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Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders

  • Author Footnotes
    1 LMH and MK contributed equally to this work.
    Leanna M. Hernandez
    Footnotes
    1 LMH and MK contributed equally to this work.
    Affiliations
    Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California

    Intellectual and Developmental Disabilities Research Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
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  • Author Footnotes
    1 LMH and MK contributed equally to this work.
    Minsoo Kim
    Footnotes
    1 LMH and MK contributed equally to this work.
    Affiliations
    Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California

    Intellectual and Developmental Disabilities Research Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California

    Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California

    Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
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  • Gil D. Hoftman
    Affiliations
    Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
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  • Jillian R. Haney
    Affiliations
    Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California

    Intellectual and Developmental Disabilities Research Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
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  • Luis de la Torre-Ubieta
    Affiliations
    Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California

    Intellectual and Developmental Disabilities Research Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
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  • Bogdan Pasaniuc
    Affiliations
    Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California

    Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California

    Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
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  • Michael J. Gandal
    Correspondence
    Address correspondence to Michael J. Gandal, M.D., Ph.D.
    Affiliations
    Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California

    Intellectual and Developmental Disabilities Research Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California

    Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California

    Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
    Search for articles by this author
  • Author Footnotes
    1 LMH and MK contributed equally to this work.
Open AccessPublished:June 11, 2020DOI:https://doi.org/10.1016/j.biopsych.2020.06.005

      Abstract

      Over the past decade, large-scale genetic studies have successfully identified hundreds of genetic variants robustly associated with risk for psychiatric disorders. However, mechanistic insight and clinical translation continue to lag the pace of risk variant identification, hindered by the sheer number of targets and their predominant noncoding localization, as well as pervasive pleiotropy and incomplete penetrance. Successful next steps require identification of “causal” genetic variants and their proximal biological consequences; placing variants within biologically defined functional contexts, reflecting specific molecular pathways, cell types, circuits, and developmental windows; and characterizing the downstream, convergent neurobiological impact of polygenicity within an individual. Here, we discuss opportunities and challenges of high-throughput transcriptomic profiling in the human brain, and how transcriptomic approaches can help pinpoint mechanisms underlying genetic risk for psychiatric disorders at a scale necessary to tackle daunting levels of polygenicity. These include transcriptome-wide association studies for risk gene prioritization through integration of genome-wide association studies with expression quantitative trait loci. We outline transcriptomic results that inform our understanding of the brain-level molecular pathology of psychiatric disorders, including autism spectrum disorder, bipolar disorder, major depressive disorder, and schizophrenia. Finally, we discuss systems-level approaches for integration of distinct genetic, genomic, and phenotypic levels, including combining spatially resolved gene expression and human neuroimaging maps. Results highlight the importance of understanding gene expression (dys)regulation across human brain development as a major contributor to psychiatric disease pathogenesis, from common variants acting as expression quantitative trait loci to rare variants enriched for gene expression regulatory pathways.

      Keywords

      A major barrier to the successful treatment of psychiatric disorders is our limited understanding of pathogenic mechanisms across molecular, cellular, and systems levels. For most disorders, the majority of liability is mediated by heritable genetic variation, thereby providing a tractable framework for gaining mechanistic insights (
      • Gandal M.J.
      • Leppa V.
      • Won H.
      • Parikshak N.N.
      • Geschwind D.H.
      The road to precision psychiatry: Translating genetics into disease mechanisms.
      ). Accordingly, large-scale genetic studies have made tremendous gains identifying hundreds of risk variants. However, biological interpretation of these variants is challenged by predominant localization in noncoding regions (
      • Maurano M.T.
      • Humbert R.
      • Rynes E.
      • Thurman R.E.
      • Haugen E.
      • Wang H.
      • et al.
      Systematic localization of common disease-associated variation in regulatory DNA.
      ), substantial linkage disequilibrium, pleiotropy, incomplete penetrance, and polygenicity. Whereas brain noncoding regions were previously poorly annotated, now the opposite problem is true—there are often many functional annotations for a given variant. These challenges culminate in thousands of small-effect-size risk alleles with unknown function, which can only be confidently localized to a given linkage disequilibrium block (
      • Claussnitzer M.
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      • Collins R.
      • Cox N.J.
      • Dermitzakis E.T.
      • Hurles M.E.
      • et al.
      A brief history of human disease genetics.
      ).
      As variant discovery continues on its exponential trajectory, biological interpretation is now the major obstacle impeding translation. Mechanistic insight requires finding robust “causal” variant(s), identifying the biological effect of a variant (e.g., putative target gene), understanding how multiple variants converge onto specific downstream molecular pathways, and finally, understanding how an individual’s variants aggregate to mediate phenotypic risk. Here, we discuss how the transcriptome—the collection of RNAs expressed in a given cell/tissue—represents a proximal, quantitative readout enabling mechanistic interrogation of the biological impact of genetic variation, individually or in aggregate, across both clinical and experimental settings (Figure 1).
      Figure thumbnail gr1
      Figure 1A transcriptomic framework for mechanistic dissection of complex psychiatric traits. (A) A hierarchical neurobiological organization linking genotype to phenotype. (B) Two main approaches are highlighted. (Left) Disease-associated genetic risk variants can be integrated with gene expression measures through transcriptome-wide association studies and related approaches to prioritize proximal biological mechanisms, particularly for noncoding variants. (Right) Differential gene expression analyses of brain tissue from subjects with psychiatric disorders (case) compared with controls can be used to identify a reactive, brain-level molecular pathology of disease. (C) Gene expression patterns are highly dynamic and interpretation requires understanding the relevant biological context. circRNA, circular RNA; lncRNA, long noncoding RNA; miRNA, microRNA; mRNA, messenger RNA; piRNA, PIWI-interacting RNA; siRNA, small interfering RNA.

      Tissue Considerations

      Transcriptomics has received renewed focus in psychiatry, particularly in the post–genome-wide association study (GWAS) era. The etiologically relevant tissue for psychiatric traits is the human brain (
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      ). Interpretation is further complicated by potential reverse causality, hidden confounding, and pleiotropic effects. Nevertheless, the transcriptomic architecture of the human brain has demonstrated remarkable stability across cortical regions, individuals, and studies, including its genetic regulation (
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      ). Further, such challenges can be mitigated, in part, by associating expression changes with germline variation, across large numbers of individuals, providing a directional anchor. Finally, methods for generating neurons and 3-dimensional cortical organoids from patient-derived human induced pluripotent stem cells (hiPSCs) can now be performed at the necessary scale to fill some of these gaps, although there are still key biological differences between these models and true brain development (
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      Transcriptomic Methods

