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1.  Method for Evaluating Multiple Mediators: Mediating Effects of Smoking and COPD on the Association between the CHRNA5-A3 Variant and Lung Cancer Risk 
PLoS ONE  2012;7(10):e47705.
A mediation model explores the direct and indirect effects between an independent variable and a dependent variable by including other variables (or mediators). Mediation analysis has recently been used to dissect the direct and indirect effects of genetic variants on complex diseases using case-control studies. However, bias could arise in the estimations of the genetic variant-mediator association because the presence or absence of the mediator in the study samples is not sampled following the principles of case-control study design. In this case, the mediation analysis using data from case-control studies might lead to biased estimates of coefficients and indirect effects. In this article, we investigated a multiple-mediation model involving a three-path mediating effect through two mediators using case-control study data. We propose an approach to correct bias in coefficients and provide accurate estimates of the specific indirect effects. Our approach can also be used when the original case-control study is frequency matched on one of the mediators. We employed bootstrapping to assess the significance of indirect effects. We conducted simulation studies to investigate the performance of the proposed approach, and showed that it provides more accurate estimates of the indirect effects as well as the percent mediated than standard regressions. We then applied this approach to study the mediating effects of both smoking and chronic obstructive pulmonary disease (COPD) on the association between the CHRNA5-A3 gene locus and lung cancer risk using data from a lung cancer case-control study. The results showed that the genetic variant influences lung cancer risk indirectly through all three different pathways. The percent of genetic association mediated was 18.3% through smoking alone, 30.2% through COPD alone, and 20.6% through the path including both smoking and COPD, and the total genetic variant-lung cancer association explained by the two mediators was 69.1%.
PMCID: PMC3471886  PMID: 23077662
2.  Comparison of Pathway Analysis Approaches Using Lung Cancer GWAS Data Sets 
PLoS ONE  2012;7(2):e31816.
Pathway analysis has been proposed as a complement to single SNP analyses in GWAS. This study compared pathway analysis methods using two lung cancer GWAS data sets based on four studies: one a combined data set from Central Europe and Toronto (CETO); the other a combined data set from Germany and MD Anderson (GRMD). We searched the literature for pathway analysis methods that were widely used, representative of other methods, and had available software for performing analysis. We selected the programs EASE, which uses a modified Fishers Exact calculation to test for pathway associations, GenGen (a version of Gene Set Enrichment Analysis (GSEA)), which uses a Kolmogorov-Smirnov-like running sum statistic as the test statistic, and SLAT, which uses a p-value combination approach. We also included a modified version of the SUMSTAT method (mSUMSTAT), which tests for association by averaging χ2 statistics from genotype association tests. There were nearly 18000 genes available for analysis, following mapping of more than 300,000 SNPs from each data set. These were mapped to 421 GO level 4 gene sets for pathway analysis. Among the methods designed to be robust to biases related to gene size and pathway SNP correlation (GenGen, mSUMSTAT and SLAT), the mSUMSTAT approach identified the most significant pathways (8 in CETO and 1 in GRMD). This included a highly plausible association for the acetylcholine receptor activity pathway in both CETO (FDR≤0.001) and GRMD (FDR = 0.009), although two strong association signals at a single gene cluster (CHRNA3-CHRNA5-CHRNB4) drive this result, complicating its interpretation. Few other replicated associations were found using any of these methods. Difficulty in replicating associations hindered our comparison, but results suggest mSUMSTAT has advantages over the other approaches, and may be a useful pathway analysis tool to use alongside other methods such as the commonly used GSEA (GenGen) approach.
PMCID: PMC3283683  PMID: 22363742
3.  Investigating Multiple Candidate Genes and Nutrients in the Folate Metabolism Pathway to Detect Genetic and Nutritional Risk Factors for Lung Cancer 
PLoS ONE  2013;8(1):e53475.
Folate metabolism, with its importance to DNA repair, provides a promising region for genetic investigation of lung cancer risk. This project investigates genes (MTHFR, MTR, MTRR, CBS, SHMT1, TYMS), folate metabolism related nutrients (B vitamins, methionine, choline, and betaine) and their gene-nutrient interactions.
We analyzed 115 tag single nucleotide polymorphisms (SNPs) and 15 nutrients from 1239 and 1692 non-Hispanic white, histologically-confirmed lung cancer cases and controls, respectively, using stochastic search variable selection (a Bayesian model averaging approach). Analyses were stratified by current, former, and never smoking status.
Rs6893114 in MTRR (odds ratio [OR] = 2.10; 95% credible interval [CI]: 1.20–3.48) and alcohol (drinkers vs. non-drinkers, OR = 0.48; 95% CI: 0.26–0.84) were associated with lung cancer risk in current smokers. Rs13170530 in MTRR (OR = 1.70; 95% CI: 1.10–2.87) and two SNP*nutrient interactions [betaine*rs2658161 (OR = 0.42; 95% CI: 0.19–0.88) and betaine*rs16948305 (OR = 0.54; 95% CI: 0.30–0.91)] were associated with lung cancer risk in former smokers. SNPs in MTRR (rs13162612; OR = 0.25; 95% CI: 0.11–0.58; rs10512948; OR = 0.61; 95% CI: 0.41–0.90; rs2924471; OR = 3.31; 95% CI: 1.66–6.59), and MTHFR (rs9651118; OR = 0.63; 95% CI: 0.43–0.95) and three SNP*nutrient interactions (choline*rs10475407; OR = 1.62; 95% CI: 1.11–2.42; choline*rs11134290; OR = 0.51; 95% CI: 0.27–0.92; and riboflavin*rs8767412; OR = 0.40; 95% CI: 0.15–0.95) were associated with lung cancer risk in never smokers.
