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1.  Total Exposure Study Analysis consortium: a cross-sectional study of tobacco exposures 
BMC Public Health  2015;15:866.
Background
The Total Exposure Study was a stratified, multi-center, cross-sectional study designed to estimate levels of biomarkers of tobacco-specific and non-specific exposure and of potential harm in U.S. adult current cigarette smokers (≥one manufactured cigarette per day over the last year) and tobacco product non-users (no smoking or use of any nicotine containing products over the last 5 years). The study was designed and sponsored by a tobacco company and implemented by contract research organizations in 2002–2003. Multiple analyses of smoking behavior, demographics, and biomarkers were performed. Study data and banked biospecimens were transferred from the sponsor to the Virginia Tobacco and Health Research Repository in 2010, and then to SRI International in 2012, for independent analysis and dissemination.
Methods
We analyzed biomarker distributions overall, and by biospecimen availability, for comparison with existing studies, and to evaluate generalizability to the entire sample. We calculated genome-wide statistical power for a priori hypotheses. We performed clinical chemistries, nucleic acid extractions and genotyping, and report correlation and quality control metrics.
Results
Vital signs, clinical chemistries, and laboratory measures of tobacco specific and non-specific toxicants are available from 3585 current cigarette smokers, and 1077 non-users. Peripheral blood mononuclear cells, red blood cells, plasma and 24-h urine biospecimens are available from 3073 participants (2355 smokers and 719 non-users). In multivariate analysis, participants with banked biospecimens were significantly more likely to self-identify as White, to be older, to have increased total nicotine equivalents per cigarette, decreased serum cotinine, and increased forced vital capacity, compared to participants without. Effect sizes were small (Cohen’s d-values ≤ 0.11). Power for a priori hypotheses was 57 % in non-Hispanic Black (N = 340), and 96 % in non-Hispanic White (N = 1840), smokers. All DNA samples had genotype completion rates ≥97.5 %; 68 % of RNA samples yielded RIN scores ≥6.0.
Conclusions
Total Exposure Study clinical and laboratory assessments and biospecimens comprise a unique resource for cigarette smoke health effects research. The Total Exposure Study Analysis Consortium seeks to perform molecular studies in multiple domains and will share data and analytic results in public repositories and the peer-reviewed literature. Data and banked biospecimens are available for independent or collaborative research.
Electronic supplementary material
The online version of this article (doi:10.1186/s12889-015-2212-5) contains supplementary material, which is available to authorized users.
doi:10.1186/s12889-015-2212-5
PMCID: PMC4561475  PMID: 26346437
2.  Drug Metabolizing Enzyme and Transporter Gene Variation, Nicotine Metabolism, Prospective Abstinence, and Cigarette Consumption 
PLoS ONE  2015;10(7):e0126113.
The Nicotine Metabolite Ratio (NMR, ratio of trans-3’-hydroxycotinine and cotinine), has previously been associated with CYP2A6 activity, response to smoking cessation treatments, and cigarette consumption. We searched for drug metabolizing enzyme and transporter (DMET) gene variation associated with the NMR and prospective abstinence in 2,946 participants of laboratory studies of nicotine metabolism and of clinical trials of smoking cessation therapies. Stage I was a meta-analysis of the association of 507 common single nucleotide polymorphisms (SNPs) at 173 DMET genes with the NMR in 449 participants of two laboratory studies. Nominally significant associations were identified in ten genes after adjustment for intragenic SNPs; CYP2A6 and two CYP2A6 SNPs attained experiment-wide significance adjusted for correlated SNPs (CYP2A6 PACT=4.1E-7, rs4803381 PACT=4.5E-5, rs1137115, PACT=1.2E-3). Stage II was mega-regression analyses of 10 DMET SNPs with pretreatment NMR and prospective abstinence in up to 2,497 participants from eight trials. rs4803381 and rs1137115 SNPs were associated with pretreatment NMR at genome-wide significance. In post-hoc analyses of CYP2A6 SNPs, we observed nominally significant association with: abstinence in one pharmacotherapy arm; cigarette consumption among all trial participants; and lung cancer in four case:control studies. CYP2A6 minor alleles were associated with reduced NMR, CPD, and lung cancer risk. We confirmed the major role that CYP2A6 plays in nicotine metabolism, and made novel findings with respect to genome-wide significance and associations with CPD, abstinence and lung cancer risk. Additional multivariate analyses with patient variables and genetic modeling will improve prediction of nicotine metabolism, disease risk and smoking cessation treatment prognosis.
doi:10.1371/journal.pone.0126113
PMCID: PMC4488893  PMID: 26132489
3.  Complex System Approaches to Genetic Analysis: Bayesian Approaches 
Advances in genetics  2010;72:47-71.