      RNA sequencing (RNA-seq) enables accurate, quantitative transcriptomic profiling with wide dynamic range, including across protein-coding and noncoding genes, splicing events, and unannotated genomic regions (
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      ). Recent developments in single-cell and long-read sequencing enable interrogation of individual cells and full-length isoforms, respectively (
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      ). RNA-seq quantifications, however, are relative and often biased by technical factors (Figure 2), including differential amplification, GC content, RNA quality, gene and mitochondrial mapping rates, and gene body coverage uniformity, among others (
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      ). The importance of experimental batch correction was recognized for microarrays (
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      ), and with RNA-seq, these can be introduced at many steps. To enable direct comparison, libraries should be processed together in parallel, and multiplexed in random groups where possible (
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      Statistical design and analysis of RNA sequencing data.
      ). RNA quality of most human brains is lower than that of cell lines or tissues from experimental models. Proper detection, visualization, and correction for such factors should therefore be a critical aspect of any workflow (
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      ,
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      ). Achieving an appropriate balance between under- and overcorrection remains a challenge, as many commonly used latent variable approaches are effective, but also prone to removing true biological signal (
      • Stegle O.
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      Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses.
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      A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies.
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      ).
      Figure thumbnail gr2
      Figure 2Factors influencing gene expression patterns across cortical brain samples in GTEx. Sequencing-related technical factors, along with estimated cell proportions, are top drivers of expression variance across anterior cingulate and frontal cortex brain samples in GTEx (v8; n = 629 samples). Sequencing-related technical covariates were computed using PicardTools (“collectMultipleMetrics”) and combined with sample and subject-level metadata provided GTEx. Collinear covariates were removed, and gene expression variance explained by each covariate was calculated using a linear mixed effects model using the variancePartition package (
      • Hoffman G.E.
      • Schadt E.E.
      variancePartition: Interpreting drivers of variation in complex gene expression studies.
      ). Sample-specific proportions for major cortical cell types were estimated with Bisque using single-nucleus RNA-seq data as a reference (
      • Zhu Z.
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      Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets.
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      Accurate estimation of cell composition in bulk expression through robust integration of single-cell information.
      ). BMI, body mass index; GTEx, Genotype-Tissue Expression; mtDNA, mitochondrial DNA; OPC, oligodendrocyte progenitor cells; RNA-seq, RNA sequencing; UTR; untranslated region.
      The human brain transcriptome exhibits a robust, hierarchical organization of coexpression patterns, reflecting cell types, subcellular organelles, and region-, sex-, and age-specific processes (
      • Hawrylycz M.J.
      • Lein E.S.
      • Guillozet-Bongaarts A.L.
      • Shen E.H.
      • Ng L.
      • Miller J.A.
      • et al.
      An anatomically comprehensive atlas of the adult human brain transcriptome.
      ,
      • Oldham M.C.
      • Konopka G.
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      • Langfelder P.
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      • Horvath S.
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      Functional organization of the transcriptome in human brain.
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      ). Dimensionality reduction techniques can capture coexpression “modules” reflecting these processes, boosting power and facilitating interpretability (
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      Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders.
      ). Weighted gene correlation network analysis is a popular unsupervised approach (
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      A general framework for weighted gene co-expression network analysis.
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      ,
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      ). Because subtle shifts in cellular proportion contribute to the largest source of expression variation across samples (Figure 2), particularly in the brain, the most connected (“hub") genes of a module often show strong, selective enrichment for cell type–specific markers (
      • Oldham M.C.
      • Konopka G.
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      Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes.
      ). Modules can be further functionally annotated by assessing overlap with protein-protein interaction databases, brain-relevant Gene Ontology pathways (
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      An evidence-based, expert-curated knowledge base for the synapse.
      ), and transcription factor or microRNA (miRNA) targets, among others. Finally, additional insight is provided by gene connectivity within a module, as hubs are more likely to act as drivers or regulators, making them useful targets for experimental validation (
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      ).

