Sanjay Shete

University of Texas MD Anderson Cancer Center, Houston, Texas, United States

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Publications (166)864.16 Total impact

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    Rajesh Talluri, Sanjay Shete
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    ABSTRACT: Epistasis helps to explain how multiple single-nucleotide polymorphisms (SNPs) interact to cause disease. A variety of tools have been developed to detect epistasis. In this article, we explore the strengths and weaknesses of an information theory approach for detecting epistasis and compare it to the logistic regression approach through simulations. We consider several scenarios to simulate the involvement of SNPs in an epistasis network with respect to linkage disequilibrium patterns among them and the presence or absence of main and interaction effects. We conclude that the information theory approach more efficiently detects interaction effects when main effects are absent, whereas, in general, the logistic regression approach is appropriate in all scenarios but results in higher false positives. We compute epistasis networks for SNPs in the FSD1L gene using a two-phase head and neck cancer genome-wide association study involving 2,185 cases and 4,507 controls to demonstrate the practical application of the methods.
    Cancer informatics 02/2015; 14(Suppl 2):17-23. DOI:10.4137/CIN.S17289
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    ABSTRACT: Glioma is a rare, but highly fatal, cancer that accounts for the majority of malignant primary brain tumors. Inherited predisposition to glioma has been consistently observed within non-syndromic families. Our previous studies, which involved non-parametric and parametric linkage analyses, both yielded significant linkage peaks on chromosome 17q. Here, we use data from next generation and Sanger sequencing to identify familial glioma candidate genes and variants on chromosome 17q for further investigation. We applied a filtering schema to narrow the original list of 4830 annotated variants down to 21 very rare (<0.1% frequency), non-synonymous variants. Our findings implicate the MYO19 and KIF18B genes and rare variants in SPAG9 and RUNDC1 as candidates worthy of further investigation. Burden testing and functional studies are planned.
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    ABSTRACT: Gliomas are the most common brain tumor, with several histological subtypes of various malignancy grade. The genetic contribution to familial glioma is not well understood. Using whole exome sequencing of 90 individuals from 55 families, we identified two families with mutations in POT1 (p.G95C, p.E450X), a member of the telomere shelterin complex, shared by both affected individuals in each family and predicted to impact DNA binding and TPP1 binding, respectively. Validation in a separate cohort of 264 individuals from 246 families identified an additional mutation in POT1 (p.D617Efs), also predicted to disrupt TPP1 binding. All families with POT1 mutations had affected members with oligodendroglioma, a specific subtype of glioma more sensitive to irradiation. These findings are important for understanding the origin of glioma and could have importance for the future diagnostics and treatment of glioma. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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    ABSTRACT: The underlying ethos of dbGaP is that access to these data by secondary data analysts facilitates advancement of science. NIH has required that genome-wide association study data be deposited in the Database of Genotypes and Phenotypes (dbGaP) since 2003. In 2013, a proposed updated policy extended this requirement to next-generation sequencing data. However, recent literature and anecdotal reports suggest lingering logistical and ethical concerns about subject identifiability, informed consent, publication embargo enforcement, and difficulty in accessing dbGaP data. We surveyed the International Genetic Epidemiology Society (IGES) membership about their experiences. One hundred and seventy five (175) individuals completed the survey, a response rate of 27%. Of respondents who received data from dbGaP (43%), only 32% perceived the application process as easy but most (75%) received data within five months. Remaining challenges include difficulty in identifying an institutional signing official and an overlong application process. Only 24% of respondents had contributed data to dbGaP. Of these, 31% reported local IRB restrictions on data release; an additional 15% had to reconsent study participants before depositing data. The majority of respondents (56%) disagreed that the publication embargo period was sufficient. In response, we recommend longer embargo periods and use of varied data-sharing models rather than a one-size-fits-all approach.
