Consensus molecular environment of schizophrenia risk genes in co-expression networks shifting across age and brain regions

This is the online data repository accompanying the following manuscript:
Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions

Giulio Pergola^1,2,3,*, Madhur Parihar¹, Leonardo Sportelli^1,2, Rahul Bharadwaj¹, Christopher Borcuk², Eugenia Radulescu¹, Loredana Bellantuono^2,5, Giuseppe Blasi^2,4, Qiang Chen¹, Joel E. Kleinman^1,3, Yanhong Wang¹, Srinidhi Rao Sripathy¹, Brady J. Maher^1,3,7, Alfonso Monaco^5,9, Fabiana Rossi^1,2, Joo Heon Shin¹, Thomas M. Hyde^1,3,6, Alessandro Bertolino^2,4,*, Daniel R. Weinberger^1,7,8,*

Affiliations:

1)Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (USA)
2)Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
3)Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
4)Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
5)Istituto Nazionale di Fisica Nucleare (INFN), Bari, Italy
6)Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
7)Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland
8)Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
9)Dipartimento Interateneo di fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy

Abstract:

Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.

Citation: Giulio Pergola et al. ,Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions.Sci. Adv.9, eade2812(2023).DOI:10.1126/sciadv.ade2812

Data Files:
DLPFC hit.genes_kb_200__online.version.zip:
Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For Sankey plots, hover mouse over the links to see the list of genes. Also supports zoom, drag and selection.
DLPFC hit.genes_kb_200__paper.version.zip:
Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)
DLPFC all.genes_kb_200__online.version.zip:
Interactive Sankey plot for age-parsed DLPFC networks with all genes
DLPFC all.genes_kb_200__paper.version.zip:
Interactive Sankey plot for age-parsed DLPFC networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)
HP hit.genes_kb_200__online.version.zip:
Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only
HP hit.genes_kb_200__paper.version.zip:
Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)
HP all.genes_kb_200__online.version.zip:
Interactive Sankey plot for age-parsed Hippocampus networks with all genes
HP all.genes_kb_200__paper.version.zip:
Interactive Sankey plot for age-parsed Hippocampus networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)
Modulewise SCZ enrichment(1.0).xlsx:
Excel file contains module level SCZ enrichment results for all networks
wide_form_test_slidingwindow_NC_SchizoNew(v1.4)_final.xlsx:
Excel file contains WGCNA output for sliding window networks
wide_form_WGCNA(v3.7.1)_final.xlsx:
Excel file contains WGCNA output for our generated networks and from previously published networks
libdnetworks(NC).preprocessed.exp.RData:
Preprocessed ranknormalised expression assay for age-parsed/nonparsed NC networks (DLPFC, HP, CAUDATE, DENTATE). For fixed window and sliding window study.
libdnetworks(SCZ).preprocessed.exp.RData:
Preprocessed ranknormalised expression assay for nonparsed SCZ networks (DLPFC, HP, CAUDATE, DENTATE). For the sliding window study.
sample_matched_HP_DG_qsva(NC).preprocessed.exp.RData:
Preprocessed ranknormalised expression assay for the sample-matched HP-DG. QSVA removed pipeline. For Cell population enrichment study.
sample_matched_HP_DG_noqsva(NC).preprocessed.exp.RData:
Preprocessed ranknormalised expression assay for the sample-matched HP-DG. No QSVA removed pipeline. For Cell population enrichment study.
stemcell.preprocessed.exp.RData:
Preprocessed ranknormalised expression assay for the iPSC network. For replication in human iPSC data study. Neuronal samples averaged for each “RealGenome”.
SCZ.ref.list.sciadv.ade2812.rds: List of All Biotypes/ Protein Coding Schizophrenia reference genelist for following bins: PGC3, 0 kbp, 20 kbp, 50 kbp, 100 kbp, 150 kbp, 200 kbp, 250 kbp, 500 kbp.

Accompanying code can be found at: https://github.com/LieberInstitute/Brain_WGCNA
Data from this repository is also available at: https://nets.libd.org/age_wgcna/

For any data inquiries please contact:
Giulio Pergola: Giulio.Pergola@libd.org