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Coexpression network architecture reveals the brain-wide and multiregional basis of disease susceptibility

Nature Neuroscience volume 24, pages 1313–1323 (2021)Cite this article

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Abstract

Gene networks have yielded numerous neurobiological insights, yet an integrated view across brain regions is lacking. We leverage RNA sequencing in 864 samples representing 12 brain regions to robustly identify 12 brain-wide, 50 cross-regional and 114 region-specific coexpression modules. Nearly 40% of genes fall into brain-wide modules, while 25% comprise region-specific modules reflecting regional biology, such as oxytocin signaling in the hypothalamus, or addiction pathways in the nucleus accumbens. Schizophrenia and autism genetic risk are enriched in brain-wide and multiregional modules, indicative of broad impact; these modules implicate neuronal proliferation and activity-dependent processes, including endocytosis and splicing, in disease pathophysiology. We find that cell-type-specific long noncoding RNA and gene isoforms contribute substantially to regional synaptic diversity and that constrained, mutation-intolerant genes are primarily enriched in neurons. We leverage these data using an omnigenic-inspired network framework to characterize how coexpression and gene regulatory networks reflect neuropsychiatric disease risk, supporting polygenic models.

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Fig. 1: Human whole-brain coexpression atlas.
Fig. 2: Cell-type heterogeneity relates to coexpression modules, mutation intolerance and evolution.
Fig. 3: Creating a catalog of cell-type-specific isoforms.
Fig. 4: Region-specific gene upregulation reflects region-specific cell types and ribosomal turnover.
Fig. 5: Gene-level module enrichment for de novo protein-truncating variants, GWAS summary statistics and differential expression.
Fig. 6: Ontologies, PPI networks and expression profiles of ASD-associated modules.
Fig. 7: Characterizing core–periphery structure of high-impact neuropsychiatric disease genes across multiple networks.

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Data availability

Processed data are available at http://geschwindlab.org/gclabapps/hubgene/home/.

Code availability

Supporting code for network construction and network genetic analysis is available at https://github.com/dhglab/multiregional-networks/.

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Acknowledgements

D.H.G., C.L.H. and G.R. were supported by the National Institute of Mental Health (NIMH; R01MH110927 and U01MH115746) and the Simons Foundation Autism Research Initiative. G.R. was supported by NIMH award 1F32MH114620. K.L. and G.P. are supported by institutional funding from the Stanley Center for Psychiatric Research at the Broad Institute, the NIMH (R01 MH109903 and U01 MH121499), the Simons Foundation Autism Research Initiative (awards 515064 and 735604), the Lundbeck Foundation (R223-2016-721 and R350-2020-963). A.B., A.S. and P.P. were supported by NIMH award R01MH110927. We thank members of the laboratories of D.H.G., K.L. and A.B. for stimulating discussions. We are also grateful for data made publicly available by the GTEx consortium and S. Deverasetty for creating the HUBgene web browser.

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Authors and Affiliations

  1. Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA, USA

    Christopher L. Hartl

  2. Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Christopher L. Hartl, Gokul Ramaswami, William G. Pembroke & Daniel H. Geschwind

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Sandrine Muller, Greta Pintacuda & Kasper Lage

  4. Department of Surgery, Massachusetts General Hospital, Boston, MA, USA

    Sandrine Muller & Kasper Lage

  5. Department of Stem Cell and Regenerative Medicine, Harvard University, Cambridge, MA, USA

    Greta Pintacuda

  6. Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

    Ashis Saha, Princy Parsana & Alexis Battle

  7. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Alexis Battle

  8. Institute for Biological Psychiatry, Mental Health Center Sct. Hans, University of Copenhagen, Roskilde, Denmark

    Kasper Lage

  9. Department of Psychiatry and Biobehavioral Sciences, Semel Institue, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Daniel H. Geschwind

  10. Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Daniel H. Geschwind

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

    Daniel H. Geschwind

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Contributions

D.H.G., C.L.H., A.B. and K.L. conceived the study design. A.B., A.S. and P.P. reprocessed RNA-seq data. C.L.H., S.M., P.P., W.G.P., G.R. and A.S. built networks and performed analyses. G.P. differentiated cell lines and performed the ANK2 western blot. C.L.H. and D.H.G. prepared the manuscript and figures. A.B. and K.L. contributed to editing.

