and schizophrenia. , 2010, American journal of human genetics.

[92]  Josyf Mychaleckyj,et al.  Robust relationship inference in genome-wide association studies , 2010, Bioinform..

[93]  D. Cutler,et al.  Microdeletions of 3q29 confer high risk for schizophrenia. , 2010, American journal of human genetics.

[94]  Wolfgang Viechtbauer,et al.  Conducting Meta-Analyses in R with the metafor Package , 2010 .

[95]  Miho Nakajima,et al.  Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells , 2010, Nucleic acids research.

[96]  T. Woo,et al.  Extracellular matrix-glial abnormalities in the amygdala and entorhinal cortex of subjects diagnosed with schizophrenia. , 2010, Archives of general psychiatry.

[97]  M. Barnes,et al.  Analysis of gene expression in two large schizophrenia cohorts identifies multiple changes associated with nerve terminal function , 2009, Molecular Psychiatry.

[98]  Jessica R. Wolff,et al.  Microduplications of 16p11.2 are Associated with Schizophrenia , 2009, Nature Genetics.

[99]  Judy H. Cho,et al.  Finding the missing heritability of complex diseases , 2009, Nature.

[100]  Guoping Fan,et al.  Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells , 2009, BMC Genomics.

[101]  E. Birney,et al.  Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt , 2009, Nature Protocols.

[102]  P. Donnelly,et al.  A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies , 2009, PLoS genetics.

[103]  Robert T. Schultz,et al.  Autism genome-wide copy number variation reveals ubiquitin and neuronal genes , 2009, Nature.

[104]  Elvira Bramon,et al.  Disruption of the neurexin 1 gene is associated with schizophrenia. , 2009, Human molecular genetics.

[105]  Sangita B. Patil,et al.  Elevated immune response in the brain of autistic patients , 2009, Journal of Neuroimmunology.

[106]  S. Horvath,et al.  WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.

[107]  Steven R. Head,et al.  Molecular profiles of schizophrenia in the CNS at different stages of illness , 2008, Brain Research.

[108]  S. Horvath,et al.  Functional organization of the transcriptome in human brain , 2008, Nature Neuroscience.

[109]  P. Visscher,et al.  Rare chromosomal deletions and duplications increase risk of schizophrenia , 2008, Nature.

[110]  Thomas W. Mühleisen,et al.  Large recurrent microdeletions associated with schizophrenia , 2008, Nature.

[111]  Pan Du,et al.  lumi: a pipeline for processing Illumina microarray , 2008, Bioinform..

[112]  Jack Satsangi,et al.  Regional variation in gene expression in the healthy colon is dysregulated in ulcerative colitis , 2008, Gut.

[113]  Károly Mirnics,et al.  Immune transcriptome alterations in the temporal cortex of subjects with autism , 2008, Neurobiology of Disease.

[114]  Bin Zhang,et al.  Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R , 2008, Bioinform..

[115]  Joshua M. Korn,et al.  Association between microdeletion and microduplication at 16p11.2 and autism. , 2008, The New England journal of medicine.

[116]  D. Pinto,et al.  Structural variation of chromosomes in autism spectrum disorder. , 2008, American journal of human genetics.

[117]  S. Rhodes,et al.  Roles of the LHX3 and LHX4 LIM-homeodomain factors in pituitary development , 2007, Molecular and Cellular Endocrinology.

[118]  D. Reich,et al.  Population Structure and Eigenanalysis , 2006, PLoS genetics.

[119]  Paul A Clemons,et al.  The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease , 2006, Science.

[120]  S. Horvath,et al.  Statistical Applications in Genetics and Molecular Biology , 2011 .

[121]  Kazuya Iwamoto,et al.  Altered expression of mitochondria-related genes in postmortem brains of patients with bipolar disorder or schizophrenia, as revealed by large-scale DNA microarray analysis. , 2005, Human molecular genetics.

[122]  Giovanni Parmigiani,et al.  MergeMaid: R Tools for Merging and Cross-Study Validation of Gene Expression Data , 2004, Statistical applications in genetics and molecular biology.

[123]  Giovanni Parmigiani,et al.  A Cross-Study Comparison of Gene Expression Studies for the Molecular Classification of Lung Cancer , 2004, Clinical Cancer Research.

[124]  Gordon K Smyth,et al.  Statistical Applications in Genetics and Molecular Biology Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2011 .

[125]  Benjamin M. Bolstad,et al.  affy - analysis of Affymetrix GeneChip data at the probe level , 2004, Bioinform..

[126]  P. Mortensen,et al.  The Danish Psychiatric Central Register. , 1997, Danish medical bulletin.

[127]  P. Visscher,et al.  Meta-analysis of the heritability of human traits based on fifty years of twin studies. , 2015, Nature genetics.

[128]  J. A. Laurence,et al.  Glial fibrillary acidic protein is elevated in superior frontal, parietal and cerebellar cortices of autistic subjects , 2008, The Cerebellum.

[129]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[130]  Chris T. A. Evelo,et al.  Bioinformatics Applications Note Databases and Ontologies Go-elite: a Flexible Solution for Pathway and Ontology Over-representation , 2022 .

[131]  Steve Horvath,et al.  Molecular Systems Biology 5; Article number 291; doi:10.1038/msb.2009.46 Citation: Molecular Systems Biology 5:291 , 2022 .