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.

INTERACTION OF BACILLUS ANTHRACIS EXOSPORIUM PROTEIN BCLA WITH COMPLEMENT FACTOR H AND SPORE PERSISTENCE IN THE LUNG

Anthrax outbreaks in the United States and Europe and its potential use as a bioweapon have made Bacillus anthracis an interest of study. Anthrax infections are caused by the entry of B. anthracis spores into the host via the respiratory system, the gastrointestinal tract, cuts or wounds in the skin, and injection. Among these four forms, inhalational anthrax has the highest lethality rate and persistence of spores in the lungs of animals following pulmonary exposure has been noted for decades. However, details or mechanisms of spore persistence were not known. In this study, we investigated spore persistence in a mouse model. The results suggest that B. anthracis spores have special properties that promote persistence in the lung, and that there may be multiple mechanisms contributing to spore persistence. Moreover, recent discoveries from our laboratory suggest that spores evolved a sophisticated mechanism to interact with the host complement system. The complement system is a crucial part of the host defense mechanism against foreign microorganisms. Knowledge of the specific interactions that occur between the complement system and B. anthracis was limited. Studies performed in our laboratory have suggested that spores of B. anthracis can target specific proteins, such as Factor H (fH) of the complement system. Spores of B. anthracis are enclosed by an exosporium, which consists of a basal layer surrounded by a nap of hair-like filaments. The major structural component of the filaments is called Bacillus collagen-like protein of anthracis (BclA), which comprises a central collagen-like region and a globular C-terminal domain. BclA is the first point of contact with the innate system of an infected host. In this study, we investigated the molecular details of BclA-fH interaction with respect to the specific binding mechanism and the functional significance of this interaction in a murine model of anthrax infection. We hypothesized that the recruitment of fH to the spore surface by BclA limits the extent of complement activation and promotes pathogen survival and persistence in the infected host. Findings from this study are significant to understanding how to treat post-exposure prophylaxis and improve our knowledge of spores with the host immune system.

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.

THE MOLECULAR INTERACTION BETWEEN TYPE II DIABETES AND ALZHEIMER’S DISEASE THROUGH CROSS-SEEDING OF PROTEIN MISFOLDING

With the population of the world aging, the prominence of diseases such as Type II Diabetes (T2D) and Alzheimer’s disease (AD) are on the rise. In addition, patients with T2D have an increased risk of developing AD compared to age-matched individuals, and the number of AD patients with T2D is higher than among aged-matched non-AD patients. AD is a chronic and progressive dementia characterized by amyloid-beta (Aβ) plaques, neurofibrillary tangles (NFTs), neuronal loss, brain inflammation, and cognitive impairment. T2D involves the dysfunctional use of pancreatic insulin by the body resulting in insulin resistance, hyperglycemia, hyperinsulinemia, pancreatic beta cell (β-cell) death, and other complications. T2D and AD are considered protein misfolding disorders (PMDs). PMDs are characterized by the presence of misfolded protein aggregates, such as in T2D pancreas (islet amyloid polypeptide - IAPP) and in AD brain (amyloid– Aβ) of affected individuals. The misfolding and accumulation of these proteins follows a seeding-nucleation model where misfolded soluble oligomers act as nuclei to propagate misfolding by recruiting other native proteins. Cross-seeding occurs when oligomers composed by one protein seed the aggregation of a different protein. Our hypothesis is that the pathological interactions between T2D and AD may in part occur through cross-seeding of protein misfolding. To test this hypothesis, we examined how each respective aggregate (Aβ or IAPP) affects the disparate disease pathology through in vitro and in vivo studies. Assaying Aβ aggregates influence on T2D pathology, IAPP+/+/APPSwe+/- double transgenic (DTg) mice exhibited exacerbated T2D-like pathology as seen in elevated hyperglycemia compared to controls; in addition, IAPP levels in the pancreas are highest compared to controls. Moreover, IAPP+/+/APPSwe+/- animals demonstrate abundant plaque formation and greater plaque density in cortical and hippocampal areas in comparison to controls. Indeed, IAPP+/+/APPSwe+/- exhibit a colocalization of both misfolded proteins in cerebral plaques suggesting IAPP may directly interact with Aβ and aggravate AD pathology. In conclusion, these studies suggest that cross-seeding between IAPP and Aβ may occur, and that these protein aggregates exacerbate and accelerate disease pathology, respectively. Further mechanistic studies are necessary to determine how these two proteins interact and aggravate both pancreatic and brain pathologies.

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^

Interactions and structural analysis of the zinc -binding motif in decorin, a small leucine rich proteoglycan in extracellular matrix