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Correspondence to Daniel H. Geschwind.

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Extended data

Extended Data Fig. 1 Brain network QC and algorithm comparisons (related to Fig. 1).

a, Standard boxplot (box: quartiles, whiskers: 1.5xIQR) of ePC and HCP loadings onto canonical cell type genes, showing significant heterogeneity of loadings across cell types, N = 114 (Neuron), 79 (Astrocyte), 242 (Microglia), 103 (Oligodendrocyte), 176 (Endothelial). b, Standard boxplot (box: quartiles, whiskers: 1.5xIQR) of ePC loadings after covariate correction using HCP and LM base correction, showing that cell type heterogeneity of the 1st component of expression is lost after HCP correction. Gene set sizes as in a; significance (two-sided T-test) ***: < 0.001. c, Network-based GO prediction accuracy for each brain region. The same gene holdouts are used in 10-fold cross validation, generating 10 values for the AUC difference of each GO category, which are used to generate a Z-score for the expected AUC difference. d, Relative improvement to the integrated correlation coefficient for BRNHYP genes, for linear model and HCP based corrections. e, Pairwise co-clustering statistics for the 4 algorithms compared in Fig. 1. X-axis denotes which modules are taken as the reference set. f-h, Pairwise module overlaps between 3 of the 4 algorithms compared in Fig. 1 (GLASSO yielded too many modules to visualize here). i, t-SNE embedding of gene features from whole-brain tensor decomposition, colored by DBSCAN clusters. j, As (i), but colored and annotated with whole-brain modules. k,l, Overlap between whole-brain consensus and tensor-decomposition+DBSCAN modules. Color scheme as in (f-h). m, Standard boxplot (box: quartiles, whiskers: 1.5xIQR, N = 10 bootstrap re-samplings) of within-module recall values for hub-gene co-clustering, demonstrating that at 100 samples, the recall is above 50% for most modules.

Extended Data Fig. 2 Exploration of cell-type modules (related to Fig. 2).

a, Cell-type marker enrichment for brain-wide modules, extended with markers of microglial activation and deactivation, and markers of reactive gliosis and A1/A2 reactive astrocytes. b, Plots of the marginal rate (solid: mean, shade: 95% CI of GAM) of LoF-intolerant (pLI>0.9) genes, as a function of BW-M1 (most enriched) and BW-M2 (most depleted) kME. c, Gene ontology enrichment for BW-M5. d, Marginal LoF-intolerance rates (solid: mean, shade: 95% CI of GAM), by gene kME, for neuronal subtype modules. e,f, Standard boxplots (box: quartiles, whiskers: 1.5xIQR) of module mean topological overlap, and gene expression, for 5 whole-brain modules in ASD cases and matched controls (Parikshak 2016). The case/control difference in lncRNA is closely matched by the same difference in randomly-selected, matched coding genes. g, LoF-intolerance enrichment for neuronal subtype modules, using pLI and o/e bins as response variables, and a linear model correcting for gene GC and length (logit link, p-values: coefficient T-test). All modules except BROD-M8 show strong enrichment, and BROD-M8 shows enrichment when using soft-membership instead of hard membership.

Extended Data Fig. 3 Glial cell-type isoforms (related to Fig. 3).

a, Replicate of main Fig. 3(b) in astrocytes, showing a strong positive relationship between astrocyte module membership, and relative expression in astrocyte cells. b-e, Relationship between module kME and cell type relative expression for transcripts across 4 neuron/astrocyte isoform switch genes, demonstrating concordance between high kME, and high relative expression. f, Unsigned Fisher’s exact test of the contingency of ‘assigned to module’ and ‘top-ranked cell type marker’ for varying kME thresholds for (left) oligodendrocytes and (right) astrocytes; for marker rankings based on both absolute and relative expression within the cell-sorted data. Thresholds in the range 0.45-0.55 appear to balance significance and odds ratio across absolute and relative rankings.

