Head and neck cancers have well-documented associations with tobacco and alcohol exposure, but the disease develops in only a small fraction of users, which implies an important role for genetic susceptibility. Therefore, head and neck cancers are an excellent model for studying genetic susceptibility to environmental carcinogens. The primary goal of this R01 application is to perform a comprehensive two-stage, high-density, genome-wide single-nucleotide polymorphism (SNP) analysis of head and neck cancer cases and corresponding frequency matched controls to identify novel genetic risk factors for head and neck cancer. This proposal builds upon a well-annotated existing DNA repository of cases and controls. One of the unique features of our study is the availability of DNA repair assay data on most of the cases and controls in this study, which will allow us to conduct genotype/phenotype analyses. We also have access to genome-wide association data from 1200 white control subjects from the same source population.
In aim 1, we will perform genotyping on 1000 randomly selected head and neck cancer cases and 500 controls using a 370K Illumina Infinium HapMap HumanCNV370-Duo SNP Chip. We will perform association analyses (1000 cases and 1700 controls) in the first stage using outcome variable as case-control status as well as DNA repair capacity assay data.
Our second aim i s to perform second-stage analysis of the SNPs selected in stage 1 using 900 additional cases and corresponding controls from the same source and from UCLA. We will use efficient joint analysis of cases and controls from the first and second aims, for a total of 1900 cases and 2600 controls. Finally, in aim 3, we will apply novel statistical tools such as the latent variable approach with Tukey's one-degree-of-freedom test and support vector machines to identify gene-gene and gene-environment (using environmental factors such as smoking and alcohol use) interactions that contribute to the risk of head and neck cancer. We are an experienced investigative team proposing a comprehensive analysis that incorporates epidemiological, behavioral, and functional data. Identification of novel genetic risk factors and their interactions with environmental factors will contribute to the early diagnosis of head and neck cancers.
The identification of novel genetic risk factors and their interactions with environmental factors will contribute to the early diagnosis of the disease and help identify individuals at highest risk for the development of head and neck cancer on the basis of their personal exposure patterns and their genetic risk profiles.
|Wang, Jian; Shete, Sanjay (2017) Estimation of indirect effect when the mediator is a censored variable. Stat Methods Med Res :962280217690414|
|Dai, Tianjiao; Shete, Sanjay (2016) Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects. BMC Med Res Methodol 16:112|
|Zhu, Xuan; Wang, Jian; Peng, Bo et al. (2016) Empirical estimation of sequencing error rates using smoothing splines. BMC Bioinformatics 17:177|
|Talluri, Rajesh; Shete, Sanjay (2016) Using the weighted area under the net benefit curve for decision curve analysis. BMC Med Inform Decis Mak 16:94|
|Reyes-Gibby, Cielito C; Wang, Jian; Silvas, Mary Rose T et al. (2016) Genome-wide association study suggests common variants within RP11-634B7.4 gene influencing severe pre-treatment pain in head and neck cancer patients. Sci Rep 6:34206|
|Reyes-Gibby, Cielito C; Wang, Jian; Silvas, Mary Rose T et al. (2016) MAPK1/ERK2 as novel target genes for pain in head and neck cancer patients. BMC Genet 17:40|
|Reyes-Gibby, Cielito C; Wang, Jian; Yeung, Sai-Ching J et al. (2015) Informative gene network for chemotherapy-induced peripheral neuropathy. BioData Min 8:24|
|Talluri, Rajesh; Shete, Sanjay (2015) Evaluating methods for modeling epistasis networks with application to head and neck cancer. Cancer Inform 14:17-23|
|Talluri, Rajesh; Wang, Jian; Shete, Sanjay (2014) Calculation of exact p-values when SNPs are tested using multiple genetic models. BMC Genet 15:75|
|Talluri, Rajesh; Wilkinson, Anna V; Spitz, Margaret R et al. (2014) A risk prediction model for smoking experimentation in Mexican American youth. Cancer Epidemiol Biomarkers Prev 23:2165-74|
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