Genetic epidemiology is increasingly focused on complex diseases involving multiple genes and environmental factors, often interacting in complex ways. Although standard frequentist methods still have a role in hypothesis generation and testing for discovery of novel main effects and interactions, Bayesian methods are particularly well suited to modeling the relationships in an integrated “systems biology” manner. In this chapter, we provide an overview of the principles of Bayesian analysis and their advantages in this context and describe various approaches to applying them for both model building and discovery in a genome-wide setting. In particular, we highlight the ability of Bayesian methods to construct complex probability models via a hierarchical structure and to account for uncertainty in model specification by averaging over large spaces of alternative models.
doi:10.1016/B978-0-12-380862-2.00003-5
PMCID: PMC4190044  PMID: 21029848
4.  A scalable, knowledge-based analysis framework for genetic association studies 
BMC Bioinformatics  2013;14:312.
Background
Testing for marginal associations between numerous genetic variants and disease may miss complex relationships among variables (e.g., gene-gene interactions). Bayesian approaches can model multiple variables together and offer advantages over conventional model building strategies, including using existing biological evidence as modeling priors and acknowledging that many models may fit the data well. With many candidate variables, Bayesian approaches to variable selection rely on algorithms to approximate the posterior distribution of models, such as Markov-Chain Monte Carlo (MCMC). Unfortunately, MCMC is difficult to parallelize and requires many iterations to adequately sample the posterior. We introduce a scalable algorithm called PEAK that improves the efficiency of MCMC by dividing a large set of variables into related groups using a rooted graph that resembles a mountain peak. Our algorithm takes advantage of parallel computing and existing biological databases when available.
Results
By using graphs to manage a model space with more than 500,000 candidate variables, we were able to improve MCMC efficiency and uncover the true simulated causal variables, including a gene-gene interaction. We applied PEAK to a case-control study of childhood asthma with 2,521 genetic variants. We used an informative graph for oxidative stress derived from Gene Ontology and identified several variants in ERBB4, OXR1, and BCL2 with strong evidence for associations with childhood asthma.
Conclusions
We introduced an extremely flexible analysis framework capable of efficiently performing Bayesian variable selection on many candidate variables. The PEAK algorithm can be provided with an informative graph, which can be advantageous when considering gene-gene interactions, or a symmetric graph, which simply divides the model space into manageable regions. The PEAK framework is compatible with various model forms, allowing for the algorithm to be configured for different study designs and applications, such as pathway or rare-variant analyses, by simple modifications to the model likelihood and proposal functions.
doi:10.1186/1471-2105-14-312
PMCID: PMC4015032  PMID: 24152222
5.  Meta-analysis of Genome-wide Association Studies of Asthma In Ethnically Diverse North American Populations 
Torgerson, Dara G. | Ampleford, Elizabeth J. | Chiu, Grace Y. | Gauderman, W. James | Gignoux, Christopher R. | Graves, Penelope E. | Himes, Blanca E. | Levin, Albert M. | Mathias, Rasika A. | Hancock, Dana B. | Baurley, James W. | Eng, Celeste | Stern, Debra A. | Celedón, Juan C. | Rafaels, Nicholas | Capurso, Daniel | Conti, David V. | Roth, Lindsey A. | Soto-Quiros, Manuel | Togias, Alkis | Li, Xingnan | Myers, Rachel A. | Romieu, Isabelle | Van Den Berg, David J. | Hu, Donglei | Hansel, Nadia N. | Hernandez, Ryan D. | Israel, Elliott | Salam, Muhammad T. | Galanter, Joshua | Avila, Pedro C. | Avila, Lydiana | Rodriquez-Santana, Jose R. | Chapela, Rocio | Rodriguez-Cintron, William | Diette, Gregory B. | Adkinson, N. Franklin | Abel, Rebekah A. | Ross, Kevin D. | Shi, Min | Faruque, Mezbah U. | Dunston, Georgia M. | Watson, Harold R. | Mantese, Vito J. | Ezurum, Serpil C. | Liang, Liming | Ruczinski, Ingo | Ford, Jean G. | Huntsman, Scott | Chung, Kian Fan | Vora, Hita | Li, Xia | Calhoun, William J. | Castro, Mario | Sienra-Monge, Juan J. | del Rio-Navarro, Blanca | Deichmann, Klaus A. | Heinzmann, Andrea | Wenzel, Sally E. | Busse, William W. | Gern, James E. | Lemanske, Robert F. | Beaty, Terri H. | Bleecker, Eugene R. | Raby, Benjamin A. | Meyers, Deborah A. | London, Stephanie J. | Gilliland, Frank D. | Burchard, Esteban G. | Martinez, Fernando D. | Weiss, Scott T. | Williams, L. Keoki | Barnes, Kathleen C. | Ober, Carole | Nicolae, Dan L.
