Matrix metalloproteinase-1 as a prognosticator in malignant glioma

The overall purpose of this study was to assess the relationship between the promoter region polymorphism (-2607 1G/2G) of matrix metalloproteinase-1 (MMP-1) polymorphism and outcome in brain tumor patients diagnosed with a primary brain tumor between 1994 and 2000 at The University of Texas M. D. Anderson Cancer Center. The MMP-1 polymorphism was genotyped for all brain tumor patients who participated in the Family Brain Tumor Study and for whom blood samples were available. Relevant covariates were abstracted from medical records for all cases from the original protocol, including information on demographics, tumor histology, therapy and outcome was obtained. The hypothesis was that brain tumor patients with the 2G allele have a poorer prognosis and shorter survival than brain tumor patients with the 1G allele. ^ Experimental Design: Genetic variants for the MMP-1 enzyme were determined by a polymerase chain reaction-restriction fragment length polymorphism assay. Comparison was made between the overall survival for cases with the 2G polymorphism and overall survival for cases with the 1G polymorphism using multivariable Cox Proportional-Hazard analysis, controlling for age, sex, Karnofsky Performance Scale (KPS), extent of surgery, tumor histology and treatment received. Kaplan-Meier and Cox Proportional-Hazard analyses were utilized to assess if the MMP-1 polymorphisms were related to overall survival. Results: Overall survival was not statistically significantly different between the 2G allele brain tumor patients and the 1G allele patients and there was no statistically significant difference between tumor types. ^ Conclusions: No association was found between MMP-1 polymorphisms and survival in patients with malignant gliomas. ^

Comparison of two statistical methods for rare variant association analysis

Studies have shown that rare genetic variants have stronger effects in predisposing common diseases, and several statistical methods have been developed for association studies involving rare variants. In order to better understand how these statistical methods perform, we seek to compare two recently developed rare variant statistical methods (VT and C-alpha) on 10,000 simulated re-sequencing data sets with disease status and the corresponding 10,000 simulated null data sets. The SLC1A1 gene has been suggested to be associated with diastolic blood pressure (DBP) in previous studies. In the current study, we applied VT and C-alpha methods to the empirical re-sequencing data for the SLC1A1 gene from 300 whites and 200 blacks. We found that VT method obtains higher power and performs better than C-alpha method with the simulated data we used. The type I errors were well-controlled for both methods. In addition, both VT and C-alpha methods suggested no statistical evidence for the association between the SLC1A1 gene and DBP. Overall, our findings provided an important comparison of the two statistical methods for future reference and provided preliminary and pioneer findings on the association between the SLC1A1 gene and blood pressure.^

Role of obesity, weight gain, physical activity, and polymorphisms in the mTOR pathway and the risk of renal cell carcinoma

The interplay between obesity, physical activity, weight gain and genetic variants in mTOR pathway have not been studied in renal cell carcinoma (RCC). We examined the associations between obesity, weight gain, physical activity and RCC risk. We also analyzed whether genetic variants in the mTOR pathway could modify the association. Incident renal cell carcinoma cases and healthy controls were recruited from the University of Texas MD Anderson Cancer Center in Houston, Texas. Cases and controls were frequency-matched by age (±5 years), ethnicity, sex, and county of residence. Epidemiologic data were collected via in-person interview. A total of 577 cases and 593 healthy controls (all white) were included. One hundred ninety-two (192) SNPs from 22 genes were available and their genotyping data were extracted from previous genome-wide association studies. Logistic regression and regression spline were performed to obtain odds ratios. Obesity at age 20, 40, and 3 years prior to diagnosis/recruitment, and moderate and large weight gain from age 20 to 40 were each significantly associated with increased RCC risk. Low physical activity was associated with a 4.08-fold (95% CI: 2.92-5.70) increased risk. Five single nucleotide polymorphisms (SNPs) were significantly associated with RCC risk and their cumulative effect increased the risk by up to 72% (95% CI: 1.20-2.46). Strata specific effects for weight change and genotyping cumulative groups were observed. However, no interaction was suggested by our study. In conclusion, energy balance related risk factors and genetic variants in the mTOR pathway may jointly influence susceptibility to RCC. ^

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.

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.

Instrumental variable method for the causal relationship between soluble receptor for advanced glycation end-products (sRAGE) and risk of pancreatic cancer

