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

This is the online data repository accompanying the following manuscript: Consensus molecular environment of schizophrenia risk genes in co-expression networks shifting across brain development, age and region Giulio Pergola1,2,*, Madhur Parihar1, Leonardo Sportelli1,2, Rahul Bharadwaj1, Christopher Borcuk2, Eugenia Radulescu1, Loredana Bellantuono2,5, Giuseppe Blasi2,3, Qiang Chen1, Joel E. Kleinman1,4, Yanhong Wang1, Srinidhi Rao Sripathy1, Brady J. Maher1,4,7, Alfonso Monaco5,9, Fabiana Rossi1,2,10, Joo Heon Shin1, Thomas M. Hyde1,4,6, Alessandro Bertolino2,3,*, Daniel R. Weinberger1,7,8,* 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)Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy 4)Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland 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 10)Campus Bio-Medico University of Rome, Rome, Italy Abstract: Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the lifespan. We investigated the convergence of putative schizophrenia risk genes in brain co-expression 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, while also revealing 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 putative schizophrenia risk genes in DLPFC; 21 are novel associations with schizophrenia. In iPSC-derived neurons, the unique relationship of these genes with putative schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting co-expression patterns across brain regions and across time, potentially underwriting its shifting clinical presentation. 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.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.RData: Preprocessed data for parsed/nonparsed NC networks (DLPFC, HP, CAUDATE, DENTATE). For fixed window and sliding window study. [SummarizedExperiment objects] libdnetworks(SCZ).preprocessed.RData: Preprocessed data for nonparsed SCZ networks (DLPFC, HP, CAUDATE, DENTATE). For the sliding window study. [As “SummarizedExperiment” objects in R] sample_matched_HP_DG_qsva(NC).preprocessed.RData: Preprocessed data for the sample-matched HP-DG. QSVA removed pipeline. For Cell population enrichment study. [As “SummarizedExperiment” objects in R] sample_matched_HP_DG_noqsva(NC).preprocessed.RData: Preprocessed data for the sample-matched HP-DG. No QSVA removed pipeline. For Cell population enrichment study. [As “SummarizedExperiment” objects in R] stemcell.preprocessed.RData: Preprocessed data for the iPSC network. For replication in human iPSC data study. Neuronal samples averaged for each “RealGenome”. [As “SummarizedExperiment” objects in R] 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: [e-mail]

本在线数据集配套如下学术手稿：《跨脑发育、年龄与脑区动态变化的共表达网络（co-expression networks）中精神分裂症（schizophrenia, SCZ）风险基因的共识分子环境》
作者：Giulio Pergola1,2,*，Madhur Parihar1，Leonardo Sportelli1,2，Rahul Bharadwaj1，Christopher Borcuk2，Eugenia Radulescu1，Loredana Bellantuono2,5，Giuseppe Blasi2,3，Qiang Chen1，Joel E. Kleinman1,4，Yanhong Wang1，Srinidhi Rao Sripathy1，Brady J. Maher1,4,7，Alfonso Monaco5,9，Fabiana Rossi1,2,10，Joo Heon Shin1，Thomas M. Hyde1,4,6，Alessandro Bertolino2,3,*，Daniel R. Weinberger1,7,8,*

作者单位：
1) 美国马里兰州巴尔的摩市约翰·霍普金斯医学园区利伯脑发育研究所
2) 意大利巴里市巴里阿尔多莫罗大学转化生物医学与神经科学系精神神经科学研究组
3) 意大利巴里市大学综合医院合作临床中心（Azienda Ospedaliero-Universitaria Consorziale Policlinico）
4) 约翰·霍普金斯大学医学院精神病学与行为科学系，马里兰州巴尔的摩市
5) 意大利巴里市国家核物理研究所（Istituto Nazionale di Fisica Nucleare, INFN）
6) 约翰·霍普金斯大学医学院神经病学系，马里兰州巴尔的摩市
7) 约翰·霍普金斯大学医学院神经科学系，马里兰州巴尔的摩市
8) 约翰·霍普金斯大学医学院遗传医学系，马里兰州巴尔的摩市
9) 意大利巴里市巴里阿尔多莫罗大学跨物理系（Dipartimento Interateneo di fisica）
10) 意大利罗马市罗马生物医学大学校区

