Persisting abdominal symptoms after travelers' diarrhea: Is there a genetic predisposition?

Functional gastrointestinal disorders (FGIDs) are defined as ailments of the mid or lower gastrointestinal tract which are not attributable to any discernable anatomic or biochemical defects.1 FGIDs include functional bowel disorders, also known as persisting abdominal symptoms (PAS). Irritable bowel syndrome (IBS) is one of the most common illnesses classified under PAS.2,3 This is the first prospective study that looks at the etiology and pathogenesis of post-infectious PAS in the context of environmental exposure and genetic susceptibility in a cohort of US travelers to Mexico. Our objective was to identify infectious, genetic and environmental factors that predispose to post infectious PAS. ^ Methods. This is a secondary data analysis of a prospective study on a cohort of 704 healthy North American tourists to Cuernavaca, Morelos and Guadalajara, Jalisco in Mexico. The subjects at risk for Travelers' diarrhea were assessed for chronic abdominal symptoms on enrollment and six months after the return to the US. ^ Outcomes. PAS was defined as disturbances of mid and lower gastrointestinal system without any known pathological or radiological abnormalities, or infectious, or metabolic causes. It refers to functional bowel disease, category C of functional gastrointestinal diseases as defined by the Rome II criterion. PAS was sub classified into Irritable bowel syndrome (IBS) and functional abdominal disease (FAD). ^ IBS is defined as recurrent abdominal pain or discomfort present at least 25% and associated with improvement with defecation, change in frequency and form of stool. FAD encompasses other abdominal symptoms of chronic nature that do not meet the criteria for IBS. It includes functional diarrhea, functional constipation, functional bloating: and unspecified bowel symptoms. ^ Results. Among the 704 travelers studied, there were 202 cases of PAS. The PAS cases included 175 cases of FAD and 27 cases of IBS. PAS was more frequent among subjects who developed traveler's diarrhea in Mexico compared to travelers who remained healthy during the short term visit to Mexico (52 vs. 38; OR = 1.8; CI, 1.3–2.5, P < 0.001). A statistically significant difference was noted in the mean age of subjects with PAS compared to healthy controls (28 vs. 34 yrs; OR = 0.97, CI, 0.95–0.98; P < 0.001). Travelers who experienced multiple episodes, a later onset of diarrhea in Mexico and passed greater numbers of unformed stools were more likely to be identified in PAS group at six months. Participants who developed TD caused by enterotoxigenic E.coli in Mexico showed a 2.6 times higher risk of developing FAD (P = 0.003). Infection with Providencia ssp. also demonstrated a greater risk to developing PAS. Subjects who sought treatment for diarrhea while in Mexico also displayed a significantly lower frequency of IBS at six months follow up (OR = 0.30; CI, 0.10–0.80; P = 0.02). ^ Forty six SNPs belonging to 14 genes were studied. Seven SNPs were associated with PAS at 6 months. These included four SNPs from the Caspase Recruitment Domain-Containing Protein 15 gene (CARD15), two SNPs from Surfactant Pulmonary-Associated Protein D gene (SFTPD) and one from Decay-Accelerating Factor For Complement gene (CD55). A genetic risk score (GRS) was composed based on the 7 SNPs that showed significant association with PAS. A 20% greater risk for PAS was noted for every unit increase in GRS. The risk increased by 30% for IBS. The mean GRS was high for IBS (2.2) and PAS (1.1) compared to healthy controls (0.51). These data suggests a role for these genetic polymorphisms in defining the susceptibility to PAS. ^ Conclusions. The study allows us to identify individuals at risk for developing post infectious IBS (PI-IBS) and persisting abdominal symptoms after an episode of TD. The observations in this study will be of use in developing measures to prevent and treat post-infectious irritable bowel syndrome among travelers including pre-travel counseling, the use of vaccines, antibiotic prophylaxis or the initiation of early antimicrobial therapy. This study also provides insights into the pathogenesis of post infectious PAS and IBS. (Abstract shortened by UMI.)^

