Lung eQTLs to help reveal the molecular underpinnings of asthma.

• Main Authors: Ke Hao, Yohan Bossé, David C Nickle, Peter D Paré, Dirkje S Postma, Michel Laviolette, Andrew Sandford, Tillie L Hackett, Denise Daley, James C Hogg, W Mark Elliott, Christian Couture, Maxime Lamontagne, Corry-Anke Brandsma, Maarten van den Berge, Gerard Koppelman, Alise S Reicin, Donald W Nicholson, Vladislav Malkov, Jonathan M Derry, Christine Suver, Jeffrey A Tsou, Amit Kulkarni, Chunsheng Zhang, Rupert Vessey, Greg J Opiteck, Sean P Curtis, Wim Timens, Don D Sin
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2012-01-01
• Series: PLoS Genetics
• Online Access: http://europepmc.org/articles/PMC3510026?pdf=render

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.