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1.  Germline rearrangements in families with strong family history of glioma and malignant melanoma, colon, and breast cancer 
Andersson, Ulrika | Wibom, Carl | Cederquist, Kristina | Aradottir, Steina | Borg, Åke | Armstrong, Georgina N. | Shete, Sanjay | Lau, Ching C. | Bainbridge, Matthew N. | Claus, Elizabeth B. | Barnholtz-Sloan, Jill | Lai, Rose | Il'yasova, Dora | Houlston, Richard S. | Schildkraut, Joellen | Bernstein, Jonine L. | Olson, Sara H. | Jenkins, Robert B. | Lachance, Daniel H. | Wrensch, Margaret | Davis, Faith G. | Merrell, Ryan | Johansen, Christoffer | Sadetzki, Siegal | Bondy, Melissa L. | Melin, Beatrice S. | Adatto, Phyllis | Morice, Fabian | Payen, Sam | McQuinn, Lacey | McGaha, Rebecca | Guerra, Sandra | Paith, Leslie | Roth, Katherine | Zeng, Dong | Zhang, Hui | Yung, Alfred | Aldape, Kenneth | Gilbert, Mark | Weinberger, Jeffrey | Colman, Howard | Conrad, Charles | de Groot, John | Forman, Arthur | Groves, Morris | Levin, Victor | Loghin, Monica | Puduvalli, Vinay | Sawaya, Raymond | Heimberger, Amy | Lang, Frederick | Levine, Nicholas | Tolentino, Lori | Saunders, Kate | Thach, Thu-Trang | Iacono, Donna Dello | Sloan, Andrew | Gerson, Stanton | Selman, Warren | Bambakidis, Nicholas | Hart, David | Miller, Jonathan | Hoffer, Alan | Cohen, Mark | Rogers, Lisa | Nock, Charles J | Wolinsky, Yingli | Devine, Karen | Fulop, Jordonna | Barrett, Wendi | Shimmel, Kristen | Ostrom, Quinn | Barnett, Gene | Rosenfeld, Steven | Vogelbaum, Michael | Weil, Robert | Ahluwalia, Manmeet | Peereboom, David | Staugaitis, Susan | Schilero, Cathy | Brewer, Cathy | Smolenski, Kathy | McGraw, Mary | Naska, Theresa | Rosenfeld, Steven | Ram, Zvi | Blumenthal, Deborah T. | Bokstein, Felix | Umansky, Felix | Zaaroor, Menashe | Cohen, Avi | Tzuk-Shina, Tzeela | Voldby, Bo | Laursen, René | Andersen, Claus | Brennum, Jannick | Henriksen, Matilde Bille | Marzouk, Maya | Davis, Mary Elizabeth | Boland, Eamon | Smith, Marcel | Eze, Ogechukwu | Way, Mahalia | Lada, Pat | Miedzianowski, Nancy | Frechette, Michelle | Paleologos, Nina | Byström, Gudrun | Svedberg, Eva | Huggert, Sara | Kimdal, Mikael | Sandström, Monica | Brännström, Nikolina | Hayat, Amina | Tihan, Tarik | Zheng, Shichun | Berger, Mitchel | Butowski, Nicholas | Chang, Susan | Clarke, Jennifer | Prados, Michael | Rice, Terri | Sison, Jeannette | Kivett, Valerie | Duo, Xiaoqin | Hansen, Helen | Hsuang, George | Lamela, Rosito | Ramos, Christian | Patoka, Joe | Wagenman, Katherine | Zhou, Mi | Klein, Adam | McGee, Nora | Pfefferle, Jon | Wilson, Callie | Morris, Pagan | Hughes, Mary | Britt-Williams, Marlin | Foft, Jessica | Madsen, Julia | Polony, Csaba | McCarthy, Bridget | Zahora, Candice | Villano, John | Engelhard, Herbert | Borg, Ake | Chanock, Stephen K | Collins, Peter | Elston, Robert | Kleihues, Paul | Kruchko, Carol | Petersen, Gloria | Plon, Sharon | Thompson, Patricia | Johansen, C. | Sadetzki, S. | Melin, B. | Bondy, Melissa L. | Lau, Ching C. | Scheurer, Michael E. | Armstrong, Georgina N. | Liu, Yanhong | Shete, Sanjay | Yu, Robert K. | Aldape, Kenneth D. | Gilbert, Mark R. | Weinberg, Jeffrey | Houlston, Richard S. | Hosking, Fay J. | Robertson, Lindsay | Papaemmanuil, Elli | Claus, Elizabeth B. | Claus, Elizabeth B. | Barnholtz-Sloan, Jill | Sloan, Andrew E. | Barnett, Gene | Devine, Karen | Wolinsky, Yingli | Lai, Rose | McKean-Cowdin, Roberta | Il'yasova, Dora | Schildkraut, Joellen | Sadetzki, Siegal | Yechezkel, Galit Hirsh | Bruchim, Revital Bar-Sade | Aslanov, Lili | Sadetzki, Siegal | Johansen, Christoffer | Kosteljanetz, Michael | Broholm, Helle | Bernstein, Jonine L. | Olson, Sara H. | Schubert, Erica | DeAngelis, Lisa | Jenkins, Robert B. | Yang, Ping | Rynearson, Amanda | Andersson, Ulrika | Wibom, Carl | Henriksson, Roger | Melin, Beatrice S. | Cederquist, Kristina | Aradottir, Steina | Borg, Åke | Merrell, Ryan | Lada, Patricia | Wrensch, Margaret | Wiencke, John | Wiemels, Joe | McCoy, Lucie | McCarthy, Bridget J. | Davis, Faith G.
Neuro-Oncology  2014;16(10):1333-1340.
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; 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.
PMCID: PMC4165415  PMID: 24723567
CDKN2A/B; family history; glioma; MLH1; MSH2; TP53
2.  Selection of X-chromosome Inactivation Model 
Cancer Informatics  2017;16:1176935117747272.
