1. Investigating trait variability of gene co-expression network architecture in brain by controlling for genomic risk of schizophrenia.
- Author
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Radulescu, Eugenia, Chen, Qiang, Pergola, Giulio, Di Carlo, Pasquale, Han, Shizhong, Shin, Joo Heon, Hyde, Thomas M., Kleinman, Joel E., and Weinberger, Daniel R.
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GENE expression , *SCHIZOPHRENIA , *DISEASE risk factors , *REGULATOR genes , *NEURAL transmission , *GENE regulatory networks - Abstract
The effect of schizophrenia (SCZ) genetic risk on gene expression in brain remains elusive. A popular approach to this problem has been the application of gene co-expression network algorithms (e.g., WGCNA). To improve reliability with this method it is critical to remove unwanted sources of variance while also preserving biological signals of interest. In this WCGNA study of RNA-Seq data from postmortem prefrontal cortex (78 neurotypical donors, EUR ancestry), we tested the effects of SCZ genetic risk on co-expression networks. Specifically, we implemented a novel design in which gene expression was adjusted by linear regression models to preserve or remove variance explained by biological signal of interest (GWAS genomic scores for SCZ risk—(GS-SCZ), and genomic scores- GS of height (GS-Ht) as a negative control), while removing variance explained by covariates of non-interest. We calculated co-expression networks from adjusted expression (GS-SCZ and GS-Ht preserved or removed), and consensus between them (representative of a "background" network free of genomic scores effects). We then tested the overlap between GS-SCZ preserved modules and background networks reasoning that modules with reduced overlap would be most affected by GS-SCZ biology. Additionally, we tested these modules for convergence of SCZ risk (i.e., enrichment in PGC3 SCZ GWAS priority genes, enrichment in SCZ risk heritability and relevant biological ontologies. Our results highlight key aspects of GS-SCZ effects on brain co-expression networks, specifically: 1) preserving/removing SCZ genetic risk alters the co-expression modules; 2) biological pathways enriched in modules affected by GS-SCZ implicate processes of transcription, translation and metabolism that converge to influence synaptic transmission; 3) priority PGC3 SCZ GWAS genes and SCZ risk heritability are enriched in modules associated with GS-SCZ effects. Overall, our results indicate that gene co-expression networks that selectively integrate information about genetic risk can reveal novel combinations of biological pathways involved in schizophrenia. Author summary: Genetic risk for schizophrenia (SCZ) is linked to brain connectivity at multiple levels, from molecular interactions to coherent cognitive processing. Biological mechanisms by which SCZ genetic risk affects brain connectivity are generally unknown. In this study we sought to uncover some of these mechanisms by focusing on gene co-expression networks calculated from RNA-Seq data extracted from postmortem DLPFC. We created co-expression networks from gene expression adjusted to account for, or to remove the likely effects of genomic (polygenic) scores of SCZ risk. We minimized potential confounding effects of illness state or treatment on gene expression by using only brains from donors without a history of mental disorder. We used as a control, co-expression networks from gene expression adjusted to account for effects of genomic scores of height- a normative trait. Our results show subtle and widespread effects of SCZ genomic scores on DLPFC co-expression networks. Gene sets from co-expression modules adjusted for SCZ genetic risk harbor biological significance represented by neuronal functions, but also by interacting pathways of general cellular processes like transcription, translation and metabolism. Our findings highlight possible avenues for identifying regulatory targets of gene expression in schizophrenia. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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