Lung eQTLs to Help Reveal the Molecular Underpinnings of Asthma

Abstract

Genome-wide association studies (GWAS) have identified loci reproducibly associated with pulmonary diseases; however, the molecular mechanism underlying these associations are largely unknown. The objectives of this study were to discover genetic variants affecting gene expression in human lung tissue, to refine susceptibility loci for asthma identified in GWAS studies, and to use the genetics of gene expression and network analyses to find key molecular drivers of asthma. We performed a genome-wide search for expression quantitative trait loci (eQTL) in 1,111 human lung samples. The lung eQTL dataset was then used to inform asthma genetic studies reported in the literature. The top ranked lung eQTLs were integrated with the GWAS on asthma reported by the GABRIEL consortium to generate a Bayesian gene expression network for discovery of novel molecular pathways underpinning asthma. We detected 17,178 cis- and 593 trans- lung eQTLs, which can be used to explore the functional consequences of loci associated with lung diseases and traits. Some strong eQTLs are also asthma susceptibility loci. For example, rs3859192 on chr17q21 is robustly associated with the mRNA levels of GSDMA (P = 3.55×10−151). The genetic-gene expression network identified the SOCS3 pathway as one of the key drivers of asthma. The eQTLs and gene networks identified in this study are powerful tools for elucidating the causal mechanisms underlying pulmonary disease. This data resource offers much-needed support to pinpoint the causal genes and characterize the molecular function of gene variants associated with lung diseases.

Translating Lung Function Genome-Wide Association Study (GWAS) Findings

“…eQTL mapping can be particularly useful for identifying potential candidate genes contained within a broad association region. For example, our research group prioritized genes at locus 4q24 previously associated with the lung function measure FEV1 and risk of COPD by identifying eQTL SNPs in a range of datasets generated in lymphoblastoid cell line, liver and brain tissue (Obeidat et al, 2013 (Hao et al, 2012;Obeidat et al, 2015), blood cells (Westra et al, 2013) and airway epithelial cells (X. Li et al, 2015;Luo et al, 2015).…”

Genome-Wide Association Study Identification of Novel Loci Associated with Airway Responsiveness in Chronic Obstructive Pulmonary Disease

“…Airway responsiveness was calculated as a quantitative measure (the concentration of methacholine at which a greater than 20% decrease in FEV 1 was achieved [PC 20 ]), and all analyses were repeated using alternative definitions of airway responsiveness: (1) dichotomization of airway responsiveness (PC 20 , 10 versus PC 20 > 10 mg/ml), (2) …”

“…The genetic contribution to airway responsiveness was assessed at baseline and Year 5 by linear regression using PLINK (19) adjusting for sex, age at baseline, clinic site, log 10 (weight in kilograms), FEV 1 (liters), FEV 1 /FVC, and smoking status at Year 5; data were combined using a correlated meta-analysis. Analysis of dichotomized PC 20 was assessed using logistic regression. A prespecified threshold for genome-wide significance of P , 9.57 3 10 28 was determined based on a Bonferroni correction for multiple comparisons for 546,355 SNPs.…”

“…Details of the gene expression study have been published (20). Briefly, the normalized expression data were adjusted for age, sex, and smoking status in a robust linear model, and eQTL mapping was performed with linear regression (21).…”

Genome-wide genetic ancestry measurements to predict lung function in European populations

“…The population used in this study is part of a lung expression quantitative trait loci (eQTL) mapping study [6,7]. Briefly, research participants were recruited from three academic sites: Laval University (Québec, QC, Canada), University of British Columbia (Vancouver, BC, Canada), and Groningen University (Groningen, the Netherlands), henceforth referred to as Laval, UBC, and Groningen, respectively.…”