    International Journal of Environmental Research and Public Health 08/2014; 11(8):8383-8398. DOI:10.3390/ijerph110808383 · 1.99 Impact Factor
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    ABSTRACT: Background: Smoking experimentation in Mexican American youth is problematic. In light of the research showing that preventing smoking experimentation is a valid strategy for smoking prevention, there is a need to identify Mexican American youth at high risk for experimentation. Methods: A prospective population-based cohort of 1179 adolescents of Mexican descent was followed for 5 years starting in 2005-06. Participants completed a baseline interview at a home visit followed by three telephone interviews at intervals of approximately 6 months and additional interviews at two home visits in 2008-09 and 2010-11. The primary end point of interest in this study was smoking experimentation. Information regarding social, cultural, and behavioral factors (e.g., acculturation, susceptibility to experimentation, home characteristics, household influences) was collected at baseline using validated questionnaires. Results: Age, sex, cognitive susceptibility, household smoking behavior, peer influence, neighborhood influence, acculturation, work characteristics, positive outcome expectations, family cohesion, degree of tension, ability to concentrate, and school discipline were found to be associated with smoking experimentation. In a validation dataset, the proposed risk prediction model had an AUC of 0.719 (95% confidence interval, 0.637 to 0.801) for predicting absolute risk for smoking experimentation within 1 year. Conclusions: The proposed risk prediction model is able to quantify the risk of smoking experimentation in Mexican American adolescents. Impact: Accurately identifying Mexican American adolescents who are at higher risk for smoking experimentation who can be intervened will substantially reduce the incidence of smoking and thereby subsequent health risks.
    Cancer Epidemiology Biomarkers & Prevention 07/2014; 23(10). DOI:10.1158/1055-9965.EPI-14-0467 · 4.32 Impact Factor
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    ABSTRACT: Survival outcomes in patients with squamous cell carcinoma of the head and neck (HNSCC) vary by extent of disease, behavioral, and socioeconomic factors. We assessed the extent to which pre-treatment pain influences survival in 2340 newly diagnosed patients with HNSCC, adjusting for disease stage, symptoms, pain medications, comorbidities, smoking, alcohol consumption, age, sex, and race/ethnicity. Patients rated their pain at presentation to the cancer center (0= 'no pain' and 10= 'pain as bad as you can imagine'). Survival time was calculated from the date of diagnosis to the date of death of any cause or last follow-up. Five year overall survival was calculated for all the variables assessed in the study. Severe pain (>7) was most prevalent among those with oral cancer (20.4%; pharynx=18.8%; larynx=16.1%) and significantly varied by tumor stage, fatigue severity, smoking status, comorbid lung disease, and race (all p<0.05) across cancer diagnoses. Overall 5 year survival varied by pain for oral (severe pain=31% versus non-severe=52%; p<0.001) and pharyngeal cancer (severe pain=33%, versus non-severe=53%; p<0.001). Multivariable analyses showed that pain persisted as an independent prognostic factor for survival. Pain reported prior to treatment should be considered in understanding survival outcomes in HNSCC patients.
    Journal of Pain 07/2014; 15(10). DOI:10.1016/j.jpain.2014.07.003 · 4.22 Impact Factor
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    ABSTRACT: Genome-wide association studies (GWAS) have mapped risk alleles for at least ten distinct cancers to a small region of 63,000 bp on chromosome 5p15.33. This region harbors the TERT and CLPTM1L genes; the former encodes the catalytic subunit of telomerase reverse transcriptase and the latter may play a role in apoptosis. To investigate further the genetic architecture of common susceptibility alleles in this region, we conducted an agnostic subset-based meta-analysis (ASSET) across six distinct cancers in 34,248 cases and 45,036 controls. Based on sequential conditional analysis, we identified as many as six independent risk loci marked by common single nucleotide polymorphisms (SNPs): five in the TERT gene (region 1: rs7726159, P=2.10x10-39; region 3: rs2853677, P=3.30x10-36 and PConditional=2.36x10-8; region 4: rs2736098, P=3.87x10-12 and PConditional=5.19x10-6, region 5: rs13172201, P=0.041 and PConditional=2.04x10-6; and region 6: rs10069690, P=7.49x10-15 and PConditional=5.35x10-7) and one in the neighboring CLPTM1L gene (region 2: rs451360; P=1.90x10-18 and PConditional=7.06x10-16). Between three and five cancers mapped to each independent locus with both risk-enhancing and protective effects. Allele specific effects on DNA methylation were seen for a subset of risk loci indicating that methylation and subsequent effects on gene expression may contribute to the biology of risk variants on 5p15.33. Our results provide strong support for extensive pleiotropy across this region of 5p15.33, to an extent not previously observed in other cancer susceptibility loci.
