Abstract
Most studies of gene expression in the brains of individuals with schizophrenia have focused on cortical regions, but subcortical nuclei such as the striatum are prominently implicated in the disease, and current antipsychotic drugs target the striatum’s dense dopaminergic innervation. Here, we performed a comprehensive analysis of the genetic and transcriptional landscape of schizophrenia in the postmortem caudate nucleus of the striatum of 443 individuals (245 neurotypical individuals, 154 individuals with schizophrenia and 44 individuals with bipolar disorder), 210 from African and 233 from European ancestries. Integrating expression quantitative trait loci analysis, Mendelian randomization with the latest schizophrenia genome-wide association study, transcriptome-wide association study and differential expression analysis, we identified many genes associated with schizophrenia risk, including potentially the dopamine D2 receptor short isoform. We found that antipsychotic medication has an extensive influence on caudate gene expression. We constructed caudate nucleus gene expression networks that highlight interactions involving schizophrenia risk. These analyses provide a resource for the study of schizophrenia and insights into risk mechanisms and potential therapeutic targets.
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Data availability
Processed data (Supplementary Data 1–13 and additional data files) and accession codes to raw RNA-Seq FASTQ files and genotypes used in this study are available from https://erwinpaquolalab.libd.org/caudate_eqtl/. Additional data files include Brainseq_caudate_4features_mash_associations.tar.gz (full set of transancestry caudate eQTL mash model results) and Brainseq_LIBD_brainregions_allpairs_genes.txt.gz (full set of brain region interaction eQTL mash model results).
Code availability
Code and jupyter notebooks are available through GitHub at https://github.com/LieberInstitute/BrainSeqPhase3Caudate.
References
Kahn, R. S. et al. Schizophrenia. Nat. Rev. Dis. Prim. 1, 15067 (2015).
Schizophrenia Working Group of the Psychiatric Genomics Consortium Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).
Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502–508 (2022).
Carlsson, A. Does dopamine play a role in schizophrenia? Psychol. Med. 7, 583–597 (1977).
Creese, I., Burt, D. R. & Snyder, S. H. Dopamine receptor binding predicts clinical and pharmacological potencies of antischizophrenic drugs. Science 192, 481–483 (1976).
Collado-Torres, L. et al. Regional heterogeneity in gene expression, regulation, and coherence in the frontal cortex and hippocampus across development and schizophrenia. Neuron 103, 203–216 (2019).
Jaffe, A. E. et al. Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis. Nat. Neurosci. 21, 1117–1125 (2018).
Gandal, M. J. et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693–697 (2018).
Hoffman, G. E. et al. CommonMind Consortium provides transcriptomic and epigenomic data for schizophrenia and bipolar disorder. Sci. Data 6, 180 (2019).
Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).
Fusar-Poli, P. & Meyer-Lindenberg, A. Striatal presynaptic dopamine in schizophrenia, part II: meta-analysis of [18F/11C]-DOPA PET studies. Schizophrenia Bull. 39, 33–42 (2013).
Seeman, P. & Niznik, H. B. Dopamine receptors and transporters in Parkinson’s disease and schizophrenia. FASEB J. 4, 2737–2744 (1990).
Wong, D. F. et al. Positron emission tomography reveals elevated D2 dopamine receptors in drug-naive schizophrenics. Science 234, 1558–1563 (1986).
Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).
GTEx Consortium Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
GTEx Consortium The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
Urbut, S. M., Wang, G., Carbonetto, P. & Stephens, M. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nat. Genet. 51, 187–195 (2019).
Dal Toso, R. et al. The dopamine D2 receptor: two molecular forms generated by alternative splicing. EMBO J. 8, 4025–4034 (1989).
Centonze, D. et al. Differential contribution of dopamine D2S and D2L receptors in the modulation of glutamate and GABA transmission in the striatum. Neuroscience 129, 157–166 (2004).
Montmayeur, J. P. et al. Differential expression of the mouse D2 dopamine receptor isoforms. FEBS Lett. 278, 239–243 (1991).
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).
Barbeira, A. N. et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol. 22, 49 (2021).
Gusev, A. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50, 538–548 (2018).
Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).
Mancuso, N. et al. Integrating gene expression with summary association statistics to identify genes associated with 30 complex traits. Am. J. Hum. Genet. 100, 473–487 (2017).
Perzel Mandell, K. A. et al. Molecular phenotypes associated with antipsychotic drugs in the human caudate nucleus. Mol. Psychiatry 27, 2061–2067 (2022).
Kim, Y. et al. Comparative genomic evidence for the involvement of schizophrenia risk genes in antipsychotic effects. Mol. Psychiatry 23, 708–712 (2018).
Chong, V. Z., Young, L. T. & Mishra, R. K. cDNA array reveals differential gene expression following chronic neuroleptic administration: implications of synapsin II in haloperidol treatment. J. Neurochem. 82, 1533–1539 (2002).
Korostynski, M. et al. Novel drug-regulated transcriptional networks in brain reveal pharmacological properties of psychotropic drugs. BMC Genomics 14, 606 (2013).