      Prioritization of Target Genes and Proximal Mechanisms Underlying GWAS Loci

      GWAS loci are largely localized within noncoding, regulatory regions (
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      Systematic localization of common disease-associated variation in regulatory DNA.
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      A brief history of human disease genetics.
      ) that control gene expression and/or splicing, often in a species- and cell type–specific manner. However, regulatory elements exhibit temporally dynamic and pleiotropic biological effects, necessitating high-throughput unbiased interrogation of their impact across distinct contexts (
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      Dynamic genetic regulation of gene expression during cellular differentiation.
      ). Along these lines, large-scale efforts have been undertaken to map expression and splicing quantitative trait loci (QTLs) across development (
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      GTEx Consortium
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      ). The PsychENCODE Consortium has compiled the largest panel to date with >30,000 eGenes identified with a cis expression QTL (eQTL), but results are restricted to the bulk frontal cortex (
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      ). Such differences are driven by unique cell type compositions (
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      ), which are obscured when profiling bulk tissue. Further, QTL mapping is typically performed after removing dozens of large expression variance components (e.g., “PEER” factors), which maximizes identification of cis-eQTLs but also removes distinct cell type signals and other trans-acting factors (
      • Stegle O.
      • Parts L.
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      Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses.
      ). Temporal eQTL variability also remains underexplored but is likely to uncover additional hidden signals, particularly during fetal and early postnatal time points (
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      Under the assumption that gene expression mediates the effect of genetic variation on a complex trait, several methods integrate GWAS and cis-eQTL signals to prioritize candidate risk genes (
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      ). Under multivariate predictive models, transcriptomic imputation methods (e.g., transcriptome-wide association study [TWAS] or S-PrediXcan) test for genetic correlation between the cis-regulated component of gene expression and a trait (
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      • et al.
      Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics.
      ). Here, a sparse set of local single nucleotide polymorphism (SNP) predictors is trained for each gene, using a large tissue-specific reference panel, followed by imputation into an association cohort to prioritize candidate risk genes and their direction of dysregulation. By aggregating effects of multiple SNPs onto specific features, these methods increase power for detection of associations potentially even outside GWAS loci. However, as these are association tests, they remain susceptible to potential confounds of linkage and pleiotropy (
      • Wainberg M.
      • Sinnott-Armstrong N.
      • Mancuso N.
      • Barbeira A.N.
      • Knowles D.A.
      • Golan D.
      • et al.
      Opportunities and challenges for transcriptome-wide association studies.
      ). Recent frameworks have been developed to identify potential associations driven by linkage (
      • Zhu Z.
      • Zhang F.
      • Hu H.
      • Bakshi A.
      • Robinson M.R.
      • Powell J.E.
      • et al.
      Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets.
      ,
      • Barbeira A.N.
      • Pividori M.
      • Zheng J.
      • Wheeler H.E.
      • Nicolae D.L.
      • Im H.K.
      Integrating predicted transcriptome from multiple tissues improves association detection.
      ), control for pleiotropy (
      • Porcu E.
      • Rüeger S.
      • Lepik K.
      • Santoni F.A.
      • et al.
      eQTLGen ConsortiumBIOS Consortium
      Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits.
      ), and provide probabilistic interpretation for locus-specific associations (
      • Mancuso N.
      • Freund M.K.
      • Johnson R.
      • Shi H.
      • Kichaev G.
      • Gusev A.
      • Pasaniuc B.
      Probabilistic fine-mapping of transcriptome-wide association studies.
      ). Ultimately, this is an active research area that will continue to improve with incorporation of additional annotations from specific cell types, tissues (
      • Huckins L.M.
      • Dobbyn A.
      • Ruderfer D.M.
      • Hoffman G.
      • Wang W.
      • Pardiñas A.F.
      • et al.
      Gene expression imputation across multiple brain regions provides insights into schizophrenia risk.
      ), and other (e.g., epigenetic) regulatory mechanisms (
      • Zhang W.
      • Voloudakis G.
      • Rajagopal V.M.
      • Readhead B.
      • Dudley J.T.
      • Schadt E.E.
      • et al.
      Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits.
      ).
      These methods have prioritized several high-confidence psychiatric risk genes. For example, in schizophrenia (SCZ), increased expression of C4A can (partially) explain the top GWAS-associated locus in Europeans (
      • O’Brien H.E.
      • Hannon E.
      • Hill M.J.
      • Toste C.C.
      • Robertson M.J.
      • Morgan J.E.
      • et al.
      Expression quantitative trait loci in the developing human brain and their enrichment in neuropsychiatric disorders.
      ,
      • Werling D.M.
      • Pochareddy S.
      • Choi J.
      • An J.-Y.
      • Sheppard B.
      • Peng M.
      • et al.
      Whole-genome and RNA sequencing reveal variation and transcriptomic coordination in the developing human prefrontal cortex.
      ,
      • Sekar A.
      • Bialas A.R.
      • de Rivera H.
      • Davis A.
      • Hammond T.R.
      • Kamitaki N.
      • et al.
      Schizophrenia risk from complex variation of complement component 4.
      ,
      • Gusev A.
      • Mancuso N.
      • Won H.
      • Kousi M.
      • Finucane H.K.
      • Reshef Y.
      • et al.
      Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights.
      ,
      • Gandal M.J.
      • Zhang P.
      • Hadjimichael E.
      • Walker R.L.
      • Chen C.
      • Liu S.
      • et al.
      Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder.
      ). The CommonMind Consortium performed colocalization of frontal cortex cis-eQTLs, prioritizing several SCZ risk genes, including SNAP91, FURIN, and TSNARE1 (
      • Fromer M.
      • Roussos P.
      • Sieberts S.K.
      • Johnson J.S.
      • Kavanagh D.H.
      • Perumal T.M.
      • et al.
      Gene expression elucidates functional impact of polygenic risk for schizophrenia.
      ), and summary-data-based Mendelian randomization and TWAS have concordantly prioritized SNX19, among others (
      • Zhu Z.
      • Zhang F.
      • Hu H.
      • Bakshi A.
      • Robinson M.R.
      • Powell J.E.
      • et al.
      Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets.
      ,
      • Gusev A.
      • Mancuso N.
      • Won H.
      • Kousi M.
      • Finucane H.K.
      • Reshef Y.
      • et al.
      Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights.
      ). PsychENCODE, with its larger frontal cortex reference panel, prioritized many of these candidate genes and several more, including RERE, SETD6, SETD8, MCHR1, JKAMP, and AKT3—all of which were also concordantly differentially expressed (DE) in SCZ (
      • Gandal M.J.
      • Zhang P.
      • Hadjimichael E.
      • Walker R.L.
      • Chen C.
      • Liu S.
      • et al.
      Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder.
      ). Finally, incorporating splicing into the TWAS framework uncovered a number of candidates not identified through expression (
      • Gusev A.
      • Mancuso N.
      • Won H.
      • Kousi M.
      • Finucane H.K.
      • Reshef Y.
      • et al.
      Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights.
      ). Highlighting the importance of the reference panel tissue, TWAS-prioritized genes from the fetal brain (
      • Walker R.L.
      • Ramaswami G.
      • Hartl C.
      • Mancuso N.
      • Gandal M.J.
      • de la Torre-Ubieta L.
      • et al.
      Genetic control of expression and splicing in developing human brain informs disease mechanisms.
      ) or within distinct GTEx (Genotype-Tissue Expression) brain tissues (
      • Huckins L.M.
      • Dobbyn A.
      • Ruderfer D.M.
      • Hoffman G.
      • Wang W.
      • Pardiñas A.F.
      • et al.
      Gene expression imputation across multiple brain regions provides insights into schizophrenia risk.
      ) showed only modest overlap.
      Such discrepancies highlight the need for validation, although true experimental replication remains challenging. For GWAS loci with a single high-probability credible/causal variant and a specific eQTL colocalization, CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/Cas9) genome editing can potentially be used for target validation (
      • Schrode N.
      • Ho S.-M.
      • Yamamuro K.
      • Dobbyn A.
      • Huckins L.
      • Matos M.R.
      • et al.
      Synergistic effects of common schizophrenia risk variants.
      ). Yet, such strategies cannot distinguish between pleiotropic associations if a given variant shows a functional effect across multiple genes. Integration of orthogonal annotations, such as chromosomal interactions with Hi-C or chromatin accessibility with ATAC-seq (assay for transposase-accessible chromatin with sequencing) (
      • Wang D.
      • Liu S.
      • Warrell J.
      • Won H.
      • Shi X.
      • Navarro F.C.P.
      • et al.
      Comprehensive functional genomic resource and integrative model for the human brain.
      ,
      • de la Torre-Ubieta L.
      • Stein J.L.
      • Won H.
      • Opland C.K.
      • Liang D.
      • Lu D.
      • Geschwind D.H.
      The dynamic landscape of open chromatin during human cortical neurogenesis.
      ), can provide additional evidence of functionality and connect enhancers to genes but remains susceptible to pleiotropy and generally lacks the resolution to pinpoint effects of individual variants. Integration with robustly associated rare coding variants for the same trait may provide validation if available (
      • Freund M.K.
      • Burch K.S.
      • Shi H.
      • Mancuso N.
      • Kichaev G.
      • Garske K.M.
      • et al.
      Phenotype-specific enrichment of Mendelian disorder genes near GWAS regions across 62 complex traits.
      ,
      • Barbeira A.N.
      • Bonazzola R.
      • Gamazon E.R.
      • Liang Y.
      • Park Y.
      • Kim-Hellmuth S.
      • et al.
      Widespread dose-dependent effects of RNA expression and splicing on complex diseases and traits.
      ). In autism spectrum disorder (ASD), for example, the lysine methyltransferase KMT2E shows genome-wide significant associations across both common and rare variant studies (
      • Satterstrom F.K.
      • Kosmicki J.A.
      • Wang J.
      • Breen M.S.
      • De Rubeis S.
      • An J.-Y.
      • et al.
      Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism.
      ,
      • Grove J.
      • Ripke S.
      • Als T.D.
      • Mattheisen M.
      • Walters R.K.
      • Won H.
      • et al.
      Identification of common genetic risk variants for autism spectrum disorder.
      ). We recommend a probabilistic interpretation of prioritized candidate genes (
      • Mancuso N.
      • Freund M.K.
      • Johnson R.
      • Shi H.
      • Kichaev G.
      • Gusev A.
      • Pasaniuc B.
      Probabilistic fine-mapping of transcriptome-wide association studies.
      ), including a potential null result, and hypothesize that integration of biologically informed pathway-level priors will significantly boost performance.
      Finally, these methods assume that genetic risk for disease is mediated through regulation of gene expression. Yet, recent evidence suggests that the overall proportion of disease heritability mediated by cis-eQTLs is much lower than previously thought, around ∼10% in SCZ at least with current bulk tissue reference panels (
      • Yao D.W.
      • O’Connor L.J.
      • Price A.L.
      • Gusev A.
      Quantifying genetic effects on disease mediated by assayed gene expression levels.
      ). The missing signal may be explained by distal QTLs, by missing cis-eQTLs for lowly expressed genes (e.g., ncRNAs), by those not captured in bulk tissue (cell type–specific QTLs) (
      • Kim-Hellmuth S.
      • Aguet F.
      • Oliva M.
      • Muñoz-Aguirre M.
      • Kasela S.
      • Wucher V.
      • et al.
      Cell type–specific genetic regulation of gene expression across human tissues.
      ,
      • Jaffe A.E.
      • Hoeppner D.J.
      • Saito T.
      • Blanpain L.
      • Ukaigwe J.
      • Burke E.E.
      • et al.
      Profiling gene expression in the human dentate gyrus granule cell layer reveals insights into schizophrenia and its genetic risk.
      ), or in distinct biological contexts, such as the fetal brain (
      • Walker R.L.
      • Ramaswami G.
      • Hartl C.
      • Mancuso N.
      • Gandal M.J.
      • de la Torre-Ubieta L.
      • et al.
      Genetic control of expression and splicing in developing human brain informs disease mechanisms.
      ). Effects other than expression regulation, including SNP-tagging structural variants, splicing or isoform QTLs, methylation- or chromatin-accessibility QTLs, coding variants, or even variants within ncRNA exons, may explain additional missing variance.

