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

• Main Authors: Dong Jun Kim (Author), Ji Eun Lim (Author), Hae-Un Jung (Author), Ju Yeon Chung (Author), Eun Ju Baek (Author), Hyein Jung (Author), Shin Young Kwon (Author), Han Kyul Kim (Author), Ji-One Kang (Author), Kyungtaek Park (Author), Sungho Won (Author), Tae-Bum Kim (Author), Bermseok Oh (Author)
• Format: Book
• Published: BMC, 2023-10-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12920-023-01677-7; 1755-8794
• Keywords: Asthma; Expression quantitative trait loci; Genome-wide association study; Colocalization; Summary-based Mendelian Randomization; Transcriptome-wide association study; Internal medicine; RC31-1245; Genetics; QH426-470
• Source: BMC Medical Genomics, Vol 16, Iss 1, Pp 1-12 (2023)

Abstract 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.