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.

中文翻译：

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通过控制精神分裂症的基因组风险来研究大脑中基因共表达网络结构的性状变异性。

精神分裂症（SCZ）遗传风险对大脑基因表达的影响仍然难以捉摸。解决这个问题的一种流行方法是应用基因共表达网络算法（例如，WGCNA）。为了提高这种方法的可靠性，消除不需要的方差来源，同时保留感兴趣的生物信号至关重要。在这项针对死后前额叶皮层（78 名神经典型供体，欧洲血统）的 RNA 测序数据的 WCGNA 研究中，我们测试了 SCZ 遗传风险对共表达网络的影响。具体来说，我们实施了一种新颖的设计，其中通过线性回归模型调整基因表达，以保留或消除由感兴趣的生物信号解释的方差（SCZ 风险的 GWAS 基因组评分 - (GS-SCZ) 和身高的基因组评分 - GS ( GS-Ht）作为阴性对照），同时消除由非兴趣协变量解释的方差。我们根据调整后的表达（保留或删除 GS-SCZ 和 GS-Ht）计算共表达网络，以及它们之间的共识（代表不受基因组评分影响的“背景”网络）。然后，我们测试了 GS-SCZ 保留模块和背景网络之间的重叠，推断重叠减少的模块将受到 GS-SCZ 生物学的影响最大。此外，我们测试了这些模块的 SCZ 风险收敛性（即 PGC3 SCZ GWAS 优先基因的富集、SCZ 风险遗传性和相关生物本体论的富集）。我们的结果强调了 GS-SCZ 对大脑共表达网络影响的关键方面，特别是：1）保留/去除 SCZ 遗传风险会改变共表达模块；2) 受 GS-SCZ 影响的模块中丰富的生物途径涉及转录、翻译和代谢过程，这些过程汇聚在一起影响突触传递；3)优先PGC3 SCZ GWAS基因和SCZ风险遗传力在与GS-SCZ效应相关的模块中得到丰富。总的来说，我们的结果表明，选择性整合遗传风险信息的基因共表达网络可以揭示与精神分裂症有关的生物途径的新组合。