A comparative analysis of CHIP Perinatal policy in twelve states

The purpose of this comparative analysis of CHIP Perinatal policy (42 CFR § 457) was to provide a basis for understanding the variation in policy outputs across the twelve states that, as of June 2007, implemented the Unborn Child rule. This Department of Health and Human Services regulation expanded in 2002 the definition of “child” to include the period from conception to birth, allowing states to consider an unborn child a “targeted low-income child” and therefore eligible for SCHIP coverage. ^ Specific study aims were to (1) describe typologically the structural and contextual features of the twelve states that adopted a CHIP Perinatal policy; (2) describe and differentiate among the various designs of CHIP Perinatal policy implemented in the states; and (3) develop a conceptual model that links the structural and contextual features of the adopting states to differences in the forms the policy assumed, once it was implemented. ^ Secondary data were collected from publicly available information sources to describe characteristics of states’ political system, health system, economic system, sociodemographic context and implemented policy attributes. I posited that socio-demographic differences, political system differences and health system differences would directly account for the observed differences in policy output among the states. ^ Exploratory data analysis techniques, which included median polishing and multidimensional scaling, were employed to identify compelling patterns in the data. Scaled results across model components showed that economic system was most closely related to policy output, followed by health system. Political system and socio-demographic characteristics were shown to be weakly associated with policy output. Goodness-of-fit measures for MDS solutions implemented across states and model components, in one- and two-dimensions, were very good. ^ This comparative policy analysis of twelve states that adopted and implemented HHS Regulation 42 C.F.R. § 457 contributes to existing knowledge in three areas: CHIP Perinatal policy, public health policy and policy sciences. First, the framework allows for the identification of CHIP Perinatal program design possibilities and provides a basis for future studies that evaluate policy impact or performance. Second, studies of policy determinants are not well represented in the health policy literature. Thus, this study contributes to the development of the literature in public health policy. Finally, the conceptual framework for policy determinants developed in this study suggests new ways for policy makers and practitioners to frame policy arguments, encouraging policy change or reform. ^

Determinants of patients' expectations about total knee arthroplasty outcomes

Knee osteoarthritis (OA) is the most prevalent form of arthritis in the US, affecting approximately 37% of adults. Approximately 300,000 total knee arthroplasty (TKA) procedures take place in the United States each year. Total knee arthroplasty is an elective procedure available to patients as an irreversible treatment after failure of previous medical treatments. Some patients sacrifice quality of life and endure many years of pain before making the decision to undergo total knee replacement. In making their decision, it is therefore imperative for patients to understand the procedure, risks and surgical outcomes to create realistic expectations and increase outcome satisfaction. ^ From 2004-2007, 236 OA patients who underwent TKA participated in the PEAKS (Patient Expectations About Knee Surgery) study, an observational longitudinal cohort study, completed baseline and 6 month follow-up questionnaires after the surgery. We performed a secondary data analysis of the PEAKS study to: (1) determine the specific presurgical patient characteristics associated with patients’ presurgical expectations of time to functional recovery; and (2) determine the association between presurgical expectations of time to functional recovery and postsurgical patient capabilities (6 months after TKA). We utilized the WOMAC to measure knee pain and function, the SF-36 to measure health-related quality of life, and the DASS and MOS-SSS to measure psychosocial quality of life variables. Expectation and capability measures were generated from panel of experts. A list of 10 activities was used for this analysis to measure functional expectations and postoperative functional capabilities. ^ The final cohort consisted of 236 individuals, was predominately White with 154 women and 82 men. The mean age was 65 years. Patients were optimistic about their time to functional recovery. Expectation time of being able to perform the list activities per patient had a median of less than 3 months. Patients who expected to be able to perform the functional activities by 3 months had better knee function, less pain and better overall health-related quality of life. Despite expectation differences, all patients showed significant improvement 6 months after surgery. Participant expectation of time to functional recovery was not an independent predictor of capability to perform functional activities at 6 months. Better presurgical patient characteristics were, however, associated with a higher likelihood of being able to perform all activities at 6 months. ^ This study gave us initial insight on the relationship between presurgical patient characteristics and their expectations of functional recovery after total knee replacement. Future studies clarifying the relationship between patient presurgical characteristics and postsurgical functional capabilities are needed.^

A path analysis of an after-school health and exercise program for elementary school children in El Paso, Texas

