Calibrating a CGE model with NTBs that Incorporates Standard Models of Modern Trade Theory

We propose a way to incorporate NTBs for the four workhorse models of the modern trade literature in computable general equilibrium models (CGEs). CGE models feature intermediate linkages and thus allow us to study global value chains (GVCs). We show that the Ethier-Krugman monopolistic competition model, the Melitz firm heterogeneity model and the Eaton and Kortum model can be defined as an Armington model with generalized marginal costs, generalized trade costs and a demand externality. As already known in the literature in both the Ethier-Krugman model and the Melitz model generalized marginal costs are a function of the amount of factor input bundles. In the Melitz model generalized marginal costs are also a function of the price of the factor input bundles. Lower factor prices raise the number of firms that can enter the market profitably (extensive margin), reducing generalized marginal costs of a representative firm. For the same reason the Melitz model features a demand externality: in a larger market more firms can enter. We implement the different models in a CGE setting with multiple sectors, intermediate linkages, non-homothetic preferences and detailed data on trade costs. We find the largest welfare effects from trade cost reductions in the Melitz model. We also employ the Melitz model to mimic changes in Non tariff Barriers (NTBs) with a fixed cost-character by analysing the effect of changes in fixed trade costs. While we work here with a model calibrated to the GTAP database, the methods developed can also be applied to CGE models based on the WIOD database.

Changes in miRNA Expression in a Model of Microcephaly

miRNAs function to regulate gene expression through post-transcriptional mechanisms to potentially regulate multiple aspects of physiology and development. Whole transcriptome analysis has been conducted on the citron kinase mutant rat, a mutant that shows decreases in brain growth and development. The resulting differences in RNA between mutant and wild-type controls can be used to identify genetic pathways that may be regulated differentially in normal compared to abnormal neurogenesis. The goal of this thesis was to verify, with quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), changes in miRNA expression in Cit-k mutants and wild types. In addition to confirming miRNA expression changes, bio-informatics software TargetScan 5.1 was used to identify potential mRNA targets of the differentially expressed miRNAs. The miRNAs that were confirmed to change include: rno-miR-466c, mmu-miR-493, mmu-miR-297a, hsa-miR-765, and hsa-miR-1270. The TargetScan analysis revealed 347 potential targets which have known roles in development. A subset of these potential targets include genes involved in the Wnt signaling pathway which is known to be an important regulator of stem cell development.

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

Metastatic lung cancer in a new mouse model inheriting p53 and latent K-ras mutations

Lung cancer is a devastating disease with very poor prognosis. The design of better treatments for patients would be greatly aided by mouse models that closely resemble the human disease. The most common type of human lung cancer is adenocarcinoma with frequent metastasis. Unfortunately, current models for this tumor are inadequate due to the absence of metastasis. Based on the molecular findings in human lung cancer and metastatic potential of osteosarcomas in mutant p53 mouse models, I hypothesized that mice with both K-ras and p53 missense mutations might develop metastatic lung adenocarcinomas. Therefore, I incorporated both K-rasLA1 and p53RI72HΔg alleles into mouse lung cells to establish a more faithful model for human lung adenocarcinoma and for translational and mechanistic studies. Mice with both mutations ( K-rasLA1/+ p53R172HΔg/+) developed advanced lung adenocarcinomas with similar histopathology to human tumors. These lung adenocarcinomas were highly aggressive and metastasized to multiple intrathoracic and extrathoracic sites in a pattern similar to that seen in lung cancer patients. This mouse model also showed gender differences in cancer related death and developed pleural mesotheliomas in 23.2% of them. In a preclinical study, the new drug Erlotinib (Tarceva) decreased the number and size of lung lesions in this model. These data demonstrate that this mouse model most closely mimics human metastatic lung adenocarcinoma and provides an invaluable system for translational studies. ^ To screen for important genes for metastasis, gene expression profiles of primary lung adenocarcinomas and metastases were analyzed. Microarray data showed that these two groups were segregated in gene expression and had 79 highly differentially expressed genes (more than 2.5 fold changes and p<0.001). Microarray data of Bub1b, Vimentin and CCAM1 were validated in tumors by quantitative real-time PCR (QPCR). Bub1b , a mitotic checkpoint gene, was overexpressed in metastases and this correlated with more chromosomal abnormalities in metastatic cells. Vimentin, a marker of epithelial-mesenchymal transition (EMT), was also highly expressed in metastases. Interestingly, Twist, a key EMT inducer, was also highly upregulated in metastases by QPCR, and this significantly correlated with the overexpression of Vimentin in the same tumors. These data suggest EMT occurs in lung adenocarcinomas and is a key mechanism for the development of metastasis in K-ras LA1/+ p53R172HΔg/+ mice. Thus, this mouse model provides a unique system to further probe the molecular basis of metastatic lung cancer.^

