Genome-wide mapping of susceptibility genes was conducted in two complex disorders of hypertension and epilepsy, allowing the dissection of the genetic architecture of these common diseases and related quantitative traits. The study performed comprehensive genetic analyses in a genome-wide scale, using different structure of data – sib-pairs and case-control samples. To identify genes influencing hypertension and blood pressure, a combined linkage and association study was conducted using over half a million SNPs genotyped in 328 siblings. Regions of significant linkage were identified for blood pressure traits on chromosomes 2q22.3 and 5p13.2, respectively. Further family-based association analysis of the linkage peak on chromosome 5 yielded a significant association (rs1605685, P < 7  10-5) for hypertension. One candidate gene, PDC, was replicated in the family-based association tests. A two-stage genome-wide association study (GWAS) was performed in a total of 1,087 cases and 3,444 controls, to identify common susceptibility variants of epilepsy in Chinese. The combined analysis identified two association signals in CAMSAP1L1, rs2292096 [G] (P=1.0×10-8, OR =0.63) and rs6660197 [T] (P=9.9×10-7, OR=0.69), which are highly correlated, achieving genome-wide significance. One SNP (rs9390754, P = 1.7 × 10-5) in GRIK2 was refined as a previously-implicated association. In addition to SNPs, the assessment of CNVs in GWAS was performed, which could provide valuable clues to discover genes contributing to the heritability of epilepsy. A genome-wide scan for epilepsy through the use of DNA pooling also provides an alternative approach to reducing the substantial cost and thus increase efficiency in large-scale genetic association studies. The genome-wide mapping studies in families and unrelated individuals are complementary and together offer a comprehensive catalog of common variations and structural variants implicated for both quantitative and qualitative traits. / published_or_final_version / Psychiatry / Doctoral / Doctor of Philosophy

published_or_final_version / Medicine / Doctoral / Doctor of Philosophy

Although essential hypertension has long been recognized to involve a strong genetic predisposition, the genes that increase susceptibility remain virtually unknown. With recent advances in molecular biology and statistical methods, it has become feasible to study candidate genes which may contribute to the pathogenesis of essential hypertension in humans. We identified the polymorphisms of five genes by applying a micoarray genotyping system for multiplex analysis of a panel of single nucleotide polymorphisms (SNPs) in genes involved in the regulation of blood pressure, then determined whether specific SNPs in genes were related to blood pressure in Hong Kong Chinese. / Based on the study results, we conclude that blood pressure levels are determined by complex interactions between genetic and environmental factors. The AGT gene 235T and the DD1R gene -48G alleles strongly predicted the development of hypertension in Hong Kong Chinese. The variants of the DD2R gene and the UCP1 gene A-3826G variant only weakly impacted upon blood regulation. However, the three variants of the INSR gene and the L10F variant of the AGT gene were not detected in Hong Kong Chinese. Although success in identifying single genes contributing to hypertension has been limited, the use of intermediate phenotypes and dense mapping of candidate genes shows the influence of gene-gene interaction on hypertension or obesity-related hypertension in our hypertensive families of Hong Kong Chinese. / Families were recruited if the proband was found to have hypertension and had siblings resident in Hong Kong. We identified 126 families with at least one hypertensive sibling. A total of 434 siblings were studied. All subjects underwent clinical and biochemical investigation to exclude those with either secondary hypertension, impaired glucose tolerance or type 2 diabetes mellitus. / Twelve SNPs in five candidate genes, which included the M235T, T174M, G-217A and L10F polymorphisms of the angiotensinogen gene (AGT); A-48G polymorphism of the dopamine D1 receptor gene (DD1R); the TaqI A, -141C Ins/Del and A-241G polymorphisms of the dopamine D2 receptor gene (DD2R); Phe382Val, Lys460Glu, and Gly1008Val polymorphisms of the insulin receptor gene (INSR); and the A-3826G polymorphism of the uncoupling protein 1 gene (UCP1). / by Fang Yujing. / "January 2006." / Adviser: Brian Tomlinson. / Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6298. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 148-181). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