To address the complexity of the X-chromosome inactivation (XCI) process, we previously developed a unified approach for the association test for X-chromosomal single-nucleotide polymorphisms (SNPs) and the disease of interest, accounting for different biological possibilities of XCI: random, skewed, and escaping XCI. In the original study, we focused on the SNP-disease association test but did not provide knowledge regarding the underlying XCI models. One can use the highest likelihood ratio (LLR) to select XCI models (max-LLR approach). However, that approach does not formally compare the LLRs corresponding to different XCI models to assess whether the models are distinguishable. Therefore, we propose an LLR comparison procedure (comp-LLR approach), inspired by the Cox test, to formally compare the LLRs of different XCI models to select the most likely XCI model that describes the underlying XCI process. We conduct simulation studies to investigate the max-LLR and comp-LLR approaches. The simulation results show that compared with the max-LLR, the comp-LLR approach has higher probability of identifying the correct underlying XCI model for the scenarios when the underlying XCI process is random XCI, escaping XCI, or skewed XCI to the deleterious allele. We applied both approaches to a head and neck cancer genetic study to investigate the underlying XCI processes for the X-chromosomal genetic variants.
PMCID: PMC5751921
X-chromosome inactivation; skewness; escaping X-chromosome inactivation; nonnested model comparison; Cox test; likelihood ratio
3.  Assessing current temporal and space-time anomalies of disease incidence 
PLoS ONE  2017;12(11):e0188065.
Approaches used to early and accurately characterize epidemiologic patterns of disease incidence in a temporal and spatial series are becoming increasingly important. Cluster tests are generally designed for retrospective detection of epidemiologic anomalies in a temporal or space-time series. Timely identification of anomalies of disease or poisoning incidence during ongoing surveillance or an outbreak requires the use of sensitive statistical methods that recognize an incidence pattern at the time of occurrence. This report describes 2 novel analytical methods that focus on detecting anomalies of incidence at the time of occurrence in a temporal and space-time series. The first method describes the paucity of incidence at the time of occurrence in an ongoing surveillance and is designed to evaluate whether a decline in incidence occurs on the single current day or during the most recent few days. The second method provides an overall assessment of current clustering or paucity of incidence in a space-time series, allowing for several space regions. We illustrate the application of these methods using a subsample of a temporal series of data on the largest dengue outbreak in Taiwan in 2015 since World War II and demonstrate that they are useful to efficiently monitor incoming data for current clustering and paucity of incidence in a temporal and space-time series. In light of the recent global emergence and resurgence of Zika, dengue, and chikungunya infection, these approaching for detecting current anomalies of incidence in the ongoing surveillance of disease are particularly desired and needed.
PMCID: PMC5683561  PMID: 29131869
4.  Correlates of Sun Protection and Sunburn in Children of Melanoma Survivors 
Sunburns during childhood increase melanoma risk. Children of melanoma survivors are at higher risk, but little is known about their sunburn and sun protection. One study showed that almost half of melanoma survivors’ children experienced sunburn in the past year. This study evaluated sunburn and sun protection in melanoma survivors’ children, and relevant survivor characteristics from Social Cognitive Theory and the Health Belief Model.
Melanoma survivors (N=340) were recruited from a comprehensive cancer center. Survivors completed a baseline questionnaire administered by telephone to report on the behavior of their children (N=340) as part of an RCT of a sun protection intervention. Data were collected in 2008 and analyzed in 2015.
In the prior 6 months, 28% of children experienced sunburn. “Always” or “frequent” sun protection varied by behavior: sunscreen, 69%; lip balm, 15%; wide-brimmed hats, 9%; sleeved shirts, 28%; pants, 48%; sunglasses, 10%; shade, 33%; and limiting time outdoors, 45%. Survivors’ sunburn and sun protection were positively associated with these outcomes in children. Correlates of sunburn also included older child age and higher risk perceptions. Correlates of sun protection behaviors included younger child age; stronger intentions, higher self-efficacy, and more positive outcome expectations about sun protection; and greater number of melanomas in survivors.
Melanoma survivors may have a heightened awareness of the importance of their children’s sun protection, but their children are not routinely protected. Correlates of children’s sunburn and sun protection suggest subgroups of survivors to target with interventions to improve sun protection.
PMCID: PMC5482415  PMID: 27067306
5.  Genome-wide association study of glioma subtypes identifies specific differences in genetic susceptibility to glioblastoma and non-glioblastoma tumors 
Nature genetics  2017;49(5):789-794.
Genome-wide association studies (GWAS) have transformed our understanding of glioma susceptibility, but individual studies have had limited power to identify risk loci. We performed a meta-analysis of existing GWAS and two new GWAS, which totaled 12,496 cases and 18,190 controls. We identified five new loci for glioblastoma (GBM) at 1p31.3 (rs12752552; P = 2.04 × 10−9, odds ratio (OR) = 1.22), 11q14.1 (rs11233250; P = 9.95 × 10−10, OR = 1.24), 16p13.3 (rs2562152; P = 1.93 × 10−8, OR = 1.21), 16q12.1 (rs10852606; P = 1.29 × 10−11, OR = 1.18) and 22q13.1 (rs2235573; P = 1.76 × 10−10, OR = 1.15), as well as eight loci for non-GBM tumors at 1q32.1 (rs4252707; P = 3.34 × 10−9, OR = 1.19), 1q44 (rs12076373; P = 2.63 × 10−10, OR = 1.23), 2q33.3 (rs7572263; P = 2.18 × 10−10, OR = 1.20), 3p14.1 (rs11706832; P = 7.66 × 10−9, OR = 1.15), 10q24.33 (rs11598018; P = 3.39 × 10−8, OR = 1.14), 11q21 (rs7107785; P = 3.87 × 10−10, OR = 1.16), 14q12 (rs10131032; P = 5.07 × 10−11, OR = 1.33) and 16p13.3 (rs3751667; P = 2.61 × 10−9, OR = 1.18). These data substantiate that genetic susceptibility to GBM and non-GBM tumors are highly distinct, which likely reflects different etiology.
