Identification of asthma-related genes using asthmatic blood eQTLs of Korean patients

• Published in: BMC medical genomics Vol. 16; no. 1; pp. 1 - 12
• Main Authors: Kim, Dong Jun, Lim, Ji Eun, Jung, Hae-Un, Chung, Ju Yeon, Baek, Eun Ju, Jung, Hyein, Kwon, Shin Young, Kim, Han Kyul, Kang, Ji-One, Park, Kyungtaek, Won, Sungho, Kim, Tae-Bum, Oh, Bermseok
• Format: Journal Article
• Language: English
• Published: London BioMed Central 24.10.2023; BioMed Central Ltd; BMC
• Subjects: Asthma; Biobanks; Biomedical and Life Sciences; Biomedicine; Blood; Colocalization; Comparative analysis; Consortia; Disease; Expression quantitative trait loci; Gene Expression; Genes; Genetic diversity; Genome-wide association studies; Genome-wide association study; Genomes; Genomics; Genotype & phenotype; Genotypes; Human Genetics; Microarrays; Quality control; Quantitative genetics; Quantitative trait loci; Summary-based Mendelian Randomization; Therapeutic targets; Transcriptome-wide association study; Transcriptomes; Transcriptomics; South Korea; Japan; United States > US; Colocalization; Expression quantitative trait loci; Summary-based Mendelian Randomization; Genome-wide association study; Transcriptome-wide association study; Asthma
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/s12920-023-01677-7

Background More 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. Methods We 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. Results We 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. Conclusions We 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.