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.

Sources of familial aggregation and co-aggregation of fasting blood glucose and nutritional factors among Mexican -Americans in Starr County, Texas

The purpose of this study was to determine the effects of nutrient intake, genetic factors and common household environmental factors on the aggregation of fasting blood glucose among Mexican-Americans in Starr County, Texas. This study was designed to determine: (a) the proportion of variation of fasting blood glucose concentration explained by unmeasured genetic and common household environmental effects; (b) the degree of familial aggregation of measures of nutrient intake; and (c) the extent to which the familial aggregation of fasting blood glucose is explained by nutrient intake and its aggregation. The method of path analysis was employed to determine these various effects.^ Genes play an important role in fasting blood glucose: Genetic variation was found to explain about 40% of the total variation in fasting blood glucose. Common household environmental effects, on the other hand, explained less than 3% of the variation in fasting blood glucose levels among individuals. Common household effects, however, did have significant effects on measures of nutrient intake, though it explained only about 10% of the total variance in nutrient intake. Finally, there was significant familial aggregation of nutrient intake measures, but their aggregation did not contribute significantly to the familial aggregation of fasting blood glucose. These results imply that similarities among relatives for fasting blood glucose are not due to similarities in nutrient intake among relatives. ^

Rapid effects of 1alpha,25-dihydroxyvitamin D3 on osteoblasts: Studies of membrane transducers that affect calcium signals

1,25-dihydroxyvitamin D3 [1,25(OH)2D 3] exerts pleiotropic effects on osteoblasts via both long-term nuclear receptor-mediated and rapid membrane-initiated pathways during bone remodeling and mineral homeostasis. This study explored the membrane transducers that mediate rapid effects of 1,25(OH)2D3 on osteoblasts, including sphingomyelinase (SMase) and L-type voltage sensitive calcium channels (VSCCs). ^ It was previously demonstrated that 1,25(OH)2D3 stimulates transmembrane influx of Ca2+ through VSCCs in ROS 17/2.8 osteoblasts, however the molecular identity of 1,25(OH)2D 3-regulated VSCC has not been known. In this study, on the basis of in vitro tests of three unique ribozymes specifically cleaving a1C mRNA, I transfected ROS 17/2.8 cells with vectors coding recombinant ribozyme modified with U1 snRNA structure, and successfully selected stable clonal cells in which the expression of a1C was strikingly reduced. Ca2+ influx studies in these cells compared to control transfectants showed selective attenuation of depolarization- and 1,25(OH)2D3-regulated Ca2+ responses. These results allow us to conclude that the cardiac ( a1C ) subtype of the L-type VSCC is the major membrane transducer of Ca 2+ influx in osteoblasts. ^ I also demonstrated that 1,25(OH)2D3 induces a rapid hydrolysis of membrane sphingomyelin (SM) in ROS 17/2.8 cells, with the concomitant generation of ceramide, detectable at 15 minute, and maximal at 1 hour after addition. Sphingosine, sphingosine-1-phosphate (SPP) and sphingosylphosphorylcholine (SPC), downstream products of SM hydrolysis, but not ceramide, elicit Ca 2+ release from intracellular stores. Considering ceramide, sphingosine, and SPP as second messengers modulating intracellular kinases or phosphatases, these findings implicate sphingolipid-signaling pathways in transducing rapid effects of 1,25(OH)2D3 on osteoblasts. In structure/function analyses of sphingolipid signaling, it was observed that psychosine elicits Ca2+ release from intracellular stores. This challenges the dogma that sphingosine phosphorylation permits mobilization of Ca2+ , because psychosine is a sphingosine analog galactosylated at 1-carbon, preventing phosphorylation at that site. Psychosine is the pathological metabolite found in patients with Krabbe's disease, suggesting that psychosine disrupts the physiological sphingolipid signaling by chronic release of Ca2+ from intracellular stores. ^ Slower SM turnover than Ca2+ influx through VSCCs in response to 1,25(OH)2D3 demonstrates ceramide does not mediate the 1,25(OH)2D3-induced Ca2+ signaling, a conclusion endorsed further by the failure of ceramide to induce Ca 2+ signaling. ^

Increased geranylgeranylated K-Ras contributes to antineoplastic effects of farnesyltransferase inhibitors.

The Ras family of small GTPases (N-, H-, and K-Ras) is a group of important signaling mediators. Ras is frequently activated in some cancers, while others maintain low level activity to achieve optimal cell growth. In cells with endogenously low levels of active Ras, increasing Ras signaling through the ERK and p38 MAPK pathways can cause growth arrest or cell death. Ras requires prenylation – the addition of a 15-carbon (farnesyl) or 20-carbon (geranylgeranyl) group – to keep the protein anchored into membranes for effective signaling. N- and K-Ras can be alternatively geranylgeranylated (GG’d) if farnesylation is inhibited but are preferentially farnesylated. Small molecule inhibitors of farnesyltransferase (FTIs) have been developed as a means to alter Ras signaling. Our initial studies with FTIs in malignant and non-malignant cells revealed FTI-induced cell cycle arrest, reduced proliferation, and increased Ras signaling. These findings led us to the hypothesis that FTI induced increased GG’d Ras. We further hypothesized that the specific effects of FTI on cell cycle and growth result from increased signal strength of GG’d Ras. Our results did show that increase in GG’d K-Ras in particular results in reduced cell viability and cell cycle arrest. Genetically engineered constructs capable of only one type of prenylation confirmed that GG’d K-Ras recapitulated the effect of FTI in 293T cells. In tumor cell lines ERK and p38 MAPK pathways were both strongly activated in response to FTI, indicating the increased activity of GG’d K-Ras results in antiproliferative signals specifically through these pathways. These results collectively indicate FTI increases active GG’d K-Ras which activates ERK and p38 MAPKs to reduced cell viability and induce cell cycle arrest in malignant cells. This is the first report that identifies increased activity of GG’d K-Ras contributes to antineoplastic effects from FTI by increasing the activity of downstream MAPKs. Our observations suggest increased GG’d K-Ras activity, rather than inhibition of farnesylated Ras, is a major source of the cytostatic and cytotoxic effects of FTI. Our data may allow for determination of which patients would benefit from FTI by excluding tumors or diseases which have strong K-Ras signaling.