    Human Molecular Genetics 07/2014; DOI:10.1093/hmg/ddu363 · 6.68 Impact Factor
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    Jian Wang, Robert Yu, Sanjay Shete
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    ABSTRACT: X-chromosome inactivation (XCI) is the process in which one of the two copies of the X-chromosome in females is randomly inactivated to achieve the dosage compensation of X-linked genes between males and females. That is, 50% of the cells have one allele inactive and the other 50% of the cells have the other allele inactive. However, studies have shown that skewed or nonrandom XCI is a biological plausibility wherein more than 75% of cells have the same allele inactive. Also, some of the X-chromosome genes escape XCI, i.e., both alleles are active in all cells. Current statistical tests for X-chromosome association studies can either account for random XCI (e.g., Clayton's approach) or escape from XCI (e.g., PLINK software). Because the true XCI process is unknown and differs across different regions on the X-chromosome, we proposed a unified approach of maximizing likelihood ratio over all biological possibilities: random XCI, skewed XCI, and escape from XCI. A permutation-based procedure was developed to assess the significance of the approach. We conducted simulation studies to compare the performance of the proposed approach with Clayton's approach and PLINK regression. The results showed that the proposed approach has higher powers in the scenarios where XCI is skewed while losing some power in scenarios where XCI is random or XCI is escaped, with well-controlled type I errors. We also applied the approach to the X-chromosomal genetic association study of head and neck cancer.
    Genetic Epidemiology 07/2014; DOI:10.1002/gepi.21814 · 2.95 Impact Factor
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    ABSTRACT: IntroductionThe genetic heritability for sensation-seeking tendencies ranges from 40 to 60%. Sensation-seeking behaviors typically manifest during adolescence and are associated with alcohol and cigarette experimentation in adolescents. Social disinhibition is an aspect of sensation-seeking that is closely tied to cigarette and alcohol experimentation.Methods We examined the contribution of candidate genes to social disinhibition among 1132 Mexican origin youth in Houston, Texas, adjusting for established demographic and psychosocial risk factors. Saliva samples were obtained at baseline in 2005–06, and social disinhibition and other psychosocial data were obtained in 2008–09. Participants were genotyped for 672 functional and tagging SNPs potentially related to sensation-seeking, risk-taking, smoking, and alcohol use.ResultsSix SNPs were significantly associated with social disinhibition scores, after controlling for false discovery and adjusting for population stratification and relevant demographic/psychosocial characteristics. Minor alleles for three of the SNPs (rs1998220 on OPRM1; rs9534511 on HTR2A; and rs4938056 on HTR3B) were associated with increased risk of social disinhibition, while minor alleles for the other three SNPs (rs1003921 on KCNC1; rs16116 downstream of NPY; and rs16870286 on LINC00518) exhibited a protective effect. Age, linguistic acculturation, thrill and adventure-seeking, and drug and alcohol-seeking were all significantly positively associated with increased risk of social disinhibition in a multivariable model (P < 0.001).Conclusions These results add to our knowledge of genetic risk factors for social disinhibition. Additional research is needed to verify whether these SNPs are associated with social disinhibition among youth of different ethnicities and nationalities, and to elucidate whether and how these SNPs functionally contribute to social disinhibition.
    07/2014; 4(4). DOI:10.1002/brb3.236
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    Rajesh Talluri, Jian Wang, Sanjay Shete
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    ABSTRACT: Several methods have been proposed to account for multiple comparisons in genetic association studies. However, investigators typically test each of the SNPs using multiple genetic models. Association testing using the Cochran-Armitage test for trend assuming an additive, dominant, or recessive genetic model, is commonly performed. Thus, each SNP is tested three times. Some investigators report the smallest p-value obtained from the three tests corresponding to the three genetic models, but such an approach inherently leads to inflated type 1 errors. Because of the small number of tests (three) and high correlation (functional dependence) among these tests, the procedures available for accounting for multiple tests are either too conservative or fail to meet the underlying assumptions (e.g., asymptotic multivariate normality or independence among the tests).
    BMC Genetics 06/2014; 15(1):75. DOI:10.1186/1471-2156-15-75 · 2.36 Impact Factor
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    Jian Wang, Robert Yu, Sanjay Shete
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    ABSTRACT: Identifying genetic variants associated with complex diseases is an important task in genetic research. Although association studies based on unrelated individuals (ie, case-control genome-wide association studies) have successfully identified common single-nucleotide polymorphisms for many complex diseases, these studies are not so likely to identify rare genetic variants. In contrast, family-based association studies are particularly useful for identifying rare-variant associations. Recently, there has been some interest in employing multilevel models in family-based genetic association studies. However, the performance of such models in these studies, especially for longitudinal family-based sequence data, has not been fully investigated. Therefore, in this study, we investigated the performance of the multilevel model in the family-based genetic association analysis and compared it with the conventional family-based association test, by examining the powers and type I error rates of the 2 approaches using 3 data sets from the Genetic Analysis Workshop 18 simulated data: genome-wide association single-nucleotide polymorphism data, sequence data, and rare-variants-only data. Compared with the univariate family-based association test, the multilevel model had slightly higher power to identify most of the causal genetic variants using the genome-wide association single-nucleotide polymorphism data and sequence data. However, both approaches had low power to identify most of the causal single-nucleotide polymorphisms, especially those among the relatively rare genetic variants. Therefore, we suggest a unified method that combines both approaches and incorporates collapsing strategy, which may be more powerful than either approach alone for studying genetic associations using family-based data.