This thesis project is motivated by the potential problem of using observational data to draw inferences about a causal relationship in observational epidemiology research when controlled randomization is not applicable. Instrumental variable (IV) method is one of the statistical tools to overcome this problem. Mendelian randomization study uses genetic variants as IVs in genetic association study. In this thesis, the IV method, as well as standard logistic and linear regression models, is used to investigate the causal association between risk of pancreatic cancer and the circulating levels of soluble receptor for advanced glycation end-products (sRAGE). Higher levels of serum sRAGE were found to be associated with a lower risk of pancreatic cancer in a previous observational study (255 cases and 485 controls). However, such a novel association may be biased by unknown confounding factors. In a case-control study, we aimed to use the IV approach to confirm or refute this observation in a subset of study subjects for whom the genotyping data were available (178 cases and 177 controls). Two-stage IV method using generalized method of moments-structural mean models (GMM-SMM) was conducted and the relative risk (RR) was calculated. In the first stage analysis, we found that the single nucleotide polymorphism (SNP) rs2070600 of the receptor for advanced glycation end-products (AGER) gene meets all three general assumptions for a genetic IV in examining the causal association between sRAGE and risk of pancreatic cancer. The variant allele of SNP rs2070600 of the AGER gene was associated with lower levels of sRAGE, and it was neither associated with risk of pancreatic cancer, nor with the confounding factors. It was a potential strong IV (F statistic = 29.2). However, in the second stage analysis, the GMM-SMM model failed to converge due to non- concaveness probably because of the small sample size. Therefore, the IV analysis could not support the causality of the association between serum sRAGE levels and risk of pancreatic cancer. Nevertheless, these analyses suggest that rs2070600 was a potentially good genetic IV for testing the causality between the risk of pancreatic cancer and sRAGE levels. A larger sample size is required to conduct a credible IV analysis.^

CHARACTERIZING A NOVEL GENETIC LOCUS ASSOCIATED WITH FAMILIAL CO-OCCURRENCE OF THORACIC AORTIC ANEURYSMS AND INTRACRANIAL ANEURYSMS

The Mendelian inheritance of genetic mutations can lead to adult-onset cardiovascular disease. Several genetic loci have been mapped for the familial form of Thoracic Aortic Aneurysms (TAA), and many causal mutations have been identified for this disease. Intracranial Aneurysms (ICA) also show linkage heterogeneity, but no mutations have been identified causing familial ICA alone. Here, we characterized a large family (TAA288) with an autosomal dominant pattern of inherited aneurysms. It is intriguing that female patients predominantly present with ICA and male patients predominantly with TAA in this family. To identify a causal mutation in this family, a genome-wide linkage analysis was previously performed on nine members of this family using the 50k GenChips Hind array from Affymetrix. This analysis eventually identified a single disease-segregating locus, on chromosome 5p15. We build upon this previous analysis in this study, hypothesizing that a genetic mutation inherited in this locus leads to the sex-specific phenotype of TAA and ICA in this family First we refined the boundaries of the 5p15 disease linked locus down to the genomic coordinates 5p15: 3,424,465- 6,312,925 (GRCh37/hg19 Assembly). This locus was named the TAA288 critical interval. Next, we sequenced candidate genes within the TAA288 critical interval. The selection of genes was simplified by the relatively small number of well-characterized genetic elements within the region. Seeking novel or rare disease-segregating variants, we initially observed a single point alteration in the metalloproteinase gene ADAMTS16 fulfilling this criteria. This variant was later classified as a low-frequency population polymorphism (rs72647757), but we continued to explore the potential role of the ADAMTS16 as the cause of disease in TAA288. We observed that fibroblasts cultured from TAA288 patients consistently upregulated the expression of this gene more strongly compared to matched control fibroblasts when treated with the cytokine TGF-β1, though there was some variation in the exact nature of this expression. We also observed evidence that this protein is expressed at elevated levels in aortic aneurysm tissue from patients with mutations in the gene TGFBR2 and Marfan syndrome, shown by immunohistochemical detection of this protein.

Genetic variation in genes for the xenobiotic-metabolizing enzymes CYP1A1, EPHX1, GSTM1, GSTT1, and GSTP1 and susceptibility to colorectal cancer in Lynch syndrome.

Individuals with Lynch syndrome are predisposed to cancer due to an inherited DNA mismatch repair gene mutation. However, there is significant variability observed in disease expression likely due to the influence of other environmental, lifestyle, or genetic factors. Polymorphisms in genes encoding xenobiotic-metabolizing enzymes may modify cancer risk by influencing the metabolism and clearance of potential carcinogens from the body. In this retrospective analysis, we examined key candidate gene polymorphisms in CYP1A1, EPHX1, GSTT1, GSTM1, and GSTP1 as modifiers of age at onset of colorectal cancer among 257 individuals with Lynch syndrome. We found that subjects heterozygous for CYP1A1 I462V (c.1384A>G) developed colorectal cancer 4 years earlier than those with the homozygous wild-type genotype (median ages, 39 and 43 years, respectively; log-rank test P = 0.018). Furthermore, being heterozygous for the CYP1A1 polymorphisms, I462V and Msp1 (g.6235T>C), was associated with an increased risk for developing colorectal cancer [adjusted hazard ratio for AG relative to AA, 1.78; 95% confidence interval, 1.16-2.74; P = 0.008; hazard ratio for TC relative to TT, 1.53; 95% confidence interval, 1.06-2.22; P = 0.02]. Because homozygous variants for both CYP1A1 polymorphisms were rare, risk estimates were imprecise. None of the other gene polymorphisms examined were associated with an earlier onset age for colorectal cancer. Our results suggest that the I462V and Msp1 polymorphisms in CYP1A1 may be an additional susceptibility factor for disease expression in Lynch syndrome because they modify the age of colorectal cancer onset by up to 4 years.