PMCID: PMC5558246  PMID: 28346443
6.  Effect of yoga training on inflammatory cytokines and C-reactive protein in employees of small-scale industries 
The present study intends to see the effect of yoga practices on lipid profile, interleukin (IL)-6, tumor necrosis factor (TNF)-α, and high-sensitivity-C-reactive protein (hs-CRP) among apparently healthy adults exposed to occupational hazards.
In the present study, 48 participants aged 30–58 years (41.5 ± 5.2) who were exposed to occupational hazards were randomized into two groups, that is, experimental and wait-list control. All the participants were assessed for lipid profile, IL-6, TNF-α, and hs-CRP at the baseline and after completion of 3 months of yoga training intervention. The experimental group underwent yoga training intervention for 1 h for 6 days a week for 3 months, whereas control group continued with their daily activities except yoga training. Data analysis was done using statistical software SPSS Version 20.0. Data were analyzed using paired t-tests and independent t-test.
The results of within group comparison revealed highly significant changes in cholesterol (P < 0.001), high-density lipoprotein (P < 0.001), low-density lipoprotein (LDL)(P < 0.01), hs-CRP (P < 0.01), IL-6 (P < 0.001), and TNF-α (P < 0.001) in experimental group. Comparison between experimental and control group revealed significant changes in cholesterol (P < 0.01), LDL (P < 0.05), IL-6 (P < 0.01), TNF-α (P < 0.01), and hs-CRP (P < 0.01).
A yoga-based lifestyle intervention seems to be a highly promising alternative therapy which favorably alters inflammatory markers and metabolic risk factors.
PMCID: PMC5561768  PMID: 28856165
High-sensitivity-C-reactive protein; inflammatory markers; interleukin-6; yoga
7.  Identifying novel genes and biological processes relevant to the development of cancer therapy-induced mucositis: An informative gene network analysis 
PLoS ONE  2017;12(7):e0180396.
Mucositis is a complex, dose-limiting toxicity of chemotherapy or radiotherapy that leads to painful mouth ulcers, difficulty eating or swallowing, gastrointestinal distress, and reduced quality of life for patients with cancer. Mucositis is most common for those undergoing high-dose chemotherapy and hematopoietic stem cell transplantation and for those being treated for malignancies of the head and neck. Treatment and management of mucositis remain challenging. It is expected that multiple genes are involved in the formation, severity, and persistence of mucositis. We used Ingenuity Pathway Analysis (IPA), a novel network-based approach that integrates complex intracellular and intercellular interactions involved in diseases, to systematically explore the molecular complexity of mucositis. As a first step, we searched the literature to identify genes that harbor or are close to the genetic variants significantly associated with mucositis. Our literature review identified 27 candidate genes, of which ERCC1, XRCC1, and MTHFR were the most frequently studied for mucositis. On the basis of this 27-gene list, we used IPA to generate gene networks for mucositis. The most biologically significant novel molecules identified through IPA analyses included TP53, CTNNB1, MYC, RB1, P38 MAPK, and EP300. Additionally, uracil degradation II (reductive) and thymine degradation pathways (p = 1.06−08) were most significant. Finally, utilizing 66 SNPs within the 8 most connected IPA-derived candidate molecules, we conducted a genetic association study for oral mucositis in the head and neck cancer patients who were treated using chemotherapy and/or radiation therapy (186 head and neck cancer patients with oral mucositis vs. 699 head and neck cancer patients without oral mucositis). The top ranked gene identified through this association analysis was RB1 (rs2227311, p-value = 0.034, odds ratio = 0.67). In conclusion, gene network analysis identified novel molecules and biological processes, including pathways related to inflammation and oxidative stress, that are relevant to mucositis development, thus providing the basis for future studies to improve the management and treatment of mucositis in patients with cancer.
PMCID: PMC5498049  PMID: 28678827
8.  Childhood Onset Essential Hypertension and the Family Structure 
The prevalence and effect of single parent families in childhood onset essential hypertension (EH) is unknown. Children with EH and age, gender- and ethnicity-matched controls were enrolled. Family structure data were obtained by in-person interview. A total of 148 families (76 hypertension probands, 72 control probands; median 14 years) were prospectively enrolled in the study. A single parent status was seen in 42% of the families; with and without EH (38% vs. 46%, p=0.41; OR: 0.7, 95%CI: 0.4–1.4). Upon multivariable analysis we did not identify a statistically significant socio-familial contributor to the development of childhood onset EH. A significant numbers of single parent families (42%), majority with single mothers were found in our pedigree study. Sociofamilial factors are known to contribute to the expression of the adult onset EH, but findings in our study suggest that they appear to contribute less in the expression of childhood onset EH.
PMCID: PMC4821812  PMID: 26435293
primary hypertension; pediatrics; single parent; pedigree; marital; socioeconomic
9.  Approaching a Scientific Consensus on the Association between Allergies and Glioma Risk: A Report from the Glioma International Case-Control Study 
Several previous studies have found inverse associations between glioma susceptibility and a history of allergies or other atopic conditions. Some evidence indicates that respiratory allergies are likely to be particularly relevant with regard to glioma risk. Using data from the Glioma International Case-Control Study (GICC), we examined the effects of respiratory allergies and other atopic conditions on glioma risk.
The GICC contains detailed information on history of atopic conditions for 4533 cases and 4171 controls, recruited from 14 study sites across five countries. Using two-stage random-effects restricted maximum likelihood modeling to calculate meta-analysis odds ratios, we examined the associations between glioma and allergy status, respiratory allergy status, asthma, and eczema.
Having a history of respiratory allergies was associated with an approximately 30% lower glioma risk, compared to not having respiratory allergies (mOR: 0.72, 95% CI: 0.58–0.90). This association was similar when restricting to high-grade glioma cases. Asthma and eczema were also significantly protective against glioma.
A substantial amount of data on the inverse association between atopic conditions and glioma has accumulated, and findings from the GICC study further strengthen the existing evidence that the relationship between atopy and glioma is unlikely to be coincidental.
