Abstract
The most significant common variant association for schizophrenia (SCZ) reflects increased expression of the complement component 4A (C4A). Yet, it remains unclear how C4A interacts with other SCZ risk genes or whether the complement system more broadly is implicated in SCZ pathogenesis. Here, we integrate several existing, large-scale genetic and transcriptomic datasets to interrogate the functional role of the complement system and C4A in the human brain. Unexpectedly, we find no significant genetic enrichment among known complement system genes for SCZ. Conversely, brain co-expression network analyses using C4A as a seed gene reveal that genes downregulated when C4A expression increases exhibit strong and specific genetic enrichment for SCZ risk. This convergent genomic signal reflects synaptic processes, is sexually dimorphic and most prominent in frontal cortical brain regions, and is accentuated by smoking. Overall, these results indicate that synaptic pathways—rather than the complement system—are the driving force conferring SCZ risk.
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Data availability
PsychENCODE raw genotype and RNA-seq data that support the findings of this study are available at https://doi.org/10.7303/syn12080241. Processed PsychENCODE summary-level data are available at http://resource.psychencode.org. GTEx genotype and RNA-seq data used for the analyses described in this manuscript were obtained from the GTEx Portal (http://www.gtexportal.org) and dbGaP (accession number phs000424.v7.p2).
Code availability
The code used to perform bioinformatic analyses are available at https://github.com/gandallab/C4A-network.
References
Sullivan, P. F., Kendler, K. S. & Neale, M. C. Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch. Gen. Psychiatry 60, 1187–1192 (2003).
Gandal, M. J., Leppa, V., Won, H., Parikshak, N. N. & Geschwind, D. H. The road to precision psychiatry: translating genetics into disease mechanisms. Nat. Neurosci. 19, 1397–1407 (2016).
Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).
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).
Hyman, S. E. The daunting polygenicity of mental illness: making a new map. Phil. Trans. R. Soc. Lond. B 373, 20170031 (2018).
Parikshak, N. N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013).
Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat. Neurosci. 18, 199–209 (2015).
Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).
Stephan, A. H., Barres, B. A. & Stevens, B. The complement system: an unexpected role in synaptic pruning during development and disease. Annu. Rev. Neurosci. 35, 369–389 (2012).
Coulthard, L. G., Hawksworth, O. A. & Woodruff, T. M. Complement: The emerging architect of the developing brain. Trends Neurosci. 41, 373–384 (2018).
Feinberg, I. Schizophrenia: caused by a fault in programmed synaptic elimination during adolescence? J. Psychiatr. Res. 17, 319–334 (1982).
Keshavan, M. S., Anderson, S. & Pettegrew, J. W. Is schizophrenia due to excessive synaptic pruning in the prefrontal cortex? The Feinberg hypothesis revisited. J. Psychiatr. Res. 28, 239–265 (1994).
Glantz, L. A. & Lewis, D. A. Decreased dendritic spine density on prefrontal cortical pyramidal neurons in schizophrenia. Arch. Gen. Psychiatry 57, 65–73 (2000).
van Erp, T. G. M. et al. Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the enhancing neuro imaging genetics through meta analysis (ENIGMA) Consortium. Biol. Psychiatry 84, 644–654 (2018).
MacDonald, M. L. et al. Selective loss of smaller spines in schizophrenia. Am. J. Psychiatry 174, 586–594 (2017).
Sellgren, C. M. et al. Increased synapse elimination by microglia in schizophrenia patient-derived models of synaptic pruning. Nat. Neurosci. 22, 374–385 (2019)
Stein, J. L. et al. A quantitative framework to evaluate modeling of cortical development by neural stem cells. Neuron 83, 69–86 (2014).
Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).
Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).
GTEx Consortium. et al. Genetic effects on gene expression across human tissues. Nature 550, 204 (2017).
Parikshak, N. N., Gandal, M. J. & Geschwind, D. H. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat. Rev. Genet. 16, 441–458 (2015).
Mah, W. & Won, H. The three-dimensional landscape of the genome in human brain tissue unveils regulatory mechanisms leading to schizophrenia risk. Schizophr. Res. 217, 17–25 (2019).
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
Li, T. et al. A scored human protein-protein interaction network to catalyze genomic interpretation. Nat. Methods 14, 61–64 (2017).
Marshall, C. R. et al. Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nat. Genet. 49, 27–35 (2017).
Singh, T. et al. Exome sequencing identifies rare coding variants in 10 genes which confer substantial risk for schizophrenia. Preprint at https://www.medrxiv.org/content/10.1101/2020.09.18.20192815v1 (2020).
