The functional and structural interactions between v-{\it mos\/} proteins and cell cycle control elements

The v-mos gene of Moloney murine sarcoma virus (Mo-MuSv) encodes a serine/threonine protein kinase capable of inducing cellular transformation. The c-mos protein is an important cell cycle regulator that functions during meiotic cell division cycles in germ cells. The overall function of c-mos in controlling meiosis is becoming better understood but the role of v-mos in malignant transformation of cells is largely unknown.^ In this study, v-mos protein was shown to be phosphorylated by M phase kinase in vitro and in vivo. The kinase activity and neoplastic transforming ability of v-mos is positively regulated by the phosphorylation. Together with the earlier finding of activation of M phase kinase by c-mos, these results raise the possibility of mutual regulation between M phase kinase and mos kinases.^ In addition to its functional interaction with the M phase kinase, the v-mos protein was shown to be present in the same protein complex with a cyclin-dependent kinase (cdk). In addition, an antibody that recognizes the cdk proteins was shown to co-precipitate the v-mos proteins in the interphase and mitotic cells transformed by p85$\sp{\rm gag-mos}$. Cdk proteins have been shown to be associated with nonmitotic cyclins which are potential oncogenes. The perturbation of cdk kinase or the activation of non-mitotic cyclins as oncogenes by v-mos could contribute directly to v-mos induced cellular transformation. v-mos proteins were also shown to interact with tubulin and vimentin, the essential components of microtubules and type IV intermediate filaments, respectively. The organizations of both microtubules and intermediate filaments are cell cycle-regulated. These results suggest that the v-mos kinase could be directly involved in inducing morphological changes typically seen in transformed cells.^ The interactions between the v-mos protein and these cell cycle control elements in regards to v-mos induced neoplastic transformation are discussed in detail in the text. ^

Defective pituitary function and gamete interactions in $\beta$1,4-galactosyltransferase null mice

Despite much attention, the function of oligosaccharide chains of glycoproteins remains largely unknown. Our understanding of oligosaccharide function in vivo has been limited to the use of reagents and targeted mutations that eliminate entire oligosaccharide chains. However, most, if not all biological functions for oligosaccharides have been attributed to specific terminal sequences on these oligosaccharides, yet there have been few studies to examine the consequences of modifying terminal oligosaccharide structures in vivo. To address this issue, mice were created bearing a targeted mutation in $\beta$1,4-galactosyltransferase, an enzyme responsible for elaboration of many of the proposed biologically-active carbohydrate epitopes. Most galactosyltransferase-null mice died within the first few weeks after birth and were characterized by stunted growth, thin skin, sparse hair, and dehydration. In addition, the adrenal cortices were poorly stratified and spermatogenesis was delayed. The few surviving adults had puffy skin (myxedema), difficulty delivering pups at birth (dystocia), and failed to lactate (agalactosis). All of these defects are consistant with endocrine insufficiency, which was confirmed by markedly decreased levels of serum thyroxine. The anterior pituitary gland appeared functionally delayed in newborn mutant mice, since the constituent cells were quiescent and nonsecretory, unlike that of control littermates. However, the anterior pituitary acquired a normal secretory phenotype during neonatal development, although it remained abnormally small and its glycoprotein hormones were devoid of $\beta$1,4-galactosyl residues. These results support in vitro studies suggesting that incomplete glycosylation of pituitary hormones leads to the creation of hormone antagonists that down regulate subsequent endocrine function producing polyglandular endocrine insufficiency. More surprisingly, the fact that some mice survive this neonatal period indicates the presence of a previously unrecognized compensatory pathway for glycoprotein hormone glycosylation and/or action.^ In addition to its well-studied biosynthetic function in the Golgi complex, a GalTase isoform is also expressed on the sperm surface where it functions as a gamete receptor during fertilization by binding to its oligosaccharide ligand on the egg coat glycoprotein, ZP3. Aggregation of GalTase by multivalent ZP3 oligosaccharides activates a G-protein cascade leading to the acrosome reaction. Although GalTase-null males are fertile, the mutant sperm bind less ZP3 than wild-type sperm, and are unable to undergo the acrosome reaction in response to either zona pellucida glycoproteins or to anti-GalTase anti-serum, as do wild-type sperm. However, mutant and wild-type sperm undergo the acrosome reaction normally in response to calcium ionophore which bypasses the requirement for ZP3 binding. Interestingly, the phenotype of the GalTase-null sperm is reciprocal to that of sperm that overexpress surface GalTAse and which bind more ZP3 leading to precocious acrosome reactions. These results confirm that GalTase functions as at least one of the sperm receptors for ZP3, and that GalTase participates in the ZP3-induced signal transduction pathway during zona pellucida-induced acrosome reactions. ^