      Identifying Biological Convergence Through Transcriptomics

      Owing to the overwhelming polygenicity of psychiatric disorders, two unrelated affected individuals likely possess distinct combinations of risk variants. As such, the next challenge is to characterize whether the effects of multiple risk variants converge onto “key” downstream molecular pathways. Early examples came from ASD-associated copy number variants, found to be enriched for neuronal and synaptic cell adhesion genes (
      • Glessner J.T.
      • Wang K.
      • Cai G.
      • Korvatska O.
      • Kim C.E.
      • Wood S.
      • et al.
      Autism genome-wide copy number variation reveals ubiquitin and neuronal genes.
      ). Exome sequencing studies found enrichment for genes harboring ASD-associated rare, de novo protein-disrupting variants among synaptic, chromatin, and gene regulation pathways (
      • De Rubeis S.
      • He X.
      • Goldberg A.P.
      • Poultney C.S.
      • Samocha K.
      • Cicek A.E.
      • et al.
      Synaptic, transcriptional and chromatin genes disrupted in autism.
      ). Further, although the overall rare, de novo protein-disrupting variant burden in SCZ is smaller, similar pathways including synaptic genes, glutamate signaling, and activity-regulated cytoskeletal pathways are implicated (
      • Fromer M.
      • Pocklington A.J.
      • Kavanagh D.H.
      • Williams H.J.
      • Dwyer S.
      • Gormley P.
      • et al.
      De novo mutations in schizophrenia implicate synaptic networks.
      ,
      • Purcell S.M.
      • Moran J.L.
      • Fromer M.
      • Ruderfer D.
      • Solovieff N.
      • Roussos P.
      • et al.
      A polygenic burden of rare disruptive mutations in schizophrenia.
      ). Common variants across SCZ, major depressive disorder, and bipolar disorder (BD), in aggregate, show similar enrichments across synaptic and gene regulatory pathways (
      Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium
      Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways.
      ).
      Such enrichments are based on manually annotated pathways, which are unlikely to capture the full genomic complexity of the human brain (
      • Koopmans F.
      • van Nierop P.
      • Andres-Alonso M.
      • Byrnes A.
      • Cijsouw T.
      • Coba M.P.
      • et al.
      An evidence-based, expert-curated knowledge base for the synapse.
      ). Transcriptomics provides a natural, bottom-up framework for systematically extending such annotations. The molecular underpinnings of human brain development are under exquisite spatiotemporal regulation, prompting several efforts to map expression across brain regions throughout development (
      • Kang H.J.
      • Kawasawa Y.I.
      • Cheng F.
      • Zhu Y.
      • Xu X.
      • Li M.
      • et al.
      Spatio-temporal transcriptome of the human brain.
      ,
      • Colantuoni C.
      • Lipska B.K.
      • Ye T.
      • Hyde T.M.
      • Tao R.
      • Leek J.T.
      • et al.
      Temporal dynamics and genetic control of transcription in the human prefrontal cortex.
      ,
      • Li M.
      • Santpere G.
      • Imamura Kawasawa Y.
      • Evgrafov O.V.
      • Gulden F.O.
      • Pochareddy S.
      • et al.
      Integrative functional genomic analysis of human brain development and neuropsychiatric risks.
      ). Regional “patterning” of gene expression has been well captured in the fetal (
      • Miller J.A.
      • Ding S.-L.
      • Sunkin S.M.
      • Smith K.A.
      • Ng L.
      • Szafer A.
      • et al.
      Transcriptional landscape of the prenatal human brain.
      ) and adult (
      • Hawrylycz M.J.
      • Lein E.S.
      • Guillozet-Bongaarts A.L.
      • Shen E.H.
      • Ng L.
      • Miller J.A.
      • et al.
      An anatomically comprehensive atlas of the adult human brain transcriptome.
      ) human brain, a trend that will only be amplified with the advent of spatial transcriptomic profiling (
      • Rodriques S.G.
      • Stickels R.R.
      • Goeva A.
      • Martin C.A.
      • Murray E.
      • Vanderburg C.R.
      • et al.
      Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution.
      ). These data enable systematic characterization of potential spatiotemporal, regional, and cell type–specific convergence of the molecular genetic underpinnings of psychiatric disease. Along these lines, nearly all well-powered psychiatric genetic studies show enrichment for brain-expressed genes (
      • Finucane H.K.
      • Reshef Y.A.
      • Anttila V.
      • Slowikowski K.
      • Gusev A.
      • Byrnes A.
      • et al.
      Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types.
      ,
      • Satterstrom F.K.
      • Kosmicki J.A.
      • Wang J.
      • Breen M.S.
      • De Rubeis S.
      • An J.-Y.
      • et al.
      Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism.
      ,
      • Grove J.
      • Ripke S.
      • Als T.D.
      • Mattheisen M.
      • Walters R.K.
      • Won H.
      • et al.
      Identification of common genetic risk variants for autism spectrum disorder.
      ,
      • Genovese G.
      • Fromer M.
      • Stahl E.A.
      • Ruderfer D.M.
      • Chambert K.
      • Landén M.
      • et al.
      Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia.
      ,
      • Wray N.R.
      • Ripke S.
      • Mattheisen M.
      • Trzaskowski M.
      • Byrne E.M.
      • Abdellaoui A.
      • et al.
      Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression.
      ,
      • Demontis D.
      • Walters R.K.
      • Martin J.
      • Mattheisen M.
      • Als T.D.
      • Agerbo E.
      • et al.
      Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder.
      ). Temporally, rare, de novo protein-disrupting variants associated with ASD and SCZ show enrichment among midfetal cortex gene networks (
      • Willsey A.J.
      • Sanders S.J.
      • Li M.
      • Dong S.
      • Tebbenkamp A.T.
      • Muhle R.A.
      • et al.
      Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism.
      ,
      • Parikshak N.N.
      • Luo R.
      • Zhang A.
      • Won H.
      • Lowe J.K.
      • Chandran V.
      • et al.
      Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism.
      ,
      • Gulsuner S.
      • Walsh T.
      • Watts A.C.
      • Lee M.K.
      • Thornton A.M.
      • Casadei S.
      • et al.
      Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network.
      ). Experimentally defined genomic targets of the RNA-binding proteins FMRP, RBFOX1, and CELF4 exhibit among the strongest enrichments for cross-disorder genetic risk (
      • Gandal M.J.
      • Zhang P.
      • Hadjimichael E.
      • Walker R.L.
      • Chen C.
      • Liu S.
      • et al.
      Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder.
      ,
      • Fromer M.
      • Pocklington A.J.
      • Kavanagh D.H.
      • Williams H.J.
      • Dwyer S.
      • Gormley P.
      • et al.
      De novo mutations in schizophrenia implicate synaptic networks.
      ,
      • Wray N.R.
      • Ripke S.
      • Mattheisen M.
      • Trzaskowski M.
      • Byrne E.M.
      • Abdellaoui A.
      • et al.
      Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression.
      ,
      • Pardiñas A.F.
      • Holmans P.
      • Pocklington A.J.
      • Escott-Price V.
      • Ripke S.
      • Carrera N.
      • et al.
      Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection.
      ,
      Cross-Disorder Group of the Psychiatric Genomics Consortium
      Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders.
      ).
      In addition to RNA-binding proteins, ncRNAs such as miRNAs are known to play an important role in human brain development and are implicated in psychiatric disease risk, as reviewed (
      • Issler O.
      • Chen A.
      Determining the role of microRNAs in psychiatric disorders.
      ). miRNAs fine-tune gene expression by binding to the 3′ untranslated region of specific target genes, inhibiting translation or promoting degradation. Several established genetic loci associated with psychiatric disorders harbor miRNAs, notably miR-137 as one of the top SCZ GWAS hits (
      Schizophrenia Working Group of the Psychiatric Genomics Consortium
      Biological insights from 108 schizophrenia-associated genetic loci.
      ), as well as miR-130B, miR-185, and DGCR8 in the 22q11.2 region. The exact nature and extent of dysregulation of miRNAs or other short ncRNAs (e.g., small nucleolar RNAs) in psychiatric disorders needs to be further explored.

      Transcriptomic Insight into the Molecular Pathology Associated With Psychiatric Disorders

