Development of a Bayesian Joint Logistic Model to Better Study the Association between Haplotypes and Disease

In 2011, there will be an estimated 1,596,670 new cancer cases and 571,950 cancer-related deaths in the US. With the ever-increasing applications of cancer genetics in epidemiology, there is great potential to identify genetic risk factors that would help identify individuals with increased genetic susceptibility to cancer, which could be used to develop interventions or targeted therapies that could hopefully reduce cancer risk and mortality. In this dissertation, I propose to develop a new statistical method to evaluate the role of haplotypes in cancer susceptibility and development. This model will be flexible enough to handle not only haplotypes of any size, but also a variety of covariates. I will then apply this method to three cancer-related data sets (Hodgkin Disease, Glioma, and Lung Cancer). I hypothesize that there is substantial improvement in the estimation of association between haplotypes and disease, with the use of a Bayesian mathematical method to infer haplotypes that uses prior information from known genetics sources. Analysis based on haplotypes using information from publically available genetic sources generally show increased odds ratios and smaller p-values in both the Hodgkin, Glioma, and Lung data sets. For instance, the Bayesian Joint Logistic Model (BJLM) inferred haplotype TC had a substantially higher estimated effect size (OR=12.16, 95% CI = 2.47-90.1 vs. 9.24, 95% CI = 1.81-47.2) and more significant p-value (0.00044 vs. 0.008) for Hodgkin Disease compared to a traditional logistic regression approach. Also, the effect sizes of haplotypes modeled with recessive genetic effects were higher (and had more significant p-values) when analyzed with the BJLM. Full genetic models with haplotype information developed with the BJLM resulted in significantly higher discriminatory power and a significantly higher Net Reclassification Index compared to those developed with haplo.stats for lung cancer. Future analysis for this work could be to incorporate the 1000 Genomes project, which offers a larger selection of SNPs can be incorporated into the information from known genetic sources as well. Other future analysis include testing non-binary outcomes, like the levels of biomarkers that are present in lung cancer (NNK), and extending this analysis to full GWAS studies.

A systematic review of community-based participatory research interventions in pediatric and adolescent populations

Background. Community-based participatory research (CBPR) is a collaborative approach to research actively involving community members in all aspects of the research process. CBPR is not a new research method, but an approach that has gained increased attention in the field of public health over the last several years. Recognition of the inequalities in health status associated with social and environmental factors have led to calls for a renewed focus on ecological approaches to research. Ecological approaches acknowledge that the health of the community is dependent on an interaction between behavioral and environmental factors affecting the entire population. While many published studies document the benefits of CBPR in difficult-to-reach populations and describe successful implementation of this approach in adult populations, relatively few studies have been conducted in child and adolescent populations. Given that children and adolescents are particularly sensitive to the effects of their physical environments and may also be distrustful of outsiders, ecological approaches involving the community as partners, such as CBPR, may be especially useful in this population. ^ Objective. This thesis reviews published studies using a community-based participatory research approach in children and adolescents to assess the appropriateness of this approach in this population. ^ Method. Studies using CBPR in youth populations were identified using Medline and other Internet searches through both MeSH heading and text-word searches. ^ Results. A total of 16 studies were identified and analyzed for this review. Nine of the sixteen studies were experimental or quasi-experimental design, with Asthma being the most commonly studied disease. ^ Conclusions. While many studies using CBPR were not conducted with the level of scientific rigor typically found in clinical trial research, the studies reviewed each contributed to a greater understanding of the problems they investigated. Furthermore, interventional studies provided lasting benefits to communities under study above what would be found in studies using more traditional research approaches. While CBPR may not be appropriate for all research situations due to the time and resources required, we conclude that is a useful approach and should be considered when conducting community-based research for pediatric and adolescent populations.^

Decomposition of $\rm R\times C$ contingency table chi -square: Application to binned DNA fragment size data and population structure analysis

The purpose of this research is to develop a new statistical method to determine the minimum set of rows (R) in a R x C contingency table of discrete data that explains the dependence of observations. The statistical power of the method will be empirically determined by computer simulation to judge its efficiency over the presently existing methods. The method will be applied to data on DNA fragment length variation at six VNTR loci in over 72 populations from five major racial groups of human (total sample size is over 15,000 individuals; each sample having at least 50 individuals). DNA fragment lengths grouped in bins will form the basis of studying inter-population DNA variation within the racial groups are significant, will provide a rigorous re-binning procedure for forensic computation of DNA profile frequencies that takes into account intra-racial DNA variation among populations. ^