Gene expression elucidates functional impact of polygenic risk for schizophrenia

Abstract

Over 100 genetic loci harbor schizophrenia-associated variants, yet how these variants confer liability is uncertain. The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of people with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, 20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Altering expression of FURIN, TSNARE1 or CNTN4 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.

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Figure 1: Enrichment of cis-eQTL in regulatory and other genomic elements.
Figure 2: Overlap of GWAS for schizophrenia with eQTL in the DLPFC.
Figure 3: Neuroanatomical phenotypes upon suppression or overexpression of genes at SCZ risk loci.
Figure 4: Decreasing FURIN expression in human NPCs perturbs neural migration.
Figure 5: Differential expression between schizophrenia cases and controls in the DLPFC.
Figure 6: Co-expression network analysis in control DLPFC samples.
Figure 7: Power to detect differential expression.

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Gene Expression Omnibus

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Acknowledgements

We thank the patients and families who donated material for these studies. We thank T. Lehner for his early and inspirational ideas about this project, as well as organizational and intellectual support. We thank X. He for discussions regarding Sherlock, J. Scarpa for help running and interpreting WGCNA, L. Essioux for support in establishing and managing interactions with the Consortium, and A. Bertolino and A. Ghosh for continuous encouragement. Data and results were generated as part of the CommonMind Consortium supported by funding from Takeda Pharmaceuticals Company Limited, F. Hoffman-La Roche Ltd; grants R01MH093725-02S1 (J.D.B.), P50MH066392 (J.D.B.), R01MH097276 (P.S., E.E.S.), R01MH075916 (C.-G.H.), P50MH096891 C.-G.H., REG, P50MH084053-S1 (D.A.L.), R37MH057881 (B.D.), R37MH057881S1 (B.D.), R01MH085542-S1 (P.S.), U01MH096296-S2 (P.S.), HHSN271201300031C (V.H.), VA VISN3 MIRECC (V.H.), P50MH066392 (J.D.B.), NIMH Intramural program (B.K.L.), R01MH101454 (K.J.B.), R01MH109677 (P.R.), R01AG050986 (P.R.), VA Merit BX002395 (P.R.) and R01 AG036836 (P.D.H.); New York Stem Cell Foundation (K.J.B.); the Silvio O Conte Center grant P50MH094268 (N.K.); NARSAD (E.C.O.) and NARSAD Young Investigator (D.M.R., P.R., E.A.S.); the Stanley Medical Research Institute for Funding for Non-Human Primate Research; and NIMH grants R01MH074313 (S.E.H.); R01AG036836, U01AG046152 and R01AG017917 (D.A.B. and P.I.D.J.); R01AG046170 (E.S.S., B.Z., J.Z., and P.R.); and R01MH109706 (E.C.O.). Brain tissues for the study were obtained from the following brain bank collections: the Mount Sinai NIH Brain and Tissue Repository, the University of Pennsylvania Alzheimer's Disease Core Center, the University of Pittsburgh Brain Tissue Donation Program, the NIMH Human Brain Collection Core and Wake Forest University. CMC Leadership: P. Sklar and J.D. Buxbaum (Icahn School of Medicine at Mount Sinai), B. Devlin and D.A. Lewis (University of Pittsburgh), R.E. Gur and C.-G. Hahn (University of Pennsylvania), K. Hirai and H. Toyoshiba (Takeda Pharmaceuticals Company Limited), E. Domenici and L. Essioux (F. Hoffman-La Roche Ltd), L.M. Mangravite and M.A. Peters (Sage Bionetworks), and T. Lehner and B.K. Lipska (NIMH).