Extended Data Fig. 4 Validation of neuron-astrocyte isoform switching (related to Fig. 3).

(left) Unmodified Western Blot corresponding to Fig. 3 (right) Same blot, annotated with source of input material and band identities.

Extended Data Fig. 5 Overlap with published modules containing disease genes.

a–d, Overlaps between published modules and the consensus whole-brain co-expression modules identified in this paper, demonstrating that the majority of modules show a high overlap, particularly to the neuronal module BW-M4. P-values: signed Fisher’s exact test. These modules were been selected because of published enrichment for neuropsychiatric disease risk genes. (see Methods).

Extended Data Fig. 6 Module BW-M4 functional annotation (related to Fig. 5).

a, Signed gene ontology enrichments (logistic regression controlling for gene length and GC, p-value from coefficient T-test) for MAGMA-significant ASD genes in module set BW-M4 across all regions in which a BW-M4 module is present. Bar color reflects the enrichment odds ratio (black: 5, light blue: 25); outline color reflects the GWAS source (pink: Grove et al., 2017; green: iPsych (Robinson et al., Nat. Genet. 2016), blue: PGC-2017). b, Meta-GSEA scores for significant MAGMA genes in BW-M4 across all tissues, implicating synaptic transmission and calcium transport as sources of neuronal dysfunction in SCZ. Red line denotes FDR significance level.

Extended Data Fig. 7 Regional AUPR curves for CEREB-M1 and CTX-M1.

Nearest-neighbor precision-recall curves for CEREB-M1 labels across all region-level co-expression networks; showing significantly higher AUPR for cerebellar regions, but substantial AUPR for all remaining regions. Right. Nearest-neighbor precision-recall curves for CTX-M3.

Extended Data Fig. 8 Exploration of an omnigenic-like model across different networks (related to Fig. 7).

Plot of Phi statistics for InWeb brain PPI network (‘PPI’) and four regulatorycircuits.org (‘RC’) networks: Hippocampus (‘Hippo’), amygdala (‘Amy’), NEU+ neurons, astrocytes, and neuroprogenitor cells (‘NPC’). Vertical breaks represent the study used to calculate phi, while the colors represent those studies used to define proposed core genes, or network central genes.

Supplementary information

Supplementary Information

Supplementary Note.

Reporting Summary

Supplementary Table 1

Per-region and multiregional module definitions and gene-module kME values.

Supplementary Table 2

Pathway and cell marker enrichments for regional and multiregional modules.

Supplementary Table 3

Correlations between module eigengenes and nonnegative matrix factorization loadings onto PsychEncode factors.

Supplementary Table 4

Table of gene kME values with pLI and overexpression values from ExAC.

Supplementary Table 5

Table of lncRNAs, their inferred module from xgboost-based projection, expression in cell types from single-cell data, and differential expression statistics in neuropsychiatric disease.

Supplementary Table 6

Table of isoform quantification correlation to total-expression modules across all regions. This is a very large file and may be difficult to download or open; please contact the authors for alternative formats.

Supplementary Table 7

RCT results for regional and multiregional contrasts.

Supplementary Table 8

Table of significant overlapping odds ratios and P values between whole-brain modules and previously published disease modules.

Supplementary Table 9

Phi statistics and P values for blood, brain and developing brain networks.

Supplementary Table 10

Preservation statistics for GLASSO, vMF and ARACNe–produced modules in orthogonal microarray datasets.

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Hartl, C.L., Ramaswami, G., Pembroke, W.G. et al. Coexpression network architecture reveals the brain-wide and multiregional basis of disease susceptibility. Nat Neurosci 24, 1313–1323 (2021). https://doi.org/10.1038/s41593-021-00887-5

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