As the literature approaches a consensus on the impact of allergies in glioma risk, future research can begin to shift focus to what the underlying biological mechanism behind this association may be, which could, in turn, yield new opportunities for immunotherapy or cancer prevention.
PMCID: PMC4874516  PMID: 26908595
glioma; glioblastoma; allergies; asthma; eczema
10.  The Glioma International Case-Control Study: A Report From the Genetic Epidemiology of Glioma International Consortium 
American Journal of Epidemiology  2015;183(2):85-91.
Decades of research have established only a few etiological factors for glioma, which is a rare and highly fatal brain cancer. Common methodological challenges among glioma studies include small sample sizes, heterogeneity of tumor subtypes, and retrospective exposure assessment. Here, we briefly describe the Glioma International Case-Control (GICC) Study (recruitment, 2010–2013), a study being conducted by the Genetic Epidemiology of Glioma International Consortium that integrates data from multiple data collection sites, uses a common protocol and questionnaire, and includes biospecimen collection. To our knowledge, the GICC Study is the largest glioma study to date that includes collection of blood samples, which will allow for genetic analysis and interrogation of gene-environment interactions.
PMCID: PMC4706682  PMID: 26656478
cancer; case-control studies; glioblastoma; glioma; methodology; study profile
11.  Effect of yoga training on lipid metabolism in industrial workers with reference to body constitution (Prakriti) 
The progressive increase in dyslipidemia and physical inactivity are considered to be major risk factors for the onset of non communicable diseases. Awareness of body constitution plays a vital role to regularise optimum health. The present study was planned to evaluate the effect of yoga practices on lipid metabolism with reference to specific body constitution (Prakriti).
A self-as-control study was conducted on 36 male healthy volunteers between age group of 30–58 years. Their prakriti analysis was done using standardized, validated questionnaire and were divided into Vata-Pitta (n = 16) and Pitta-Kapha (n = 20) groups. The assessment of lipid profile was done in fasting blood samples before and after 12 weeks of yoga training. Data were analyzed using paired t-test and independent t-test.
After yoga intervention, the result of within group comparison revealed that in Vata-Pitta (V-P) group, significant decrease in the levels of TC, LDL (p < 0.001) and significant increase in HDL (p < 0.01) was observed. While, Pitta-Kapha (P-K) group showed significant decrease in TC (p < 0.001), TG (p < 0.01), LDL (p < 0.001) and VLDL (p < 0.05) levels. Further, the results between groups revealed that P-K group has significantly higher baseline levels of TC, TG and VLDL as compared to V-P group (p < 0.05).
The study concludes that yoga practices can effectively regulate lipid metabolism and total body energy expenditure with reference to specific constitutional type (Prakriti) that may act as a tool to assess magnitude of metabolic functions.
Graphical abstract
Image 1
PMCID: PMC5506662
Yoga training; Prakriti; Lipid profile; Energy metabolism; Non communicable diseases
13.  Whole-exome sequencing reveals an inherited R566X mutation of the epithelial sodium channel β-subunit in a case of early-onset phenotype of Liddle syndrome 
To comprehensively evaluate a European–American child with severe hypertension, whole-exome sequencing (WES) was performed on the child and parents, which identified causal variation of the proband's early-onset disease. The proband's hypertension was resistant to treatment, requiring a multiple drug regimen including amiloride, spironolactone, and hydrochlorothiazide. We suspected a monogenic form of hypertension because of the persistent hypokalemia with low plasma levels of renin and aldosterone. To address this, we focused on rare functional variants and indels, and performed gene-based tests incorporating linkage scores and allele frequency and filtered on deleterious functional mutations. Drawing upon clinical presentation, 27 genes were selected evidenced to cause monogenic hypertension and matched to the gene-based results. This resulted in the identification of a stop-gain mutation in an epithelial sodium channel (ENaC), SCNN1B, an established Liddle syndrome gene, shared by the child and her father. Interestingly, the father also harbored a missense mutation (p.Trp552Arg) in the α-subunit of the ENaC trimer, SCNN1A, possibly pointing to pseudohypoaldosteronism type I. This case is unique in that we present the early-onset disease and treatment response caused by a canonical stop-gain mutation (p.Arg566*) as well as ENaC digenic hits in the father, emphasizing the utility of WES informing precision medicine.
PMCID: PMC5111009  PMID: 27900368
elevated diastolic blood pressure; elevated mean arterial pressure; elevated systolic blood pressure
14.  Genome-wide association study suggests common variants within RP11-634B7.4 gene influencing severe pre-treatment pain in head and neck cancer patients 
Scientific Reports  2016;6:34206.
Pain is often one of the first signs of squamous cell carcinoma of the head and neck (HNSCC). Pain at diagnosis is an important prognostic marker for the development of chronic pain, and importantly, for the overall survival time. To identify variants influencing severe pre-treatment pain in 1,368 patients newly diagnosed with HNSCC, we conducted a genome-wide association study based on 730,525 tagging SNPs. The patients were all previously untreated for cancer. About 15% of the patients had severe pre-treatment pain, defined as pain score ≥7 (0 = “no pain” and 10 = “worst pain”). We identified 3 common genetic variants in high linkage disequilibrium for severe pre-treatment pain, representing one genomic region at 1q44 (rs3862188, P = 3.45 × 10−8; rs880143, P = 3.45 × 10−8; and rs7526880, P = 4.92 × 10−8), which maps to the RP11-634B7.4 gene, a novel antisense gene to three olfactory receptor genes. Olfactory receptor genes, upstream effectors of the MAPK signaling cascade, might be novel target genes for pain in HNSCC patients. Future experimental validation to explore biological mechanisms will be key to defining the role of the intronic variants and non-coding RNA for pain in patients with HNSCC.
PMCID: PMC5037456  PMID: 27670397
15.  Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects 
In a standard two-stage SMART design, the intermediate response to the first-stage intervention is measured at a fixed time point for all participants. Subsequently, responders and non-responders are re-randomized and the final outcome of interest is measured at the end of the study. To reduce the side effects and costs associated with first-stage interventions in a SMART design, we proposed a novel time-varying SMART design in which individuals are re-randomized to the second-stage interventions as soon as a pre-fixed intermediate response is observed. With this strategy, the duration of the first-stage intervention will vary.
