DESIGN AND SYNTHESIS OF NOVEL SYNTHETIC ANTIOXIDANTS FOR THE TREATMENT OF OXIDATIVE STRESS RELATED DISEASES

Free radicals play an important role in many physiological processes that occur in the human body such as cellular defense responses to infectious agents and a variety of cellular signaling pathways. While at low concentrations free radicals are involved in many significant metabolic reactions, high levels of free radicals can have deleterious effects on biomolecules like proteins, lipids, and DNA. Many physiological disorders such as diabetes, ageing, neurodegenerative diseases, and ischemia-reperfusion (I/R) injury are associated with oxidative stress.1 In particular, the deleterious effects caused by I/R injury developed during organ transplantation, cardiac infarct, and stroke have become the main cause of death in the United States and Europe.1,2 In this context, we synthesized and characterized a series of novel indole-amino acid conjugates as potential antioxidants for I/R injury. The synthesis of indole-phenol conjugate compounds is also discussed. Phenolic derivatives such as caffeic acid, butylated hydroxytoluene (BHT), butylated hydroxyanisole (BHA), resveratrol, and its analogues are known for their significant antioxidative properties. A series of resveratrol analogues have been designed and synthesized as potential antioxidants. The radical scavenging mechanisms for potential antioxidants and assays for the in vitro evaluation of antioxidant activities are also discussed.

Genetic association studies under the population stratification, family pedigree and application to genome-wide association studies

This dissertation has three separate parts: the first part deals with the general pedigree association testing incorporating continuous covariates; the second part deals with the association tests under population stratification using the conditional likelihood tests; the third part deals with the genome-wide association studies based on the real rheumatoid arthritis (RA) disease data sets from Genetic Analysis Workshop 16 (GAW16) problem 1. Many statistical tests are developed to test the linkage and association using either case-control status or phenotype covariates for family data structure, separately. Those univariate analyses might not use all the information coming from the family members in practical studies. On the other hand, the human complex disease do not have a clear inheritance pattern, there might exist the gene interactions or act independently. In part I, the new proposed approach MPDT is focused on how to use both the case control information as well as the phenotype covariates. This approach can be applied to detect multiple marker effects. Based on the two existing popular statistics in family studies for case-control and quantitative traits respectively, the new approach could be used in the simple family structure data set as well as general pedigree structure. The combined statistics are calculated using the two statistics; A permutation procedure is applied for assessing the p-value with adjustment from the Bonferroni for the multiple markers. We use simulation studies to evaluate the type I error rates and the powers of the proposed approach. Our results show that the combined test using both case-control information and phenotype covariates not only has the correct type I error rates but also is more powerful than the other existing methods. For multiple marker interactions, our proposed method is also very powerful. Selective genotyping is an economical strategy in detecting and mapping quantitative trait loci in the genetic dissection of complex disease. When the samples arise from different ethnic groups or an admixture population, all the existing selective genotyping methods may result in spurious association due to different ancestry distributions. The problem can be more serious when the sample size is large, a general requirement to obtain sufficient power to detect modest genetic effects for most complex traits. In part II, I describe a useful strategy in selective genotyping while population stratification is present. Our procedure used a principal component based approach to eliminate any effect of population stratification. The paper evaluates the performance of our procedure using both simulated data from an early study data sets and also the HapMap data sets in a variety of population admixture models generated from empirical data. There are one binary trait and two continuous traits in the rheumatoid arthritis dataset of Problem 1 in the Genetic Analysis Workshop 16 (GAW16): RA status, AntiCCP and IgM. To allow multiple traits, we suggest a set of SNP-level F statistics by the concept of multiple-correlation to measure the genetic association between multiple trait values and SNP-specific genotypic scores and obtain their null distributions. Hereby, we perform 6 genome-wide association analyses using the novel one- and two-stage approaches which are based on single, double and triple traits. Incorporating all these 6 analyses, we successfully validate the SNPs which have been identified to be responsible for rheumatoid arthritis in the literature and detect more disease susceptibility SNPs for follow-up studies in the future. Except for chromosome 13 and 18, each of the others is found to harbour susceptible genetic regions for rheumatoid arthritis or related diseases, i.e., lupus erythematosus. This topic is discussed in part III.