Integration of genetically regulated gene expression and pharmacological library provides therapeutic drug candidates
Konuma T., Ogawa K., Okada Y.
Meta-analysis with a reported sample of 900 on Hip, published in Hum Mol Genet (2021) — summary generated from the PubMed abstract.
Relatively higher-quality human studies compared with other topics in this database, e.g. multiple RCTs or systematic reviews. This does not mean it is standard or approved care.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Meta-analysis
- Journal
- Hum Mol Genet (2021)
- Reported sample size
- 900
- Source database
- Europe PMC
- PMID
- 33577681
- PMCID
- PMC7928862
- DOI
- 10.1093/hmg/ddab049
- Citations
- 20
Abstract (original English)
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.
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
Evidence level
Relatively higher-quality human studies compared with other topics in this database, e.g. multiple RCTs or systematic reviews. This does not mean it is standard or approved care.
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