We developed a time-varying mixed effects model and a joint model that allows for modeling the outcomes of interest (intermediate and final) and the random durations of the first-stage interventions simultaneously. The joint model borrows strength from the survival sub-model in which the duration of the first-stage intervention (i.e., time to response to the first-stage intervention) is modeled. We performed a simulation study to evaluate the statistical properties of these models.
Our simulation results showed that the two modeling approaches were both able to provide good estimations of the means of the final outcomes of all the embedded interventions in a SMART. However, the joint modeling approach was more accurate for estimating the coefficients of first-stage interventions and time of the intervention.
We conclude that the joint modeling approach provides more accurate parameter estimates and a higher estimated coverage probability than the single time-varying mixed effects model, and we recommend the joint model for analyzing data generated from time-varying SMART designs. In addition, we showed that the proposed time-varying SMART design is cost-efficient and equally effective in selecting the optimal embedded adaptive intervention as the standard SMART design.
Electronic supplementary material
The online version of this article (doi:10.1186/s12874-016-0202-7) contains supplementary material, which is available to authorized users.
PMCID: PMC5006275  PMID: 27578254
Adaptive interventions; Sequential multiple assignment randomized trial (SMART); Time-varying mixed effects model (TVMEM); Longitudinal model; Joint model
Background and Objectives
To assess tobacco use among lesbian, gay, bisexual, and transgender (LGBT) individuals from the 2014 Houston Pride Parade and Festival in Houston, Texas (TX).
Cross-sectional study using convenience sample of LGBT individuals (n=99) examining tobacco use, sexual orientation, and other socio-demographic factors through survey participation.
Findings showed a high prevalence of tobacco and electronic cigarettes use. White LGBT individuals had greater odds of using any type of tobacco product.
Discussion and Conclusions
Despite a high smoking prevalence among the surveyed LGBT individuals, this study sample did not identify tobacco use as a health issue.
Scientific Significance
Supports the need for further investigation on tobacco-related disparities among LGBT individuals in Houston, TX.
PMCID: PMC4516602  PMID: 26009978
17.  Developments in our understanding of the genetic basis of birth defects 
Birth defects are a major cause of morbidity and mortality worldwide. There has been much progress in understanding the genetic basis of familial and syndromic forms of birth defects. However, the etiology of nonsydromic birth defects is not well-understood. Although there is still much work to be done, we now have the tools to accomplish the task. Advances in next-generation sequencing have introduced a sea of possibilities, from disease-gene discovery to clinical screening and diagnosis. These advances have been fruitful in identifying a host of candidate disease genes, spanning the spectrum of birth defects. With the advent of CRISPR-Cas9 gene editing, researchers now have a precise tool for characterizing this genetic variation in model systems. Work in model organisms has also illustrated the importance of epigenetics in human development and birth defects etiology. Here we review past and current knowledge in birth defects genetics. We describe genotyping and sequencing methods for the detection and analysis of rare and common variants. We remark on the utility of model organisms and explore epigenetics in the context of structural malformation. We conclude by highlighting approaches that may provide insight into the complex genetics of birth defects.
PMCID: PMC4537658  PMID: 26033863
18.  Using the weighted area under the net benefit curve for decision curve analysis 
Risk prediction models have been proposed for various diseases and are being improved as new predictors are identified. A major challenge is to determine whether the newly discovered predictors improve risk prediction. Decision curve analysis has been proposed as an alternative to the area under the curve and net reclassification index to evaluate the performance of prediction models in clinical scenarios. The decision curve computed using the net benefit can evaluate the predictive performance of risk models at a given or range of threshold probabilities. However, when the decision curves for 2 competing models cross in the range of interest, it is difficult to identify the best model as there is no readily available summary measure for evaluating the predictive performance. The key deterrent for using simple measures such as the area under the net benefit curve is the assumption that the threshold probabilities are uniformly distributed among patients.
We propose a novel measure for performing decision curve analysis. The approach estimates the distribution of threshold probabilities without the need of additional data. Using the estimated distribution of threshold probabilities, the weighted area under the net benefit curve serves as the summary measure to compare risk prediction models in a range of interest.
We compared 3 different approaches, the standard method, the area under the net benefit curve, and the weighted area under the net benefit curve. Type 1 error and power comparisons demonstrate that the weighted area under the net benefit curve has higher power compared to the other methods. Several simulation studies are presented to demonstrate the improvement in model comparison using the weighted area under the net benefit curve compared to the standard method.
The proposed measure improves decision curve analysis by using the weighted area under the curve and thereby improves the power of the decision curve analysis to compare risk prediction models in a clinical scenario.
Electronic supplementary material
The online version of this article (doi:10.1186/s12911-016-0336-x) contains supplementary material, which is available to authorized users.
PMCID: PMC4949771  PMID: 27431531
Decision curve analysis; Clinical decision making; Area under the curve; Net benefit curves; Threshold probabilities
19.  Empirical estimation of sequencing error rates using smoothing splines 
BMC Bioinformatics  2016;17:177.
Next-generation sequencing has been used by investigators to address a diverse range of biological problems through, for example, polymorphism and mutation discovery and microRNA profiling. However, compared to conventional sequencing, the error rates for next-generation sequencing are often higher, which impacts the downstream genomic analysis. Recently, Wang et al. (BMC Bioinformatics 13:185, 2012) proposed a shadow regression approach to estimate the error rates for next-generation sequencing data based on the assumption of a linear relationship between the number of reads sequenced and the number of reads containing errors (denoted as shadows). However, this linear read-shadow relationship may not be appropriate for all types of sequence data. Therefore, it is necessary to estimate the error rates in a more reliable way without assuming linearity. We proposed an empirical error rate estimation approach that employs cubic and robust smoothing splines to model the relationship between the number of reads sequenced and the number of shadows.