Obesity and risk of multiple myeloma: A case-control study

Introduction. Several studies have reported a positive association of body mass index (BMI) with multiple myeloma; however, the period of adulthood where BMI is most important remains unclear. In addition, it is well known that body fat is associated with both sex-steroid hormone storage and with increasing insulin levels; therefore, it was hypothesized that the association between obesity and multiple myeloma may be attributed to increased aromatization of androgen in adipose tissue. Objective. The overall objective of this case-control study was to determine whether multiple myeloma cases had higher BMI and greater adult weight gain relative to healthy controls. In addition, we tested the hypothesis that hormone replacement therapy use among women will further increase the association between BMI and risk of multiple myeloma. This study used data from a pilot case-control study at M.D. Anderson Cancer Center (MDACC), entitled Etiology of multiple myeloma, directed by Dr. Sara Strom and Dr. Sergio Giralt. Methods. The pilot study recruited a total of 122 cases of histopathologically confirmed multiple myeloma from MDACC. Controls (n=183) were selected from a database of random digit dialing controls accrued in the Department of Epidemiology at MDACC and were frequency matched to the cases on age (±5 years), gender, and race/ethnicity. Demographic and risk factor information were obtained from all participants who completed a self-administered questionnaire. Items included in the questionnaire include demographic information, height and weight at age 25, 40 and current/diagnosis, medical history, family history of cancer, smoking and alcohol use. Statistical analysis. Initial descriptive analysis included Student's t-test and Pearson's chi-squared tests. Odds ratios and 95% confidence intervals were calculated to quantify the association between the variables of interest and multiple myeloma. A multivariable model will be developed using unconditional logistic regression. Results. MM cases were 1.79 times (95% CI=0.99-3.32) more likely to have been overweight or obese (BMI > 25 kg/m2) at age 25 relative to healthy controls after controlling for age, gender, race/ethnicty, education and family history of cancer. Being overweight or obese at age 40 was not significantly associated with mutliple myeloma risk (OR=1.42, 95% CI=0.86-2.34) nor was being overweight or obses at diagnosis (OR=1.43, 95% CI=0.78, 2.63). We observed a statistically significant 2-fold increased odds of multiple myeloma in individuals who gained more than 4.7 kg during between 25 and 40 years (OR=1.97, 95% CI=1.15-3.39). When assessing HRT as a modifier of the BMI and multiple myeloma association among women (N=123), no association between obesity and MM status was observed among women who have never used HRT (OR=0.60, 95% CI=0.23-1.61; n=73). Yet among women who have ever used HRT (n=50), being overweight or obese was associated with an increase in MM risk (OR=2. 93, 95% CI=0.81-10.6) after adjusting for age; however, the association was not statistically significant. Significance. This study provides further evidence that increased BMI increases the risk of multiple myeloma. Furthermore, among women, HRT use may modify risk of disease. ^

CARMA3 serves as a critical mediator of NF-kappaB in multiple signal transduction pathways