Decorin, a dermatan/chondroitin sulfate proteoglycan, is ubiquitously distributed in the extracellular matrix (ECM) of mammals. Decorin belongs to the small leucine rich proteoglycan (SLRP) family, a proteoglycan family characterized by a core protein dominated by Leucine Rich Repeat motifs. The decorin core protein appears to mediate the binding of decorin to ECM molecules, such as collagens and fibronectin. It is believed that the interactions of decorin with these ECM molecules contribute to the regulation of ECM assembly, cell adhesions, and cell proliferation. These basic biological processes play critical roles during embryonic development and wound healing and are altered in pathological conditions such as fibrosis and tumorgenesis. ^ In this dissertation, we discover that decorin core protein can bind to Zn2+ ions with high affinity. Zinc is an essential trace element in mammals. Zn2+ ions play a catalytic role in the activation of many enzymes and a structural role in the stabilization of protein conformation. By examining purified recombinant decorin and its core protein fragments for Zn2+ binding activity using Zn2+-chelating column chromatography and Zn2+-equilibrium dialysis approaches, we have located the Zn2+ binding domain to the N-terminal sequence of the decorin core protein. The decorin N-terminal domain appears to contain two Zn2+ binding sites with similar high binding affinity. The sequence of the decorin N-terminal domain does not resemble any other reported zinc-binding motifs and, therefore, represents a novel Zn 2+ binding motif. By investigating the influence of Zn2+ ions on decorin binding interactions, we found a novel Zn2+ dependent interaction with fibrinogen, the major plasma protein in blood clots. Furthermore, a recombinant peptide (MD4) consisting of a 41 amino acid sequence of mouse decorin N-terminal domain can prolong thrombin induced fibrinogen/fibrin clot formation. This suggests that in the presence of Zn2+ the decorin N-terminal domain has an anticoagulation activity. The changed Zn2+-binding activities of the truncated MD4 peptides and site-directed mutagenesis generated mutant peptides revealed that the functional MD4 peptide might contain both a structural zinc-binding site in the cysteine cluster region and a catalytic zinc site that could be created by the flanking sequences of the cysteine cluster region. A model of a loop-like structure for MD4 peptide is proposed. ^

Circumvention of cellular defense responses to DNA -directed nucleoside analogues by the protein kinase inhibitor, UCN-01

DNA-directed nucleoside analogues, such as ara-C, fludarabine, and gemcitabine, are antimetabolites effective in the treatment of a variety of cancers. However, resistance to nucleoside analogue-based chemotherapy in treatments is still a major problem in therapy. Therefore, it is essential to develop rationales for optimizing the use of nucleoside analogues in combination with other anticancer drugs or modalities such as radiation. The present study focuses on establishing mechanism-based combination strategy to overcome resistance to nucleoside analogues. ^ I hypothesized that the cytostatic concentrations of nucleoside analogues may cause S-phase arrest by activating an S-phase checkpoint that consists of a series of kinases. This may allow cells to repair damaged DNA over time and spare cytotoxicity. Thus, the ability of cells to enact an S-phase arrest in response to incorporation of potentially lethal amounts of nucleoside analogue may serve as a mechanism of resistance to S-phase-specific agents. As a corollary, the addition of a kinase inhibitor, such as UCN-01, may dysregulate the checkpoint response and abrogate the survival of S-phase-arrested cells by suppression of the survival signaling pathways. Using gemcitabine as a model of S-phase-specific nucleoside analogues in human acute myelogenous leukemia ML-1 cells, I demonstrated that cells arrested in S-phase in response to cytostatic conditions. Proliferation continued after washing the cells into drug-free medium, suggesting S-phase arrest served as a resistance mechanism of cancer cells to spare cytotoxicity of nucleoside analogues. However, nontoxic concentrations of UCN-01 rapidly killed S-phase-arrested cells by apoptosis. Furthermore, the molecular mechanism for UCN-01-induced apoptosis in S-phase-arrested cells was through inhibition of survival pathways associated with these cells. In this regard, suppression of the PI 3-kinase-Akt-Bad survival pathway as well as the NF-κB signaling pathway were associated with induction of apoptosis in S-phase-arrested cells by UCN-01, whereas the Ras-Raf-MEK-ERK pathway appeared not involved. This study has provided the rationales and strategies for optimizing the design of effective combination therapies to overcome resistance to nucleoside analogues. In fact, a clinical trial of the combination of ara-C with UCN-01 to treat relapsed or refractory AML patients has been initiated at U.T.M.D. Anderson Cancer Center. ^

Water level time series and sediment grain size model

The environment of ebb-tidal deltas between barrier island systems is characterized by a complex morphology with ebb- and flood-dominated channels, shoals and swash bars connecting the ebb-tidal delta platform to the adjacent island. These morphological features reveal characteristic surface sediment grain-size distributions and are subject to a continuous adaptation to the prevailing hydrodynamic forces. The mixed-energy tidal inlet Otzumer Balje between the East Frisian barrier islands of Langeoog and Spiekeroog in the southern North Sea has been chosen here as a model study area for the identification of relevant hydrodynamic drivers of morphology and sedimentology. We compare the effect of high-energy, wave-dominated storm conditions to mid-term, tide-dominated fair-weather conditions on tidal inlet morphology and sedimentology with a process-based numerical model. A multi-fractional approach with five grain-size fractions between 150 and 450 µm allows for the simulation of corresponding surface sediment grain-size distributions. Net sediment fluxes for distinct conditions are identified: during storm conditions, bed load sediment transport is generally onshore directed on the shallower ebb-tidal delta shoals, whereas fine-grained suspended sediment bypasses the tidal inlet by wave-driven currents. During fair weather the sediment transport mainly focuses on the inlet throat and the marginal flood channels. We show how the observed sediment grain-size distribution and the morphological response at mixed-energy tidal inlets are the result of both wave-dominated less frequent storm conditions and mid-term, tide-dominant fair-weather conditions.