Trans-ancestry epigenome-wide association meta-analysis of DNA methylation with lifetime cannabis use

Abstract

Cannabis is widely used worldwide, yet its links to health outcomes are not fully understood. DNA methylation can serve as a mediator to link environmental exposures to health outcomes. We conducted an epigenome-wide association study (EWAS) of peripheral blood-based DNA methylation and lifetime cannabis use (ever vs. never) in a meta-analysis including 9436 participants (7795 European and 1641 African ancestry) from seven cohorts. Accounting for effects of cigarette smoking, our trans-ancestry EWAS meta-analysis revealed four CpG sites significantly associated with lifetime cannabis use at a false discovery rate of 0.05 $$(p < 5.85\times {10}^{-7})$$ ( p < 5.85 × 10 − 7 ) : cg22572071 near gene ADGRF1, cg15280358 in ADAM12, cg00813162 in ACTN1, and cg01101459 near LINC01132. Additionally, our EWAS analysis in participants who never smoked cigarettes identified another epigenome-wide significant CpG site, cg14237301 annotated to APOBR. We used a leave-one-out approach to evaluate methylation scores constructed as a weighted sum of the significant CpGs. The best model can explain 3.79% of the variance in lifetime cannabis use. These findings unravel the DNA methylation changes associated with lifetime cannabis use that are independent of cigarette smoking and may serve as a starting point for further research on the mechanisms through which cannabis exposure impacts health outcomes.

Twin-based Mendelian Randomization Analyses Highlight Smoking’s Effects on Blood DNA Methylation, with Putative Reverse Causation

“…Thus, DNAm variation at this locus may plausibly reflect individual differences in immunological tolerance of cigarette smoke and, consequently, maintenance of smoking behavior. Interestingly, these CpGs were significantly associated with neither cannabis use 7 nor alcohol consumption 6 in recent large-scale EWASs. Notably, though, both these studies reported DNAm associations conditional on cigarette smoking, making them unsuitable for gauging whether the CpGs with putative effects on smoking liability are also associated with other substances.…”

“…If so, it may be prudent to report supplementary EWAS results without smoking as a covariate, as some CpGs may have a reverse or bidirectional causal relationship with smoking. Note that the EWAS of cannabis use 7 did perform such preliminary analyses but only reported the results conditional on cigarette smoking.…”

“…The current study included participants of European ancestry only. Although prior EWASs show highly concordant associations across ancestries 2 , 7 , examining the generalizability of causal estimates in non-European populations is a critical subject of further research. As MR-DoC models provide causal inference specific to the target dataset, rather than the discovery GWAS samples, future research may apply this study design to subpopulations of interest, e.g., stratified by sex or age (such as children or elderly populations), provided the results from population-wide GWAS generalize adequately.…”

“…However, some smokingassociated CpG sites may have reverse or bidirectional causal links with smoking, such that DNAm may reciprocally affect the development and maintenance of smoking behaviors 4 . Moreover, associations between smoking and DNAm could be attributable to potential confounders, such as schizophrenia 5 , alcohol consumption 6 , cannabis use 7 , and body mass index 8 . An alternative approach to causal inference in observational studies is Mendelian Randomization (MR) analysis, using genetic variants as instrumental variables (IVs) to estimate causal effects under specific assumptions 3,9 (see Methods).…”

“…However, some smoking-associated CpG sites may have reverse or bidirectional causal links with smoking, such that DNAm may reciprocally affect the development and maintenance of smoking behaviors 4 . Moreover, associations between smoking and DNAm could be attributable to potential confounders, such as schizophrenia 5 , alcohol consumption 6 , cannabis use 7 , and body mass index 8 .…”