This study identified various nutritional factors and genetic factors related to folate metabolism that jointly play a role in lung cancer risk. Performing analyses stratified by smoking status (current, former, and never), we found folate-related dietary and genetic factors, and gene*nutrient interactions were associated with lung cancer risk in current, former, and never smokers. Alcohol was associated with lung cancer risk in current smokers, while gene-nutrient interactions were associated with varying risk in former and never smokers. SNPs in MTRR were associated with lung cancer risk in current, former and never smokers, while a variant in MTHFR was associated with lung cancer risk in never smokers. An additional SNP in TYMS was found to interact with betaine to influence lung cancer risk in former smokers.
Recent research has shown mixed results regarding the association between alcohol drinking and lung cancer risk in non-Hispanic whites 
. Evidence is more consistent in never smokers for no association between alcohol and lung cancer risk 
, which aligns with the findings of the current study. For current smokers, there is less evidence regarding the association between alcohol intake and lung cancer risk 
. Recent studies suggest that the influence of alcohol may depend on the type of alcohol consumed, citing a possible protective effect for wine and increased risk for beer 
. Other studies show a marginal, non-linear relationship between alcohol intake and lung cancer risk, with moderate drinking having a protective effect 
. The strong posterior probability for alcohol seen here suggests that alcohol may be associated with lung cancer risk in current smokers; however, this study does not provide any definitive resolution regarding the mediating effects of smoking on the relationship between alcohol and lung cancer risk. Possible explanations for this apparent association are that cases stopped drinking recently relative to their diagnosis or simply under-report their drinking because of recent diagnosis.
Our analysis also identified several polymorphisms associated with lung cancer risk. For all smoking statuses, different SNPs in MTRR
exhibited strong evidence for association with lung cancer risk. In current smokers, we identified rs6893114 with increased risk, for former smokers, we identified rs13170530 with increased risk, and for never smokers we identified multiple MTRR
SNPs: rs13162612 and rs10512948 were associated with decreased risk; and rs2924471 was associated with increased risk for lung cancer. There is very little information regarding the association of MTRR
with lung cancer, with most studies focusing on MTRR A66G
. Two previous studies found no association for MTRR A66G
, while a third found increased risk 
. All three studies mentioned an interaction between smoking status and A66G
alleles. The fact that the current study also found polymorphisms in MTRR
provides further evidence of an association between MTRR
and lung cancer.
In never smokers, an additional polymorphism in MTHFR
, rs9651118, is associated with decreased lung cancer risk. Over the past decade, many researchers have focused their efforts on two particular polymorphisms of MTHFR
, owing to variants from wild-type at these loci resulting in altered serum folate levels 
. However, results concerning these two loci and their association with lung cancer risk are often inconclusive 
. In the current study, C677T
was not selected as being associated with lung cancer. The SNP associated with lung cancer in never smokers, rs9651118 (T/C), has a borderline Bayes factor (3.05), and moderate protective effect for lung cancer (37% decrease in risk). This SNP is in low LD with the C677T
polymorphisms for MTHFR
<0.20) and is located in an intronic region of MTHFR
. (We do not use D′ here, because by D′, all tag SNPs have D′>0.9 with C677T
.) Given the findings in this study, further investigation of this SNP is encouraged.