Nature genetics  2011;43(9):887-892.
Asthma is a common disease with a complex risk architecture including both genetic and environmental factors. We performed a meta-analysis of North American genome-wide association studies (GWAS) of asthma in 5,416 asthma cases representing European Americans, African Americans/African Caribbeans, and Latinos, and replicated five regions among the most significant signals in 12,649 individuals from the same ethnic groups. Four were at previously reported loci on 17q21, and near the IL1RL1, TSLP, and IL33, genes, but we report for the first time that these loci are associated with asthma risk in three ethnic groups. In addition, we identified a novel association with asthma in the PYHIN1, gene that was specific to individuals of African descent (p=3.9×10−9). These results suggest that some asthma susceptibility loci are robust to differences in ancestry when sufficiently large samples sizes are investigated, and that ancestry-specific associations also contribute to the complex genetic architecture of asthma.
doi:10.1038/ng.888
PMCID: PMC3445408  PMID: 21804549
7.  TSLP Polymorphisms are Associated with Asthma in a Sex-Specific Fashion 
Allergy  2010;65(12):1566-1575.
Background
Single nucleotide polymorphisms (SNPs) in thymic stromal lymphopoietin (TSLP) have been associated with IgE (in girls) and asthma (in general). We sought to determine whether TSLP SNPs are associated with asthma in a sex-specific fashion.
Methods
We conducted regular and sex-stratified analyses of association between SNPs in TSLP and asthma in families of asthmatic children in Costa Rica. Significant findings were replicated in white and African-American participants in the Childhood Asthma Management Program, in African Americans in the Genomic Research on Asthma in the African Diaspora study, in whites and Hispanics in the Children’s Health Study, and in whites in the Framingham Heart Study (FHS).
Main Results
Two SNPs in TSLP (rs1837253 and rs2289276) were significantly associated with a reduced risk of asthma in combined analyses of all cohorts (p values of 2×10−5 and 1×10−5, respectively). In a sex-stratified analysis, the T allele of rs1837253 was significantly associated with a reduced risk of asthma in males only (p= 3×10−6). Alternately, the T allele of rs2289276 was significantly associated with a reduced risk of asthma in females only (p= 2×10−4). Findings for rs2289276 were consistent in all cohorts except the FHS.
Conclusions
TSLP variants are associated with asthma in a sex-specific fashion.
doi:10.1111/j.1398-9995.2010.02415.x
PMCID: PMC2970693  PMID: 20560908
asthma; genetic association; sex-specific; thymic stromal lymphopoietin; TSLP
8.  Detecting Gene-Environment Interactions in Genome-Wide Association Data 
Genetic epidemiology  2009;33(Suppl 1):S68-S73.
Despite the importance of gene-environment (G×E) interactions in the etiology of common diseases, little work has been done to develop methods for detecting these types of interactions in genome-wide association study data. This was the focus of Genetic Analysis Workshop 16 Group 10 contributions, which introduced a variety of new methods for the detection of G×E interactions in both case-control and family-based data using both cross-sectional and longitudinal study designs. Many of these contributions detected significant G×E interactions. Although these interactions have not yet been confirmed, the results suggest the importance of testing for interactions. Issues of sample size, quantifying the environmental exposure, longitudinal data analysis, family-based analysis, selection of the most powerful analysis method, population stratification, and computational expense with respect to testing G×E interactions are discussed.
doi:10.1002/gepi.20475
PMCID: PMC2924567  PMID: 19924704
GAW; case-control; family-based; cross-sectional; longitudinal; rheumatoid arthritis; Framingham Heart Study

Results 1-8 (8)