The central dogma of molecular biology dictates that DNA is transcribed into RNA, which is later translated into protein. One of the early activators in this process is the transcription factor NF-κB. We have determined that an NF-κB inducer, CARMA3, is required for proper neural tube closure, similar to other NF-κB inducers. Using a genetic knockout of CARMA3, we demonstrated that it is required for Gαq-coupled GPCR-induced NF-κB activation. This is facilitated through a MAPK and IKK phosphorylation-independent mechanism, most likely by controlling NEMO-associated ubiquitination. We have also shown that CARMA3 is required for EGF and HRG-induced NF-κB activation. This activation requires the activity of both EGFR and HER2, as well as PKC. Again, we observed no defect in IKK phosphorylation, although we determined a clear defect in IKK activation. Finally, we have begun to determine the role of CARMA3 to both EGFR and HER2-induced tumorigenicity. By overexpressing a constitutive active mutant of HER2 in our CARMA3 WT and KO MEF cells, we have shown CARMA3 is important for HER2-driven soft agar colony growth. We have also shown that knockdown of endogenous CARMA3 in the EGFR-overexpressing A431 cell line abolishes EGF-induced NF-κB activation. These same cells have a dramatically reduced capacity to form colonies in soft agar as well. Using both mouse xenografts and a transgenic model of HER2-induced breast cancer, we have initiated studies which will help to determine the role of CARMA3 to in vivo tumorigenesis. Collectively, this work reveals novel roles for the CARMA3 protein in development, GPCR and EGFR/HER2 signaling. It also suggests that CARMA3 is involved in EGFR/HER2 mediated tumorigenesis, possibly indicating a novel therapeutic target for use in treatment of cancer. ^

Functional data analysis approaches for genotype-phenotype association studies from next-generation sequencing

Next-generation DNA sequencing platforms can effectively detect the entire spectrum of genomic variation and is emerging to be a major tool for systematic exploration of the universe of variants and interactions in the entire genome. However, the data produced by next-generation sequencing technologies will suffer from three basic problems: sequence errors, assembly errors, and missing data. Current statistical methods for genetic analysis are well suited for detecting the association of common variants, but are less suitable to rare variants. This raises great challenge for sequence-based genetic studies of complex diseases.^ This research dissertation utilized genome continuum model as a general principle, and stochastic calculus and functional data analysis as tools for developing novel and powerful statistical methods for next generation of association studies of both qualitative and quantitative traits in the context of sequencing data, which finally lead to shifting the paradigm of association analysis from the current locus-by-locus analysis to collectively analyzing genome regions.^ In this project, the functional principal component (FPC) methods coupled with high-dimensional data reduction techniques will be used to develop novel and powerful methods for testing the associations of the entire spectrum of genetic variation within a segment of genome or a gene regardless of whether the variants are common or rare.^ The classical quantitative genetics suffer from high type I error rates and low power for rare variants. To overcome these limitations for resequencing data, this project used functional linear models with scalar response to develop statistics for identifying quantitative trait loci (QTLs) for both common and rare variants. To illustrate their applications, the functional linear models were applied to five quantitative traits in Framingham heart studies. ^ This project proposed a novel concept of gene-gene co-association in which a gene or a genomic region is taken as a unit of association analysis and used stochastic calculus to develop a unified framework for testing the association of multiple genes or genomic regions for both common and rare alleles. The proposed methods were applied to gene-gene co-association analysis of psoriasis in two independent GWAS datasets which led to discovery of networks significantly associated with psoriasis.^

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^

Single nucleotide polymorphisms (SNPs) associated with TGF-beta pathway and their significance in systemic sclerosis - A multilevel analysis

