De novo identification of complex traits associated with asthma
IntroductionAsthma is a heterogeneous inflammatory disease often associated with other complex phenotypes. Identifying asthma-associated diseases and uncovering the molecular mechanisms mediating their interaction can help detangle the heterogeneity of asthma. Network analysis is a powerful approach...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1231492/full |
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| author | Roan E. Zaied Tayaza Fadason Tayaza Fadason Justin M. O’Sullivan Justin M. O’Sullivan Justin M. O’Sullivan Justin M. O’Sullivan Justin M. O’Sullivan |
| author_facet | Roan E. Zaied Tayaza Fadason Tayaza Fadason Justin M. O’Sullivan Justin M. O’Sullivan Justin M. O’Sullivan Justin M. O’Sullivan Justin M. O’Sullivan |
| author_sort | Roan E. Zaied |
| collection | DOAJ |
| description | IntroductionAsthma is a heterogeneous inflammatory disease often associated with other complex phenotypes. Identifying asthma-associated diseases and uncovering the molecular mechanisms mediating their interaction can help detangle the heterogeneity of asthma. Network analysis is a powerful approach for untangling such inter-disease relationships.MethodsHere, we integrated information on physical contacts between common single nucleotide polymorphisms (SNPs) and gene expression with expression quantitative trait loci (eQTL) data from the lung and whole blood to construct two tissue-specific spatial gene regulatory networks (GRN). We then located the asthma GRN (level 0) within each tissue-specific GRN by identifying the genes that are functionally affected by asthma-associated spatial eQTLs. Curated protein interaction partners were subsequently identified up to four edges or levels away from the asthma GRN. The eQTLs spatially regulating genes on levels 0–4 were queried against the GWAS Catalog to identify the traits enriched (hypergeometric test; FDR ≤ 0.05) in each level.ResultsWe identified 80 and 82 traits significantly enriched in the lung and blood GRNs, respectively. All identified traits were previously reported to be comorbid or associated (positively or negatively) with asthma (e.g., depressive symptoms and lung cancer), except 8 traits whose association with asthma is yet to be confirmed (e.g., reticulocyte count). Our analysis additionally pinpoints the variants and genes that link asthma to the identified asthma-associated traits, a subset of which was replicated in a comorbidity analysis using health records of 26,781 asthma patients in New Zealand.DiscussionOur discovery approach identifies enriched traits in the regulatory space proximal to asthma, in the tissue of interest, without a priori selection of the interacting traits. The predictions it makes expand our understanding of possible shared molecular interactions and therapeutic targets for asthma, where no cure is currently available. |
| first_indexed | 2024-03-12T13:43:40Z |
| format | Article |
| id | doaj.art-fba5e97915064f8ea0d84c7b1d959083 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-12T13:43:40Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-fba5e97915064f8ea0d84c7b1d9590832023-08-23T11:00:57ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-08-011410.3389/fimmu.2023.12314921231492De novo identification of complex traits associated with asthmaRoan E. Zaied0Tayaza Fadason1Tayaza Fadason2Justin M. O’Sullivan3Justin M. O’Sullivan4Justin M. O’Sullivan5Justin M. O’Sullivan6Justin M. O’Sullivan7The Liggins Institute, The University of Auckland, Auckland, New ZealandThe Liggins Institute, The University of Auckland, Auckland, New ZealandThe Maurice Wilkins Centre, The University of Auckland, Auckland, New ZealandThe Liggins Institute, The University of Auckland, Auckland, New ZealandThe Maurice Wilkins Centre, The University of Auckland, Auckland, New ZealandGarvan Institute of Medical Research, Sydney, NSW, AustraliaMedical Research Council (MRC) Lifecourse Epidemiology Unit, University of Southampton, Southampton, United KingdomSingapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, SingaporeIntroductionAsthma is a heterogeneous inflammatory disease often associated with other complex phenotypes. Identifying asthma-associated diseases and uncovering the molecular mechanisms mediating their interaction can help detangle the heterogeneity of asthma. Network analysis is a powerful approach for untangling such inter-disease relationships.MethodsHere, we integrated information on physical contacts between common single nucleotide polymorphisms (SNPs) and gene expression with expression quantitative trait loci (eQTL) data from the lung and whole blood to construct two tissue-specific spatial gene regulatory networks (GRN). We then located the asthma GRN (level 0) within each tissue-specific GRN by identifying the genes that are functionally affected by asthma-associated spatial eQTLs. Curated protein interaction partners were subsequently identified up to four edges or levels away from the asthma GRN. The eQTLs spatially regulating genes on levels 0–4 were queried against the GWAS Catalog to identify the traits enriched (hypergeometric test; FDR ≤ 0.05) in each level.ResultsWe identified 80 and 82 traits significantly enriched in the lung and blood GRNs, respectively. All identified traits were previously reported to be comorbid or associated (positively or negatively) with asthma (e.g., depressive symptoms and lung cancer), except 8 traits whose association with asthma is yet to be confirmed (e.g., reticulocyte count). Our analysis additionally pinpoints the variants and genes that link asthma to the identified asthma-associated traits, a subset of which was replicated in a comorbidity analysis using health records of 26,781 asthma patients in New Zealand.DiscussionOur discovery approach identifies enriched traits in the regulatory space proximal to asthma, in the tissue of interest, without a priori selection of the interacting traits. The predictions it makes expand our understanding of possible shared molecular interactions and therapeutic targets for asthma, where no cure is currently available.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1231492/fullasthmaSNP functioncomorbidityexpression quantitative trait locinetwork analysisgene regulation |
| spellingShingle | Roan E. Zaied Tayaza Fadason Tayaza Fadason Justin M. O’Sullivan Justin M. O’Sullivan Justin M. O’Sullivan Justin M. O’Sullivan Justin M. O’Sullivan De novo identification of complex traits associated with asthma Frontiers in Immunology asthma SNP function comorbidity expression quantitative trait loci network analysis gene regulation |
| title | De novo identification of complex traits associated with asthma |
| title_full | De novo identification of complex traits associated with asthma |
| title_fullStr | De novo identification of complex traits associated with asthma |
| title_full_unstemmed | De novo identification of complex traits associated with asthma |
| title_short | De novo identification of complex traits associated with asthma |
| title_sort | de novo identification of complex traits associated with asthma |
| topic | asthma SNP function comorbidity expression quantitative trait loci network analysis gene regulation |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1231492/full |
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