We performed simulation studies using a frequency-based approach to generate the read and shadow counts directly, which can mimic the real sequence counts data structure. Using simulation, we investigated the performance of the proposed approach and compared it to that of shadow linear regression. The proposed approach provided more accurate error rate estimations than the shadow linear regression approach for all the scenarios tested. We also applied the proposed approach to assess the error rates for the sequence data from the MicroArray Quality Control project, a mutation screening study, the Encyclopedia of DNA Elements project, and bacteriophage PhiX DNA samples.
The proposed empirical error rate estimation approach does not assume a linear relationship between the error-free read and shadow counts and provides more accurate estimations of error rates for next-generation, short-read sequencing data.
Electronic supplementary material
The online version of this article (doi:10.1186/s12859-016-1052-3) contains supplementary material, which is available to authorized users.
PMCID: PMC4840868  PMID: 27102907
Empirical error rate; Next-generation sequencing; Smoothing spline; Frequency-based simulation; Short reads
20.  History of chickenpox in glioma risk: a report from the glioma international case–control study (GICC) 
Cancer Medicine  2016;5(6):1352-1358.
Varicella zoster virus (VZV) is a neurotropic α‐herpesvirus that causes chickenpox and establishes life‐long latency in the cranial nerve and dorsal root ganglia of the host. To date, VZV is the only virus consistently reported to have an inverse association with glioma. The Glioma International Case‐Control Study (GICC) is a large, multisite consortium with data on 4533 cases and 4171 controls collected across five countries. Here, we utilized the GICC data to confirm the previously reported associations between history of chickenpox and glioma risk in one of the largest studies to date on this topic. Using two‐stage random‐effects restricted maximum likelihood modeling, we found that a positive history of chickenpox was associated with a 21% lower glioma risk, adjusting for age and sex (95% confidence intervals (CI): 0.65–0.96). Furthermore, the protective effect of chickenpox was stronger for high‐grade gliomas. Our study provides additional evidence that the observed protective effect of chickenpox against glioma is unlikely to be coincidental. Future studies, including meta‐analyses of the literature and investigations of the potential biological mechanism, are warranted.
PMCID: PMC4924393  PMID: 26972449
Brain tumor; chickenpox; glioma; shingles
21.  MAPK1/ERK2 as novel target genes for pain in head and neck cancer patients 
BMC Genetics  2016;17:40.
Genetic susceptibility plays an important role in the risk of developing pain in individuals with cancer. As a complex trait, multiple genes underlie this susceptibility. We used gene network analyses to identify novel target genes associated with pain in patients newly diagnosed with squamous cell carcinoma of the head and neck (HNSCC).
We first identified 36 cancer pain-related genes (i.e., focus genes) from 36 publications based on a literature search. The Ingenuity Pathway Analysis (IPA) analysis identified additional genes that are functionally related to the 36 focus genes through pathway relationships yielding a total of 82 genes. Subsequently, 800 SNPs within the 82 IPA-selected genes on the Illumina HumanOmniExpress-12v1 platform were selected from a large-scale genotyping effort. Association analyses between the 800 candidate SNPs (covering 82 genes) and pain in a patient cohort of 1368 patients with HNSCC (206 patients with severe pain vs. 1162 with non-severe pain) showed the highest significance for MAPK1/ERK2, a gene belonging to the MAP kinase family (rs8136867, p value = 8.92 × 10−4; odds ratio [OR] = 1.33, 95 % confidence interval [CI]: 1.13–1.58). Other top genes were PIK3C2G (a member of PI3K [complex], rs10770367, p value = 1.10 × 10−3; OR = 1.46, 95 % CI: 1.16–1.82), TCRA (the alpha chain of T-cell receptor, rs6572493, p value = 2.84 × 10−3; OR = 0.70, 95 % CI: 0.55–0.88), PDGFC (platelet-derived growth factor C, rs6845322, p value = 4.88 × 10−3; OR = 1.32, 95 % CI: 1.09–1.60), and CD247 (a member of CD3, rs2995082, p value = 7.79 × 10−3; OR = 0.76, 95 % CI: 0.62–0.93).
Our findings provide novel candidate genes and biological pathways underlying pain in cancer patients. Further study of the variations of these candidate genes could inform clinical decision making when treating cancer pain.
Electronic supplementary material
The online version of this article (doi:10.1186/s12863-016-0348-7) contains supplementary material, which is available to authorized users.
PMCID: PMC4752805  PMID: 26872611
Cancer pain; Head and neck cancer; MAPK1/ERK2; Ingenuity pathway analysis; Gene; SNP
22.  Health literacy, smoking, and health indicators in African American adults 
Journal of health communication  2015;20(0 2):24-33.
We examined cross-sectional associations of health literacy (HL) with smoking and other established health indicators among 1,467 African American adults. Data emanated from a longitudinal cohort study designed to investigate cancer risk factors among church-going African American adults. We conducted linear and logistic regression analyses to assess associations between HL and health indicators. HL was assessed using an established single-item screening question. Outcomes included indicators of poor physical (cigarette smoking, self-rated general and physical health) and mental health (self-rated mental health, depressive symptoms, perceived stress). Nearly 19% of participants had low HL. Low HL was significantly associated with current smoking, poorer self-rated general and physical health, and higher perceived stress (ps < .05) even after controlling for demographic variables (i.e., age, gender, relationship status) and indicators of socioeconomic status (i.e., education, income, insurance status). Low HL appears to be an independent risk factor for smoking and other indicators of poor physical and mental health in a large sample of African American adults. Future directions and clinical implications are discussed.
PMCID: PMC4725699  PMID: 26513028
health literacy; cigarette smoking; mental health; self-rated health; African American
23.  Essential Hypertension vs. Secondary Hypertension Among Children 
The aim was to determine the proportions and correlates of essential hypertension among children in a tertiary pediatric hypertension clinic.
We evaluated 423 consecutive children and collected demographic and clinical history by retrospective chart review.