Located at 1p36.3, the MTHFR
gene codes for the methyenetetrahydrofolate reductase that converts 5,10-methylenetetrahydrofolate to 5-methylenetetrahydrofolate, which is the primary circulating form of folate and provides methyl groups for synthesis of methionine, an important factor for healthy DNA methylation. MTRR
codes for methionine synthase reductase, which controls methionine synthase, which uses methionine as a methyl donor for DNA methylation. A disruption in any of these three metabolites can lead to chromosome instability and DNA under-methylation, and ultimately to cancer 
codes for thymidylate synthase, an enzyme that is key to a reaction providing thymidine, an important nucleotide used in DNA synthesis and repair. Increased activity is expected to be associated with healthier DNA, while decreased activity is expected to be associated with more DNA damage and thus higher cancer risk 
Our analysis did not detect any nutrient main effects; however, for each smoking status we did detect statistical interactions. In former smokers, we detected a statistical interaction between variants in MTRR
and betaine, and in never smokers we detected interactions between variants in MTRR
and choline and riboflavin. The Bayes factors indicated no evidence for any associations between lung cancer and the main effects of betaine, choline or riboflavin or the SNPs involved in the interactions, but as the intake of betaine and allele dose of rs2658161 (MTRR
) and rs16948305 (TYMS
) increased, our model indicated a reduction in lung cancer risk for former smokers. With never smokers, a statistical interaction with choline and rs10475407 (MTRR
) lead to an increased risk, while the interactions of choline with rs11134290 (MTRR
) and riboflavin with rs876712 (MTRR
) were modeled to decrease lung cancer risk. Researchers are just beginning to investigate choline and betaine intake in human studies, due to the recently available database linkage to FFQs for betaine and choline 
. Some human studies have linked breast cancer 
and colon cancer 
to choline and betaine intake levels, while other studies have found no association 
. Riboflavin has been reported with mixed associations with lung cancer as well 
. Therefore, the literature offers other studies that support many of the SNPs found by this Bayesian model averaging method. However, this is one of the first studies to jointly model these risk factors for lung cancer, and further validation of these findings is needed.
The findings of the current study need to be interpreted in the light of certain limitations especially for the nutrition data. First, because this study sample was restricted to non-Hispanic whites, our findings may not generalize to other ethnicities. Second, this study is a cross-sectional study and information on all variables was collected upon recruitment, and we cannot investigate any real change in behavior over time, such as a change in drinking behavior. Third, the controls were selected from an HMO in the greater Houston metropolitan area. Therefore, controls may not be fully representative of the general population. The fact that these individuals sought medical care might suggest a higher awareness of health and, perhaps, of the importance of proper nutrition. Therefore, the nutrition profiles may not accurately reflect intake in the general population. However, a previous study found the intake of various food items in this population to be comparable to those found by NHANES 
Additionally, the pattern of missing data is significantly different in smokers versus non-smokers, but since we stratify by smoking status, the bias will be minimal. The missing pattern between cases and controls are not significantly different. The nutrition data were collected using food frequency questionnaires, which have the well-known limitations of recall bias 
, minimized in this study by interviewer administration. Even though this bias was minimized by administration by trained interviewers, it may be a factor contributing to the difference in the findings here compared to results found using prospective data such as EPIC 
and ATBC 
. Once we removed the missing data, and stratified, the sample sizes are small. Yet using the Bayesian approach, we were able to control the false discovery rate to be less than 15%, which for the number of findings of the study, comes to one expected false positive per model. Even though the false discovery rate was controlled, and recall bias was minimized, an important next step is to externally validate these findings with independent, prospectively collected data sets.
We would also like to discuss our independence assumptions. When constructing our priors, we modeled genetic covariance using linkage disequilibrium, but assumed nutrition variables and gene-nutrition interactions to be independent. Prior definitions are not rigid assumptions, but rather reflection of the prior belief of the modeler 
. Previous simulation studies involving LD as a prior showed that it can reduce false positives from multicollinearity in the presence of high LD 
. This covariance argument can generalize to correlation between any covariates. As a secondary precaution we computed the false discovery rate as described in 
, and it was controlled at around 15%.
To our knowledge, this is one of the first studies to jointly assess the association between lung cancer and a comprehensive panel of candidate genes in the folate pathway and nutrients related to folate metabolism, and nutrient-gene interactions. Furthermore, we used a novel Bayesian model averaging method to explore these associations. Strengths of this study include a sample size large enough to stratify by smoking status and jointly investigate multiple factors. Jointly modeling gene and nutrient factors allowed us to comprehensively assess the impact of folate metabolism and lung cancer risk. Through our stratified models, we also show that the genetic and nutritional impact on lung cancer risk differs by smoking status. These preliminary findings suggest that the impact of dietary interventions for lung cancer risk may be modified by genotypes in key folate metabolism genes. These findings mark a first step toward more personalized interventions to reduce cancer risk. In developing dietary interventions to reduce lung cancer risk, we not only need to consider smoking status, but also potentially, the genotypes of folate metabolism genes, and how they interact with the nutrient intake levels.