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1.  Parental risk factors for oral clefts among Central Africans, Southeast Asians, and Central Americans 
Several lifestyle and environmental exposures have been suspected as risk factors for oral clefts, although few have been convincingly demonstrated. Studies across global diverse populations could offer additional insight given varying types and levels of exposures.
We performed an international case–control study in the Democratic Republic of the Congo (133 cases, 301 controls), Vietnam (75 cases, 158 controls), the Philippines (102 cases, 152 controls), and Honduras (120 cases, 143 controls). Mothers were recruited from hospitals and their exposures were collected from interviewer‐administered questionnaires. We used logistic regression modeling to estimate odds ratios (OR) and 95% confidence intervals (CI).
Family history of clefts was strongly associated with increased risk (maternal: OR = 4.7; 95% CI, 3.0–7.2; paternal: OR = 10.5; 95% CI, 5.9–18.8; siblings: OR = 5.3; 95% CI, 1.4–19.9). Advanced maternal age (5 year OR = 1.2; 95% CI, 1.0–1.3), pregestational hypertension (OR = 2.6; 95% CI, 1.3–5.1), and gestational seizures (OR = 2.9; 95% CI, 1.1–7.4) were statistically significant risk factors. Lower maternal (secondary school OR = 1.6; 95% CI, 1.2–2.2; primary school OR = 2.4, 95% CI, 1.6–2.8) and paternal education (OR = 1.9; 95% CI, 1.4–2.5; and OR = 1.8; 95% CI, 1.1–2.9, respectively) and paternal tobacco smoking (OR = 1.5, 95% CI, 1.1–1.9) were associated with an increased risk. No other significant associations between maternal and paternal factors were found; some environmental factors including rural residency, indoor cooking with wood, chemicals and water source appeared to be associated with an increased risk in adjusted models.
Our study represents one of the first international studies investigating risk factors for clefts among multiethnic underserved populations. Our findings suggest a multifactorial etiology including both maternal and paternal factors. Birth Defects Research (Part A) 103:863–879, 2015. © 2015 The Authors Birth Defects Research Part A: Clinical and Molecular Teratology Published by Wiley Periodicals, Inc.
PMCID: PMC5049483  PMID: 26466527
cleft lip/palate; cleft palate; parental risk factors; maternal health; multiethnic populations
2.  Smokescreen: a targeted genotyping array for addiction research 
BMC Genomics  2016;17:145.
Addictive disorders are a class of chronic, relapsing mental disorders that are responsible for increased risk of mental and medical disorders and represent the largest, potentially modifiable cause of death. Tobacco dependence is associated with increased risk of disease and premature death. While tobacco control efforts and therapeutic interventions have made good progress in reducing smoking prevalence, challenges remain in optimizing their effectiveness based on patient characteristics, including genetic variation. In order to maximize collaborative efforts to advance addiction research, we have developed a genotyping array called Smokescreen. This custom array builds upon previous work in the analyses of human genetic variation, the genetics of addiction, drug metabolism, and response to therapy, with an emphasis on smoking and nicotine addiction.
The Smokescreen genotyping array includes 646,247 markers in 23 categories. The array design covers genome-wide common variation (65.67, 82.37, and 90.72 % in African (YRI), East Asian (ASN), and European (EUR) respectively); most of the variation with a minor allele frequency ≥ 0.01 in 1014 addiction genes (85.16, 89.51, and 90.49 % for YRI, ASN, and EUR respectively); and nearly all variation from the 1000 Genomes Project Phase 1, NHLBI GO Exome Sequencing Project and HapMap databases in the regions related to smoking behavior and nicotine metabolism: CHRNA5-CHRNA3-CHRNB4 and CYP2A6-CYP2B6. Of the 636 pilot DNA samples derived from blood or cell line biospecimens that were genotyped on the array, 622 (97.80 %) passed quality control. In passing samples, 90.08 % of markers passed quality control. The genotype reproducibility in 25 replicate pairs was 99.94 %. For 137 samples that overlapped with HapMap2 release 24, the genotype concordance was 99.76 %. In a genome-wide association analysis of the nicotine metabolite ratio in 315 individuals participating in nicotine metabolism laboratory studies, we identified genome-wide significant variants in the CYP2A6 region (min p = 9.10E-15).
