MONUMENT, CO, United States
MONUMENT, CO, United States

Time filter

Source Type

PubMed | Wake forest University, Brown University, Baylor College of Medicine, Sloan Kettering Cancer Center and 8 more.
Type: | Journal: EBioMedicine | Year: 2016

Genome-wide association studies have identified polymorphisms linked to both smoking exposure and risk of lung cancer. The degree to which lung cancer risk is driven by increased smoking, genetics, or gene-environment interactions is not well understood.We analyzed associations between 28 single nucleotide polymorphisms (SNPs) previously associated with smoking quantity and lung cancer in 7156 African-American females in the Womens Health Initiative (WHI), then analyzed main effects of top nominally significant SNPs and interactions between SNPs, cigarettes per day (CPD) and pack-years for lung cancer in an independent, multi-center case-control study of African-American females and males (1078 lung cancer cases and 822 controls).Nine nominally significant SNPs for CPD in WHI were associated with incident lung cancer (corrected p-values from 0.027 to 6.09 10(-5)). CPD was found to be a nominally significant effect modifier between SNP and lung cancer for six SNPs, including CHRNA5 rs2036527[A](betaSNP*CPD = - 0.017, p = 0.0061, corrected p = 0.054), which was associated with CPD in a previous genome-wide meta-analysis of African-Americans.These results suggest that chromosome 15q25.1 variants are robustly associated with CPD and lung cancer in African-Americans and that the allelic dose effect of these polymorphisms on lung cancer risk is most pronounced in lighter smokers.

PubMed | Stanford University, Biorealm, University of California at San Francisco, SRI International and Oregon Research Institute
Type: Journal Article | Journal: Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco | Year: 2016

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.

PubMed | University of Tübingen, Helmholtz Center Munich, University of Pennsylvania, University of Würzburg and 14 more.
Type: | Journal: Molecular psychiatry | Year: 2016

The maintenance of normal body weight is disrupted in patients with anorexia nervosa (AN) for prolonged periods of time. Prior to the onset of AN, premorbid body mass index (BMI) spans the entire range from underweight to obese. After recovery, patients have reduced rates of overweight and obesity. As such, loci involved in body weight regulation may also be relevant for AN and vice versa. Our primary analysis comprised a cross-trait analysis of the 1000 single-nucleotide polymorphisms (SNPs) with the lowest P-values in a genome-wide association meta-analysis (GWAMA) of AN (GCAN) for evidence of association in the largest published GWAMA for BMI (GIANT). Subsequently we performed sex-stratified analyses for these 1000 SNPs. Functional ex vivo studies on four genes ensued. Lastly, a look-up of GWAMA-derived BMI-related loci was performed in the AN GWAMA. We detected significant associations (P-values <5 10

PubMed | Childrens Hospital Los Angeles, Operation Smile Philippines, Operation Smile Vietnam, Operation Smile South Africa and 4 more.
Type: Journal Article | Journal: Birth defects research. Part A, Clinical and molecular teratology | Year: 2015

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.

Figueiredo J.C.,University of Southern California | Levine A.J.,University of Southern California | Crott J.W.,Tufts University | Baurley J.,Biorealm | Haile R.W.,University of Southern California
Molecular Nutrition and Food Research | Year: 2013