Genovese, G. et al. Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia. Nat. Neurosci. 19, 1433–1441 (2016).
Koopmans, F. et al. SynGO: an evidence-based, expert-curated knowledge base for the synapse. Neuron 103, 217–234.e4 (2019).
Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362, eaat7615 (2018).
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.e8 (2019).
Skene, N. G. & Grant, S. G. N. Identification of vulnerable cell types in major brain disorders using single cell transcriptomes and expression weighted cell type enrichment. Front. Neurosci. 10, 16 (2016).
Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).
Kamitaki, N. et al. Complement genes contribute sex-biased vulnerability in diverse disorders. Nature 582, 577–581 (2020).
Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).
Neniskyte, U. & Gross, C. T. Errant gardeners: glial-cell-dependent synaptic pruning and neurodevelopmental disorders. Nat. Rev. Neurosci. 18, 658–670 (2017).
Gandal, M. J. et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693–697 (2018).
Kendler, K. S., Lönn, S. L., Sundquist, J. & Sundquist, K. Smoking and schizophrenia in population cohorts of Swedish women and men: a prospective co-relative control study. Am. J. Psychiatry 172, 1092–1100 (2015).
Lam, M. et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat. Genet. 51, 1670–1678 (2019).
Karama, S. et al. Cigarette smoking and thinning of the brain’s cortex. Mol. Psychiatry 20, 778–785 (2015).
Jones, H. J. et al. Association of combined patterns of tobacco and cannabis use in adolescence with psychotic experiences. JAMA Psychiatry 75, 240–246 (2018).
Tang, G. et al. Loss of mTOR-dependent macroautophagy causes autistic-like synaptic pruning deficits. Neuron 83, 1131–1143 (2014).
Han, Y.-G. et al. Hedgehog signaling and primary cilia are required for the formation of adult neural stem cells. Nat. Neurosci. 11, 277–284 (2008).
Wang, S., Livingston, M. J., Su, Y. & Dong, Z. Reciprocal regulation of cilia and autophagy via the MTOR and proteasome pathways. Autophagy 11, 607–616 (2015).
Foerster, P. et al. mTORC1 signaling and primary cilia are required for brain ventricle morphogenesis. Development 144, 201–210 (2017).
Park, S. M., Jang, H. J. & Lee, J. H. Roles of primary cilia in the developing brain. Front. Cell. Neurosci. 13, 218 (2019).
Marley, A. & von Zastrow, M. A simple cell-based assay reveals that diverse neuropsychiatric risk genes converge on primary cilia. PLoS ONE 7, e46647 (2012).
Nguyen, H. T. et al. Integrated Bayesian analysis of rare exonic variants to identify risk genes for schizophrenia and neurodevelopmental disorders. Genome Med 9, 114 (2017).
Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).
Povey, S. et al. The HUGO gene nomenclature committee (HGNC). Hum. Genet. 109, 678–680 (2001).
Võsa, U. et al. Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis. Preprint at bioRxiv https://doi.org/10.1101/447367 (2018).
Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).
van Rheenen, W. et al. Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis. Nat. Genet. 48, 1043–1048 (2016).
Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).
Fritsche, L. G. et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat. Genet. 48, 134–143 (2016).
Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).
Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).
Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).
Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).
Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50, 912–919 (2018).
Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).
International Multiple Sclerosis Genetics Consortium (IMSGC) et al. Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat. Genet. 45, 1353–1360 (2013).
Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).
International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) & OCD Collaborative Genetics Association Studies (OCGAS). Revealing the complex genetic architecture of obsessive-compulsive disorder using meta-analysis. Mol. Psychiatry 23, 1181–1188 (2018).
Nalls, M. A. et al. Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease. Nat. Genet. 46, 989–993 (2014).
Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).
Bentham, J. et al. Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nat. Genet. 47, 1457–1464 (2015).
Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).
Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).
1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Sanders, S. J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233 (2015).
Satterstrom, F. K. et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 80, 568–584.e23 (2020).
Ruzzo, E. K. et al. Inherited and de novo genetic risk for autism impacts shared networks. Cell 178, 850–866.e26 (2019).
Polioudakis, D. et al. A Single-cell transcriptomic atlas of human neocortical development during Mid-gestation. Neuron 103, 785–801.e8 (2019).
Kaplanis, J. et al. Evidence for 28 genetic disorders discovered by combining healthcare and research data. Nature 586, 757–762 (2020).
Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).