摘要：精神分裂症是一类神经发育性脑疾病，其遗传风险与贯穿生命周期的动态临床表型变化密切相关。本研究针对死后人类背外侧前额叶皮层（dorsolateral prefrontal cortex, DLPFC）、海马体、尾状核及齿状回颗粒细胞的共表达网络，按特定年龄阶段进行分层解析（总样本量N=833），探究潜在精神分裂症风险基因的聚集特征。结果显示，前额叶早期即参与精神分裂症相关生物学过程；同时揭示了脑区间的动态互作模式，相较于合并所有年龄阶段的分析，按年龄分层能解释更多精神分裂症风险变异。通过多数据源与已发表研究，我们在DLPFC中鉴定出28个在富集潜在精神分裂症风险基因的共表达模块中最具一致性的伙伴基因，其中21个为全新的精神分裂症关联基因。在诱导多能干细胞（induced pluripotent stem cell, iPSC）分化的神经元中，这些基因与潜在精神分裂症风险基因的独特关联模式得以验证保留。精神分裂症的遗传架构嵌入于脑区与时间维度的动态共表达模式中，或为其动态临床表型的分子基础。

数据文件：
1. `DLPFC hit.genes_kb_200__online.version.zip`：仅包含200kbp SCZ基因集的年龄分层背外侧前额叶皮层共表达网络交互式桑基图。桑基图支持鼠标悬停查看基因列表，同时支持缩放、拖拽与选中操作。
2. `DLPFC hit.genes_kb_200__paper.version.zip`：仅包含200kbp SCZ基因集的年龄分层背外侧前额叶皮层共表达网络交互式桑基图。该版本为论文配图，将小型模块合并为一个浅灰色宏模块。
3. `DLPFC all.genes_kb_200__online.version.zip`：包含所有基因的年龄分层背外侧前额叶皮层共表达网络交互式桑基图。
4. `DLPFC all.genes_kb_200__paper.version.zip`：包含所有基因的年龄分层背外侧前额叶皮层共表达网络交互式桑基图。该版本为论文配图，将小型模块合并为一个浅灰色宏模块。
5. `HP hit.genes_kb_200__online.version.zip`：仅包含200kbp SCZ基因集的年龄分层海马体共表达网络交互式桑基图。
6. `HP hit.genes_kb_200__paper.version.zip`：仅包含200kbp SCZ基因集的年龄分层海马体共表达网络交互式桑基图。该版本为论文配图，将小型模块合并为一个浅灰色宏模块。
7. `HP all.genes_kb_200__online.version.zip`：包含所有基因的年龄分层海马体共表达网络交互式桑基图。
8. `HP all.genes_kb_200__paper.version.zip`：包含所有基因的年龄分层海马体共表达网络交互式桑基图。该版本为论文配图，将小型模块合并为一个浅灰色宏模块。
9. `Modulewise SCZ enrichment.xlsx`：包含所有共表达网络的模块水平精神分裂症富集分析结果的Excel文件。
10. `wide_form_test_slidingwindow_NC_SchizoNew(v1.4)_final.xlsx`：包含滑动窗口共表达网络加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）输出结果的Excel文件。
11. `wide_form_WGCNA(v3.7.1)_final.xlsx`：包含本研究生成及已发表共表达网络的WGCNA输出结果的Excel文件。
12. `libdnetworks(NC).preprocessed.RData`：分层/未分层正常对照（NC）共表达网络（背外侧前额叶皮层、海马体、尾状核、齿状回）的预处理数据，适用于固定窗口与滑动窗口分析。[为R语言SummarizedExperiment对象]
13. `libdnetworks(SCZ).preprocessed.RData`：未分层精神分裂症共表达网络（背外侧前额叶皮层、海马体、尾状核、齿状回）的预处理数据，适用于滑动窗口分析。[为R语言SummarizedExperiment对象]
14. `sample_matched_HP_DG_qsva(NC).preprocessed.RData`：样本匹配的海马体-齿状回预处理数据，采用定量序列变异分析（quantitative sequence variation analysis, QSVA）去混杂流程，适用于细胞群体富集分析。[为R语言SummarizedExperiment对象]
15. `sample_matched_HP_DG_noqsva(NC).preprocessed.RData`：样本匹配的海马体-齿状回预处理数据，未采用QSVA去混杂流程，适用于细胞群体富集分析。[为R语言SummarizedExperiment对象]
16. `stemcell.preprocessed.RData`：诱导多能干细胞共表达网络的预处理数据，适用于人类iPSC数据验证研究。每个“真实基因组”对应的神经元样本已完成均值聚合。[为R语言SummarizedExperiment对象]

配套代码可通过以下网址获取：https://github.com/LieberInstitute/Brain_WGCNA
本数据集同时可通过以下网址获取：https://nets.libd.org/age_wgcna/
如需咨询数据相关问题，请联系：
朱利奥·佩尔戈拉（Giulio Pergola）：[e-mail]