Pleiotropy refers to the shared effects of a gene or genes on multiple phenotypes, a major reason for genetic correlation between phenotypes. For example, for osteoporosis, bone mineral densities at different skeletal sites may share common genetic factors; thus, examining the shared effects of genes may enable more effective fracture treatments. To date, methods are not available for estimating and testing the pleiotropic effects of single nucleotide polymorphisms (SNPs) in genetic association studies. In this dissertation, we explore two types of methods to evaluate the SNP-specific pleiotropic effect based on multivariate techniques. First, we propose two approaches based on variance components (VC) analysis for family-based studies, which quantify and test the pleiotropic effect by examining the contribution of specific genetic marker(s) to polygenic correlation or covariance of traits. Second, we propose a multivariate linear regression approach for population-based studies with samples of families or unrelated subjects. This method partitions the specific effect of the marker(s) from phenotypic covariance. We evaluate the performance of our proposed methods in simulation studies, compare them to existing multivariate analysis methods and illustrate their application using real data to assess candidate SNPs for osteoporosis-related phenotypes in the Framingham Osteoporosis Study. In contrast to existing methods, our newly proposed approaches allow the quantification of pleiotropic effects. The bootstrap resampling percentile method is used to construct confidence intervals for statistical hypothesis testing. Simulation results suggest that the VC-based approaches are affected by the polygenic correlation level. The covariance analysis approach outperforms the VC-based approaches, with unbiased estimates and better power, which remain consistent regardless of the polygenic correlation. In addition, the covariance analysis approach is simple to implement and can be applied to both family data and genetically unrelated data. Using simulation, we also show that existing methods, such as MANOVA, can have high rejection rates when a SNP has a large effect on a single trait, which prevent us from using them for pleiotropic effect analysis. In summary, this dissertation introduces promising new approaches in multiple phenotypic models for SNP-specific pleiotropic effect.

Alcohol addiction may increase the risk of obesity due to shared genetic components. The Copine V (CPNE5) gene is involved in Ca2+ binding and may play an important role in the development of the central nervous system. This study tested the genetic associations of 77 single-nucleotide polymorphisms (SNPs) within the CPNE5 gene with alcohol dependence (AD) and obesity using a Caucasian sample - The Study of Addiction - Genetics and Environment (SAGE) sample (1066 AD cases and 1278 non-AD controls, 422 obese cases and 1395 non-obese controls). The Marshfield sample (1442 obese cases and 2122 non-obese controls) was used for replication of obesity. Multiple logistic regression analysis was performed using the PLINK software. In the SAGE sample, we identified 10 SNPs associated with AD and 17 SNPs associated with obesity (p < 0.05). Interestingly, 6 SNPs (rs9986517, rs9470387, rs3213534, rs10456444, rs3752482, and rs9470386) were associated with both AD (OR = 0.77, 0.77, 0.83, 0.84, 0.79 and 1.14, respectively; p = 9.72 × 10-5, 1.1 × 10-4, 4.09 × 10-3, 5.26 × 10-3, 1.59 × 10-2, and 3.81 × 10-2, respectively) and obesity (OR = 0.77, 0.77, 0.78, 0.77, 0.68 and 1.18, respectively; p = 2.74 × 10-3, 2.69 × 10-3, 2.45 × 10-3, 1.01 × 10-3, 5.18 × 10-3 and 3.85 × 10-2, respectively). In the Marshfield sample, rs3752480 was associated with obesity (p = 0.0379). In addition, four SNPs (rs9986517, rs10456444, rs7763347 and rs4714010) showed associations with obesity in the meta-analysis using both samples (p = 0.00493, 0.0274, 0.00346, and 0.0141, respectively). These findings provide the first evidence of common genetic variants in the CPNE5 gene influencing both the AD and obesity; and will serve as a resource for replication in other populations.

Oxidative stress is a documented factor in the pathogenesis of inflammation and cancer. Vitamin E with its antioxidant properties holds promise for use in clinical practice. There are two main forms of vitamin E, tocopherols and tocotrienols. Palm oil contains almost 70% of tocotrienols. Tocotrienols exerts its antiproliferative activity against malignant cells but not on normal cells. Tocotrienols play an important role in counteracting cellular inflammatory response secondary to oxidative stress, thus exerting an anticancer property. Tocotrienols mediate function of NF-kappa B, STAT3 (signal transduction and activators), and COX-2. In addition to its role as an antioxidant and anti-inflammatory agent, tocotrienols also mediate multiple cell cycle pathways. More work needs to be done on animal models and in genetic models of pancreatic cancer to gather more data to eventually consider phase III clinical trial in human subjects.

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