Systemic sclerosis (SSc) or Scleroderma is a complex disease and its etiopathogenesis remains unelucidated. Fibrosis in multiple organs is a key feature of SSc and studies have shown that transforming growth factor-β (TGF-β) pathway has a crucial role in fibrotic responses. For a complex disease such as SSc, expression quantitative trait loci (eQTL) analysis is a powerful tool for identifying genetic variations that affect expression of genes involved in this disease. In this study, a multilevel model is described to perform a multivariate eQTL for identifying genetic variation (SNPs) specifically associated with the expression of three members of TGF-β pathway, CTGF, SPARC and COL3A1. The uniqueness of this model is that all three genes were included in one model, rather than one gene being examined at a time. A protein might contribute to multiple pathways and this approach allows the identification of important genetic variations linked to multiple genes belonging to the same pathway. In this study, 29 SNPs were identified and 16 of them located in known genes. Exploring the roles of these genes in TGF-β regulation will help elucidate the etiology of SSc, which will in turn help to better manage this complex disease. ^

Disease risk factors and humoral antibody response to periodontal-associated pathogens: A model to evaluate periodontal disease in older adults

Periodontal diseases (PD) are infectious, inflammatory, and tissue destructive events which affect the periodontal ligament that surround and support the teeth. Periodontal diseases are the major cause of tooth loss after age 35, with gingivitis and periodontitis affecting 75% of the adult population. A select group of bacterial organisms are associated with periodontal pathogenesis. There is a direct association between oral hygiene and prevention of PD. The importance of genetic differences and host immune response capabilities in determining host, susceptibility or resistance to PD has not been established. This study examined the risk factors and serum (humoral) immune response to periodontal diseased-associated pathogens in a 55 to 80+ year old South Texas study sample with PD. This study sample was described by: age, sex, ethnicity, the socioeconomic factors marital status, income and occupation, IgG, IgA, IgM immunoglobulin status, and the autoimmune response markers rheumatoid factor (RF) and antinuclear antibody (ANA). These variables were used to determine the risk factors associated with development of PD. Serum IgG, IgA, IgM antibodies to bacterial antigens provided evidence for disease exposure.^ A causal model for PD was constructed from associations for risk factors (ethnicity, marital status, income, and occupation) with dental exam and periodontitis. The multiple correlation between PD and ethnicity, income and dental exam was significant. Hispanics of low income were least likely to have had a dental exam in the last year and most likely to have PD. The etiologic agents for PD, as evidenced by elevated humoral antibody responses, were the Gram negative microorganisms Bacteroides gingivalis, serotypes FDC381 and SUNYaBA7A1-28, and Wolinella recta. Recommendation for a PD prevention and control program are provided. ^

Hispanic incorporation and access to health care in Houston and Harris County, Texas: Adopting an Hispanic perspective to the Aday and Andersen model

This study examines Hispanic levels of incorporation and access to health care. Applying the Aday and Andersen framework for the study of access, the study examined the relationship between two levels of Hispanic incorporation into U.S. society, i.e., mainstream versus ethnic, and potential and realized measures of access to health care. Data for the study were drawn from a 1992 telephone survey of 600 randomly selected Hispanics in Houston and Harris County.^ The hypotheses tested were: (1) Hispanics who are incorporated into mainstream society are more likely to have better potential and realized access to health care than those who are incorporated into ethnic-group enclaves regardless of their socioeconomic status (SES), health status and health needs, and (2) there is no interaction between the levels of incorporation (mainstream or ethnic) and SES, health status, and health needs in predicting potential and realized access.^ The data analysis supported Hypothesis One for the two measures of potential access. The results of bivariate and multiple logistic regression analyses indicated that for Hispanics in Houston and Harris County, being in the "mainstream" incorporation category increased their potential access to care, having "health insurance" and a "regular place of care". For the selected measure of realized access, having a "regular check-up", the analysis did not demonstrate statistically significant differences in having a regular check-up among Hispanics incorporated in the ethnic or mainstream incorporation categories.^ Hypothesis Two, that there is no interaction between the levels of incorporation and socioeconomic characteristics, health status, and health needs in predicting potential and realized access among Hispanics was supported by the data. The results of the logistic regression analysis showed that, after adjusting for socioeconomic status, health status, and health needs, the association between "level of incorporation" and the two measures of potential access ("health insurance" and having a "usual place of care") was not modified by the control variables nor by their interaction with level of incorporation. That is, the effect of incorporation on Hispanics' health insurance coverage, and having a usual place of care, was homogenous across Hispanics with different SES and health status.^ The main research implication of this dissertation is the employment of a theoretical framework for the assessment of cultural factors essential to research on migrating heterogeneous subpopulations. It also provided strategies to solve practical and methodological difficulties in the secondary analyses of data on these populations. ^