Scope: The metabolism of folate involves a complex network of polymorphic enzymes that may explain a proportion of the risk associated with colorectal neoplasia. Over 60 observational studies primarily in non-Hispanic White populations have been conducted on selected genetic variants in specific genes, MTHFR, MTR, MTRR, CBS, TCNII, RFC, GCPII, SHMT, TYMS, and MTHFD1, including five meta-analyses on MTHFR 677C>T (rs1801133) and MTHFR 1298C>T (rs1801131); two meta-analyses on MTR-2756A>C (rs1805087); and one for MTRR 66A>G (rs1801394). Methods and results: This systematic review synthesizes these data, highlighting the consistent inverse association between MTHFR 677TT genotype and risk of colorectal cancer (CRC) and its null association with adenoma risk. Results for other variants varied across individual studies; in our meta-analyses we observed some evidence for SHMT 1420C>T (rs1979277) ((odds ratio) OR = 0.85; 95% confidence interval (CI) = 0.73-1.00 for TT v. CC) and TYMS 5' 28 bp repeat (rs34743033) and CRC risk (OR = 0.84; 95% CI = 0.75-0.94 for 2R/3R v. 3R/3R and OR = 0.82; 95% CI = 0.69-0.98 for 2R/2R v. 3R/3R). Conclusion: To gain further insight into the role of folate variants in colorectal neoplasia will require incorporating measures of the metabolites, including B-vitamin cofactors, homocysteine and S-adenosylmethionine, and innovative statistical methods to better approximate the folate one-carbon metabolism pathway. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Baurley J.W.,Binus University | Baurley J.W.,Biorealm | Conti D.V.,University of Southern California
BMC Bioinformatics | Year: 2013

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. © 2013 Baurley and Conti; licensee BioMed Central Ltd.

Agency: Department of Health and Human Services | Branch: National Institutes of Health | Program: SBIR | Phase: Phase II | Award Amount: 2.01M | Year: 2015

Not Available

Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 149.98K | Year: 2012

The Contractor shall develop two major components of the Smokescreen platform, specifically a custom genotyping microarray that will contain single-nucleotide polymorphisms (SNPs) that interrogate known and candidate tobacco addiction loci and a novel software product.

Agency: Department of Health and Human Services | Branch: National Institutes of Health | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2016

DESCRIPTION provided by applicant Tobacco attributable disease remains the largest potentially modifiable cause of mortality Strategies to reduce smoking prevalence include developing more effective smoking cessation treatments Nicotine metabolism is a predictor of smoking behaviors including response to treatment The goal of this Phase I project is to establish the technical and scientific feasibility of developing the andquot Smokescreen r Translational Analysis Platform TL andquot a service that will predict an individualandapos s nicotine metabolism from Smokescreen r Genotyping Array GTA data with content focused for pharmacogenetic analysis in individuals from multiple world populations Germline variants in the CYP A gene play a major role in nicotine metabolism activity and are difficult to genotype due to the regionandapos s complex alleles and sequence similarity with other genes We will assess the genotyping and imputation accuracy of the Smokescreen r GTA for CYP A variants using samples from public genomic projects and from three laboratory studies of nicotine metabolism This assessment will support the technical validity of using Smokescreen r GTA CYP A genotypes in predictive models We will then use existing Smokescreen r GTA genotype data from three laboratory studies of nicotine metabolism to compute CYP A haplotypes and predict metabolism in individuals of African Asian and European ancestry the results will be evaluated in relation to observed nicotine metabolism In addition we will define new nicotine metabolism pathway prediction models using the latest Bayesian variable selection methodologies that incorporate external functional clinical and pathway knowledge These results will be packaged into prototype reports for the Smokescreen r TL service and presented to translational and clinical researchers for feedback Accomplishments opportunities and challenges will be summarized for the Smokescreen r TL service Computable models of nicotine metabolism integrated into a unified genetic platform will provide opportunities for future validation in laboratory studies of diverse populations and can be used retrospectively or prospectively in clinical trials of smoking behaviors including response to smoking cessation therapies With additional research using clinical trials of smoking cessation therapies this platform will ultimately provide model estimates and prognostic information for translational research and for use in smoking cessation therapy assignment PUBLIC HEALTH RELEVANCE Quitting tobacco smoking remains a challenge for a significant portion of the U S population This project aims to create a service to help scientist identify factors related to how nicotine is processed in the body and use these factors to improve rates of quitting reduce side effects and assess risk of related diseases

PubMed | Biorealm
Type: | Journal: BMC genomics | Year: 2016

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.

Loading Biorealm collaborators
Loading Biorealm collaborators