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
de Leeuw, C., Werme, J., Savage, J., Peyrot, W. J. & Posthuma, D. Reconsidering the validity of transcriptome-wide association studies. Preprint at bioRxiv https://doi.org/10.1101/2021.08.15.456414 (2021).
Abi-Dargham, A. Schizophrenia: overview and dopamine dysfunction. J. Clin. Psychiatry 75, e31 (2014).
Farde, L. et al. Positron emission tomographic analysis of central D1 and D2 dopamine receptor occupancy in patients treated with classical neuroleptics and clozapine. Relation to extrapyramidal side effects. Arch. Gen. Psychiatry 49, 538–544 (1992).
Lipska, B. K. et al. Critical factors in gene expression in postmortem human brain: focus on studies in schizophrenia. Biol. Psychiatry 60, 650–658 (2006).
Ritchie, M. E., Carvalho, B. S., Hetrick, K. N., Tavaré, S. & Irizarry, R. A. R/Bioconductor software for Illumina’s Infinium whole-genome genotyping BeadChips. Bioinformatics 25, 2621–2623 (2009).
Carvalho, B. S., Louis, T. A. & Irizarry, R. A. Quantifying uncertainty in genotype calls. Bioinformatics 26, 242–249 (2010).
Scharpf, R. B., Irizarry, R. A., Ritchie, M. E., Carvalho, B. & Ruczinski, I. Using the R package crlmm for genotyping and copy number estimation. J. Stat. Softw. 40, 1–32 (2011).
Scharpf, R. B. et al. A multilevel model to address batch effects in copy number estimation using SNP arrays. Biostatistics 12, 33–50 (2011).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).
Fuchsberger, C., Abecasis, G. R. & Hinds, D. A. minimac2: faster genotype imputation. Bioinformatics 31, 782–784 (2015).
Loh, P.-R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).
Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
Feng, Y. -Y. et al. RegTools: integrated analysis of genomic and transcriptomic data for discovery of splicing variants in cancer. Preprint at bioRxiv https://doi.org/10.1101/436634 (2018).
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
Jaffe, A. E. et al. qSVA framework for RNA quality correction in differential expression analysis. Proc. Natl Acad. Sci. USA 114, 7130–7135 (2017).
Buja, A. & Eyuboglu, N. Remarks on parallel analysis. Multivar. Behav. Res. 27, 509–540 (1992).
Jew, B. et al. Accurate estimation of cell composition in bulk expression through robust integration of single-cell information. Nat. Commun. 11, 1971 (2020).
Tran, M. N. et al. Single-nucleus transcriptome analysis reveals cell-type-specific molecular signatures across reward circuitry in the human brain. Neuron 109, 3088–3103 (2021).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T. & Delaneau, O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479–1485 (2016).
Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).
Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).
Martin, E. R. et al. Properties of global- and local-ancestry adjustments in genetic association tests in admixed populations. Genet. Epidemiol. 42, 214–229 (2018).
Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012).
Storey, J. D. A direct approach to false discovery rates. J. R. Stat. Soc. Series B Stat. Methodol. 64, 479–498 (2002).
Wen, X. Molecular QTL discovery incorporating genomic annotations using Bayesian false discovery rate control. Ann. Appl. Stat. 10, 1619–1638 (2016).
Wen, X., Pique-Regi, R. & Luca, F. Integrating molecular QTL data into genome-wide genetic association analysis: probabilistic assessment of enrichment and colocalization. PLoS Genet. 13, e1006646 (2017).
Pividori, M. et al. PhenomeXcan: mapping the genome to the phenome through the transcriptome. Sci. Adv. 6, eaba2083 (2020).
Lee, Y., Francesca, L., Pique-Regi, R. & Wen, X. Bayesian multi-SNP genetic association analysis: control of FDR and use of summary statistics. Preprint at bioRxiv https://doi.org/10.1101/316471 (2018).
Wen, X., Lee, Y., Luca, F. & Pique-Regi, R. Efficient integrative multi-SNP association analysis via deterministic approximation of posteriors. Am. J. Hum. Genet. 98, 1114–1129 (2016).
Klopfenstein, D. V. et al. GOATOOLS: a Python library for Gene Ontology analyses. Sci. Rep. 8, 10872 (2018).
Luo, W. & Brouwer, C. Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics 29, 1830–1831 (2013).
Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at https://doi.org/10.48550/arXiv.1312.6114 (2014).
Kim, H. & Mnih, A. Disentangling by factorising. Preprint at https://doi.org/10.48550/arXiv.1802.05983 (2019).
Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).
Wickham, H. ggplot2—Elegant Graphics for Data Analysis (Springer International Publishing, 2016).
Waskom, M. seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).
Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).