Handsaker, R. E. et al. Large multiallelic copy number variations in humans. Nat. Genet. 47, 296–303 (2015).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Liberzon, A. et al. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Ballouz, S., Verleyen, W. & Gillis, J. Guidance for RNA-seq co-expression network construction and analysis: safety in numbers. Bioinformatics 31, 2123–2130 (2015).
Iancu, O. D. et al. Gene networks and haloperidol-induced catalepsy. Genes Brain Behav. 11, 29–37 (2012).
Acknowledgements
This work was supported by the Simons Foundation Bridge to Independence Award (M.J.G.), the National Institute of Mental Health (R01MH121521 to M.J.G.; R01MH123922 to M.J.G.; P50HD103557 to M.J.G.; K00MH119663 to L.M.H.; T32MH073526 to M.K.), and the UCLA Medical Scientist Training Program (T32GM008042 to M.K.). We thank G. Hoftman and members of the Gandal Lab for critical comments. Data were generated as part of the PsychENCODE Consortium, supported by U01MH103392, U01MH103365, U01MH103346, U01MH103340, U01MH103339, R21MH109956, R21MH105881, R21MH105853, R21MH103877, R21MH102791, R01MH111721, R01MH110928, R01MH110927, R01MH110926, R01MH110921, R01MH110920, R01MH110905, R01MH109715, R01MH109677, R01MH105898, R01MH105898, R01MH094714, P50MH106934, U01MH116488, U01MH116487, U01MH116492, U01MH116489, U01MH116438, U01MH116441, U01MH116442, R01MH114911, R01MH114899, R01MH114901, R01MH117293, R01MH117291, and R01MH117292 awarded to S. Akbarian (Icahn School of Medicine at Mount Sinai), G. Crawford (Duke University), S. Dracheva (Icahn School of Medicine at Mount Sinai), P. Farnham (University of Southern California), M. Gerstein (Yale University), D. Geschwind (University of California, Los Angeles), F. Goes (Johns Hopkins University), T. Hyde (Lieber Institute for Brain Development), A. Jaffe (Lieber Institute for Brain Development), J. Knowles (University of Southern California), C. Liu (SUNY Upstate Medical University), D. Pinto (Icahn School of Medicine at Mount Sinai), P. Roussos (Icahn School of Medicine at Mount Sinai), S. Sanders (University of California, San Francisco), N. Sestan (Yale University), P. Sklar (Icahn School of Medicine at Mount Sinai), M. State (University of California, San Francisco), P. Sullivan (University of North Carolina), F. Vaccarino (Yale University), D. Weinberger (Lieber Institute for Brain Development), S. Weissman (Yale University), K. White (University of Chicago), J. Willsey (University of California, San Francisco), and P. Zandi (Johns Hopkins University). 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 NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS.
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M.K. and M.J.G. planned the study and wrote the paper. M.K. performed primary analyses with additional input from J.R.H., P.Z., L.M.H., L.M.O.L., L.dlT.-U. and M.J.G. L.-K.W. and L.P.-C. validated the C4 imputation pipeline. All authors read and approved the final manuscript.
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Extended data
Extended Data Fig. 1 Ancestry of PsychENCODE subjects.
Principal component analysis was performed using PLINK after merging the PsychENCODE genotype data with the 1000 Genomes Project reference panel. The PsychENCODE genotype data was available for a total 1,864 subjects to begin with. Each point represents an individual and points are color-coded by corresponding ethnicity. Global ancestry was inferred by k-nearest neighbors algorithm with the first five principal components. Downstream analyses were restricted to samples of European ancestry (n = 812).
Extended Data Fig. 2 Number of PsychENCODE samples with high-quality C4 imputation.
Total 552 samples had average imputed probabilistic dosage > 0.7. These samples were subsequently used to generate C4A-seeded networks.
Extended Data Fig. 3 Replication of PsychENCODE seeded network in GTEx.
a, Shown are Venn diagrams of the number of overlapping C4A-positive and C4A-negative genes in PsychENCODE and GTEx (OR’s = 19 and 16, P’s < 10−16, respectively). These networks were constructed from frontal cortex samples of non-psychiatric controls with C4A CN = 2. b, Shown is correlation of effect sizes (that is, PCC) of each gene that is shared between the two networks (R = 0.68, two-sided P < 10−16).