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.^

Assessing and applying evidence-based interventions at the community level in India as a model policy to reduce neonatal mortality rates in Nigeria

Background. The United Nations' Millennium Development Goal (MDG) 4 aims for a two-thirds reduction in death rates for children under the age of five by 2015. The greatest risk of death is in the first week of life, yet most of these deaths can be prevented by such simple interventions as improved hygiene, exclusive breastfeeding, and thermal care. The percentage of deaths in Nigeria that occur in the first month of life make up 28% of all deaths under five years, a statistic that has remained unchanged despite various child health policies. This paper will address the challenges of reducing the neonatal mortality rate in Nigeria by examining the literature regarding efficacy of home-based, newborn care interventions and policies that have been implemented successfully in India. ^ Methods. I compared similarities and differences between India and Nigeria using qualitative descriptions and available quantitative data of various health indicators. The analysis included identifying policy-related factors and community approaches contributing to India's newborn survival rates. Databases and reference lists of articles were searched for randomized controlled trials of community health worker interventions shown to reduce neonatal mortality rates. ^ Results. While it appears that Nigeria spends more money than India on health per capita ($136 vs. $132, respectively) and as percent GDP (5.8% vs. 4.2%, respectively), it still lags behind India in its neonatal, infant, and under five mortality rates (40 vs. 32 deaths/1000 live births, 88 vs. 48 deaths/1000 live births, 143 vs. 63 deaths/1000 live births, respectively). Both countries have comparably low numbers of healthcare providers. Unlike their counterparts in Nigeria, Indian community health workers receive training on how to deliver postnatal care in the home setting and are monetarily compensated. Gender-related power differences still play a role in the societal structure of both countries. A search of randomized controlled trials of home-based newborn care strategies yielded three relevant articles. Community health workers trained to educate mothers and provide a preventive package of interventions involving clean cord care, thermal care, breastfeeding promotion, and danger sign recognition during multiple postnatal visits in rural India, Bangladesh, and Pakistan reduced neonatal mortality rates by 54%, 34%, and 15–20%, respectively. ^ Conclusion. Access to advanced technology is not necessary to reduce neonatal mortality rates in resource-limited countries. To address the urgency of neonatal mortality, countries with weak health systems need to start at the community level and invest in cost-effective, evidence-based newborn care interventions that utilize available human resources. While more randomized controlled studies are urgently needed, the current available evidence of models of postnatal care provision demonstrates that home-based care and health education provided by community health workers can reduce neonatal mortality rates in the immediate future.^

Bayesian estimation of multilevel item response model with missing data

This study proposed a novel statistical method that modeled the multiple outcomes and missing data process jointly using item response theory. This method follows the "intent-to-treat" principle in clinical trials and accounts for the correlation between outcomes and missing data process. This method may provide a good solution to chronic mental disorder study. ^ The simulation study demonstrated that if the true model is the proposed model with moderate or strong correlation, ignoring the within correlation may lead to overestimate of the treatment effect and result in more type I error than specified level. Even if the within correlation is small, the performance of proposed model is as good as naïve response model. Thus, the proposed model is robust for different correlation settings if the data is generated by the proposed model.^

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.