Acknowledgements
We thank the Offices of the Chief Medical Examiner of Washington, DC, Northern Virginia, and Maryland for the provision of brain tissue used in this study. We also thank L.B. Bigelow and members of the LIBD Neuropathology Section for their work in assembling and curating the clinical and demographic information and organizing the Human Brain Tissue Repository of the Lieber Institute. Finally, we thank the families that have donated this tissue to advance our understanding of psychiatric disorders. This work is supported by the LIBD, the BrainSeq Consortium, the NIH T32 fellowship (T32MH015330) to K.J.M.B, NIH R01 (MH123183) to L.A.H and L.C.-T. and an NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation to J.A.E.
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K.J.M.B., J.A.E., D.R.W. and A.C.M.P. designed the study. K.J.M.B.and A.C.M.P. performed main data analysis and interpretation and led the writing of the manuscript. Q.C. performed SMR analysis and interpretation. J.H.S. and R.T. performed RNA sequencing data generation (RNA extraction, library preparation and sequencing) and QC analyses. A.E.J., L.C.-T., J.M.S. and E.E.B. performed data processing of RNA sequencing and genotypes. L.A.H.-M. and L.C.-T. performed cell type deconvolution analysis and interpretation. K.J.M.B., A.S.F., A.R.B., E.R., G.P. and A.C.M.P. performed gene network analysis and interpretation. R.A. contributed to differential expression analysis and interpretation. A.C.M.P. conceived and developed GNVAE. W.S.U. created the user-friendly database and website for eQTL visualization. A.E.J., L.C.-T. and the BrainSeq Consortium provided feedback on the manuscript and contributed to the interpretation of results. A.D.-S. obtained consent from and clinically characterized human brain donors. T.M.H. and J.E.K. obtained consent from donors, curated medical data, collected, characterized and dissected human brain tissue and contributed to the design of the study. K.J.M.B., J.A.E., D.R.W. and A.C.M.P. wrote the manuscript. J.A.E., D.R.W. and A.C.M.P. supervised the study.
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The following BrainSeq Consortium members have competing interests. M.M., T.S., K.T. and D.J.H. are employees of Astellas Pharma. D.A.C. and B.B.M. are employees of Eli Lilly and Company. K.M. is an employee of UCB Pharma and past employee of Eli Lilly and Company. M.F., D.H. and H.K. are employees of Janssen Research & Development LLC and Johnson and Johnson. M.D. and L.F. are employees of H. Lundbeck A/S. T.K.-T. and D.M. are employees of F. Hoffmann-La Roche. The primary role of these BrainSeq Consortium members was study conceptualization, project administration and funding acquisition. The remaining authors declare no competing interests.
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Supplementary Information
Legends for Supplementary Data 1–13, Supplementary Figs. 1–32 and Tables 1–8.
Supplementary Data 1
Excel file with quality control metrics (RIN, percent alignment and rRNA mapping rate) across BrainSeq brain regions with total RNA RNA-sequencing (caudate, DLPFC and hippocampus), CommonMind DLPFC and GTEx brain regions.
Supplementary Data 2
Compressed tar-zipped directory containing compressed text files for mash model results for the brain region interaction meta-analysis for all eFeatures (gene, transcript, exon and junction) containing mash results of strong signals (top variants).
Supplementary Data 3
Compressed tar-zipped directory containing fastENLOC results for schizophrenia GWAS (PGC2 + CLOZUK and PGC3).
Supplementary Data 4
Compressed tar-zipped directory containing compressed text files with SMR significant results (SMR FDR < 0.05 and HEIDI P > 0.01) for genes, transcripts, exons and junctions. Additionally, GTEx caudate replication results are in this compressed directory.
Supplementary Data 5
Compressed text file of TWAS results in EA for the caudate nucleus with the latest schizophrenia GWAS (PGC3) across all features.
Supplementary Data 6
Text file of caudate TWAS genes overlapping PGC3-prioritized genes.
Supplementary Data 7
Text file of TWAS genes summarized across caudate nucleus, DLPFC and hippocampus in EA using PGC2 + CLOZUK schizophrenia GWAS.
Supplementary Data 8
Compressed text file of differential expression results for the caudate nucleus of schizophrenia versus neurotypical individuals across all features.
Supplementary Data 9
Excel file of discordant differential expressed genes for schizophrenia comparing the caudate nucleus with DLPFC and hippocampus.
Supplementary Data 10
Text file of demographic information for caudate nucleus samples.
Supplementary Data 11
Compressed text file of differential expression results for the caudate nucleus of schizophrenia with and without antipsychotics present at time of death versus neurotypical individuals across all features (gene, transcript, exon and junction)
Supplementary Data 12
Compressed tar-zipped directory containing GNVAE output: table of latent variables for all genes, module membership tables, GO enrichment analysis tables and GO word clouds.
Supplementary Data 13
Compressed tar-zipped directory containing WGCNA output: eigengenes, module membership tables and GO enrichment analysis tables.
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Benjamin, K.J.M., Chen, Q., Jaffe, A.E. et al. Analysis of the caudate nucleus transcriptome in individuals with schizophrenia highlights effects of antipsychotics and new risk genes. Nat Neurosci 25, 1559–1568 (2022). https://doi.org/10.1038/s41593-022-01182-7
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DOI: https://doi.org/10.1038/s41593-022-01182-7
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