    BMC proceedings 06/2014; 8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S30. DOI:10.1186/1753-6561-8-S1-S30
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    ABSTRACT: We are now well into the sequencing era of genetic analysis, and methods to investigate rare variants associated with disease remain in high demand. Currently, the more common rare variant analysis methods are burden tests and variance component tests. This report introduces a burden test known as the modified replication based sum statistic and evaluates its performance, and the performance of other common burden and variance component tests under the setting of a small sample size (103 total cases and controls) using the Genetic Analysis Workshop 18 simulated data with complete knowledge of the simulation model. Specifically we look at the variable threshold sum statistic, replication-based sum statistics, the C-alpha, and sequence kernel association test. Using minor allele frequency thresholds of less than 0.05, we find that the modified replication based sum statistic is competitive with all methods and that using 103 individuals leads to all methods being vastly underpowered. Much larger sample sizes are needed to confidently find truly associated genes.
    BMC proceedings 06/2014; 8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S13. DOI:10.1186/1753-6561-8-S1-S13
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    Rajesh Talluri, Sanjay Shete
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    ABSTRACT: Graphical models are increasingly used in genetic analyses to take into account the complex relationships between genetic and nongenetic factors influencing the phenotypes. We propose a model for determining the network structure of quantitative traits while accounting for the correlated nature of the family-based samples using the kinship coefficient. The Gaussian graphical model of age, systolic blood pressure, diastolic blood pressure, hypertension, blood pressure medication use, and smoking status was derived for three time points using real data. We also explored binary sparse graphical models of single-nucleotide polymorphisms (SNPs), covariates, and quantitative traits for exploratory analysis of the data. We validated the applicability of this method by producing a network graph using 20 causal variants, 21 noncausal variants, and 6 binary and quantitative phenotypes using the simulated data. To improve the model's ability to identify associations between the causal variants and the phenotypes, we intend to conduct follow-up studies investigating how to use the relationships between SNPs and between SNPs and phenotypes when analyzing genome wide association data with multiple phenotypes.
    BMC proceedings 06/2014; 8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S99. DOI:10.1186/1753-6561-8-S1-S99
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    ABSTRACT: The aim was to determine the proportions and correlates of essential hypertension among children in a tertiary pediatric hypertension clinic.
    American Journal of Hypertension 05/2014; DOI:10.1093/ajh/hpu083 · 3.40 Impact Factor
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    ABSTRACT: Although familial susceptibility to glioma is known, the genetic basis for this susceptibility remains unidentified in the majority of glioma-specific families. An alternative approach to identifying such genes is to examine cancer pedigrees, which include glioma as one of several cancer phenotypes, to determine whether common chromosomal modifications might account for the familial aggregation of glioma and other cancers. Germline rearrangements in 146 glioma families (from the Gliogene Consortium; http://www.gliogene.org/) were examined using multiplex ligation-dependent probe amplification. These families all had at least 2 verified glioma cases and a third reported or verified glioma case in the same family or 2 glioma cases in the family with at least one family member affected with melanoma, colon, or breast cancer.The genomic areas covering TP53, CDKN2A, MLH1, and MSH2 were selected because these genes have been previously reported to be associated with cancer pedigrees known to include glioma. We detected a single structural rearrangement, a deletion of exons 1-6 in MSH2, in the proband of one family with 3 cases with glioma and one relative with colon cancer. Large deletions and duplications are rare events in familial glioma cases, even in families with a strong family history of cancers that may be involved in known cancer syndromes.
