Visualisation files for multimorbidity analysis

Chromatin interactions (using Hi-C) and functional (using eQTL) data were used to identify long-range regulatory associations involving >20,000 GWAS variants and their target genes (i.e. eGenes) in >1,350 phenotypes in the GWAS Catalog. Using convex biclustering, we segregated phenotypes based on the eGenes they share, which implies a common underlying molecular mechanism. Understanding the roles the eGenes play has potential applications in understanding multimorbidities and drug repurposing.This datasource underlie the figures generated in the study

利用染色质相互作用（Hi-C）和功能数据（eQTL），本研究旨在识别涉及超过20,000个GWAS变体及其靶基因（即eGenes）在超过1,350个表型中的长距离调控关联。通过凸二分聚类方法，我们根据共享的eGenes对表型进行了细分，这暗示了共同的潜在分子机制。阐释eGenes所扮演的角色，在理解多病态和药物再利用方面具有潜在应用价值。该数据源为本研究中生成的图表提供了基础。