As schools are pressured to perform on academics and standardized examinations, schools are reluctant to dedicate increased time to physical activity. After-school exercise and health programs may provide an opportunity to engage in more physical activity without taking time away from coursework during the day. The current study is a secondary data analysis of data from a randomized trial of a 10-week after-school program (six schools, n = 903) that implemented an exercise component based on the CATCH physical activity component and health modules based on the culturally-tailored Bienestar health education program. Outcome variables included BMI and aerobic capacity, health knowledge and healthy food intentions as assessed through path analysis techniques. Both the baseline model (χ2 (df = 8) = 16.90, p = .031; RMSEA = .035 (90% CI of .010–.058), NNFI = 0.983 and the CFI = 0.995) and the model incorporating intervention participation proved to be a good fit to the data (χ2 (df = 10) = 11.59, p = .314. RMSEA = .013 (90% CI of .010–.039); NNFI = 0.996 and CFI = 0.999). Experimental group participation was not predictive of changes in health knowledge, intentions to eat healthy foods or changes in Body Mass Index, but it was associated with increased aerobic capacity, β = .067, p < .05. School characteristics including SES and Language proficiency proved to be significantly associated with changes in knowledge and physical indicators. Further effects of school level variables on intervention outcomes are recommended so that tailored interventions can be developed aimed at the specific characteristics of each participating school. ^

Secondary analysis of effectivness of tinidazole-containing-quadruple eradication therapy in asymptomatic H. pylori infected children in El Paso

Helicobacter pylori infection is frequently acquired during childhood. This microorganism is known to cause gastritis, and duodenal ulcer in pediatric patients, however most children remain completely asymptomatic to the infection. Currently there is no consensus in favor of treatment of H. pylori infection in asymptomatic children. The firstline of treatment for this population is triple medication therapy including two antibacterial agents and one proton pump inhibitor for a 2 week duration course. Decreased eradication rate of less than 75% has been documented with the use of this first-line therapy but novel tinidazole-containing quadruple sequential therapies seem worth investigating. None of the previous studies on such therapy has been done in the United States of America. As part of an iron deficiency anemia study in asymptomatic H. pylori infected children of El Paso, Texas, we conducted a secondary data analysis of study data collected in this trial to assess the effectiveness of this tinidazole-containing sequential quadruple therapy compared to placebo on clearing the infection. Subjects were selected from a group of asymptomatic children identified through household visits to 11,365 randomly selected dwelling units. After obtaining parental consent and child assent a total of 1,821 children 3-10 years of age were screened and 235 were positive to a novel urine immunoglobulin class G antibodies test for H. pylori infection and confirmed as infected using a 13C urea breath test, using a hydrolysis urea rate >10 μg/min as cut-off value. Out of those, 119 study subjects had a complete physical exam and baseline blood work and were randomly allocated to four groups, two of which received active H. pylori eradication medication alone or in combination with iron, while the other two received iron only or placebo only. Follow up visits to their houses were done to assess compliance and occurrence of adverse events and at 45+ days post-treatment, a second urea breath test was performed to assess their infection status. The effectiveness was primarily assessed on intent to treat basis (i.e., according to their treatment allocation), and the proportion of those who cleared their infection using a cut-off value >10 μg/min of for urea hydrolysis rate, was the primary outcome. Also we conducted analysis on a per-protocol basis and according to the cytotoxin associated gene A product of the H. pylori infection status. Also we compared the rate of adverse events across the two arms. On intent-to-treat and per-protocol analyses, 44.3% and 52.9%, respectively, of the children receiving the novel quadruple sequential eradication cleared their infection compared to 12.2% and 15.4% in the arms receiving iron or placebo only, respectively. Such differences were statistically significant (p<0.001). The study medications were well accepted and safe. In conclusion, we found in this study population, of mostly asymptomatically H. pylori infected children, living in the US along the border with Mexico, that the quadruple sequential eradication therapy cleared the infection in only half of the children receiving this treatment. Research is needed to assess the antimicrobial susceptibility of the strains of H. pylori infecting this population to formulate more effective therapies. ^

Demographic analysis of Certified Industrial Hygienists (CIH) with the use of survival analysis techniques