Functions and intramolecular interactions of ribosomal RNA in decoding of termination codons

Two regions in the 3$\prime$ domain of 16S rRNA (the RNA of the small ribosomal subunit) have been implicated in decoding of termination codons. Using segment-directed PCR random mutagenesis, I isolated 33 translational suppressor mutations in the 3$\prime$ domain of 16S rRNA. Characterization of the mutations by both genetic and biochemical methods indicated that some of the mutations are defective in UGA-specific peptide chain termination and that others may be defective in peptide chain termination at all termination codons. The studies of the mutations at an internal loop in the non-conserved region of helix 44 also indicated that this structure, in a non-conserved region of 16S rRNA, is involved in both peptide chain termination and assembly of 16S rRNA.^ With a suppressible trpA UAG nonsense mutation, a spontaneously arising translational suppressor mutation was isolated in the rrnB operon cloned into a pBR322-derived plasmid. The mutation caused suppression of UAG at two codon positions in trpA but did not suppress UAA or UGA mutations at the same trpA positions. The specificity of the rRNA suppressor mutation suggests that it may cause a defect in UAG-specific peptide chain termination. The mutation is a single nucleotide deletion (G2484$\Delta$) in helix 89 of 23S rRNA (the large RNA of the large ribosomal subunit). The result indicates a functional interaction between two regions of 23S rRNA. Furthermore, it provides suggestive in vivo evidence for the involvement of the peptidyl-transferase center of 23S rRNA in peptide chain termination. The $\Delta$2484 and A1093/$\Delta$2484 (double) mutations were also observed to alter the decoding specificity of the suppressor tRNA lysT(U70), which has a mutation in its acceptor stem. That result suggests that there is an interaction between the stem-loop region of helix 89 of 23S rRNA and the acceptor stem of tRNA during decoding and that the interaction is important for the decoding specificity of tRNA.^ Using gene manipulation procedures, I have constructed a new expression vector to express and purify the cellular protein factors required for a recently developed, realistic in vitro termination assay. The gene for each protein was cloned into the newly constructed vector in such a way that expression yielded a protein with an N-terminal affinity tag, for specific, rapid purification. The amino terminus was engineered so that, after purification, the unwanted N-terminal tag can be completely removed from the protein by thrombin cleavage, yielding a natural amino acid sequence for each protein. I have cloned the genes for EF-G and all three release factors into this new expression vector and the genes for all the other protein factors into a pCAL-n expression vector. These constructs will allow our laboratory group to quickly and inexpensively purify all the protein factors needed for the new in vitro termination assay. (Abstract shortened by UMI.) ^

Negotiated settlements: The utilities, multiple regulators and clean air

Utilities have made strides in reducing air pollutant levels, but the proposed 1990 Clean Air Act Amendments call for even greater reductions and more stringent enforcement. Federal and state air enforcement agencies now encourage the use of negotiated settlements as a way to bring about compliance. This research examines the operation of such procedures in 19 case studies and a formal survey with the negotiators to account for the differences in the nature of the settlements and to identify the factors contributing to their perceived success. ^

Characterization of Jak, STAT, and Src interactions in Head and Neck Squamous Cell Carcinoma

