BackgroundSmoking is a leading cause of preventable death. Early studies based on samples of twins have linked the lifetime smoking practices to genetic predisposition. The flavin‐containing monooxygenase (FMO) protein family consists of a group of enzymes that metabolize drugs and xenobiotics. Both FMO1 and FMO3 were potentially susceptible genes for nicotine metabolism process.MethodsIn this study, we investigated the potential of FMO genes to confer risk of nicotine dependence via deep targeted sequencing in 2,820 study subjects comprising 1,583 nicotine dependents and 1,237 controls from European American and African American. Specifically, we focused on the two genomic segments including FMO1,FMO3, and pseudo gene FMO6P, and aimed to investigate the potential association between FMO genes and nicotine dependence. Both common and low‐frequency/rare variants were analyzed using different algorithms. The potential functional significance of SNPs with association signal was investigated with relevant bioinformatics tools.ResultsWe identified different clusters of significant common variants in European (with most significant SNP rs6674596, p = .0004, OR = 0.67, MAF_EA = 0.14, FMO1) and African Americans (with the most significant SNP rs6608453, p = .001, OR = 0.64, MAF_AA = 0.1, FMO6P). No significant signals were identified through haplotype‐based analyses. Gene network investigation indicated that both FMO1 and FMO3 have a strong relation with a variety of genes belonging to CYP gene families (with combined score greater than 0.9). Most of the significant variants identified were SNPs located within intron regions or with unknown functional significance, indicating a need for future work to understand the underlying functional significance of these signals.ConclusionsOur findings indicated significant association between FMO genes and nicotine dependence. Replications of our findings in other ethnic groups were needed in the future. Most of the significant variants identified were SNPs located within intronic regions or with unknown functional significance, indicating a need for future work to understand the underlying functional significance of these signals.

Rare variants have been proposed to play a significant role in the onset and development of common diseases. However, traditional analysis methods have difficulties in detecting association signals for rare causal variants because of a lack of statistical power. We propose a two-stage, gene-based method for association mapping of rare variants by applying four different noncollapsing algorithms. Using the Genome Analysis Workshop18 whole genome sequencing data set of simulated blood pressure phenotypes, we studied and contrasted the false-positive rate of each algorithm using receiver operating characteristic curves. The statistical power of these methods was also evaluated and compared through the analysis of 200 simulated replications in a smaller genotype data set. We showed that the Fisher's method was superior to the other 3 noncollapsing methods, but was no better than the standard method implemented with famSKAT. Further investigation is needed to explore the potential statistical properties of these approaches.