Waterpipe (also called hookah, shisha, or narghile) smoking is a common form of tobacco use in the Middle East. Its use is becoming more prevalent in Western societies, especially among young adults as an alternative form of tobacco use to traditional cigarettes. While the risk to cigarette smoking is well documented, the risk to waterpipe smoking is not well defined with limited information on its health impact at the epidemiologic, clinical and biologic levels with respect to lung disease. Based on the knowledge that airway epithelial cell DNA methylation is modified in response to cigarette smoke and in cigarette smoking-related lung diseases, we assessed the impact of light-use waterpipe smoking on DNA methylation of the small airway epithelium (SAE) and whether changes in methylation were linked to the transcriptional output of the cells. Small airway epithelium was obtained from 7 nonsmokers and 7 light-use (2.6 ± 1.7 sessions/wk) waterpipe-only smokers. Genome-wide comparison of SAE DNA methylation of waterpipe smokers to nonsmokers identified 727 probesets differentially methylated (fold-change >1.5, p<0.05) representing 673 unique genes. Dominant pathways associated with these epigenetic changes include those linked to G-protein coupled receptor signaling, aryl hydrocarbon receptor signaling and xenobiotic metabolism signaling, all of which have been associated with cigarette smoking and lung disease. Of the genes differentially methylated, 11.3% exhibited a corresponding significant (p<0.05) change in gene expression with enrichment in pathways related to regulation of mRNA translation and protein synthesis (eIF2 signaling and regulation of eIF4 and p70S6K signaling). Overall, these data demonstrate that light-use waterpipe smoking is associated with epigenetic changes and related transcriptional modifications in the SAE, the cell population demonstrating the earliest pathologic abnormalities associated with chronic cigarette smoking.

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL.

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