Recurrence of Head and Neck Squamous Cell Carcinoma (HNSCC) is common; thus, it is essential to improve the effectiveness and reduce toxicity of current treatments. Proteins in the Src/Jak/STAT pathway represent potential therapeutic targets, as this pathway is hyperactive in HNSCC and it has roles in cell migration, metastasis, proliferation, survival, and angiogenesis. During short-term Src inhibition, Janus kinase (Jak) 2, and signal transducer and activator of transcription (STAT) 3 and STAT5 are dephosphorylated and inactivated. Following sustained Src inhibition, STAT5 remains inactive, but Jak2 and STAT3 are reactivated following their early inhibition. To further characterize the mechanism of this novel feedback pathway we performed several experiments to look at the interactions between Src, Jak2, STAT5 and STAT3. We attempted to develop a non-radioactive kinase assay using purified recombinant Jak2 and Src proteins, but found that phospho-tyrosine antibodies were non-specifically binding to purified recombinant proteins. We then performed in vitro kinase assays (IVKAs) using purified recombinant Jak2, Src, STAT3, and STAT5 proteins with and without Src and Jak2 pharmacologic inhibitors. We also examined the interactions of these proteins in intact HNSCC cells. We found that recombinant Jak2, STAT3, and STAT5 are direct substrates of Src and that recombinant Src, STAT3, and STAT5 are direct substrates of Jak2 in the IVKA. To our knowledge, the finding that Src is a Jak substrate is novel and has not been shown before. In intact HNSCC cells we find that STAT3 can be reactivated despite continuous Src inhibition and that STAT5 continues to be inhibited despite Jak2 reactivation. Also, Jak2 inhibition did not affect Src or STAT5 activity but it did cause STAT3 inhibition. We hypothesized that the differences between the intact cells and the IVKA assays were due to a potential need for binding partners in intact HNSCC cells. One potential binding partner that we examined is the epidermal growth factor receptor (EGFR). We found that EGFR activation caused increased activation of Src and STAT5 but not Jak2. Our results demonstrate that although STAT3 and STAT5 are capable of being Src and Jak2 substrates, in intact HNSCC cells Src predominantly regulates STAT5 and Jak2 regulates STAT3. Regulation of STAT5 by Src may involve interactions between Src and EGFR. This knowledge along with future studies will better define the mechanisms of STAT regulation in HNSCC cells and ultimately result in an ideal combination of therapeutic agents for HNSCC.

Evaluation of racial and ethnic disparities of glycemic control in the Health and Retirement Study. 2003 Diabetes Survey

Objective. To examine and evaluate racial and ethnic disparities in glycemic control among HRS respondents with diabetes aged 55-94 years. ^ Methods. The HRS Diabetes 2003 database provides data on blood-drawn glycemic control and self-reported demographics, socioeconomic status, clinical, health access and self-care characteristics. 1,141 non-Hispanic White, non-Hispanic Black, and Hispanic respondents were included in multiple logistic regression of glycemic control. ^ Results. The rate of poor control was significantly higher among Blacks (61.5%, 105/171) and Hispanics (65.3% 72/110) than among Whites (45.0% 387/860) (p < 0.01). After controlling for influential covariates and interactions, Blacks and Hispanics had a three-fold increased risk for poor control compared to Whites when duration was five years or less. ^ Conclusions. Clinical and self-perception variables, like duration, medication, and self-rated poor diabetes control affected glycemic control independent of race and ethnicity, but there remains unexplained racial and ethnic disparities for newly-diagnosed individuals. This is the first study to find an interaction between duration and race and ethnicity on glycemic control. Future research should incorporate cultural beliefs and attitudes about diabetes control that may explain the racial and ethnic disparity. ^

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.

