Objective: To determine the role of candidate gene polymorphisms in patients who sustained myocardial infarction at a young age and examine their relationship, if any, to risk factors. Since angiotensin II is known to play a pathophysiological role at the myocardial and vascular level, the genes to be studied are those regulating the renin angiotensin system and tissue metabolism. Design: The risk factors and genetic profile is described in 117 young Indians with myocardial infarction recruited over a period of thirty months (Dec 1997 - Jun 1999). Controls comprised 80 normal subjects with no clinical evidence of coronary heart disease (CHD) and with a normal effort response. The key features of this study are the selection of young subjects with myocardial infarction, (mean age 43 ± 6.8 years) in whom the possibility of a genetic basis for the disease was felt to be more likely since the confounding effect of age as a risk factor was reduced. Setting: Patients recruited 3 -12 months after myocardial infarction from Addington Hospital, Durban. This hospital subserves the Indian community in the north of Durban. The majority of patients were from the Phoenix settlement area. Results: 1. The clinical profile of the young Indian with myocardial infarction is a young man, slightly overweight with a high prevalence of risk factors, particularly smoking and diabetes, coupled with sedentary behaviour and risk-prone dietary patterns characterised by high red meat intake and low fruit and vegetable consumption, resulting in increased BMI and W/H ratios. 2. There were no differences in the patterns of gene polymorphism in the reninangiotensin system between the study and control groups. This finding extended across all candidate gene loci studied i.e. those involving aldosterone, G-protein, TGF-B and homocysteine metabolism. Serum triglycerides, haemoglobin AlC and urine microalbumin levels were elevated in the probands together with low HDL-C levels (p = 0.001). 3. A striking finding of this study was the substantial proportion of patients found to have diabetes mellitus, totalling 47% of the proband group. Of the 53 diabetic patients, (45 males and 8 females) four (3 males, 1 female) had impaired glucose tolerance. Cigarette smoking, a positive family history of hypertension/diabetes and a family history for premature CHD emerged as important risk predictors for MI. Conclusion: This study, the first to report candidate gene polymorphisms in young Indians with coronary heart disease, has shown no obvious association between the genetic loci studied and acute myocardial infarction. Instead a high prevalence of risk factors, particularly smoking and diabetes mellitus, coupled with coronary-prone behavioural patterns was observed. In the light of these findings, genome-wide screening of unaffected siblings of subjects with early onset CHD cannot be recommended in this population until common polymorphisms can be clearly identified as risk factors. Indeed this study again supports the dire need for early, school level, education in behavioural lifestyle patterns and disease predisposition. The Indian community is a very high-risk group who should be targeted, not for secondary, but for primordial disease prevention measures. The study does not rule out the role of other candidate gene polymorphisms in the pathogenesis of CHD in these subjects. The high prevalence of diabetes and insulin resistance suggests that studies of genes regulating glucose and lipid metabolism should be pursued. Such candidate genes should include genes for lipoprotein lipase and paraoxonase polymorphisms which may explain the dyslipidaemia patterns in this group. / Thesis (Ph.D.)-University of Natal, Durban, 2000.

Gene expression is regulated by local genetic sequence, and researchers have identified thousands of common genetic variants in the human population that associate with altered gene expression. These expression quantitative trait loci (eQTLs) often co-localize with genome wide association study (GWAS) loci, suggesting that they may hold the key to understanding genetic effects on human phenotype and cause disease. eQTLs are enriched in cis-regulatory elements, suggesting that many affect gene expression via non-coding mechanisms. However, many of the discovered loci lie in noncoding regions of the genome for which we lack understanding, and determining their mechanisms of action remains a challenge. To complicate matters further, genetic variants may have varied effects in different tissues or under different environmental conditions. The research presented here uses statistical methods to investigate genetic variants’ mechanisms of actions and context specificity. In Chapter 1, we introduce eQTLs and discuss challenges associated with their discovery and analysis. In Chapter 2, we investigate cross-tissue eQTL and gene expression patterns, including for GWAS genes. We find that eQTL effects show increasing, decreasing, and non-monotonic relationships with gene expression levels across tissues, and we observe higher eQTL effects and eGene expression for GWAS genes in disease-relevant tissues. In Chapter 3, we use the natural variation of transcription factor activity among tissues and between individuals to elucidate mechanisms of action of eQTL regulatory variants and understand context specificity of eQTL effects. We discover thousands of potential transcription factor mechanisms of eQTL effects, and we investigate the transcription factors’ roles with orthogonal datasets and experimental approaches. Finally, in Chapter 4, we focus on a locus implicated in coronary artery disease risk and unravel the likely causal variants and functional mechanisms of the locus’s effects on gene expression and disease. We confirm the locus’s colocalization with an eQTL for the LIPA gene, and using statistical, functional, and experimental approaches, we highlight two potential causal variants in partial linkage disequilibrium. Taken together, this work develops a framework for understanding eQTL context variability and highlights the complex genetic and environmental contributions to gene regulation. It provides a deeper understanding of gene regulation and of genetic and environmental contributions to complex traits and disease, enabling future research surrounding the context variability of genetic effects on gene expression and disease.

[Truncated abstract] This thesis investigates novel methods for the genetic association analysis of haplotype data in samples of unrelated individuals, and applies these methods to the analysis of coronary heart disease and related phenotypes. Determining the inheritance pattern of genetic variants in studies of unrelated individuals can be problematic because family members of the studied individuals are often not available. For the analysis of individual genetic loci, no problem arises because the unit of interest is the observed genotype. When the unit of interest is the linear combination of alleles along one chromosome, inherited together in a haplotype, it is not always possible to determine with certainty the inheritance pattern, and therefore statistical methods to infer these patterns must be adopted. Due to genotypic heterozygosity, mutliple possible haplotype configurations can often resolve an individual's genotype measures at multiple loci. When haplotypes are not known, but are inferred statistically, an element of uncertainty is thus inherent which, if not dealt with appropriately, can result in unreliable estimates of effect sizes in an association setting. The core aim of the research described in this thesis was to develop and implement a general method for haplotype-based association analysis using multiple imputation to appropriately deal with uncertainty haplotype assignment. Regression-based approaches to association analysis provide flexible methods to investigate the influence of a covariate on a response variable, adjusting for the effects of other variables including interaction terms. ... These methods are then applied to models accommodating binary, quantitative, longitudinal and survival data. The performance of the multiple imputation method implemented was assessed using simulated data under a range of haplotypic effect sizes and genetic inheritance patterns. The multiple imputation approach performed better, on average, than ignoring haplotypic uncertainty, and provided estimates that in most cases were similar to those observed when haplotypes were known. The haplotype association methods developed in this thesis were used to investigate the genetic epidemiology of cardiovascular disease, utilising data for the cholesteryl ester transfer protein gene (CETP), the hepatic lipase (LIPC) gene and the 15- lipoxygenase (ALOX15) gene on a total of 6,487 individuals from three Western Australian studies. Results of these analyses suggested single nucleotide polymorphisms (SNPs) and haplotypes in the CETP gene were associated with increased plasma high-density lipoprotein cholesterol (HDL-C). SNPs in the LIPC gene were also associated with increased HDL-C and haplotypes in the ALOX15 gene were associated with risk of carotid plaque among individuals with premature CHD. The research presented in this thesis is both novel and important as it provides methods for the analysis of haplotypic associations with a range of response types, while incorporating information about haplotype uncertainty inherent in populationbased studies. These methods are shown to perform well for a range of simulated and real data situations, and have been written into a statistical analysis package that has been freely released to the research community.