ArticleBMC medical genomics2023
Identification of asthma-related genes using asthmatic blood eQTLs of Korean patients.
Article in BMC medical genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 2 citations in OpenAlex.
- Post-genome-wide association study variant-to-function challenges in asthma research.The Journal of allergy and clinical immunology · 2026Review
- Estimating in silico causal effects of DNA methylation on gene expression through genetic anchors in airway epithelium in asthma.Science advances · 2026Article
- Current understanding and future directions in severe asthma through artificial intelligence-integrated multi-omic approaches.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- DNA methylation and bronchiectasis: a Mendelian randomization analysis to investigate causal link and therapeutic target.Epigenetics · 2025Article
- A scoping review of statistical methods to investigate colocalization between genetic associations and microRNA expression in osteoarthritis.Osteoarthritis and cartilage open · 2024Article
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
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Abstract
backgroundMore than 200 asthma-associated genetic variants have been identified in genome-wide association studies (GWASs). Expression quantitative trait loci (eQTL) data resources can help identify causal genes of the GWAS signals, but it can be difficult to find an eQTL that reflects the disease state because most eQTL data are obtained from normal healthy subjects.
methodsWe performed a blood eQTL analysis using transcriptomic and genotypic data from 433 Korean asthma patients. To identify asthma-related genes, we carried out colocalization, Summary-based Mendelian Randomization (SMR) analysis, and Transcriptome-Wide Association Study (TWAS) using the results of asthma GWASs and eQTL data. In addition, we compared the results of disease eQTL data and asthma-related genes with two normal blood eQTL data from Genotype-Tissue Expression (GTEx) project and a Japanese study.
resultsWe identified 340,274 cis-eQTL and 2,875 eGenes from asthmatic eQTL analysis. We compared the disease eQTL results with GTEx and a Japanese study and found that 64.1% of the 2,875 eGenes overlapped with the GTEx eGenes and 39.0% with the Japanese eGenes. Following the integrated analysis of the asthmatic eQTL data with asthma GWASs, using colocalization and SMR methods, we identified 15 asthma-related genes specific to the Korean asthmatic eQTL data.
conclusionsWe provided Korean asthmatic cis-eQTL data and identified asthma-related genes by integrating them with GWAS data. In addition, we suggested these asthma-related genes as therapeutic targets for asthma. We envisage that our findings will contribute to understanding the etiological mechanisms of asthma and provide novel therapeutic targets.
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