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Authors

Contributions

P.R., J.S.J., K.T., R.E.G., C.-G.H., D.A.L., V.H., B.K.L. and J.D.B. contributed to sample collection. S.E.H. contributed monkey brain tissue and P.F.S. contributed mouse data. M.F., P.R., S.K.S., D.H.K., T.M.P., D.M.R., K.K.D., E.C.O., A.T., T.C., M.A.P., E.D., B.D. and P.S. contributed to the writing of this manuscript. M.F., P.R., S.K.S., H.R.S., D.M.R., K.K.D., M.C.M., J.M.J.D., A.C., S.M.P., L.A.S., L.M.M., H.T., D.A.L., M.A.P., J.D.B., E.E.S., K.H., K.J.B., N.K., B.D. and P.S. contributed to experimental and study design and planning analytical strategies. L.A.S., H.T., D.A.L., B.K.L., J.D.B., E.E.S., K.H., E.D., B.D. and P.S. contributed the funding of this work. M.F., P.R., S.K.S., J.S.J., D.H.K., T.M.P., D.M.R., H.R.S., L.L.K., R.K., D.P., Z.H.G., A.E.C., L.X., A.C., K.K.D., A.B., C.L., B.R., E.A.S., T.H., J.F.F., Y.-C.W., J.T.D., B.A.L., T.R., J.Z., B.Z., P.F.S., S.M.P., E.E.S., K.R., E.D., B.D. and P.S. contributed to data analyses. E.C.O., A.T., J.X., M.P., K.J.B. and N.K. contributed to the model system experiments. T.R., D.A.B., P.L.D.J. contributed the ROS/MAP data. A.C., L.A.S., L.M.M., H.T., R.E.G., C.-G.H., D.A.L., M.A.P., B.K.L., J.D.B., K.H., E.E.S., E.D., B.D. and P.S. contributed to the management and leadership of phase 1 of the CommonMind Consortium.

Corresponding author

Correspondence to Pamela Sklar.

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Competing interests

E. Dominici was an employee of F. Hoffmann-La Roche for the first portion of the study and later served as a consultant to Roche in the area of genetic biomarkers. H. Toyoshiba and K. Hirai are employees of Takeda Pharmaceutical Company Limited and L.A. Shinobu is a former employee. D.A.L. currently receives investigator-initiated research support from Pfizer and from 2012 to 2014 served as a consultant in the areas of target identification and validation and new compound development to Autifony, Bristol-Myers Squibb, Concert Pharmaceuticals and Sunovion. M. Fromer was an employee of Mount Sinai until April 2016; he is now an employee of Google Verily.

Integrated supplementary information

Supplementary Figure 1 Experimental flow diagram and ancestry characterization.

(a) Samples were received from three brain banks and transferred to a single site for tissue processing, RNA sequencing, and DNA genotyping. Randomization of samples was performed at each of two steps: 1) prior to tissue processing and 2) again, prior to library preparation. (b) Distribution of genetic ancestry by brain bank, diagnosis, and nominal ancestry. (c) Ancestry dimension plots using assigned clusters (letters) and diagnosis (colors) to highlight additional features of the data. SCZ=blue, Control=red, Affective Disorders=orange. (d) Polygenic scoring profile for CMC. The x-axis gives the discovery p-value threshold (pT), and the y-axis the Nagelkerke’s case-control prediction R-squared.

Supplementary Figure 2 Analytic flow diagram and RNA sequencing metrics.

Top: Diagram of the mapping, QC and quantification of RNA sequencing, and basic QC results. Middle: Approach to data normalization and covariate adjustment. Bottom: Analytic strategy for differential expression, eQTLs, WGCNA with differential connectivity, and evaluation of genetic overlaps of results.

Supplementary Figure 3 qPCR validation of gene expression levels from RNA-seq.

(a) Comparisons of gene expression levels quantified using either qPCR (expression quantified as cycle number, normalized to that of beta actin, cyclophilin, and GAPDH) or voom-normalized log(CPM) levels from RNA-seq. Each point corresponds to one of N =114 individuals from the Pitt cohort: 57 SCZ cases (red) and 57 matched controls (blue). For each of the 13 genes validated, the corresponding gene symbol, Ensembl gene identifier, and Pearson correlation r between qPCR and RNA-seq are as shown. (b) Voom-normalized log(CPM) are presented by diagnosis and site for GAD1, PVALB, SLC32A1 and SST. Cases=pink, controls=green.

Supplementary Figure 4 Evaluation and selection of covariates and surrogate variables.