研究背景和目的 / 高血壓和糖尿病是人群中常見的疾病，兩者常共同存在，其共存的病理生理機制非常複雜，其中腎素血管景張素系統功能紊亂起重要作用。多個研究表明血管緊張素轉化晦抑制劑和血管緊張素II 1 型受體阻滯劑通過調節不同基因的表達，發揮其保護心血管和腎臟功能的效用。然而，目前仍缺乏遠兩類藥物影響全基因表達譜的全面調查。因此，本研究應用全基因表達譜晶片技術，檢測分析了高血壓和糖尿病並發的病人在服用安慰劑、雷米普利(ramipril)和替米沙坦(telmisartan)後的全基因表達譜的變化，從而全面評估了血管緊張素轉化臨抑制劑和血管繁張素II 1 型受體阻滯劑對相關基因的轉錄調控作用。 / 方法 / 11 名患有高血壓和糖尿病的病人(男性5 名)在服用安慰劑最少2 星期后，以隨機吹序接受為期各6 星期的雷米普利和替米沙坦治療，並分別在安慰劑期和2 個藥物治療期結束后提取心A 進行全基因表達譜分析。 / 結果 / 與服用安慰劑時的全基因表達譜相比，雷米普利治療后有267 個基因的表達降低， 99 個基因的表達增強。表達差異幅度為-2.0 至1.3 (P < 0.05) 。表達下降的基因主要與血管平滑肌收縮、炎症反應和氧化壓力相關。表達增強的基因主要與心血管炎症反應負調節相關。基因共表達網絡分析表明， 2 個共表達基因組與雷米普利的降血壓作用相闕， 3 個共表達基因組與肥胖相關。 / 與服用安慰劑時的全基因表達譜相比， 替米拉)、坦治療后有55 個基因表達降低， 158 個基因的表達增強。表達差異幅度為-1. 9 至1.3 (P < 0.05) 。表達增強的基因主要與脂質代謝、糖代謝和抗炎症因子作用相關。基因共表達網絡分析表明， 2 個共表達基因組與替米沙坦對24 小時舒張壓負荷量的作用相關， 2 個共表達基因組則與總膽固醇， 低密度脂蛋白膽固醇和C 反應蛋白相關。 / 結論 / 本論文描述了高血壓和2 型糖尿病病患全基因組表達的總體模式及經藥物治療後表達譜的相應改變， 為今後進一步研究腎素血管緊張素系統抑制劑和高血壓、糖尿病發展進程的相互作用提供了方向。 / Background and aim: Pathophysiological mechanisms underpinning the coexistence of hypertension and type 2 diabetes are complex systemic responses involving dysregulation of the renin-angiotensin system (RAS). We conducted this study to investigate the genome wide gene expression changes in patients with both hypertension and diabetes at three treatment stages, including placebo, ramipril and telmisartan. This study aimed to obtain a panoramic view of interactions between gene transcription and antihypertensive therapy by RAS inhibition. / Methods: 11 diabetic patients (S men) with hypertension were treated with placebo for at least 2 weeks followed by 6 weeks randomised crossover treatment with ramipril Smg daily and telmisartan 40mg daily, respectively. Total RNA were extracted from leukocytes at the end of placebo and each treatment period, and were hybridized to the whole transcript microarray. The limma package for R was used to identify differentially expressed genes between placebo and the 2 active treatments. The weighted gene coexpression network analysis (WGCNA) was applied to identify groups of genes (modules) highly correlated to a common biological function in pathogenesis and progression of hypertension and diabetes. / Results: There were 267 genes down-regulated and 99 genes up-regulated with ramipril. Fold changes of gene expression were ranged from -2.0 to 1.3 (P < 0.05). The down-regulated genes were involved in vascular signalling pathways responsible for vascular smooth muscle contraction, inflammation and oxidative stress. The up-regulated genes were associated with negative regulation of cardiovascular inflammation. The WGCNA identified 17 coexpression gene modules related to ramipril. The midnight blue (57 genes, r < -0.44, P < 0.05) and magenta (190 genes, r < -0.44, P < 0.05) modules were significantly correlated to blood pressure differences between placebo and ramipril. / There were 55 genes down-regulated and 158 genes up-regulated with telmisartan. Fold changes of gene expression were ranged from -1.9 to 1.3 (P < 0.05). The down-regulated genes were mainly associated with cardiovascular inflammation and oxidative stress. The up-regulated genes were associated with lipid and glucose metabolism and anti-inflammatory actions. The WGCNA identified 8 coexpression gene modules related to telmisartan. The black (56 genes, r = 0.46, P = 0.03) and turquoise (1340 genes, r = -0.48, P = 0.02) modules were correlated with diastolic blood pressure load. The blue (1027 genes) module was enriched with genes correlated with total cholesterol (r = - 0.52, P = 0.01), LDL-C (r = - 0.58, P = 0.004), and hsCRP (r = -0.57, P = 0.006). The green module (272 genes) was significantly correlated with LDL-C (r = - 0.44, P = 0.04) and hsCRP (r = - 0.59, P = 0.004). / Conclusion: Genome wide gene expression profiling in this study describes the general pattern and treatment responses in patients with hypertension and type 2 diabetes, which suggests future directions for further investigations on the interaction between actions of the RAS blockers and disease progression. