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Brain gene co-expression networks link complement signaling with convergent synaptic pathology in schizophrenia

Nature Neuroscience volume 24, pages 799–809 (2021)Cite this article

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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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Fig. 1: Limited evidence for broad genetic enrichment within the complement system.
Fig. 2: C4A-seeded co-expression networks capture convergent genetic risk for SCZ.
Fig. 3: Strong network expansion with increased C4A copy number.
Fig. 4: C4A-seeded co-expression networks identify transcriptional correlates of synaptic pruning.
Fig. 5: Sex differences in C4A co-expression highlight male-accentuated effects on mTOR signaling and neuronal cilia.
Fig. 6: Spatiotemporal patterns of C4A co-expression implicate frontal cortical regions and early adult timepoints in SCZ.
Fig. 7: Broad, bimodal differential expression of genes within the classical complement pathway in postmortem brains from individuals with SCZ.
Fig. 8: A model of the functional role of C4A in SCZ pathogenesis.

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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.

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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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Authors and Affiliations

  1. Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Minsoo Kim, Jillian R. Haney, Pan Zhang, Leanna M. Hernandez, Lee-kai Wang, Loes M. Olde Loohuis, Luis de la Torre-Ubieta & Michael J. Gandal

  2. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Minsoo Kim & Michael J. Gandal

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

    Minsoo Kim, Jillian R. Haney, Loes M. Olde Loohuis & Michael J. Gandal

  4. Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Jillian R. Haney, Lee-kai Wang & Laura Perez-Cano

  5. STALICLA DDS, Barcelona, Spain

    Laura Perez-Cano

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Contributions

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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Correspondence to Michael J. Gandal.

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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.

Reporting Summary

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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