The steps to determine which covariates were used for gene expression adjustment (by linear modeling and regression) were: (a) Determine which “simple” (low degree of freedom) covariates add most to diagnosis as a predictor. (b) Test which batch effects (each with many levels) improve the model. (c) Cluster library batch effects to simplify them (reducing the number of degrees of freedom). (d) Re-cluster library batch effects after outlier sample removal. (e) Define the final library batch clusters (“clustered LIB”). (f) Evaluate the potential utility of quadratic terms. (g) Select the number of surrogate variables (SV) using SVA. (h) Test how many genes have variance better explained by addition of SV. (i) Summarize the model R2 explained (“model fit”) by either diagnosis alone, the base model including diagnosis (panel a), inclusion of the clustered library batch (b, e), adding in RIN2 (f), and adding in the SV (h); note the increasing R2 at each successive step of model selection, indicating that more of the gene expression variance is being captured by the model.

Supplementary Figure 5 Distribution of biological covariates.

(a) CMC and (b) HBCC

Supplementary Figure 6 Genome-wide overlap of GWAS for schizophrenia with eQTL in the DLPFC.

(a) Genome-wide Manhattan plot of Sherlock GWAS-eQTL overlap significance for all 12,367 genes (with one or more eQTL) tested for potential mediation of common variant risk of schizophrenia. Blue and red colors mark genes located on alternating chromosomes, and the dashed line denotes genome-wide Bonferroni significance (Pcorrected ≤ 0.05). (b) eQTL association profiles across two loci on chromosome 3 (highlighting CNTN4) and chromosome 8 (highlighting TSNARE1). Plot details are as in Fig. 2a.

Supplementary Figure 7 Summary of eQTL evidence pointing to single genes in GWAS loci.

(a) List of 10 genes (including 3 isoforms) ranked significantly by Sherlock for having gene and/or isoform eQTL profiles matching SCZ association, where no other gene (or gene isoform) in the corresponding GWAS locus is significant. For each gene and/or isoform, shown are the predicted direction of effect for the corresponding GWAS risk allele (predicted up-regulation in red and down-regulation in green), and whether the eQTL-to-GWAS correlation passes a manual visual assessment (see corresponding plots in panel B). (b) For each of the 13 genes or isoforms listed in A, the correlation between the significance (-log10(P)) of the eQTL associations at each eSNP (eQTL significance is to the right of the vertical dotted line) and the significance of association of that SNP with SCZ (genome-wide significance for GWAS is above the horizontal dotted line) is assessed. Each plot notes the number of total SNPs depicted (those with eQTL P < 0.1), the number of significantly associated eSNPs (P < 10-3 as input to Sherlock, or at FDR ≤ 5%), and Spearman’s rho (and p-value) for the correlation between the significant eQTL SNPs and their associations with SCZ. Plots are grouped into the 7 high quality single-gene GWAS-eQTL overlap results, the SNX19 gene that is high quality only at the isoform level, and two genes ranked highly by Sherlock but not passing this manual evaluation of correlation between disease and expression association signals.

Supplementary Figure 8 Validation of furin targeting.

(a) Schematic of genomic region on zebrafish furin_a gene targeted by a splice-blocking morpholino (MO) on the exon-intron region of exon 7. Position of RT-PCR primers for MO validation are also indicated. (b) RT-PCR assessment of furin_a MO efficiency. Only the MO-injected sample showed an amplicon containing the inclusion of intron 7 as a result of deleterious splicing caused by the MO. (c) Sanger sequencing confirming the inclusion of intron 7. (d) Schematic depicting the inclusion of intron 7 as a result of targeting furin_a. (e) Quantification of head size and proliferation defects caused by suppression of furin_a that can be rescued by co-expression of human FURIN mRNA. Error bars are s.e., * P < 0.05, ** P < 0.005, *** P < 0.0005.

Supplementary Figure 9 Correlation of differential expression between CMC and two meta-analytic studies.

Estimated differential expression for 23 genes with significant differential expression for CommonMind, after covariate adjustment, and for at least one of two meta-analytic studies. (a) CMC results fit with covariates as described in manuscript; (b) CMC results fit with only diagnosis as the covariate. Note that for the diagnosis-only model, none of these genes would be significant for CMC.

