Lung eQTLs to help reveal the molecular underpinnings of asthma.
- Authors
- Hao, Ke; BossΓ©, Yohan; Nickle, David C; ParΓ©, Peter D; Postma, Dirkje S; Laviolette, Michel; Sandford, Andrew; Hackett, Tillie L; Daley, Denise; Hogg, James C; Elliott, W Mark; Couture, Christian; Lamontagne, Maxime; Brandsma, Corry-Anke; van den Berge, Maarten; Koppelman, Gerard; Reicin, Alise S; Nicholson, Donald W; Malkov, Vladislav; Derry, Jonathan M; Suver, Christine; Tsou, Jeffrey A; Kulkarni, Amit; Zhang, Chunsheng; Vessey, Rupert; Opiteck, Greg J; Curtis, Sean P; Timens, Wim; Sin, Don D
- Year
- 2012
- Journal
- PLoS genetics
- PMID
- 23209423
- DOI
- 10.1371/journal.pgen.1003029
- PMCID
- PMC3510026
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 Γ 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.
Overall study design and analysis workflow.
LLM interpretation
This figure is a flow diagram illustrating the study design and analysis workflow starting from lung tissue samples. The process splits into two parallel pipelines: a DNA path involving SNP genotyping, quality control, and imputation, and an mRNA path involving expression profiling, quality control, and adjustments for smoking, age, and gender. These paths converge to identify within-cohort expressional QTLs (eQTLs) for meta-analysis across three cohorts, while the adjusted expression profile is separately used for Bayesian Networks and Key Driver Analysis (KDA).
Lung eQTLs found on chromosome 17q21.(a) The upper panel is a region of chromosome 17q21 from the UCSC browser showing the genes located in this region. The next panel shows the results from the GABRIEL consortium [9] (each bar represents a SNP and the y-axis shows βlog10 P values for association with asthma). The following three panels show the lung eQTL results for expression levels of ORMDL3 (blue bars), GSDMB (green bars), and GSDMA (red bars), respectively. The y-axis represents βlog10 P values derived from the meta-analysis of gene expression. The black horizontal lines are drawn at P = 0.05. (b) Boxplots of lung gene expression levels for GSDMA according to genotype groups for SNPs rs3859192 and rs7216389 in 1,111 subjects.
LLM interpretation
Figure (a) consists of a genomic browser track and four Manhattan-style plots showing $-\log_{10} P$ values for asthma association (GABRIEL consortium) and lung eQTLs for *ORMDL3* (blue), *GSDMB* (green), and *GSDMA* (red) on chromosome 17q21. Figure (b) contains two boxplots showing standardized *GSDMA* expression levels across genotypes (CC, CT, TT) for SNPs rs3859192 ($P=3.55\text{E-}151$) and rs7216389 ($P=8.78\text{E-}25$). In both boxplots, expression levels decrease as the number of T alleles increases.
Relative expression of GSDMA and GSDMB.Primary human airway epithelial cells in monolayer culture were analyzed for; (a) the relative abundance of mRNA for GSDMA and GSDMB (n = 7) and (b) Protein expression of GSDMA and GSDMB by Western blot normalized to expression of Ξ²-tubulin (n = 10). (c) Representative image of GSDMA expression in human conducting airway by immunohistochemistry, which shows expression of GSDMA in both basal and apical cells within the airway epithelium.
LLM interpretation
This figure evaluates the expression of GSDMA and GSDMB in primary human airway epithelial cells. Panel (a) is a bar chart showing significantly higher mRNA expression of GSDMA compared to GSDMB on a $\text{Log}_{10}$ scale. Panel (b) displays Western blot bands showing detectable GSDMA protein expression and negligible GSDMB expression, normalized to $\beta$-tubulin. Panel (c) consists of immunohistochemistry images showing GSDMA staining in both basal and apical cells of the airway epithelium compared to an isotype control.
QβQ plot for the GABRIEL meta-analysis among eSNPs found in the lung eQTL study.Among all SNPs surveyed by the GABRIEL meta-analysis, 60,530 were eSNPs in the Lung eQTL study and considered in this Q-Q plot. As is apparent eQTLs were enriched for lower P values (enrichment P value<2.2Eβ16). Panel (a) is GABRIEL eSNPs plotted against GABRIEL SNPs, panel (b) is eSNPs of canonical asthma genes plotted against GABRIEL SNPs and panel (c) is eSNPs of canonical asthma genes plotted against GABRIEL eSNPs.
LLM interpretation
This figure consists of three Q-Q plots (a, b, and c) comparing $-\log_{10}(p\text{-values})$ of different SNP subsets against reference groups. In all three panels, the observed data points deviate upward from the red diagonal identity line, indicating an enrichment of lower p-values. The x-axes represent the $-\log_{10}(p\text{-values})$ of all Gabriel SNPs (a, b) or Gabriel eSNPs (c), while the y-axes represent the $-\log_{10}(p\text{-values})$ of Gabriel eSNPs (a) or eSNPs of canonical asthma genes (b, c).
The meta-analysis Bayesian network with the top 6 KDA genes and their nearest neighbors.KDA genes are in yellow, canonical genes are in blue, and all other genes are in grey. P values for each of the key driver genes are in Table S3.
LLM interpretation
This figure is a Bayesian network diagram illustrating the relationships between key driver genes (KDA), canonical genes, and other genes. KDA genes (CXCR3, CCL2, SOCS3, CXCL2, CD200R1, and IL1B) are highlighted in yellow, canonical genes are in blue, and other genes are in grey, with directed edges indicating the network connections. The visualization maps the interactions and influence of the top 6 KDA genes on their nearest neighboring genes.
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