Konuma Takahiro, Ogawa Kotaro, Okada Yukinori
Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.
Central Pharmaceutical Research Institute, JAPAN TOBACCO INC., Takatsuki 569-1125, Japan.
Hum Mol Genet. 2021 Apr 26;30(3-4):294-304. doi: 10.1093/hmg/ddab049.
Approaches toward new therapeutics using disease genomics, such as genome-wide association study (GWAS), are anticipated. Here, we developed Trans-Phar [integration of transcriptome-wide association study (TWAS) and pharmacological database], achieving in silico screening of compounds from a large-scale pharmacological database (L1000 Connectivity Map), which have inverse expression profiles compared with tissue-specific genetically regulated gene expression. Firstly we confirmed the statistical robustness by the application of the null GWAS data and enrichment in the true-positive drug-disease relationships by the application of UK-Biobank GWAS summary statistics in broad disease categories, then we applied the GWAS summary statistics of large-scale European meta-analysis (17 traits; naverage = 201 849) and the hospitalized COVID-19 (n = 900 687), which has urgent need for drug development. We detected potential therapeutic compounds as well as anisomycin in schizophrenia (false discovery rate (FDR)-q = 0.056) and verapamil in hospitalized COVID-19 (FDR-q = 0.068) as top-associated compounds. This approach could be effective in disease genomics-driven drug development.
人们期待采用疾病基因组学的新疗法，例如全基因组关联研究（GWAS）。在此，我们开发了Trans-Phar[转录组全关联研究（TWAS）与药理学数据库的整合]，实现了从大规模药理学数据库（L1000连接图谱）中对化合物进行计算机筛选，这些化合物与组织特异性基因调控的基因表达具有相反的表达谱。首先，我们通过应用无效GWAS数据确认了统计稳健性，并通过在广泛疾病类别中应用英国生物银行GWAS汇总统计数据，在真阳性药物-疾病关系中进行富集。然后，我们应用了大规模欧洲荟萃分析（17个特征；平均样本量=201849）和住院COVID-19（样本量=900687）的GWAS汇总统计数据，而住院COVID-19迫切需要药物开发。我们检测到精神分裂症中的潜在治疗化合物以及茴香霉素（错误发现率（FDR）-q=0.056）和住院COVID-19中的维拉帕米（FDR-q=0.068）作为最相关的化合物。这种方法在疾病基因组学驱动的药物开发中可能是有效的。