      Psychiatric disorders lack a clearly defined anatomic pathology. Yet, given strong genetic roots, the question becomes the following: what are the downstream biological consequences of these genetic risk factors? This question has fueled hundreds of case-control studies attempting to characterize group-level differences in molecular biomarkers, such as gene expression (
      • Horváth S.
      • Mirnics K.
      Schizophrenia as a disorder of molecular pathways.
      ,
      • Parikshak N.N.
      • Gandal M.J.
      • Geschwind D.H.
      Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders.
      ). Here, transcriptomics can provide a strategy for high-throughput, comprehensive molecular phenotyping of affected neural systems to characterize the current reactive state of a biological sample. Tissue and sample size are again critical, particularly given substantial levels of heterogeneity.
      Initial seminal studies employed expression microarrays or in situ hybridization in the human postmortem prefrontal cortex from matched pairs of SCZ cases and control subjects. These studies identified notable dysregulation in synaptic machinery (
      • Mirnics K.
      • Middleton F.A.
      • Lewis D.A.
      • Levitt P.
      Analysis of complex brain disorders with gene expression microarrays: Schizophrenia as a disease of the synapse.
      ), interneuron markers (
      • Hashimoto T.
      • Volk D.W.
      • Eggan S.M.
      • Mirnics K.
      • Pierri J.N.
      • Sun Z.
      • et al.
      Gene expression deficits in a subclass of GABA neurons in the prefrontal cortex of subjects with schizophrenia.
      ), mitochondrial processes (
      • Middleton F.A.
      • Mirnics K.
      • Pierri J.N.
      • Lewis D.A.
      • Levitt P.
      Gene expression profiling reveals alterations of specific metabolic pathways in schizophrenia.
      ), and myelin-related genes (
      • Hakak Y.
      • Walker J.R.
      • Li C.
      • Wong W.H.
      • Davis K.L.
      • Buxbaum J.D.
      • et al.
      Genome-wide expression analysis reveals dysregulation of myelination-related genes in chronic schizophrenia.
      ), among others, as reviewed (
      • Horváth S.
      • Mirnics K.
      Schizophrenia as a disorder of molecular pathways.
      ). Following on this initial work, postmortem brain transcriptomics became widely performed across many disorders, fueled by the growth of brain banks (
      • Deep-Soboslay A.
      • Benes F.M.
      • Haroutunian V.
      • Ellis J.K.
      • Kleinman J.E.
      • Hyde T.M.
      Psychiatric brain banking: Three perspectives on current trends and future directions.
      ) and advances in high-throughput profiling. Decreased expression of interneuron markers was also reported in BD and major depressive disorder (
      • Sibille E.
      • Morris H.M.
      • Kota R.S.
      • Lewis D.A.
      GABA-related transcripts in the dorsolateral prefrontal cortex in mood disorders.
      ), as well as mitochondrial dysfunction in BD (
      • Iwamoto K.
      • Bundo M.
      • Kato T.
      Altered expression of mitochondria-related genes in postmortem brains of patients with bipolar disorder or schizophrenia, as revealed by large-scale DNA microarray analysis.
      ,
      • Konradi C.
      • Eaton M.
      • MacDonald M.L.
      • Walsh J.
      • Benes F.M.
      • Heckers S.
      Molecular evidence for mitochondrial dysfunction in bipolar disorder.
      ). Synaptic dysregulation was also observed in the ASD cortex, as were elevated neuroinflammatory markers and notable splicing changes (
      • Voineagu I.
      • Wang X.
      • Johnston P.
      • Lowe J.K.
      • Tian Y.
      • Horvath S.
      • et al.
      Transcriptomic analysis of autistic brain reveals convergent molecular pathology.
      ). Cross-disorder comparisons have been conducted, with layer-specific pyramidal cell changes observed in SCZ but not in BD or major depressive disorder (
      • Arion D.
      • Huo Z.
      • Enwright J.F.
      • Corradi J.P.
      • Tseng G.
      • Lewis D.A.
      Transcriptome alterations in prefrontal pyramidal cells distinguish schizophrenia from bipolar and major depressive disorders.
      ). Further, when directly compared, transcriptomic changes were substantially larger in ASD than in SCZ or BD (
      • Gandal M.J.
      • Haney J.R.
      • Parikshak N.N.
      • Leppa V.
      • Ramaswami G.
      • Hartl C.
      • et al.
      Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap.
      ). However, results have been variable across studies, particularly for individual DE genes—discrepancies that have been attributed to methodological, analytic, and/or cohort-specific effects. Consistency tends to be greater at the level of pathways, cell types, and networks, for example, and with meta- and mega-analytic approaches (
      • Gandal M.J.
      • Haney J.R.
      • Parikshak N.N.
      • Leppa V.
      • Ramaswami G.
      • Hartl C.
      • et al.
      Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap.
      ,
      • Elashoff M.
      • Higgs B.W.
      • Yolken R.H.
      • Knable M.B.
      • Weis S.
      • Webster M.J.
      • et al.
      Meta-analysis of 12 genomic studies in bipolar disorder.
      ,
      • Toker L.
      • Mancarci B.O.
      • Tripathy S.
      • Pavlidis P.
      Transcriptomic evidence for alterations in astrocytes and parvalbumin interneurons in subjects with bipolar disorder and schizophrenia.
      ).
      To address these challenges, consortia such as CommonMind (
      • Fromer M.
      • Roussos P.
      • Sieberts S.K.
      • Johnson J.S.
      • Kavanagh D.H.
      • Perumal T.M.
      • et al.
      Gene expression elucidates functional impact of polygenic risk for schizophrenia.
      ) and BrainSeq (
      • Jaffe A.E.
      • Straub R.E.
      • Shin J.H.
      • Tao R.
      • Gao Y.
      • Collado-Torres L.
      • et al.
      Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis.
      ) were formed to aggregate and uniformly profile large numbers of psychiatric brain samples. The CommonMind Consortium profiled more than 500 prefrontal cortex brain samples from subjects with SCZ and control subjects (>250 per group), prioritizing several new candidate risk genes through eQTL and GWAS colocalization (
      • Fromer M.
      • Roussos P.
      • Sieberts S.K.
      • Johnson J.S.
      • Kavanagh D.H.
      • Perumal T.M.
      • et al.
      Gene expression elucidates functional impact of polygenic risk for schizophrenia.