Knowledge, beliefs, risk behaviors, and social skills for HIV/STD prevention in junior college students (grades 11--12) in Bombay, India

1230 year 11 and 12 college students, modal age 16 and 17, in three colleges in Bombay, India, were studied on sexual behaviors or risk of sexual behaviors, beliefs about sex, HIV/STD knowledge, perceived norms regarding sexual behaviors, and the relationships between social skills/anxieties in HIV/STD prevention and actual and anticipated sexual behaviors. A quantitative questionnaire examining HIV/STD risk behaviors, knowledge, attitudes and beliefs, and the AIDS Social Assertiveness Scale (ASAS) were administered to these 1230 college students. Data indicated that 8% of males and 1% of females had had sexual experience, but over one third were not sure at all of being able to abstain from sexual activity with either steady or casual partners. Perceived norms were slanted toward sexual abstinence for the majority of the sample. Knowledge of protective effects of condoms was high, although half of those who had had sex did not use condoms. Logistic regression showed knowledge was higher among males, those who believed it was OK to have sex with a steady partner and that they should not wait until they were older, those who believed that condoms should be used even if the partner is known, and those who believed it was acceptable to have multiple partners. Gender differences in sexual activity and beliefs about sexual activity showed males were less likely to believe in abstaining from sexual activity. The 5 scales of the ASAS were scored and compared on ANOVA on: those who had had sexual experience (HS), those who anticipated being unable to refuse sex (AS), and those who did not anticipate problems in refusing sex (DS). Those in the AS group had greater anxieties about refusing sexual or other risk behaviors than HS and DS groups. There were greater anxieties about dealing with condoms in the AS and DS groups compared with the HS group. Confiding sexual or HIV/STD-related problems to significant others was more anxiety-provoking for the AS group compared with the HS group, and the AS group were more anxious about interactions with people with HIV. Factor analysis produced the same 5 factors as those found in previous studies. Of these, condom interactions and confiding in significant others were most anxiety provoking, and condom interactions most variable based on demographic and attitudinal factors.^ This age group is appropriate for HIV/STD reduction education given the low rate of sexual activity but despite knowledge of the importance of condom use, social skills to apply this knowledge are lacking. Social skills training in sexual negotiations, condom negotiations, and confiding HIV/STD-related concerns to significant others should reduce the risks of Indian college students having unwanted or unprotected sex. ^

Diabetes genes and risk of prostate cancer in the Atherosclerosis Risk in Communities study

Prostate cancer (PrCa) is a leading cause of morbidity and mortality, yet the etiology remains uncertain. Meta-analyses show that PrCa risk is reduced by 16% in men with type 2 diabetes (T2D), but the mechanism is unknown. Recent genome-wide association studies and meta-analyses have found single nucleotide polymorphisms (SNPs) that consistently predict T2D risk. We evaluated associations of incident PrCa with 14 T2D SNPs in the Atherosclerosis Risk in Communities (ARIC) study. From 1987-2000, there were 397 incident PrCa cases ascertained from state or local cancer registries among 6,642 men (1,560 blacks and 5,082 whites) aged 45-64 years at baseline. Genotypes were determined by TaqMan assay. Cox proportional hazards models were used to assess the association between PrCa and increasing number of T2D risk-raising alleles for individual SNPs and for genetic risk scores (GRS) comprised of the number of T2D risk-raising alleles across SNPs. Two-way gene-gene interactions were evaluated with likelihood ratio tests. Using additive genetic models, the T2D risk-raising allele was associated with significantly reduced risk of PrCa for IGF2BP2 rs4402960 (hazard ratio [HR]=0.79; P=0.07 among blacks only), SLC2A2 rs5400 (race-adjusted HR=0.85; P=0.05) and UCP2 rs660339 (race-adjusted HR=0.84; P=0.02), but significantly increased risk of PrCa for CAPN10 rs3792267 (race-adjusted HR=1.20; P=0.05). No other SNPs were associated with PrCa using an additive genetic model. However, at least one copy of the T2D risk-raising allele for TCF7L2 rs7903146 was associated with reduced PrCa risk using a dominant genetic model (race-adjusted HR=0.79; P=0.03). These results imply that the T2D-PrCa association may be partly due to shared genetic variation, but these results should be verified since multiple tests were performed. When the combined, additive effects of these SNPs were tested using a GRS, there was nearly a 10% reduction in risk of PrCa per T2D risk-raising allele (race-adjusted HR=0.92; P=0.02). SNPs in IGF2BP2, KCNJ11 and SLC2A2 were also involved in multiple synergistic gene-gene interactions on a multiplicative scale. In conclusion, it appears that the T2D-PrCa association may be due, in part, to common genetic variation. Further knowledge of T2D gene-PrCa mechanisms may improve understanding of PrCa etiology and may inform PrCa prevention and treatment.^