Supplementary Figure 10 In situ hybridization

Images are from the Allen Human Brain Atlas for selected genes among significant differentially expressed genes in CMC. Images are taken from the ISH data from the Neurotransmitter Study (176 genes across cortical regions and 88 genes across subcortical regions in 4 control cases). Images are taken from Frontal Cortex, Structure Name: middle frontal gyrus, right, Specimen H0351.1016 (White or Caucasian, Age 55 yrs, Male). Images suggest neuron specific expression for CALB1 (5.241) and GABRA5 (5.925); glial cell for ALDH1A1 (6.038) and SLC6A13 (1.512); endothelial cell expression for SLC38A5 (2.356); mixed expression for GABRB3 (7.795), predominantly neuronal; for GRIN3A (4.783) and CHRN2 (1.316), predominantly glial. The top right figure is a representative Nissl staining image of the same area. In brackets the average expression (pre-adjusted logCPM) determined by RNASeq in the CMC cohort is reported.

Supplementary Figure 11 Differential isoform expression.

(a) The number of isoforms per gene for all isoforms analyzed after filtering. (b) Analysis of differential expression of gene isoforms between SCZ and controls resulted in some consistency with results at the gene level, but also yielded additional genes that were not found to be differential when considered without isoform resolution. (c) Overlap between the genes differential at FDR ≤ 5% with the genes with isoforms differential at FDR ≤ 5%. (d) Prototypical examples for 4 genes with differential isoform expression (selected because each is found in the M2c co-expression module enriched for differential expression, see Fig. 6a). The mean log2 fold change, 95% confidence interval, and FDR value is plotted for each isoform. The top two genes (TBC1D15 and MSL1) each have at least one isoform differentially down-regulated in SCZ cases, but in aggregate (across all isoforms), the gene is not differentially expressed; for MSL1, this is likely due to the presence of 2 differential isoforms moving in opposite directions. On the other hand, the bottom two genes (N4BP2 and AFF2) are ranked as differential at the gene level, which seems to be mostly resulting from a single significant isoform in each instance.

Supplementary Figure 12 Cell type proportions.

Estimated cell fractions from human cortical tissue using (a) the program CIBERSORT to deconvolve the mixture based on 11,992 genes from the Zhang matrix of mouse cell-specific expression, (b) CIBERSORT to deconvolve the mixture based on 415 human cell-specific markers (markers estimated from the data), and (c) the CellMix package, lsfit option, using 11,992 genes from the Zhang matrix.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12, Supplementary Information, and Supplementary Tables 1–3 (PDF 3496 kb)

Supplementary Methods Checklist (PDF 408 kb)

Supplementary Data File 1

Published post-mortem genome-wide differential gene expression studies in schizophrenia (XLSX 18 kb)

Supplementary Data File 2

Summary of regions with genes ranked highly by Sherlock; Genes with max-eQTL ranked as credible by PGC SCZ2 GWAS data; Isoforms with max-eQTL ranked as credible by PGC SCZ2 GWAS data (XLSX 31 kb)

Supplementary Data File 3

Differentially expressed genes; Differentially expressed isoforms; Comparison of CMC and HBCC (XLSX 1035 kb)

Supplementary Data File 4

Enrichment of differential expression in hypothesis-driven and hypothesis-free gene sets (XLSX 163 kb)

Supplementary Data File 5

Module assignments and connectivity values in the control network; Module assignments and connectivity values in the schizophrenia network (XLSX 2718 kb)

Supplementary Data File 6

Overlap of differentially expressed genes with modules in the control network; Overlap of differentially expressed genes with modules in the case network (XLSX 51 kb)

Supplementary Data File 7

Geneset enrichment for modules in the control network; Geneset enrichment for modules in the schizophrenia network (XLSX 157 kb)

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Fromer, M., Roussos, P., Sieberts, S. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci 19, 1442–1453 (2016). https://doi.org/10.1038/nn.4399

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