      ). Several hundred DE genes were identified, and although effect sizes were modest, modeling estimated that >40% of the transcriptome is perturbed in SCZ—a level of polygenicity that parallels GWAS findings to date. A neuronal coexpression module, associated with SCZ DE genes, was enriched for common and rare variation as well as for postsynaptic density and synaptic signaling pathways, pointing to a convergent disease biology. More recently, the PsychENCODE Consortium has compiled and meta-analyzed transcriptomic data from >2000 samples, including hundreds of individuals across SCZ, ASD, and BD as well as ∼1000 control subjects for frontal cortex (
      • Wang D.
      • Liu S.
      • Warrell J.
      • Won H.
      • Shi X.
      • Navarro F.C.P.
      • et al.
      Comprehensive functional genomic resource and integrative model for the human brain.
      ,
      • Gandal M.J.
      • Zhang P.
      • Hadjimichael E.
      • Walker R.L.
      • Chen C.
      • Liu S.
      • et al.
      Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder.
      ). Concordant results include interneuron marker downregulation, especially PVALB and SST, across SCZ and ASD (
      • Sibille E.
      • Morris H.M.
      • Kota R.S.
      • Lewis D.A.
      GABA-related transcripts in the dorsolateral prefrontal cortex in mood disorders.
      ,
      • Gandal M.J.
      • Haney J.R.
      • Parikshak N.N.
      • Leppa V.
      • Ramaswami G.
      • Hartl C.
      • et al.
      Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap.
      ,
      • Chung D.W.
      • Chung Y.
      • Bazmi H.H.
      • Lewis D.A.
      Altered ErbB4 splicing and cortical parvalbumin interneuron dysfunction in schizophrenia and mood disorders.
      ). Many neuronal processes were dysregulated, particularly in ASD and SCZ, including those related to synaptic signaling and/or regulated by RBFOX1 (
      • Jaffe A.E.
      • Straub R.E.
      • Shin J.H.
      • Tao R.
      • Gao Y.
      • Collado-Torres L.
      • et al.
      Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis.
      ,
      • Gandal M.J.
      • Zhang P.
      • Hadjimichael E.
      • Walker R.L.
      • Chen C.
      • Liu S.
      • et al.
      Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder.
      ,
      • Voineagu I.
      • Wang X.
      • Johnston P.
      • Lowe J.K.
      • Tian Y.
      • Horvath S.
      • et al.
      Transcriptomic analysis of autistic brain reveals convergent molecular pathology.
      ,
      • Gandal M.J.
      • Haney J.R.
      • Parikshak N.N.
      • Leppa V.
      • Ramaswami G.
      • Hartl C.
      • et al.
      Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap.
      ). Notably, neuronal isoform–level changes showed the greatest effect size changes in ASD and SCZ as well as the largest genetic enrichments (
      • Gandal M.J.
      • Zhang P.
      • Hadjimichael E.
      • Walker R.L.
      • Chen C.
      • Liu S.
      • et al.
      Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder.
      ). Mitochondrial and metabolic processes were broadly disrupted (
      • Arion D.
      • Huo Z.
      • Enwright J.F.
      • Corradi J.P.
      • Tseng G.
      • Lewis D.A.
      Transcriptome alterations in prefrontal pyramidal cells distinguish schizophrenia from bipolar and major depressive disorders.
      ,
      • Gandal M.J.
      • Haney J.R.
      • Parikshak N.N.
      • Leppa V.
      • Ramaswami G.
      • Hartl C.
      • et al.
      Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap.
      ,
      • Pacifico R.
      • Davis R.L.
      Transcriptome sequencing implicates dorsal striatum-specific gene network, immune response and energy metabolism pathways in bipolar disorder.
      ), as were blood-brain barrier markers (
      • Gandal M.J.
      • Zhang P.
      • Hadjimichael E.
      • Walker R.L.
      • Chen C.
      • Liu S.
      • et al.
      Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder.
      ). In contrast, a number of processes were concordantly upregulated, such as NF-κB (nuclear factor-κB) and interferon response pathways along with astrocyte genes in SCZ and ASD (
      • Gandal M.J.
      • Zhang P.
      • Hadjimichael E.
      • Walker R.L.
      • Chen C.
      • Liu S.
      • et al.
      Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder.
      ,
      • Ramaker R.C.
      • Bowling K.M.
      • Lasseigne B.N.
      • Hagenauer M.H.
      • Hardigan A.A.
      • Davis N.S.
      • et al.
      Post-mortem molecular profiling of three psychiatric disorders.
      ,
      • Volk D.W.
      • Moroco A.E.
      • Roman K.M.
      • Edelson J.R.
      • Lewis D.A.
      The role of the nuclear factor-κB transcriptional complex in cortical immune activation in schizophrenia.
      ). Microglial genes exhibited more distinct changes, with upregulation in ASD and downregulation in SCZ and BD (
      • Gandal M.J.
      • Zhang P.
      • Hadjimichael E.
      • Walker R.L.
      • Chen C.
      • Liu S.
      • et al.
      Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder.
      ). Many of these ASD results were recapitulated by a recent single-nucleus RNA-seq (snRNA-seq) study (
      • Velmeshev D.
      • Schirmer L.
      • Jung D.
      • Haeussler M.
      • Perez Y.
      • Mayer S.
      • et al.
      Single-cell genomics identifies cell type-specific molecular changes in autism.
      ). Importantly, regional differences need to be characterized, particularly for noncortical brain structures, which can exhibit highly distinctive changes (
      • Parikshak N.N.
      • Swarup V.
      • Belgard T.G.
      • Irimia M.
      • Ramaswami G.
      • Gandal M.J.
      • et al.
      Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism.
      ,
      • Collado-Torres L.
      • Burke E.E.
      • Peterson A.
      • Shin J.
      • Straub R.E.
      • Rajpurohit A.
      • et al.
      Regional heterogeneity in gene expression, regulation, and coherence in the frontal cortex and hippocampus across development and schizophrenia.
      ).
      Interpretation of such changes is challenging, however, as they may reflect a compensatory or reactive consequence of disease, rather than a true causal pathophysiology. Integration of established genetic risk factors, imparted at birth, with identified expression changes can provide a directional framework for interpretation, although pleiotropy can still confound observed associations. DE genes or coexpression modules can be assessed for GWAS enrichment with methods such as linkage disequilibrium score regression (
      • Finucane H.K.
      • Bulik-Sullivan B.
      • Gusev A.
      • Trynka G.
      • Reshef Y.
      • Loh P.-R.
      • et al.
      Partitioning heritability by functional annotation using genome-wide association summary statistics.
      ) and MAGMA (
      • de Leeuw C.A.
      • Mooij J.M.
      • Heskes T.
      • Posthuma D.
      MAGMA: Generalized gene-set analysis of GWAS data.
      ).

