Molecular mechanisms underlying variations in lung function: a systems genetics analysis

Obeidat, Ma'en; Hao, Ke; Boss�, Yohan; Nickle, David C.; Nie, Yunlong; Postma, Dirkje S.; Laviolette, Michel; Sandford, Andrew J.; Daley, Denise D.; Hogg, James C.; Elliott, W. Mark; Fishbane, Nick; Timens, Wim; Hysi, Pirro G.; Kaprio, Jaakko; Wilson, James F.; Hui, Jennie; Rawal, Rajesh; Schulz, Holger; Stubbe, Beate; Hayward, Caroline; Polasek, Ozren; J�rvelin, Marjo-Riitta; Zhao, Jing Hua; Jarvis, Deborah; K�h�nen, Mika; Franceschini, Nora; North, Kari E.; Loth, Daan W.; Brusselle, Guy G.; Smith, Albert Vernon; Gudnason, Vilmundur; Bartz, Traci M; Wilk, Jemma B.; O'Connor, George T.; Cassano, Patricia A.; Tang, Wenbo; Wain, Louise V.; Artigas, Mar�a Soler; Gharib, Sina A.; Strachan, David P.; Sin, Don D.; Tobin, Martin D.; London, Stephanie J.; Hall, Ian P.; Par�, Peter D.

Abstract

Background: Lung function measures reflect the physiological state of the lung, and are essential to the diagnosis of chronic obstructive pulmonary disease (COPD). The SpiroMeta-CHARGE consortium undertook the largest genome wide association study (GWAS) so far (n=48 201) for forced expiratory volume in 1 s (FEV1) and the ratio of FEV1 to forced vital capacity (FEV1/FVC) in the general population. The lung expression quantitative trait loci (eQTLs) study mapped the genetic architecture of gene expression in lung tissue from 1111 individuals. We used a systems genetics approach to identify single nucleotide polymorphisms (SNPs) associated with lung function that act as eQTLs and change the level of expression of their target genes in lung tissue; termed eSNPs.
Methods: The SpiroMeta-CHARGE GWAS results were integrated with lung eQTLs to map eSNPs and the genes and pathways underlying the associations in lung tissue. For comparison, a similar analysis was done in peripheral blood. The lung mRNA expression levels of the eSNP-regulated genes were tested for associations with lung function measures in 727 individuals. Additional analyses identified the pleiotropic effects of eSNPs from the published GWAS catalogue, and mapped enrichment in regulatory regions from the ENCODE project. Finally, the Connectivity Map database was used to identify potential therapeutics in silico that could reverse the COPD lung tissue gene signature.
Findings: SNPs associated with lung function measures were more likely to be eQTLs and vice versa. The integration mapped the specific genes underlying the GWAS signals in lung tissue. The eSNP-regulated genes were enriched for developmental and inflammatory pathways; by comparison, SNPs associated with lung function that were eQTLs in blood, but not in lung, were only involved in inflammatory pathways. Lung function eSNPs were enriched for regulatory elements and were overrepresented among genes showing differential expression during fetal lung development. An mRNA gene expression signature for COPD was identified in lung tissue and compared with the Connectivity Map. This in-silico drug repurposing approach suggested several compounds that reverse the COPD gene expression signature, including a nicotine receptor antagonist. These findings represent novel therapeutic pathways for COPD.
Interpretation: The system genetics approach identified lung tissue genes driving the variation in lung function and susceptibility to COPD. The identification of these genes and the pathways in which they are enriched is essential to understand the pathophysiology of airway obstruction and to identify novel therapeutic targets and biomarkers for COPD, including drugs that reverse the COPD gene signature in silico.

Citation

Obeidat, M., Hao, K., Bossé, Y., Nickle, D. C., Nie, Y., Postma, D. S., …Paré, P. D. (2015). Molecular mechanisms underlying variations in lung function: a systems genetics analysis. Lancet Respiratory Medicine, 3(10), https://doi.org/10.1016/S2213-2600%2815%2900380-X

• Journal Article Type: Article
• Acceptance Date: Jun 8, 2015
• Online Publication Date: Sep 21, 2015
• Publication Date: Oct 1, 2015
• Deposit Date: Jul 21, 2017
• Journal: Lancet Respiratory Medicine
• Print ISSN: 2213-2600
• Electronic ISSN: 2213-2619
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 3
• Issue: 10
• DOI: https://doi.org/10.1016/S2213-2600%2815%2900380-X
• Public URL: https://nottingham-repository.worktribe.com/output/759954
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S221326001500380X?via%3Dihub
• Contract Date: Jul 21, 2017