We developed a comprehensive genotyping array for addiction research and demonstrated its analytic validity and utility through pilot genotyping of HapMap and study samples. This array allows researchers to perform genome-wide, candidate gene, and pathway-based association analyses of addiction, tobacco-use, treatment response, comorbidities, and associated diseases in a standardized, high-throughput platform.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-016-2495-7) contains supplementary material, which is available to authorized users.
PMCID: PMC4769529  PMID: 26921259
Addiction; Nicotine dependence; Nicotine metabolism; Pharmacogenomics; Smoking cessation; Genome-wide association study; Bioinformatics; Biomarkers
3.  Genome-Wide Association of the Laboratory-Based Nicotine Metabolite Ratio in Three Ancestries 
Nicotine & Tobacco Research  2016;18(9):1837-1844.
Metabolic enzyme variation and other patient and environmental characteristics influence smoking behaviors, treatment success, and risk of related disease. Population-specific variation in metabolic genes contributes to challenges in developing and optimizing pharmacogenetic interventions. We applied a custom genome-wide genotyping array for addiction research (Smokescreen), to three laboratory-based studies of nicotine metabolism with oral or venous administration of labeled nicotine and cotinine, to model nicotine metabolism in multiple populations. The trans-3′-hydroxycotinine/cotinine ratio, the nicotine metabolite ratio (NMR), was the nicotine metabolism measure analyzed.
Three hundred twelve individuals of self-identified European, African, and Asian American ancestry were genotyped and included in ancestry-specific genome-wide association scans (GWAS) and a meta-GWAS analysis of the NMR. We modeled natural-log transformed NMR with covariates: principal components of genetic ancestry, age, sex, body mass index, and smoking status.
African and Asian American NMRs were statistically significantly (P values ≤ 5E-5) lower than European American NMRs. Meta-GWAS analysis identified 36 genome-wide significant variants over a 43 kilobase pair region at CYP2A6 with minimum P = 2.46E-18 at rs12459249, proximal to CYP2A6. Additional minima were located in intron 4 (rs56113850, P = 6.61E-18) and in the CYP2A6-CYP2A7 intergenic region (rs34226463, P = 1.45E-12). Most (34/36) genome-wide significant variants suggested reduced CYP2A6 activity; functional mechanisms were identified and tested in knowledge-bases. Conditional analysis resulted in intergenic variants of possible interest (P values < 5E-5).
This meta-GWAS of the NMR identifies CYP2A6 variants, replicates the top-ranked single nucleotide polymorphism from a recent Finnish meta-GWAS of the NMR, identifies functional mechanisms, and provides pan-continental population biomarkers for nicotine metabolism.
This multiple ancestry meta-GWAS of the laboratory study-based NMR provides novel evidence and replication for genome-wide association of CYP2A6 single nucleotide and insertion–deletion polymorphisms. We identify three regions of genome-wide significance: proximal, intronic, and distal to CYP2A6. We replicate the top-ranking single nucleotide polymorphism from a recent GWAS of the NMR in Finnish smokers, identify a functional mechanism for this intronic variant from in silico analyses of RNA-seq data that is consistent with CYP2A6 expression measured in postmortem lung and liver, and provide additional support for the intergenic region between CYP2A6 and CYP2A7.
PMCID: PMC4978985  PMID: 27113016
4.  Total Exposure Study Analysis consortium: a cross-sectional study of tobacco exposures 
BMC Public Health  2015;15:866.
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.
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.
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.
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.
PMCID: PMC4561475  PMID: 26346437
5.  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.
PMCID: PMC4488893  PMID: 26132489
6.  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.
PMCID: PMC4190044  PMID: 21029848
7.  A scalable, knowledge-based analysis framework for genetic association studies 
BMC Bioinformatics  2013;14:312.
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.
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.
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.
PMCID: PMC4015032  PMID: 24152222
8.  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.
PMCID: PMC3445408  PMID: 21804549
10.  TSLP Polymorphisms are Associated with Asthma in a Sex-Specific Fashion 
Allergy  2010;65(12):1566-1575.
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.
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.
TSLP variants are associated with asthma in a sex-specific fashion.
PMCID: PMC2970693  PMID: 20560908
asthma; genetic association; sex-specific; thymic stromal lymphopoietin; TSLP
11.  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.
PMCID: PMC2924567  PMID: 19924704
GAW; case-control; family-based; cross-sectional; longitudinal; rheumatoid arthritis; Framingham Heart Study

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