      Cell Type Specificity

      Interpretation of bulk tissue transcriptomic results is further challenged because of its component mixture of individual cell types. Indeed, as much as ∼85% of tissue-level RNA-seq data in the human brain are driven by cell type proportional differences (
      • Wang D.
      • Liu S.
      • Warrell J.
      • Won H.
      • Shi X.
      • Navarro F.C.P.
      • et al.
      Comprehensive functional genomic resource and integrative model for the human brain.
      ). Computational approaches, including unsupervised coexpression network analysis or supervised cell type deconvolution methods, can be used to gain inference into specific cell classes at a population level (
      • Kim-Hellmuth S.
      • Aguet F.
      • Oliva M.
      • Muñoz-Aguirre M.
      • Kasela S.
      • Wucher V.
      • et al.
      Cell type–specific genetic regulation of gene expression across human tissues.
      ,
      • Wang D.
      • Liu S.
      • Warrell J.
      • Won H.
      • Shi X.
      • Navarro F.C.P.
      • et al.
      Comprehensive functional genomic resource and integrative model for the human brain.
      ,
      • Gandal M.J.
      • Haney J.R.
      • Parikshak N.N.
      • Leppa V.
      • Ramaswami G.
      • Hartl C.
      • et al.
      Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap.
      ,
      • Miller J.A.
      • Cai C.
      • Langfelder P.
      • Geschwind D.H.
      • Kurian S.M.
      • Salomon D.R.
      • Horvath S.
      Strategies for aggregating gene expression data: The collapseRows R function.
      ,
      • Miller J.A.
      • Cai C.
      • Langfelder P.
      • Geschwind D.H.
      • Kurian S.M.
      • Salomon D.R.
      • Horvath S.
      Strategies for aggregating gene expression data: The collapseRows R function.
      ). However, individual cellular-level variability is lost, and these methods are often limited to common cell types. Initial targeted experimental methods, such as laser capture microdissection, flow cytometry, or immunopanning, used cell type–specific visualization or labeling to enrich for a given population followed by transcriptomic profiling (
      • Poulin J.-F.
      • Tasic B.
      • Hjerling-Leffler J.
      • Trimarchi J.M.
      • Awatramani R.
      Disentangling neural cell diversity using single-cell transcriptomics.
      ), although these approaches can be labor intensive and limited in throughput.
      Advances in high-throughput single-cell RNA-seq or snRNA-seq now provide cellular-level resolution to the aforementioned approaches (
      • Macosko E.Z.
      • Basu A.
      • Satija R.
      • Nemesh J.
      • Shekhar K.
      • Goldman M.
      • et al.
      Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
      ,
      • Kulkarni A.
      • Anderson A.G.
      • Merullo D.P.
      • Konopka G.
      Beyond bulk: A review of single cell transcriptomics methodologies and applications.
      ). Microfluidic technologies capture individual cells in an oil droplet along with a barcoded bead and enzymes. Cells are lysed and polyA+ messenger RNAs are reverse-transcribed into complementary DNA, which is amplified, fragmented, and sequenced. Barcoded complementary DNA are pooled for polymerase chain reaction amplification, library construction, and fragmentation. Libraries are typically only sequenced at the 3′ end of a transcript, which is sufficient to quantify overall gene abundance. Transcripts from the same cell contain the same barcode, whereas a unique molecular index controls for biases owing to polymerase chain reaction amplification. To avoid artifacts from tissue dissociation, snRNA-seq using frozen tissue samples has become popular, as nuclei are more resistant to the stresses of freeze-thaw during isolation (
      • Habib N.
      • Avraham-Davidi I.
      • Basu A.
      • Burks T.
      • Shekhar K.
      • Hofree M.
      • et al.
      Massively parallel single-nucleus RNA-seq with DroNc-seq.
      ). snRNA-seq is generally comparable to single-cell RNA-seq, although it detects more intronic reads and fewer total genes (
      • Habib N.
      • Avraham-Davidi I.
      • Basu A.
      • Burks T.
      • Shekhar K.
      • Hofree M.
      • et al.
      Massively parallel single-nucleus RNA-seq with DroNc-seq.
      ,
      • Bakken T.E.
      • Hodge R.D.
      • Miller J.A.
      • Yao Z.
      • Nguyen T.N.
      • Aevermann B.
      • et al.
      Single-nucleus and single-cell transcriptomes compared in matched cortical cell types.
      ). Resulting datasets are large but sparse, with substantial gene dropout and noisy quantification of at most a few thousand genes per cell. Analytic methods are rapidly evolving, including quality control, normalization, batch correction, and multimodal integration (
      • Kulkarni A.
      • Anderson A.G.
      • Merullo D.P.
      • Konopka G.
      Beyond bulk: A review of single cell transcriptomics methodologies and applications.
      ,
      • Stuart T.
      • Satija R.
      Integrative single-cell analysis.
      ,
      • Welch J.D.
      • Kozareva V.
      • Ferreira A.
      • Vanderburg C.
      • Martin C.
      • Macosko E.Z.
      Single-cell multi-omic integration compares and contrasts features of brain cell identity.
      ). Nonlinear dimensionality reduction methods identify unique cell type clusters, which can then be contrasted across experimental conditions (
      • Kulkarni A.
      • Anderson A.G.
      • Merullo D.P.
      • Konopka G.
      Beyond bulk: A review of single cell transcriptomics methodologies and applications.
      ). Emerging methods can infer cellular lineage and “pseudo-temporal” trajectories to characterize cell type transition states and branch points (
      • Stuart T.
      • Satija R.
      Integrative single-cell analysis.
      ,
      • La Manno G.
      • Soldatov R.
      • Zeisel A.
      • Braun E.
      • Hochgerner H.
      • Petukhov V.
      • et al.
      RNA velocity of single cells.
      ). Nevertheless, as most methods rely on polyA+ 3′ sequencing, these data generally fail to capture the full transcriptome, missing many noncoding genes and splicing changes that are likely important contributors to psychiatric pathophysiology (
      • Gandal M.J.
      • Zhang P.
      • Hadjimichael E.
      • Walker R.L.
      • Chen C.
      • Liu S.
      • et al.
      Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder.
      ,
      • Parikshak N.N.
      • Swarup V.
      • Belgard T.G.
      • Irimia M.
      • Ramaswami G.
      • Gandal M.J.
      • et al.
      Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism.
      ,
      • Li Y.I.
      • van de Geijn B.
      • Raj A.
      • Knowles D.A.
      • Petti A.A.
      • Golan D.
      • et al.
      RNA splicing is a primary link between genetic variation and disease.
      ,
      • Takata A.
      • Matsumoto N.
      • Kato T.
      Genome-wide identification of splicing QTLs in the human brain and their enrichment among schizophrenia-associated loci.
      ).
      Large-scale single-cell/nucleus RNA-seq efforts are underway to fully elucidate the “parts list” of the human brain across development, including the midgestational human fetal brain (
      • Polioudakis D.
      • de la Torre-Ubieta L.
      • Langerman J.
      • Elkins A.G.
      • Shi X.
      • Stein J.L.
      • et al.
      A single-cell transcriptomic atlas of human neocortical development during mid-gestation.
      ,
      • Nowakowski T.J.
      • Bhaduri A.
      • Pollen A.A.
      • Alvarado B.
      • Mostajo-Radji M.A.
      • Di Lullo E.
      • et al.
      Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex.
      ) and adult cortex (
      • Lake B.B.
      • Chen S.
      • Sos B.C.
      • Fan J.
      • Kaeser G.E.
      • Yung Y.C.
      • et al.
      Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain.
      ,
      • Hodge R.D.
      • Bakken T.E.
      • Miller J.A.
      • Smith K.A.
      • Barkan E.R.
      • Graybuck L.T.
      • et al.
      Conserved cell types with divergent features in human versus mouse cortex.
      ) across multiple regions, as well as the hippocampus, striatum (
      • Krienen F.M.
      • Goldman M.
      • Zhang Q.
      • Del Rosario R.C.H.
      • Florio M.
      • Machold R.
      • et al.
      Innovations present in the primate interneuron repertoire.
      ), and substantia nigra (
      • Welch J.D.
      • Kozareva V.
      • Ferreira A.
      • Vanderburg C.
      • Martin C.
      • Macosko E.Z.
      Single-cell multi-omic integration compares and contrasts features of brain cell identity.
      ). Results highlight notable species-specific differences, including primate-specific striatal interneuron populations (
      • Krienen F.M.
      • Goldman M.
      • Zhang Q.
      • Del Rosario R.C.H.
      • Florio M.
      • Machold R.
      • et al.
      Innovations present in the primate interneuron repertoire.
      ) and human-specific cell type expression patterns for several serotonin and glutamate receptors (
      • Hodge R.D.
      • Bakken T.E.
      • Miller J.A.
      • Smith K.A.
      • Barkan E.R.
      • Graybuck L.T.
      • et al.
      Conserved cell types with divergent features in human versus mouse cortex.
      ). GWAS enrichment analyses have identified a set of genetically “vulnerable” cell types in SCZ, including cortical pyramidal cells, interneurons, and DRD2+ medium spiny neurons (
      • Skene N.G.
      • Bryois J.
      • Bakken T.E.
      • Breen G.
      • Crowley J.J.
      • Gaspar H.A.
      • et al.
      Genetic identification of brain cell types underlying schizophrenia.
      ). Rare variants associated with ASD, which are most highly expressed early during brain development, show particular enrichment among excitatory and inhibitory neuronal lineages (
      • Satterstrom F.K.
      • Kosmicki J.A.
      • Wang J.
      • Breen M.S.
      • De Rubeis S.
      • An J.-Y.
      • et al.
      Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism.
      ,
      • Polioudakis D.
      • de la Torre-Ubieta L.
      • Langerman J.
      • Elkins A.G.
      • Shi X.
      • Stein J.L.
      • et al.
      A single-cell transcriptomic atlas of human neocortical development during mid-gestation.
      ,
      • Nowakowski T.J.
      • Bhaduri A.
      • Pollen A.A.
      • Alvarado B.
      • Mostajo-Radji M.A.
      • Di Lullo E.
      • et al.
      Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex.
      ). Concordantly, snRNA-seq profiling of the ASD cortex found notable alterations in synaptic gene expression in upper-layer excitatory neurons (
      • Velmeshev D.
      • Schirmer L.
      • Jung D.
      • Haeussler M.
      • Perez Y.
      • Mayer S.
      • et al.
      Single-cell genomics identifies cell type-specific molecular changes in autism.
      ). Finally, there is hope that cell type–specific eQTL mapping will provide critical missing annotations for psychiatric GWAS loci and uncover hidden disease mechanisms. Some evidence already supports this (
      • Jaffe A.E.
      • Hoeppner D.J.
      • Saito T.
      • Blanpain L.
      • Ukaigwe J.
      • Burke E.E.
      • et al.
      Profiling gene expression in the human dentate gyrus granule cell layer reveals insights into schizophrenia and its genetic risk.
      ), and while cell type–specific analyses will undoubtedly identify new eQTLs, many non–expression-based mechanisms (e.g., splicing) will remain hidden.