We identified 275 (65%) hypertensive children (blood pressure >95th percentile per the “Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents”) from 423 children referred to the clinic for history of elevated blood pressure. The remainder of the patients had normotension (11%), white coat hypertension (11%), prehypertension (10%), and pending diagnosis (3%). Among the 275 hypertensive children, 43% (n = 119; boys = 56%; median age = 12 years; range = 3–17 years) had essential hypertension and 57% (n = 156; boys = 66%; median age = 9 years; range = 0.08–19 years) had secondary hypertension. When compared with those with secondary hypertension, those with essential hypertension had a significantly older age at diagnosis (P = 0.0002), stronger family history of hypertension (94% vs. 68%; P < 0.0001), and lower prevalence of preterm birth (20% vs. 46%; P < 0.001). There was a bimodal distribution of age of diagnosis in those with secondary hypertension.
The phenotype of essential hypertension can present as early as 3 years of age and is the predominant form of hypertension in children after age of 6 years. Among children with hypertension, those with essential hypertension present at an older age, have a stronger family history of hypertension, and have lower prevalence of preterm birth.
PMCID: PMC4318949  PMID: 24842390
blood pressure; etiology; hypertension; pediatrics; primary hypertension; secondary hypertension.
24.  Germline Mutations in Shelterin Complex Genes Are Associated With Familial Glioma 
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.
PMCID: PMC4296199  PMID: 25482530
25.  Imputation and subset-based association analysis across different cancer types identifies multiple independent risk loci in the TERT-CLPTM1L region on chromosome 5p15.33 
Wang, Zhaoming | Zhu, Bin | Zhang, Mingfeng | Parikh, Hemang | Jia, Jinping | Chung, Charles C. | Sampson, Joshua N. | Hoskins, Jason W. | Hutchinson, Amy | Burdette, Laurie | Ibrahim, Abdisamad | Hautman, Christopher | Raj, Preethi S. | Abnet, Christian C. | Adjei, Andrew A. | Ahlbom, Anders | Albanes, Demetrius | Allen, Naomi E. | Ambrosone, Christine B. | Aldrich, Melinda | Amiano, Pilar | Amos, Christopher | Andersson, Ulrika | Andriole, Gerald | Andrulis, Irene L. | Arici, Cecilia | Arslan, Alan A. | Austin, Melissa A. | Baris, Dalsu | Barkauskas, Donald A. | Bassig, Bryan A. | Beane Freeman, Laura E. | Berg, Christine D. | Berndt, Sonja I. | Bertazzi, Pier Alberto | Biritwum, Richard B. | Black, Amanda | Blot, William | Boeing, Heiner | Boffetta, Paolo | Bolton, Kelly | Boutron-Ruault, Marie-Christine | Bracci, Paige M. | Brennan, Paul | Brinton, Louise A. | Brotzman, Michelle | Bueno-de-Mesquita, H. Bas | Buring, Julie E. | Butler, Mary Ann | Cai, Qiuyin | Cancel-Tassin, Geraldine | Canzian, Federico | Cao, Guangwen | Caporaso, Neil E. | Carrato, Alfredo | Carreon, Tania | Carta, Angela | Chang, Gee-Chen | Chang, I-Shou | Chang-Claude, Jenny | Che, Xu | Chen, Chien-Jen | Chen, Chih-Yi | Chen, Chung-Hsing | Chen, Constance | Chen, Kuan-Yu | Chen, Yuh-Min | Chokkalingam, Anand P. | Chu, Lisa W. | Clavel-Chapelon, Francoise | Colditz, Graham A. | Colt, Joanne S. | Conti, David | Cook, Michael B. | Cortessis, Victoria K. | Crawford, E. David | Cussenot, Olivier | Davis, Faith G. | De Vivo, Immaculata | Deng, Xiang | Ding, Ti | Dinney, Colin P. | Di Stefano, Anna Luisa | Diver, W. Ryan | Duell, Eric J. | Elena, Joanne W. | Fan, Jin-Hu | Feigelson, Heather Spencer | Feychting, Maria | Figueroa, Jonine D. | Flanagan, Adrienne M. | Fraumeni, Joseph F. | Freedman, Neal D. | Fridley, Brooke L. | Fuchs, Charles S. | Gago-Dominguez, Manuela | Gallinger, Steven | Gao, Yu-Tang | Gapstur, Susan M. | Garcia-Closas, Montserrat | Garcia-Closas, Reina | Gastier-Foster, Julie M. | Gaziano, J. Michael | Gerhard, Daniela S. | Giffen, Carol A. | Giles, Graham G. | Gillanders, Elizabeth M. | Giovannucci, Edward L. | Goggins, Michael | Gokgoz, Nalan | Goldstein, Alisa M. | Gonzalez, Carlos | Gorlick, Richard | Greene, Mark H. | Gross, Myron | Grossman, H. Barton | Grubb, Robert | Gu, Jian | Guan, Peng | Haiman, Christopher A. | Hallmans, Goran | Hankinson, Susan E. | Harris, Curtis C. | Hartge, Patricia | Hattinger, Claudia | Hayes, Richard B. | He, Qincheng | Helman, Lee | Henderson, Brian E. | Henriksson, Roger | Hoffman-Bolton, Judith | Hohensee, Chancellor | Holly, Elizabeth A. | Hong, Yun-Chul | Hoover, Robert N. | Hosgood, H. Dean | Hsiao, Chin-Fu | Hsing, Ann W. | Hsiung, Chao Agnes | Hu, Nan | Hu, Wei | Hu, Zhibin | Huang, Ming-Shyan | Hunter, David J. | Inskip, Peter