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

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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10599017/
https://www.ncbi.nlm.nih.gov/pubmed/37875944
http://dx.doi.org/10.1186/s12920-023-01677-7
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author 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
author_facet 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
author_sort Kim, Dong Jun
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01677-7.
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spelling pubmed-105990172023-10-26 Identification of asthma-related genes using asthmatic blood eQTLs of Korean patients 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 BMC Med Genomics Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01677-7. BioMed Central 2023-10-24 /pmc/articles/PMC10599017/ /pubmed/37875944 http://dx.doi.org/10.1186/s12920-023-01677-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
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
Identification of asthma-related genes using asthmatic blood eQTLs of Korean patients
title Identification of asthma-related genes using asthmatic blood eQTLs of Korean patients
title_full Identification of asthma-related genes using asthmatic blood eQTLs of Korean patients
title_fullStr Identification of asthma-related genes using asthmatic blood eQTLs of Korean patients
title_full_unstemmed Identification of asthma-related genes using asthmatic blood eQTLs of Korean patients
title_short Identification of asthma-related genes using asthmatic blood eQTLs of Korean patients
title_sort identification of asthma-related genes using asthmatic blood eqtls of korean patients
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10599017/
https://www.ncbi.nlm.nih.gov/pubmed/37875944
http://dx.doi.org/10.1186/s12920-023-01677-7
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