    Neuro-Oncology 04/2014; 16(10). DOI:10.1093/neuonc/nou052 · 5.29 Impact Factor
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    ABSTRACT: Birth defects are a leading cause of infant morbidity and mortality worldwide. The vast majority of birth defects are nonsyndromic, and although their etiologies remain mostly unknown, evidence supports the hypothesis that they result from the complex interaction of genetic, epigenetic, environmental, and lifestyle factors. Since our last review published in 2002 describing the basic tools of genetic epidemiology used to study nonsyndromic structural birth defects, many new approaches have become available and have been used with varying success. Through rapid advances in genomic technologies, investigators are now able to investigate large portions of the genome at a fraction of previous costs. With next-generation sequencing, research has progressed from assessing a small percentage of single-nucleotide polymorphisms to assessing the entire human protein-coding repertoire (exome)-an approach that is starting to uncover rare but informative mutations associated with nonsyndromic birth defects. Herein, we report on the current state of the genetic epidemiology of birth defects and comment on future challenges and opportunities. We consider issues of study design, and we discuss common variant approaches, including candidate gene studies and genome-wide association studies. We also discuss the complexities embedded in exploring interactions between genes and the environment. We complete our review by describing new and promising next-generation sequencing technologies and examining how the study of epigenetic mechanisms could become the key to unraveling the complex etiologies of nonsyndromic structural birth defects.
    02/2014; 168(4). DOI:10.1001/jamapediatrics.2013.4858
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    ABSTRACT: Renal cell carcinoma (RCC) accounts for ∼4% of all human malignancies and is the 9th leading cause of male cancer death in the United States. The purpose of this study was to determine the effect of variation within microRNA (miRNA)-binding sites of genes in the VHL-HIF1α pathway on RCC risk. We identified 429 miRNA-binding site single-nucleotide polymorphisms (SNPs) in 102 pathway genes and assessed 53 tagging-SNPs for 31 of these genes for risk in a case-control study consisting of 894 RCC cases and 1,516 controls. Results showed that five SNPs were significantly associated with RCC risk. The most significant finding was rs743409 in MAPK1. Under the additive model, the variant was associated with a 10% risk reduction (OR: 0.90, 95% CI, 0.77-0.98). Other significant findings were for SNPs in CDCP1, TFRC, and DEC1. Cumulative effects analysis showed that subjects carrying four or five unfavorable genotypes had a 2.14-fold increase in risk (95% CI, 1.03-4.43, P = 0.04) than those with no unfavorable genotypes. Potential higher-order gene-gene interactions were identified and categorized subjects into different risk groups. The OR of the high-risk group defined by two SNPs: CDCP1:rs6773576 (GG) and DEC1:rs10982724 (GG) was 4.46 times higher than that of low-risk reference group (95% CI, 1.31-15.08). Overall, our study provides the first evidence supporting a connection between miRNA-binding site SNPs within the VHL-HIF1α pathway and RCC risk. These novel genetic risk factors might help identify individuals at high risk to enable detection of tumors at an early, curable stage. © 2012 Wiley Periodicals, Inc.
    Molecular Carcinogenesis 01/2014; 53(1). DOI:10.1002/mc.21917 · 4.77 Impact Factor
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    ABSTRACT: Because smoking has a profound impact on socioeconomic disparities in illness and death, it is crucial that vulnerable populations of smokers be targeted with treatment. The U.S. Public Health Service recommends that all patients be asked about their smoking at every visit and that smokers be given brief advice to quit and referred to treatment. Initiatives to facilitate these practices include the 5A's (ask, advise, assess, assist, arrange) and Ask-Advise-Refer (AAR). Unfortunately, primary care referrals are low, and most smokers referred fail to enroll. This study evaluated the efficacy of the Ask-Advise-Connect (AAC) approach to linking smokers with treatment in a large, safety net public healthcare system. The study design was a pair-matched group-randomized trial with two treatment arms. Ten safety net clinics in Houston TX. Clinics were randomized to AAC (n=5; intervention) or AAR (n=5; control). Licensed vocational nurses (LVNs) were trained to assess and record the smoking status of all patients at all visits in the electronic health record. Smokers were given brief advice to quit. In AAC, the names and phone numbers of smokers who agreed to be connected were sent electronically to the Texas quitline daily, and patients were proactively called by the quitline within 48 hours. In AAR, smokers were offered a quitline referral card and encouraged to call on their own. Data were collected between June 2010 and March 2012 and analyzed in 2012. The primary outcome was impact, defined here as the proportion of identified smokers that enrolled in treatment. The impact (proportion of identified smokers who enrolled in treatment) of AAC (14.7%) was significantly greater than the impact of AAR (0.5%), t(4)=14.61, p=0.0001, OR=32.10 (95% CI=16.60, 62.06). The AAC approach to aiding smoking cessation has tremendous potential to reduce tobacco-related health disparities. This study is registered at ISRCTN78799157.
