LAUSR

Nicotine dependence is an addiction to tobacco products and a global public health concern that in part would be influenced by our genetics. Smokers are reported to have reduced MAOA activity, but the results from genetic associations with this gene have been inconclusive. Two functionally relevant variable number tandem repeat (VNTR) domains, termed uVNTR and dVNTR, in the MAOA gene are well characterized transcriptional regulatory elements. In the present study, we analyzed uVNTR and dVNTR polymorphisms in the MAOA gene in the Vietnamese male population of smokers and non-smokers in order to assess the association of MAOA with the nicotine dependence measured by the Fagerström Test for Nicotine Dependence (FTND). Individual analysis of VNTRs separately identified uVNTR to be associated with the F6 question of the FTND indicating the stronger addiction to nicotine. No associations were found between the dVNTR and smoking behavior. The combination of dVNTR and uVNTR, that predicts low expression of MAOA (10–3 haplotypes), was significantly associated with the higher nicotine dependence (FTND score), longer smoking duration, and more persistent smoking behavior (fewer quit attempts). In conclusion, our study confirms that low MAOA expression is genetically predictive to the higher nicotine dependence. Impact statement The present study combined the analysis of two transcriptional regulators, uVNTR and dVNTR, in the MAOA gene that is an enzyme responsible for the monoamine degradation and identified genetic interaction between these VNTRs in association with the nicotine dependence. The main impact is that when analyzing different populations in the genetic studies, the functionally meaningful variants should be combined rather than addressing individual elements separately (a mini polygenic risk score for a particular gene/locus). This combination is very rarely analyzed and therefore the study sets an example. Another impact is that we analyzed the genetic variability in the Asian population and therefore our data present a piece of information from underrepresented populations.