De novo identification of complex traits associated with asthma

Roan E Zaied; Tayaza Fadason; Justin M O'Sullivan

doi:10.3389/fimmu.2023.1231492

De novo identification of complex traits associated with asthma

Front Immunol. 2023 Aug 23:14:1231492. doi: 10.3389/fimmu.2023.1231492. eCollection 2023.

Authors

Roan E Zaied¹, Tayaza Fadason^{1

2}, Justin M O'Sullivan^{1

2

3

4

5}

Affiliations

¹ The Liggins Institute, The University of Auckland, Auckland, New Zealand.
² The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand.
³ Garvan Institute of Medical Research, Sydney, NSW, Australia.
⁴ Medical Research Council (MRC) Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom.
⁵ Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore.

Abstract

Introduction: Asthma 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.

Methods: Here, 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.

Results: We 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.

Discussion: Our 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.

Keywords: SNP function; asthma; comorbidity; expression quantitative trait loci; gene regulation; network analysis.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Asthma* / genetics
Gene Regulatory Networks
Humans
Lung Neoplasms*
Multifactorial Inheritance
Quantitative Trait Loci

Grants and funding

This work was funded by the Dines Family Charitable Trust (JO’S, RZ, and TF) and an HRC Explorer Grant (HRC 19/774; JO’S and TF).