    American journal of preventive medicine 12/2013; 45(6):737-41. DOI:10.1016/j.amepre.2013.07.011 · 4.28 Impact Factor
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    ABSTRACT: Neuronal nicotinic acetylcholine receptor (nAChR) genes (CHRNA5/CHRNA3/CHRNB4) have been reproducibly associated with nicotine dependence, smoking behaviors, and lung cancer risk. Of the few reports that have focused on early smoking behaviors, association results have been mixed. This meta-analysis examines early smoking phenotypes and SNPs in the gene cluster to determine: (1) whether the most robust association signal in this region (rs16969968) for other smoking behaviors is also associated with early behaviors, and/or (2) if additional statistically independent signals are important in early smoking. We focused on two phenotypes: age of tobacco initiation (AOI) and age of first regular tobacco use (AOS). This study included 56,034 subjects (41 groups) spanning nine countries and evaluated five SNPs including rs1948, rs16969968, rs578776, rs588765, and rs684513. Each dataset was analyzed using a centrally generated script. Meta-analyses were conducted from summary statistics. AOS yielded significant associations with SNPs rs578776 (beta = 0.02, P = 0.004), rs1948 (beta = 0.023, P = 0.018), and rs684513 (beta = 0.032, P = 0.017), indicating protective effects. There were no significant associations for the AOI phenotype. Importantly, rs16969968, the most replicated signal in this region for nicotine dependence, cigarettes per day, and cotinine levels, was not associated with AOI (P = 0.59) or AOS (P = 0.92). These results provide important insight into the complexity of smoking behavior phenotypes, and suggest that association signals in the CHRNA5/A3/B4 gene cluster affecting early smoking behaviors may be different from those affecting the mature nicotine dependence phenotype.
    Genetic Epidemiology 11/2013; DOI:10.1002/gepi.21760 · 2.95 Impact Factor
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    ABSTRACT: We studied whether a melanoma survivor-centered intervention was more effective than materials available to the general public in increasing children's sun protection. In a randomized controlled trial, melanoma survivors (n = 340) who had a child ≤12 years received a targeted sun protection intervention (DVD and booklets) or standard education. Primary outcomes were children's sunburns, children's sun protection, and survivors' psychosocial factors at baseline and postintervention (1 and 4 months). The intervention increased children's sunscreen reapplication at 1 month (P = 0.002) and use of wide-brimmed hats at 4 months (P = 0.045). There were no effects on other behaviors or sunburns. The intervention improved survivors' hats/clothing self-efficacy at both follow-up assessments (P = 0.026, 0.009). At 4 months, the intervention improved survivors' clothing intentions (P = 0.029), knowledge (P = 0.010), and outcome expectations for hats (P = 0.002) and clothing (P = 0.037). Children's sun protection increased with survivors' intervention use. The intervention was less effective in survivors who were female or who had a family history, older children, or children with higher baseline sun protection scores. A melanoma survivor-centered sun protection intervention can improve some child and survivor outcomes. The intervention may be more effective in survivors who have younger children or less experience with sun protection. Intervention delivery must be enhanced to maximize use. This is the first study to examine a sun protection intervention for children of melanoma survivors. Findings will guide interventions for this important population at increased melanoma risk. Cancer Epidemiol Biomarkers Prev; 22(10); 1813-24. ©2013 AACR.
    Cancer Epidemiology Biomarkers & Prevention 10/2013; 22(10):1813-1824. DOI:10.1158/1055-9965.EPI-13-0249 · 4.32 Impact Factor

Publication Stats

6k Citations
864.16 Total Impact Points

Institutions

  • 2002–2015
    • University of Texas MD Anderson Cancer Center
      • • Department of Biostatistics
      • • Department of Epidemiology
      Houston, Texas, United States
  • 2012
    • Harvard Medical School
      • Department of Medicine
      Boston, Massachusetts, United States
  • 2010
    • Baylor College of Medicine
      • Department of Pediatrics
      Houston, Texas, United States
  • 2008
    • University of Texas Health Science Center at Houston
      • Department of Internal Medicine
      Houston, TX, United States
  • 2007–2008
    • University of Houston
      Houston, Texas, United States
    • Umeå University
      Umeå, Västerbotten, Sweden
  • 2005
    • The Ohio State University
      Columbus, Ohio, United States
    • Brigham and Women's Hospital
      Boston, Massachusetts, United States
  • 2000–2003
    • Case Western Reserve University
      • Rammelkamp Center for Education and Research
      Cleveland, Ohio, United States