Extended Data Fig. 4 Enrichment for complement components among C4A-positive genes and synaptic components as well as neurodevelopmental risk genes among C4A-negative genes.
a, Seed genes were permuted 10,000 times and corresponding seeded networks were tested for enrichment of the complement system (n = 57 genes) and synaptic components (n = 1,103 genes) from SynGo. Shown is distribution of the odds ratio from Fisher’s exact test. b, C4A-positive and C4A-negative genes at FDR < 0.05 from the meta-analysis of PsychENCODE and GTEx were used for rare variant analyses (logistic regression with significance assessed through likelihood ratio test). The dotted line denotes FDR-adjusted P value at 0.05.
Extended Data Fig. 5 Relationship between C4 structural variation and C4 gene expression.
Residualized C4 gene expression (that is normalized and corrected for all known biological and technical covariates except the diagnosis status) was associated strongly with corresponding gene copy number (total n = 812; n = 20, 114, 367, and 311 for ASD, BD, CTL, and SCZ samples, respectively). Adjusted R2 values are shown for significant correlations. Of note, the best linear models for C4A and C4B expression explained up to 22% and 2.7% of variation in expression, respectively. All boxplots show median and interquartile range (IQR) with whiskers denoting 1.5 × IQR.
Extended Data Fig. 6 Larger number of C4A-positive and C4A-negative genes with increased C4A copy number.
Shown are Venn diagrams of the number of overlapping C4A-positive and C4A-negative genes across three CNV groups. Note that the sum of positive and negative genes is equal to the total number of co-expressed genes. The size of the circle is approximately proportional to the number of genes.
Extended Data Fig. 7 C4A-specific interaction with C4A copy number.
Multiple regression was performed with interaction terms between C4 copy numbers and C4 gene expression. Significant interaction effect was present only between C4A copy number and C4A expression. Several genes are highlighted to demonstrate this interaction. Also shown are fitted linear models with 95% confidence bands.
Extended Data Fig. 8 Sex and spatiotemporal differences in C4A co-expression.
a, Three different thresholds were tested, namely the number of total co-expressed genes at PCC > 0.4 and the number of C4A-positive and C4A-negative genes at FDR < 0.05. Males had more co-expressed genes than females regardless of the threshold metric used (n = 36, 38, 45, 47, 39, 45, 39, and 45 for frontal cortex, anterior cingulate cortex, hippocampus, caudate, putamen, cerebellum, hypothalamus, and nucleus accumbens, respectively; permutation test, P < 10−5). b, Similarly, frontal and anterior cingulate cortex were the two most connected regions for C4A regardless of the threshold metric used (n = 36, 38, 45, 47, 39, 45, 39, and 45 for frontal cortex, anterior cingulate cortex, hippocampus, caudate, putamen, cerebellum, hypothalamus, and nucleus accumbens, respectively; permutation test, P < 10−5). c, Leftward shift in co-expression peak was observed in SCZ cases compared to neurotypical controls across different threshold metrics (n = 30, 42, 57, 68, 47, and 32 for control samples in each age bin; n = 36, 46, 55, 45, and 47 for SCZ samples). All boxplots show median and interquartile range (IQR) with whiskers denoting 1.5 × IQR.
Extended Data Fig. 9 Pathways exhibiting differential co-expression in males and females.
Shown are GSEA enrichments for C4A compared to 10,000 random seed genes. Genes were ranked by the magnitude of co-expression in male and female networks separately, and the corresponding gene list was used for GSEA. Several pathways showed the opposite direction of effect.
Extended Data Fig. 10 Differential gene expression of the complement system in SCZ and BD.
Differential expression (DE) for brain-expressed complement system genes (n = 42 genes) was assessed in SCZ (n = 531) and BD (n = 217) compared to controls (n = 895). DE was repeated for SCZ after randomly downsampling to match the sample size of BD. DE was also repeated for SCZ while adjusting for C4A expression and/or C4A copy number. Since C4A copy number was only imputed for samples of European ancestry, a subset of PsychENCODE samples was used for such conditional analyses (n = 311 and 367 for SCZ and controls, respectively). Text shows log2FC. Asterisks denote significance at FDR < 0.1.
Supplementary information
Supplementary Information
Supplementary Figures 1–6.
Supplementary Table 1
Complement system annotations and evidence for SCZ genetic association
Supplementary Table 2
C4A-seeded networks in PsychENCODE and GTEx
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Kim, M., Haney, J.R., Zhang, P. et al. Brain gene co-expression networks link complement signaling with convergent synaptic pathology in schizophrenia. Nat Neurosci 24, 799–809 (2021). https://doi.org/10.1038/s41593-021-00847-z
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DOI: https://doi.org/10.1038/s41593-021-00847-z
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