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Identifying causal genes for migraine by integrating the proteome and transcriptome

BACKGROUND: While previous genome-wide association studies (GWAS) have identified multiple risk variants for migraine, there is a lack of evidence about how these variants contribute to the development of migraine. We employed an integrative pipeline to efficiently transform genetic associations to...

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Autores principales: Li, Shuang-jie, Shi, Jing-jing, Mao, Cheng-yuan, Zhang, Chan, Xu, Ya-fang, Fan, Yu, Hu, Zheng-wei, Yu, Wen-kai, Hao, Xiao-yan, Li, Meng-jie, Li, Jia-di, Ma, Dong-rui, Guo, Meng-nan, Zuo, Chun-yan, Liang, Yuan-yuan, Xu, Yu-ming, Wu, Jun, Sun, Shi-lei, Wang, Yong-gang, Shi, Chang-he
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Milan 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10433568/
https://www.ncbi.nlm.nih.gov/pubmed/37592229
http://dx.doi.org/10.1186/s10194-023-01649-3
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author Li, Shuang-jie
Shi, Jing-jing
Mao, Cheng-yuan
Zhang, Chan
Xu, Ya-fang
Fan, Yu
Hu, Zheng-wei
Yu, Wen-kai
Hao, Xiao-yan
Li, Meng-jie
Li, Jia-di
Ma, Dong-rui
Guo, Meng-nan
Zuo, Chun-yan
Liang, Yuan-yuan
Xu, Yu-ming
Wu, Jun
Sun, Shi-lei
Wang, Yong-gang
Shi, Chang-he
author_facet Li, Shuang-jie
Shi, Jing-jing
Mao, Cheng-yuan
Zhang, Chan
Xu, Ya-fang
Fan, Yu
Hu, Zheng-wei
Yu, Wen-kai
Hao, Xiao-yan
Li, Meng-jie
Li, Jia-di
Ma, Dong-rui
Guo, Meng-nan
Zuo, Chun-yan
Liang, Yuan-yuan
Xu, Yu-ming
Wu, Jun
Sun, Shi-lei
Wang, Yong-gang
Shi, Chang-he
author_sort Li, Shuang-jie
collection PubMed
description BACKGROUND: While previous genome-wide association studies (GWAS) have identified multiple risk variants for migraine, there is a lack of evidence about how these variants contribute to the development of migraine. We employed an integrative pipeline to efficiently transform genetic associations to identify causal genes for migraine. METHODS: We conducted a proteome-wide association study (PWAS) by combining data from the migraine GWAS data with proteomic data from the human brain and plasma to identify proteins that may play a role in the risk of developing migraine. We also combined data from GWAS of migraine with a novel joint-tissue imputation (JTI) prediction model of 17 migraine-related human tissues to conduct transcriptome-wide association studies (TWAS) together with the fine mapping method FOCUS to identify disease-associated genes. RESULTS: We identified 13 genes in the human brain and plasma proteome that modulate migraine risk by regulating protein abundance. In addition, 62 associated genes not reported in previous migraine TWAS studies were identified by our analysis of migraine using TWAS and fine mapping. Five genes including ICA1L, TREX1, STAT6, UFL1, and B3GNT8 showed significant associations with migraine at both the proteome and transcriptome, these genes are mainly expressed in ependymal cells, neurons, and glial cells, and are potential target genes for prevention of neuronal signaling and inflammatory responses in the pathogenesis of migraine. CONCLUSIONS: Our proteomic and transcriptome findings have identified disease-associated genes that may give new insights into the pathogenesis and potential therapeutic targets for migraine. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s10194-023-01649-3.
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spelling pubmed-104335682023-08-18 Identifying causal genes for migraine by integrating the proteome and transcriptome Li, Shuang-jie Shi, Jing-jing Mao, Cheng-yuan Zhang, Chan Xu, Ya-fang Fan, Yu Hu, Zheng-wei Yu, Wen-kai Hao, Xiao-yan Li, Meng-jie Li, Jia-di Ma, Dong-rui Guo, Meng-nan Zuo, Chun-yan Liang, Yuan-yuan Xu, Yu-ming Wu, Jun Sun, Shi-lei Wang, Yong-gang Shi, Chang-he J Headache Pain Research BACKGROUND: While previous genome-wide association studies (GWAS) have identified multiple risk variants for migraine, there is a lack of evidence about how these variants contribute to the development of migraine. We employed an integrative pipeline to efficiently transform genetic associations to identify causal genes for migraine. METHODS: We conducted a proteome-wide association study (PWAS) by combining data from the migraine GWAS data with proteomic data from the human brain and plasma to identify proteins that may play a role in the risk of developing migraine. We also combined data from GWAS of migraine with a novel joint-tissue imputation (JTI) prediction model of 17 migraine-related human tissues to conduct transcriptome-wide association studies (TWAS) together with the fine mapping method FOCUS to identify disease-associated genes. RESULTS: We identified 13 genes in the human brain and plasma proteome that modulate migraine risk by regulating protein abundance. In addition, 62 associated genes not reported in previous migraine TWAS studies were identified by our analysis of migraine using TWAS and fine mapping. Five genes including ICA1L, TREX1, STAT6, UFL1, and B3GNT8 showed significant associations with migraine at both the proteome and transcriptome, these genes are mainly expressed in ependymal cells, neurons, and glial cells, and are potential target genes for prevention of neuronal signaling and inflammatory responses in the pathogenesis of migraine. CONCLUSIONS: Our proteomic and transcriptome findings have identified disease-associated genes that may give new insights into the pathogenesis and potential therapeutic targets for migraine. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s10194-023-01649-3. Springer Milan 2023-08-17 /pmc/articles/PMC10433568/ /pubmed/37592229 http://dx.doi.org/10.1186/s10194-023-01649-3 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
Li, Shuang-jie
Shi, Jing-jing
Mao, Cheng-yuan
Zhang, Chan
Xu, Ya-fang
Fan, Yu
Hu, Zheng-wei
Yu, Wen-kai
Hao, Xiao-yan
Li, Meng-jie
Li, Jia-di
Ma, Dong-rui
Guo, Meng-nan
Zuo, Chun-yan
Liang, Yuan-yuan
Xu, Yu-ming
Wu, Jun
Sun, Shi-lei
Wang, Yong-gang
Shi, Chang-he
Identifying causal genes for migraine by integrating the proteome and transcriptome
title Identifying causal genes for migraine by integrating the proteome and transcriptome
title_full Identifying causal genes for migraine by integrating the proteome and transcriptome
title_fullStr Identifying causal genes for migraine by integrating the proteome and transcriptome
title_full_unstemmed Identifying causal genes for migraine by integrating the proteome and transcriptome
title_short Identifying causal genes for migraine by integrating the proteome and transcriptome
title_sort identifying causal genes for migraine by integrating the proteome and transcriptome
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10433568/
https://www.ncbi.nlm.nih.gov/pubmed/37592229
http://dx.doi.org/10.1186/s10194-023-01649-3
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