Objective. The goal of this study is to characterize the current workforce of CIHs, the lengths of professional practice careers of the past and current CIHs.^ Methods. This is a secondary data analysis of data compiled from all of the nearly 50 annual roster listings of the American Board of Industrial Hygiene (ABIH) for Certified Industrial Hygienists active in each year since 1960. Survival analysis was performed as a technique to measure the primary outcome of interest. The technique which was involved in this study was the Kaplan-Meier method for estimating the survival function.^ Study subjects: The population to be studied is all Certified Industrial Hygienists (CIHs). A CIH is defined by the ABIH as an individual who has achieved the minimum requirements for education, working experience and through examination, has demonstrated a minimum level of knowledge and competency in the prevention of occupational illnesses. ^ Results. A Cox-proportional hazards model analysis was performed by different start-time cohorts of CIHs. In this model we chose cohort 1 as the reference cohort. The estimated relative risk of the event (defined as retirement, or absent from 5 consecutive years of listing) occurred for CIHs for cohorts 2,3,4,5 relative to cohort 1 is 0.385, 0.214, 0.234, 0.299 relatively. The result show that cohort 2 (CIHs issued from 1970-1980) has the lowest hazard ratio which indicates the lowest retirement rate.^ Conclusion. The manpower of CIHs (still actively practicing up to the end of 2009) increased tremendously starting in 1980 and grew into a plateau in recent decades. This indicates that the supply and demand of the profession may have reached equilibrium. More demographic information and variables are needed to actually predict the future number of CIHs needed. ^

BEST SUBSET SELECTION FOR CATEGORICAL DATA

When choosing among models to describe categorical data, the necessity to consider interactions makes selection more difficult. With just four variables, considering all interactions, there are 166 different hierarchical models and many more non-hierarchical models. Two procedures have been developed for categorical data which will produce the "best" subset or subsets of each model size where size refers to the number of effects in the model. Both procedures are patterned after the Leaps and Bounds approach used by Furnival and Wilson for continuous data and do not generally require fitting all models. For hierarchical models, likelihood ratio statistics (G('2)) are computed using iterative proportional fitting and "best" is determined by comparing, among models with the same number of effects, the Pr((chi)(,k)('2) (GREATERTHEQ) G(,ij)('2)) where k is the degrees of freedom for ith model of size j. To fit non-hierarchical as well as hierarchical models, a weighted least squares procedure has been developed.^ The procedures are applied to published occupational data relating to the occurrence of byssinosis. These results are compared to previously published analyses of the same data. Also, the procedures are applied to published data on symptoms in psychiatric patients and again compared to previously published analyses.^ These procedures will make categorical data analysis more accessible to researchers who are not statisticians. The procedures should also encourage more complex exploratory analyses of epidemiologic data and contribute to the development of new hypotheses for study. ^

Descriptive analysis of Ben Taub Pediatric Weight Management Program

The purpose of this study is to descriptively analyze the current program at Ben Taub Pediatric Weight Management Program in Houston, Texas, a program designed to help overweight children ages three to eighteen to lose weight. In Texas, approximately one in every three children is overweight or obese. Obesity is seen at an even greater level within Ben Taub due to the hospital's high rate of service for underserved minority populations (Dehghan et al, 2005; Tyler and Horner, 2008; Hunt, 2009). The weight management program consists of nutritional, behavioral, physical activity, and medical counseling. Analysis will focus on changes in weight, BMI, cholesterol levels, and blood pressure from 2007–2010 for all participants who attended at least two weight management sessions. Recommendations will be given in response to the results of the data analysis.^

Motivational interviewing and feedback for college drinkers: Handling missing values with complete case analysis and multiple imputation

Objective: In this secondary data analysis, three statistical methodologies were implemented to handle cases with missing data in a motivational interviewing and feedback study. The aim was to evaluate the impact that these methodologies have on the data analysis. ^ Methods: We first evaluated whether the assumption of missing completely at random held for this study. We then proceeded to conduct a secondary data analysis using a mixed linear model to handle missing data with three methodologies (a) complete case analysis, (b) multiple imputation with explicit model containing outcome variables, time, and the interaction of time and treatment, and (c) multiple imputation with explicit model containing outcome variables, time, the interaction of time and treatment, and additional covariates (e.g., age, gender, smoke, years in school, marital status, housing, race/ethnicity, and if participants play on athletic team). Several comparisons were conducted including the following ones: 1) the motivation interviewing with feedback group (MIF) vs. the assessment only group (AO), the motivation interviewing group (MIO) vs. AO, and the intervention of the feedback only group (FBO) vs. AO, 2) MIF vs. FBO, and 3) MIF vs. MIO.^ Results: We first evaluated the patterns of missingness in this study, which indicated that about 13% of participants showed monotone missing patterns, and about 3.5% showed non-monotone missing patterns. Then we evaluated the assumption of missing completely at random by Little's missing completely at random (MCAR) test, in which the Chi-Square test statistic was 167.8 with 125 degrees of freedom, and its associated p-value was p=0.006, which indicated that the data could not be assumed to be missing completely at random. After that, we compared if the three different strategies reached the same results. For the comparison between MIF and AO as well as the comparison between MIF and FBO, only the multiple imputation with additional covariates by uncongenial and congenial models reached different results. For the comparison between MIF and MIO, all the methodologies for handling missing values obtained different results. ^ Discussions: The study indicated that, first, missingness was crucial in this study. Second, to understand the assumptions of the model was important since we could not identify if the data were missing at random or missing not at random. Therefore, future researches should focus on exploring more sensitivity analyses under missing not at random assumption.^