The effect of genotype-by-environment interaction on longitudinal triglyceride profiles in the Bogalusa Heart Study

Triglyceride levels are a component of plasma lipids that are thought to be an important risk factor for coronary heart disease and are influenced by genetic and environmental factors, such as single nucleotide polymorphisms (SNPs), alcohol intake, and smoking. This study used longitudinal data from the Bogalusa Heart Study, a biracial community-based survey of cardiovascular disease risk factors. A sample of 1191 individuals, 4 to 38 years of age, was measured multiple times from 1973 to 2000. The study sample consisted of 730 white and 461 African American participants. Individual growth models were developed in order to assess gene-environment interactions affecting plasma triglycerides over time. After testing for inclusion of significant covariates and interactions, final models, each accounting for the effects of a different SNP, were assessed for fit and normality. After adjustment for all other covariates and interactions, LIPC -514C/T was found to interact with age3, age2, and age and a non-significant interaction of CETP -971G/A genotype with smoking status was found (p = 0.0812). Ever-smokers had higher triglyceride levels than never smokers, but persons heterozygous at this locus, about half of both races, had higher triglyceride levels after smoking cessation compared to current smokers. Since tobacco products increase free fatty acids circulating in the bloodstream, smoking cessation programs have the potential to ultimately reduce triglyceride levels for many persons. However, due to the effect of smoking cessation on the triglyceride levels of CETP -971G/A heterozygotes, the need for smoking prevention programs is also demonstrated. Both smoking cessation and prevention programs would have a great public health impact on minimizing triglyceride levels and ultimately reducing heart disease. ^

Detecting genetic and nutritional lung cancer risk factors related to folate metabolism using Bayesian generalized linear models

Complex diseases, such as cancer, are caused by various genetic and environmental factors, and their interactions. Joint analysis of these factors and their interactions would increase the power to detect risk factors but is statistically. Bayesian generalized linear models using student-t prior distributions on coefficients, is a novel method to simultaneously analyze genetic factors, environmental factors, and interactions. I performed simulation studies using three different disease models and demonstrated that the variable selection performance of Bayesian generalized linear models is comparable to that of Bayesian stochastic search variable selection, an improved method for variable selection when compared to standard methods. I further evaluated the variable selection performance of Bayesian generalized linear models using different numbers of candidate covariates and different sample sizes, and provided a guideline for required sample size to achieve a high power of variable selection using Bayesian generalize linear models, considering different scales of number of candidate covariates. ^ Polymorphisms in folate metabolism genes and nutritional factors have been previously associated with lung cancer risk. In this study, I simultaneously analyzed 115 tag SNPs in folate metabolism genes, 14 nutritional factors, and all possible genetic-nutritional interactions from 1239 lung cancer cases and 1692 controls using Bayesian generalized linear models stratified by never, former, and current smoking status. SNPs in MTRR were significantly associated with lung cancer risk across never, former, and current smokers. In never smokers, three SNPs in TYMS and three gene-nutrient interactions, including an interaction between SHMT1 and vitamin B12, an interaction between MTRR and total fat intake, and an interaction between MTR and alcohol use, were also identified as associated with lung cancer risk. These lung cancer risk factors are worthy of further investigation.^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

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.