      Model Systems

      Transcriptomics can provide an important mechanistic readout in experimental settings, such as with animal models, hiPSC-derived neurons, or 3-dimensional cortical organoids. hiPSC-based methods provide an exciting opportunity to interrogate subject-specific neurobiology in an otherwise inaccessible context (
      • Marton R.M.
      • Pașca S.P.
      Organoid and assembloid technologies for investigating cellular crosstalk in human brain development and disease.
      ). To date, these approaches have been most fruitful characterizing effects of rare large-effect-size mutations. For example, hiPSC-derived neurons from subjects with SCZ harboring rare pathogenic deletions in NRXN1 show strong allele-specific accumulation of “mutant” isoforms and concomitant reduction in neuronal activity (
      • Flaherty E.
      • Zhu S.
      • Barretto N.
      • Cheng E.
      • Deans P.J.M.
      • Fernando M.B.
      • et al.
      Neuronal impact of patient-specific aberrant NRXN1α splicing.
      ). hiPSC-derived neurons from subjects with MECP2 mutations associated with Rett syndrome show transcriptomic signatures of increased stress and senescence (
      • Ohashi M.
      • Korsakova E.
      • Allen D.
      • Lee P.
      • Fu K.
      • Vargas B.S.
      • et al.
      Loss of MECP2 leads to activation of P53 and neuronal senescence.
      ). In the context of common variation, genetic background effects can be difficult to control, although strategies enriching for subjects with strong polygenic burden have been successfully used (
      • Hoffman G.E.
      • Hartley B.J.
      • Flaherty E.
      • Ladran I.
      • Gochman P.
      • Ruderfer D.M.
      • et al.
      Transcriptional signatures of schizophrenia in hiPSC-derived NPCs and neurons are concordant with post-mortem adult brains.
      ). High-throughput genome-editing approaches coupled with transcriptional readouts are likely to provide important insights (
      • Gasperini M.
      • Hill A.J.
      • McFaline-Figueroa J.L.
      • Martin B.
      • Kim S.
      • Zhang M.D.
      • et al.
      A genome-wide framework for mapping gene regulation via cellular genetic screens.
      , for example, identifying convergent patterns of neuronal differentiation delay or acceleration associated with repression of distinct groups of ASD risk genes (
      • Lalli M.A.
      • Avey D.
      • Dougherty J.D.
      • Milbrandt J.
      • Mitra R.D.
      High-throughput single-cell functional elucidation of neurodevelopmental disease-associated genes reveals convergent mechanisms altering neuronal differentiation.
      ). Similar approaches taken in the context of animal models have begun to elucidate convergent biological mechanisms underlying psychiatric risk mutations. For example, overlapping transcriptomic changes in mouse models of 3 distinct copy number variants associated with SCZ and ASD pinpointed convergent dysregulation of neuronal mitochondrial function (
      • Gordon A.
      • Forsingdal A.
      • Klewe I.V.
      • Nielsen J.
      • Didriksen M.
      • Werge T.
      • Geschwind D.H.
      Transcriptomic networks implicate neuronal energetic abnormalities in three mouse models harboring autism and schizophrenia-associated mutations.
      ). However, species differences in genomic regulation and brain cell type organization (
      • Hodge R.D.
      • Bakken T.E.
      • Miller J.A.
      • Smith K.A.
      • Barkan E.R.
      • Graybuck L.T.
      • et al.
      Conserved cell types with divergent features in human versus mouse cortex.
      ) pose notable challenges for translational investigation of brain-relevant traits (
      • Miller J.A.
      • Horvath S.
      • Geschwind D.H.
      Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways.
      ).

      Integrative Approaches: Linking Brain Imaging and Transcriptomics

      Magnetic resonance imaging is an important tool to investigate changes in brain structure, connectivity, and function that are associated with psychiatric traits at the macro scale. Owing to a lack of resources integrating human neuroimaging data with gene expression patterns across cortical regions, previous studies have primarily examined associations between polygenic risk scores and brain structure/function. However, the recent availability of whole-brain gene expression atlases has enabled investigation into how regional gene expression relates to spatial patterns of in vivo neuroimaging phenotypes (
      • Hawrylycz M.J.
      • Lein E.S.
      • Guillozet-Bongaarts A.L.
      • Shen E.H.
      • Ng L.
      • Miller J.A.
      • et al.
      An anatomically comprehensive atlas of the adult human brain transcriptome.
      ). The most widely used technique for integrating neuroimaging and transcriptomics links regional patterning of gene expression with spatial variation in brain structure using the Allen Human Brain Atlas. A detailed description of this approach is provided elsewhere (
      • Fornito A.
      • Arnatkevičiūtė A.
      • Fulcher B.D.
      Bridging the gap between connectome and transcriptome.
      ,
      • Arnatkevic Iūtė A.
      • Fulcher B.D.
      • Fornito A.
      A practical guide to linking brain-wide gene expression and neuroimaging data.
      ). The Allen Human Brain Atlas consists of 6 adult human brains from which T1-weighted magnetic resonance images were collected and coregistered with gene expression profiled from ∼900 neuroanatomically defined regions. The resulting dataset provides an index of gene expression by brain region, which is then related to spatial patterns of neuroimaging phenotypes. This integrative method has the potential to inform our understanding of how gene networks relate to the hierarchical organizing principles of brain structural topography (
      • Burt J.B.
      • Demirtaş M.
      • Eckner W.J.
      • Navejar N.M.
      • Ji J.L.
      • Martin W.J.
      • et al.
      Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography.
      ) and corticocortical connectivity patterns (
      • Krienen F.M.
      • Yeo B.T.T.
      • Ge T.
      • Buckner R.L.
      • Sherwood C.C.
      Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain.
      ) relevant to behavioral symptoms associated with psychiatric disorders, and may also elucidate the genetic mechanisms by which particular brain regions are susceptible to disease-associated pathology as indexed in vivo by magnetic resonance imaging. Indeed, work in this area has already shown that regional gene expression relates to atypical structural connectivity in SCZ (
      • Romme I.A.C.
      • de Reus M.A.
      • Ophoff R.A.
      • Kahn R.S.
      • van den Heuvel M.P.
      Connectome disconnectivity and cortical gene expression in patients with schizophrenia.
      ,
      • Morgan S.E.
      • Seidlitz J.
      • Whitaker K.J.
      • Romero-Garcia R.
      • Clifton N.E.
      • Scarpazza C.
      • et al.
      Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes.
      ) and regional variability of cortical thickness in ASD (
      • Romero-Garcia R.
      • Warrier V.
      • Bullmore E.T.
      • Baron-Cohen S.
      • Bethlehem R.A.I.
      Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism.
      ). Importantly, as the Allen Human Brain Atlas is derived from 6 neurotypical adults, future research integrating neurodevelopmental patterns of gene expression with age-appropriate neuroimaging data, as well as with disease-specific expression profiles, will undoubtedly shed light on how polygenic risk for mental illness relates to the emergence of psychiatric illness across the lifespan. Overall, these methods have the potential to inform our understanding of how individual variability in gene expression affects both behavioral and brain-based phenotypes of psychiatric dysfunction, as well as how genetic control of transcription relates to symptomatology and neuroendophenotypes across time.

      Conclusions and Future Directions

      Altogether, transcriptomics provides a rich resource for informing our understanding of the mechanisms underlying genetic risk for psychiatric disorders. Yet, we emphasize that this is not the only approach, nor is it without limitations. Indeed, RNA-seq provides only a static snapshot of the transcriptional state of a biological system, and RNA abundance is highly dynamic, undergoing concomitant synthesis, splicing, and degradation. Integration of orthogonal measures, including epigenetic and proteomic annotations (such as protein-protein interactions), will be highly complementary (
      • Neale B.M.
      • Kou Y.
      • Liu L.
      • Ma’ayan A.
      • Samocha K.E.
      • Sabo A.
      • et al.
      Patterns and rates of exonic de novo mutations in autism spectrum disorders.
      ,
      • Horn H.
      • Lawrence M.S.
      • Chouinard C.R.
      • Shrestha Y.
      • Hu J.X.
      • Worstell E.
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      NetSig: Network-based discovery from cancer genomes.
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      ), and detailed investigation of the full complexity of local splicing and isoform-level regulation (
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      ). Finally, the notable enrichment of psychiatric genetic risk among gene regulatory pathways, particularly during fetal time points, highlights a strong, continued need for basic research into the complex molecular genetic principles orchestrating human brain development.

      Acknowledgments and Disclosures

      This work was supported by the Simons Foundation Bridge to Independence Award (to MJG); National Institute of Mental Health Grant Nos. R01MH121521 (to MJG), K00MH119663 (to LMH), and T32MH073526 (to MK); and UCLA Medical Scientist Training Program Grant No. T32GM008042 (to MK). The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health , and by National Cancer Institute , National Human Genome Research Institute , National Heart, Lung, and Blood Institute , National Institute of Drug Abuse , National Institute of Mental Health, and National Institute of Neurological Disorders and Stroke . The data used for the analyses described in this manuscript were obtained from the GTEx Portal and database of Genotypes and Phenotypes (dbGaP) accession no. phs000424.v8.p2.
      The authors declare no biomedical financial interests or potential conflicts of interest.

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