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

The SanaViz: Human Centered Geovisualization to facilitate visual exploration of Public Health data

These three manuscripts are presented as a PhD dissertation for the study of using GeoVis application to evaluate telehealth programs. The primary reason of this research was to understand how the GeoVis applications can be designed and developed using combined approaches of HC approach and cognitive fit theory and in terms utilized to evaluate telehealth program in Brazil. First manuscript The first manuscript in this dissertation presented a background about the use of GeoVisualization to facilitate visual exploration of public health data. The manuscript covered the existing challenges that were associated with an adoption of existing GeoVis applications. The manuscript combines the principles of Human Centered approach and Cognitive Fit Theory and a framework using a combination of these approaches is developed that lays the foundation of this research. The framework is then utilized to propose the design, development and evaluation of “the SanaViz” to evaluate telehealth data in Brazil, as a proof of concept. Second manuscript The second manuscript is a methods paper that describes the approaches that can be employed to design and develop “the SanaViz” based on the proposed framework. By defining the various elements of the HC approach and CFT, a mixed methods approach is utilized for the card sorting and sketching techniques. A representative sample of 20 study participants currently involved in the telehealth program at the NUTES telehealth center at UFPE, Recife, Brazil was enrolled. The findings of this manuscript helped us understand the needs of the diverse group of telehealth users, the tasks that they perform and helped us determine the essential features that might be necessary to be included in the proposed GeoVis application “the SanaViz”. Third manuscript The third manuscript involved mix- methods approach to compare the effectiveness and usefulness of the HC GeoVis application “the SanaViz” against a conventional GeoVis application “Instant Atlas”. The same group of 20 study participants who had earlier participated during Aim 2 was enrolled and a combination of quantitative and qualitative assessments was done. Effectiveness was gauged by the time that the participants took to complete the tasks using both the GeoVis applications, the ease with which they completed the tasks and the number of attempts that were taken to complete each task. Usefulness was assessed by System Usability Scale (SUS), a validated questionnaire tested in prior studies. In-depth interviews were conducted to gather opinions about both the GeoVis applications. This manuscript helped us in the demonstration of the usefulness and effectiveness of HC GeoVis applications to facilitate visual exploration of telehealth data, as a proof of concept. Together, these three manuscripts represent challenges of combining principles of Human Centered approach, Cognitive Fit Theory to design and develop GeoVis applications as a method to evaluate Telehealth data. To our knowledge, this is the first study to explore the usefulness and effectiveness of GeoVis to facilitate visual exploration of telehealth data. The results of the research enabled us to develop a framework for the design and development of GeoVis applications related to the areas of public health and especially telehealth. The results of our study showed that the varied users were involved with the telehealth program and the tasks that they performed. Further it enabled us to identify the components that might be essential to be included in these GeoVis applications. The results of our research answered the following questions; (a) Telehealth users vary in their level of understanding about GeoVis (b) Interaction features such as zooming, sorting, and linking and multiple views and representation features such as bar chart and choropleth maps were considered the most essential features of the GeoVis applications. (c) Comparing and sorting were two important tasks that the telehealth users would perform for exploratory data analysis. (d) A HC GeoVis prototype application is more effective and useful for exploration of telehealth data than a conventional GeoVis application. Future studies should be done to incorporate the proposed HC GeoVis framework to enable comprehensive assessment of the users and the tasks they perform to identify the features that might be necessary to be a part of the GeoVis applications. The results of this study demonstrate a novel approach to comprehensively and systematically enhance the evaluation of telehealth programs using the proposed GeoVis Framework.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.