This study identified possible nutrient and genetic factors related to folate metabolism associated with lung cancer risk, which could potentially lead to nutritional interventions tailored by smoking status to reduce lung cancer risk.
PMCID: PMC3553105  PMID: 23372658
4.  Cigarette Experimentation and the Population Attributable Fraction for Associated Genetic and Non-Genetic Risk Factors 
PLoS ONE  2013;8(1):e53868.
We, and others, have shown that experimenting with cigarettes is a function of both non-genetic and genetic factors. In this analysis we ask: how much of the total risk of experimenting with cigarettes, among those who had not experimented with cigarettes when they enrolled in a prospective cohort, is attributable to genetic factors and to non-genetic factors?
Participants (N = 1,118 Mexican origin youth), recruited from a large population-based cohort study in Houston, Texas, provided prospective data on cigarette experimentation over three years. Non-genetic data were elicited twice – baseline and follow-up. Participants were genotyped for 672 functional and tagging variants in the dopamine, serotonin and opioid pathways.
In the overall model, the adjusted combined non-genetic PAF was 71.2% and the adjusted combined genetic PAF was 58.5%. Among committed never smokers the adjusted combined non-genetic PAF was 67.0% and the adjusted combined genetic PAF was 53.5%. However, among cognitively susceptible youth, the adjusted combined non-genetic PAF was 52.0% and the adjusted combined genetic PAF was 68.4%.
Our results suggest there may be differences in genotypes between youth who think they will try cigarettes in the future compared to their peers who think they will not and underscore the possibility that the relative influence of genetic vs. non-genetic factors on the uptake of smoking may vary between these two groups of youth.
A clearer understanding of the relative role of genetic vs. non-genetic factors in the uptake of smoking may have implications for the design of prevention programs.
PMCID: PMC3547034  PMID: 23342024
5.  Genetic Variants in Inflammation-Related Genes Are Associated with Radiation-Induced Toxicity Following Treatment for Non-Small Cell Lung Cancer 
PLoS ONE  2010;5(8):e12402.
Treatment of non-small cell lung cancer (NSCLC) with radiotherapy or chemoradiotherapy is often accompanied by the development of esophagitis and pneumonitis. Identifying patients who might be at increased risk for normal tissue toxicity would help in determination of the optimal radiation dose to avoid these events. We profiled 59 single nucleotide polymorphisms (SNPs) from 37 inflammation-related genes in 173 NSCLC patients with stage IIIA/IIIB (dry) disease who were treated with definitive radiation or chemoradiation. For esophagitis risk, nine SNPs were associated with a 1.5- to 4-fold increase in risk, including three PTGS2 (COX2) variants: rs20417 (HR:1.93, 95% CI:1.10–3.39), rs5275 (HR:1.58, 95% CI:1.09–2.27), and rs689470 (HR:3.38, 95% CI:1.09–10.49). Significantly increased risk of pneumonitis was observed for patients with genetic variation in the proinflammatory genes IL1A, IL8, TNF, TNFRSF1B, and MIF. In contrast, NOS3:rs1799983 displayed a protective effect with a 45% reduction in pneumonitis risk (HR:0.55, 95% CI:0.31–0.96). Pneumonitis risk was also modulated by polymorphisms in anti-inflammatory genes, including genetic variation in IL13. rs20541 and rs180925 each resulted in increased risk (HR:2.95, 95% CI:1.14–7.63 and HR:3.23, 95% CI:1.03–10.18, respectively). The cumulative effect of these SNPs on risk was dose-dependent, as evidenced by a significantly increased risk of either toxicity with an increasing number of risk genotypes (P<0.001). These results suggest that genetic variations among inflammation pathway genes may modulate the development of radiation-induced toxicity and, ultimately, help in identifying patients who are at an increased likelihood for such events.
PMCID: PMC2928273  PMID: 20811626
6.  Evaluation of Association of HNF1B Variants with Diverse Cancers: Collaborative Analysis of Data from 19 Genome-Wide Association Studies 
PLoS ONE  2010;5(5):e10858.
Genome-wide association studies have found type 2 diabetes-associated variants in the HNF1B gene to exhibit reciprocal associations with prostate cancer risk. We aimed to identify whether these variants may have an effect on cancer risk in general versus a specific effect on prostate cancer only.
Methodology/Principal Findings
In a collaborative analysis, we collected data from GWAS of cancer phenotypes for the frequently reported variants of HNF1B, rs4430796 and rs7501939, which are in linkage disequilibrium (r2 = 0.76, HapMap CEU). Overall, the analysis included 16 datasets on rs4430796 with 19,640 cancer cases and 21,929 controls; and 21 datasets on rs7501939 with 26,923 cases and 49,085 controls. Malignancies other than prostate cancer included colorectal, breast, lung and pancreatic cancers, and melanoma. Meta-analysis showed large between-dataset heterogeneity that was driven by different effects in prostate cancer and other cancers. The per-T2D-risk-allele odds ratios (95% confidence intervals) for rs4430796 were 0.79 (0.76, 0.83)] per G allele for prostate cancer (p<10−15 for both); and 1.03 (0.99, 1.07) for all other cancers. Similarly for rs7501939 the per-T2D-risk-allele odds ratios (95% confidence intervals) were 0.80 (0.77, 0.83) per T allele for prostate cancer (p<10−15 for both); and 1.00 (0.97, 1.04) for all other cancers. No malignancy other than prostate cancer had a nominally statistically significant association.
The examined HNF1B variants have a highly specific effect on prostate cancer risk with no apparent association with any of the other studied cancer types.
PMCID: PMC2878330  PMID: 20526366

Results 1-6 (6)