De novo identification of complex traits associated with asthma - Zaied, Roan E.; Fadason, Tayaza; O’Sullivan, Justin M. (Frontiers in Immunology, 2023)
Network analysis is a powerful approach for untangling the relationships between complex diseases. 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). From these GRNs, we located the genes that are functionally affected by asthmaassociated eQTLs to identify the asthma GRN. We then expanded outwards to identify curated protein-protein interactions occurring 1 to 4 edges away from the asthma GRN. The eQTLs spatially regulating the identified genes were used to interrogate the GWAS Catalog to identify enriched traits (hypergeometric test; FDR≤0.05) within the asthma GRN and each of the 4 levels.This led to the de novo identification of traits both previously reported (e.g., depressive symptoms and lung cancer) and unreported (e.g., reticulocyte count) to be comorbid or associated (positively or negatively) with asthma. It 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. 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.