Lessons Learned From Parsing Genetic Risk for Schizophrenia Into Biological Pathways

Pergola, Giulio; Penzel, Nora; Sportelli, Leonardo; Bertolino, Alessandro

doi:10.1016/j.biopsych.2022.10.009

Cited by 12 publications

(9 citation statements)

References 123 publications

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“…Moreover, our results are consistent with past findings of positive correlations between polygenic risk for SCZ and ventral striatal activation during the MID task in a large sample of healthy adolescents 55 . Importantly and again, cumulative risk outside of this filter (i.e., variants not included in C80) did not show a significant relationship with this anticipatory BOLD response; along with the similar pattern observed in our PET results, this specificity suggests that parsing the PRS into co-expression pathways can reveal previously unreported phenotypic association with risk 43 .…”

Section: Discussionsupporting

confidence: 79%

“…In sum, these results highlight a DA related striatal gene set that is prominently expressed in SCZ, implicated in SCZ genetic risk, and involved in dopamine synthesis and striatal physiological activity in vivo, suggesting that genetic risk within this pathway differentially affects SCZ-relevant striatal function. These observations provide evidence that polygenic risk for SCZ can be effectively parsed into pathways important for specific systems-level functions that is measurable even in the absence of illness 43 . Furthermore, they suggest a molecular basis of how genetic risk within the C80 pathway might affect illness relevant striatal neurochemistry and neurofunction and may open new possible avenues for studying clinical heterogeneity 19,43 and drug treatment response 43 .…”

Section: Discussionmentioning

confidence: 76%

“…These observations provide evidence that polygenic risk for SCZ can be effectively parsed into pathways important for specific systems-level functions that is measurable even in the absence of illness 43 . Furthermore, they suggest a molecular basis of how genetic risk within the C80 pathway might affect illness relevant striatal neurochemistry and neurofunction and may open new possible avenues for studying clinical heterogeneity 19,43 and drug treatment response 43 .…”

Section: Discussionmentioning

confidence: 76%

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Dopamine and schizophrenia from bench to bedside: Discovery of a striatal co-expression risk gene set that predicts in vivo measures of striatal function

Sportelli,

Eisenberg,

Passiatore

et al. 2023

Preprint

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Schizophrenia (SCZ) is characterized by a polygenic risk architecture implicating diverse molecular pathways important for synaptic function. However, how polygenic risk funnels through these pathways to translate into syndromic illness is unanswered. To evaluate biologically meaningful pathways of risk, we used tensor decomposition to characterize gene co-expression in post-mortem brain (of neurotypicals: N=154; patients with SCZ: N=84; and GTEX samples N=120) from caudate nucleus (CN), hippocampus (HP), and dorsolateral prefrontal cortex (DLPFC). We identified a CN-predominant gene set showing dopaminergic selectivity that was enriched for genes associated with clinical state and for genes associated with SCZ risk. Parsing polygenic risk score for SCZ based on this specific gene set (parsed-PRS), we found that greater pathway-specific SCZ risk predicted greater in vivo striatal dopamine synthesis capacity measured by [18F]-FDOPA PET in three independent cohorts of neurotypicals and patients (total N=235) and greater fMRI striatal activation during reward anticipation in two additional independent neurotypical cohorts (total N=141). These results reveal a "bench to bedside" translation of dopamine-linked genetic risk variation in driving in vivo striatal neurochemical and hemodynamic phenotypes that have long been implicated in the pathophysiology of SCZ.

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Section: Discussionsupporting

confidence: 79%

Section: Discussionmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 76%

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Dopamine and schizophrenia from bench to bedside: Discovery of a striatal co-expression risk gene set that predicts in vivo measures of striatal function

Sportelli,

Eisenberg,

Passiatore

et al. 2023

Preprint

Self Cite

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“…Likewise, it worth reiterating that GWAS derived genomic scores are crude measures representing mixtures of genetic signal not specific to only one complex trait of interest. As suggested in [ 42 – 43 ], parsing genomic scores by biological pathways may provide a solution to increase their portability, especially for new therapies development.…”

Section: Discussionmentioning

confidence: 99%

Investigating trait variability of gene co-expression network architecture in brain by controlling for genomic risk of schizophrenia

Radulescu,

Chen,

Pergola

et al. 2023

PLoS Genet

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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.

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“…Notably, our results based on a connectivity match between networks across brain regions and age failed to support previous findings of less connected coexpression networks in the DLPFC of patients with SCZ (5). We hypothesize that the networks available with the present work may afford greater explanatory power in the translation of postmortem brain insights into neuroscience and clinical predictions in living patients with the latest available approaches (13)(14)(15)(53)(54)(55)(56)(57)(58)(59)(60).…”

Section: Scz Gene Coexpression Changes Across the Neurotypical Life Spanmentioning

confidence: 99%

Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions

et al. 2023

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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.

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Lessons Learned From Parsing Genetic Risk for Schizophrenia Into Biological Pathways

Cited by 12 publications

References 123 publications

Dopamine and schizophrenia from bench to bedside: Discovery of a striatal co-expression risk gene set that predicts in vivo measures of striatal function

Dopamine and schizophrenia from bench to bedside: Discovery of a striatal co-expression risk gene set that predicts in vivo measures of striatal function

Investigating trait variability of gene co-expression network architecture in brain by controlling for genomic risk of schizophrenia

Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions

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