Chapter 2 (Farrow et al. 2022; Brain) Supplementary tables

This repository contains the supplementary tables for chapter 2: Establishing gene regulatory networks from Parkinson's disease risk loci. See below for supplementary table descriptions.Supplementary table 2.1: Nalls et al. 2019 GWAS dataset. Data downloaded directly from the publication Supplementary files. Odds ratios obtained from the IPDGC PD locus web browser.Supplementary table 2.2: The Hi-C datasets used in CoDeS3D analysis. List of all Hi-C datasets used in this study and their reference to original article and GEO accession numberSupplementary table 2.3a: Nalls et al. 2019 GWAS: Sig eQTLs CoDeS3D output. Detailed data of eQTL-gene interactions when interrogating the 90 PD-SNPs. Overall 76 of the 90 SNPs tested were identified as eQTLs that were associated with the regulation of 518 genes across 49 tissues.Supplementary table 2.3b: Replication of CoDeS3D whole-blood cis- eQTLs in eQTLGenSupplementary table 2.3c: eQTLGen PD curated SNPs Supplementary table 2.4: Nalls et al. 2019 GWAS: Sig eQTLs CoDeS3D output: Brain Hi-C: Brain GTEx only. Detailed data of eQTL-gene interactions when interrogating the 90 PD-SNPs in the 13 GTEx brain tissues only, and using brain-specific Hi-C cell lines (cortical plate neurons; germinal zone neurons; astrocyte of the cerebellum; brain vascular pericyte; brain microvascular endothelial primary cell; SK-N-MC; SK-N-DZ; neuronal progenitor cells; astrocyte of the spinal cord; dorsolateral prefrontal cortex cells; hippocampus cells; H1 neuronal progenitor cells)Supplementary table 2.5: SNPnexus epigenome roadmap for all 90 SNPs, including histone modifications and DNaseI marker of open chromatinSupplementary table 2.6a: Pathway analysis of the 518 genes; gProfiler (g:OST)Supplementary table 2.6b: Pathway analysis of the 165 brain-specific genes; gProfiler (g:OST)Supplementary table 2.7: The LOEUF values (downloaded from gnomAD) for each of the 518 genes included in the gene network. LOEUF score ("loss-of-function observed/expected upper bound fraction”) indicates the tolerance of a given gene to inactivation. A low LOEUF score indicates strong selection against loss-of-function variation.Supplementary table 2.8: Nalls et al. 2019 GWAS: Sig eQTLs CoDeS3D output. Detailed data of eQTL-gene interactions when interrogating the 90 PD-SNPs in the foetal cortex, using 2 foetal Hi-C cell lines (cortical plate neurons; germinal zone neurons) and a foetal cortex eQTL dataset.Supplementary table 2.9a: Gene regulatory network analysis. Protein:protein interaction analysis of 523 genes (using STRING, high confidence levels >0.700) followed by Louvain clustering identified 9 significant clusters.Supplementary table 2.9b: Risk vs. protective proportion analysis of nine clustersSupplementary table 2.10a: Pathway analysis for the genes within each of the nine significant clusters; gProfiler (g:OST) results (inc. bootstrapping analysis results)Supplementary table 2.10b: Association between cluster enriched pathways and Parkinson's disease - reference tableSupplementary table 2.11: Sig eQTLs CoDeS3D output for rs10835060 and rs4238361: These two SNPs were identified by Makarious et al. as part of a polygenic risk score for PD diagnosisSupplementary table 2.12: Correlational analysis – tissues falling outside of the 95% CI of expected eQTL numbers. Detailed data of the tissues that fall above or below the 95% CI of expected eQTL numbers, supplemental to figure 2. The data includes information for the analysis run on all 49 tissues (i.e. figure 2e-h).

本存储库包含了第二章：基于帕金森病风险位点的基因调控网络建立的补充表格。以下为补充表格的详细描述。补充表格2.1：Nalls等人2019年GWAS数据集。数据直接从出版物补充文件中下载。OR值（优势比）从IPDGC PD位点网络浏览器中获得。补充表格2.2：用于CoDeS3D分析的Hi-C数据集。列出本研究中使用的所有Hi-C数据集及其与原始文献的引用和GEO登录号。补充表格2.3a：Nalls等人2019年GWAS：Sig eQTLs CoDeS3D输出。在调查90个PD-SNPs时，eQTL-基因互作的具体数据。在49个组织中，测试的90个SNPs中，共有76个被确认为与518个基因调控相关的eQTL。补充表格2.3b：在eQTLGen中验证CoDeS3D全血顺式eQTLs。补充表格2.3c：eQTLGen PD精选SNPs。补充表格2.4：Nalls等人2019年GWAS：Sig eQTLs CoDeS3D输出：脑Hi-C：仅限于13个GTEx脑组织。在调查90个PD-SNPs时，eQTL-基因互作的具体数据，仅使用特定的脑Hi-C细胞系（皮质板神经元；生发层神经元；小脑星形胶质细胞；脑血管周细胞；脑微血管内皮原代细胞；SK-N-MC；SK-N-DZ；神经元祖细胞；脊髓星形胶质细胞；背外侧前额叶皮层细胞；海马细胞；H1神经元祖细胞）。补充表格2.5：针对所有90个SNPs的SNPnexus表观遗传学路线图，包括组蛋白修饰和开放染色质DNaseI标记。补充表格2.6a：518个基因的通路分析；gProfiler（g:OST）。补充表格2.6b：165个脑特异性基因的通路分析；gProfiler（g:OST）。补充表格2.7：包含在基因网络中的518个基因的LOEUF值（从gnomAD下载）。LOEUF评分（功能丧失观察/预期上限分数）表明了特定基因对失活耐受的程度。低LOEUF评分表示对功能丧失变异的强烈选择。补充表格2.8：Nalls等人2019年GWAS：Sig eQTLs CoDeS3D输出。在调查90个PD-SNPs时，eQTL-基因互作的具体数据，仅在胎儿皮层中，使用2个胎儿Hi-C细胞系（皮质板神经元；生发层神经元）和胎儿皮层eQTL数据集。补充表格2.9a：基因调控网络分析。蛋白质-蛋白质相互作用分析523个基因（使用STRING，置信水平高于0.700）后，通过Louvain聚类确定了9个显著簇。补充表格2.9b：九个簇的风险与保护比例分析。补充表格2.10a：对九个显著簇中每个簇内基因的通路分析；gProfiler（g:OST）结果（包括靴 strap分析结果）。补充表格2.10b：簇富集通路与帕金森病之间的关联——参考表格。补充表格2.11：Sig eQTLs CoDeS3D输出对于rs10835060和rs4238361：这两个SNPs被Makarious等人识别为PD诊断多基因风险评分的一部分。补充表格2.12：相关性分析——超出预期eQTL数量95%置信区间的组织。超出或低于预期eQTL数量95%置信区间的组织详细数据，补充图2。数据包括对所有49个组织进行的分析（即图2e-h）。