D. | Ito, Hidemi | Jacobs, Eric J. | Jacobs, Kevin B. | Jenab, Mazda | Ji, Bu-Tian | Johansen, Christoffer | Johansson, Mattias | Johnson, Alison | Kaaks, Rudolf | Kamat, Ashish M. | Kamineni, Aruna | Karagas, Margaret | Khanna, Chand | Khaw, Kay-Tee | Kim, Christopher | Kim, In-Sam | Kim, Jin Hee | Kim, Yeul Hong | Kim, Young-Chul | Kim, Young Tae | Kang, Chang Hyun | Jung, Yoo Jin | Kitahara, Cari M. | Klein, Alison P. | Klein, Robert | Kogevinas, Manolis | Koh, Woon-Puay | Kohno, Takashi | Kolonel, Laurence N. | Kooperberg, Charles | Kratz, Christian P. | Krogh, Vittorio | Kunitoh, Hideo | Kurtz, Robert C. | Kurucu, Nilgun | Lan, Qing | Lathrop, Mark | Lau, Ching C. | Lecanda, Fernando | Lee, Kyoung-Mu | Lee, Maxwell P. | Le Marchand, Loic | Lerner, Seth P. | Li, Donghui | Liao, Linda M. | Lim, Wei-Yen | Lin, Dongxin | Lin, Jie | Lindstrom, Sara | Linet, Martha S. | Lissowska, Jolanta | Liu, Jianjun | Ljungberg, Börje | Lloreta, Josep | Lu, Daru | Ma, Jing | Malats, Nuria | Mannisto, Satu | Marina, Neyssa | Mastrangelo, Giuseppe | Matsuo, Keitaro | McGlynn, Katherine A. | McKean-Cowdin, Roberta | McNeill, Lorna H. | McWilliams, Robert R. | Melin, Beatrice S. | Meltzer, Paul S. | Mensah, James E. | Miao, Xiaoping | Michaud, Dominique S. | Mondul, Alison M. | Moore, Lee E. | Muir, Kenneth | Niwa, Shelley | Olson, Sara H. | Orr, Nick | Panico, Salvatore | Park, Jae Yong | Patel, Alpa V. | Patino-Garcia, Ana | Pavanello, Sofia | Peeters, Petra H. M. | Peplonska, Beata | Peters, Ulrike | Petersen, Gloria M. | Picci, Piero | Pike, Malcolm C. | Porru, Stefano | Prescott, Jennifer | Pu, Xia | Purdue, Mark P. | Qiao, You-Lin | Rajaraman, Preetha | Riboli, Elio | Risch, Harvey A. | Rodabough, Rebecca J. | Rothman, Nathaniel | Ruder, Avima M. | Ryu, Jeong-Seon | Sanson, Marc | Schned, Alan | Schumacher, Fredrick R. | Schwartz, Ann G. | Schwartz, Kendra L. | Schwenn, Molly | Scotlandi, Katia | Seow, Adeline | Serra, Consol | Serra, Massimo | Sesso, Howard D. | Severi, Gianluca | Shen, Hongbing | Shen, Min | Shete, Sanjay | Shiraishi, Kouya | Shu, Xiao-Ou | Siddiq, Afshan | Sierrasesumaga, Luis | Sierri, Sabina | Loon Sihoe, Alan Dart | Silverman, Debra T. | Simon, Matthias | Southey, Melissa C. | Spector, Logan | Spitz, Margaret | Stampfer, Meir | Stattin, Par | Stern, Mariana C. | Stevens, Victoria L. | Stolzenberg-Solomon, Rachael Z. | Stram, Daniel O. | Strom, Sara S. | Su, Wu-Chou | Sund, Malin | Sung, Sook Whan | Swerdlow, Anthony | Tan, Wen | Tanaka, Hideo | Tang, Wei | Tang, Ze-Zhang | Tardon, Adonina | Tay, Evelyn | Taylor, Philip R. | Tettey, Yao | Thomas, David M. | Tirabosco, Roberto | Tjonneland, Anne | Tobias, Geoffrey S. | Toro, Jorge R. | Travis, Ruth C. | Trichopoulos, Dimitrios | Troisi, Rebecca | Truelove, Ann | Tsai, Ying-Huang | Tucker, Margaret A. | Tumino, Rosario | Van Den Berg, David | Van Den Eeden, Stephen K. | Vermeulen, Roel | Vineis, Paolo | Visvanathan, Kala | Vogel, Ulla | Wang, Chaoyu | Wang, Chengfeng | Wang, Junwen | Wang, Sophia S. | Weiderpass, Elisabete | Weinstein, Stephanie J. | Wentzensen, Nicolas | Wheeler, William | White, Emily | Wiencke, John K. | Wolk, Alicja | Wolpin, Brian M. | Wong, Maria Pik | Wrensch, Margaret | Wu, Chen | Wu, Tangchun | Wu, Xifeng | Wu, Yi-Long | Wunder, Jay S. | Xiang, Yong-Bing | Xu, Jun | Yang, Hannah P. | Yang, Pan-Chyr | Yatabe, Yasushi | Ye, Yuanqing | Yeboah, Edward D. | Yin, Zhihua | Ying, Chen | Yu, Chong-Jen | Yu, Kai | Yuan, Jian-Min | Zanetti, Krista A. | Zeleniuch-Jacquotte, Anne | Zheng, Wei | Zhou, Baosen | Mirabello, Lisa | Savage, Sharon A. | Kraft, Peter | Chanock, Stephen J. | Yeager, Meredith | Landi, Maria Terese | Shi, Jianxin | Chatterjee, Nilanjan | Amundadottir, Laufey T.
Human Molecular Genetics  2014;23(24):6616-6633.
Genome-wide association studies (GWAS) have mapped risk alleles for at least 10 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 (association analysis based on subsets) 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: five in the TERT gene (Region 1: rs7726159, P = 2.10 × 10−39; Region 3: rs2853677, P = 3.30 × 10−36 and PConditional = 2.36 × 10−8; Region 4: rs2736098, P = 3.87 × 10−12 and PConditional = 5.19 × 10−6, Region 5: rs13172201, P = 0.041 and PConditional = 2.04 × 10−6; and Region 6: rs10069690, P = 7.49 × 10−15 and PConditional = 5.35 × 10−7) and one in the neighboring CLPTM1L gene (Region 2: rs451360; P = 1.90 × 10−18 and PConditional = 7.06 × 10−16). 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.
PMCID: PMC4240198  PMID: 25027329

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