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Deng, Hanbing. / "December 2011." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 198-256). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Declaration --- p.i / Publications --- p.ii / Abstract --- p.iv / 論文摘要 --- p.vi / Acknowledgements --- p.viii / Table of Contents --- p.x / List of tables --- p.xiv / List of figures --- p.xv / List of appendices --- p.xvii / List of abbreviations --- p.xviii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Epidemiology --- p.6 / Chapter 1.2.1 --- Epidemiology of hypertension --- p.9 / Chapter 1.2.2 --- Epidemiology of type 2 diabetes --- p.10 / Chapter 1.3 --- Aetiology --- p.13 / Chapter 1.3.1 --- Ageing --- p.13 / Chapter 1.3.1.1 --- Age-induced artery stiffness --- p.14 / Chapter 1.3.1.2 --- Age-related endothelial dysfunction --- p.14 / Chapter 1.3.2 --- The renin-angiotensin system (RAS) --- p.16 / Chapter 1.3.2.1 --- The local RAS --- p.20 / Chapter 1.3.2.2 --- The RAS and insulin resistance --- p.22 / Chapter 1.3.2.3 --- The RAS and inflammation --- p.26 / Chapter 1.3.2.4 --- The RAS and oxidative stress --- p.28 / Chapter 1.3.3 --- Obesity --- p.31 / Chapter 1.3.3.1 --- Obesity and renin-angiotensin system (RAS) --- p.33 / Chapter 1.3.3.2 --- Obesity and insulin resistance --- p.36 / Chapter 1.3.3.3 --- Obesity and oxidative stress --- p.38 / Chapter 1.3.3.4 --- Obesity and sympathetic nervous system (SNS) --- p.38 / Chapter 1.4 --- Pharmacogenomics of antihypertensive therapy --- p.39 / Chapter 1.4.1 --- Angiotensin-converting enzyme inhibitors (ACEIs) --- p.41 / Chapter 1.4.2 --- Angiotensin II type 1 receptor blockers (ARBs) --- p.43 / Chapter Chapter 2 --- Aim --- p.59 / Chapter Chapter 3 --- Methods --- p.60 / Chapter 3.1 --- Subjects --- p.60 / Chapter 3.1.1 --- Subject recruitment protocol --- p.60 / Chapter 3.1.2 --- Definition of type 2 diabetes --- p.62 / Chapter 3.1.3 --- Definition of obesity --- p.62 / Chapter 3.1.4 --- Definition of dyslipidaemia --- p.63 / Chapter 3.2 --- Study design and procedure --- p.64 / Chapter 3.2.1 --- Blood pressure assessments --- p.65 / Chapter 3.2.2 --- Anthropometric measurements --- p.68 / Chapter 3.2.3 --- Medical history, life style and side effect evaluation --- p.68 / Chapter 3.2.4 --- RNA isolation --- p.68 / Chapter 3.2.5 --- RNA quality assessment --- p.70 / Chapter 3.2.6 --- Oligonucleotide microarrays --- p.71 / Chapter 3.2.7 --- DNA extraction --- p.75 / Chapter 3.2.8 --- Biomedical measurements --- p.76 / Chapter 3.2.8.1 --- Glycosylated haemoglobin Alc (HbA₁c) --- p.77 / Chapter 3.2.8.2 --- Fasting plasma glucose (FP G) --- p.77 / Chapter 3.2.8.3 --- Fasting insulin --- p.77 / Chapter 3.2.8.4 --- Plasma urate --- p.77 / Chapter 3.2.8.5 --- High sensitive C-reactive protein (hsCRP) --- p.78 / Chapter 3.2.8.6 --- Fasting plasma triglycerides (TG) --- p.78 / Chapter 3.2.8.7 --- Fasting plasma cholesterols --- p.78 / Chapter 3.2.8.8 --- Renal and liver functions --- p.78 / Chapter 3.2.8.9 --- Urinary parameters --- p.79 / Chapter 3.3 --- Statistical Analysis --- p.79 / Chapter 3.3.1 --- Statistical analysis of clinical and biomedical data --- p.79 / Chapter 3.3.2 --- Analysis of microarray data --- p.80 / Chapter 3.3.2.1 --- Raw data assessment --- p.80 / Chapter 3.3.2.2 --- Data normalisation --- p.92 / Chapter 3.3.2.3 --- Data filtering --- p.96 / Chapter 3.3.2.4 --- Linear models for assessment of differential expression --- p.96 / Chapter 3.3.2.5 --- Weighted gene coexpression network analysis --- p.101 / Chapter 3.3.2.6 --- Network visualisation and gene ontology analysis --- p.102 / Chapter 3.3.3 --- Sample size calculation --- p.103 / Chapter Chapter 4 --- Results --- p.104 / Chapter 4.1 --- Demographic and biomedical characteristics at baseline --- p.104 / Chapter 4.1.1 --- Hypertension and diabetes status at baseline --- p.108 / Chapter 4.1.2 --- Prevalence of dyslipidaemia --- p.108 / Chapter 4.1.3 --- Prevalence of obesity --- p.109 / Chapter 4.1.4 --- Prevalence of metabolic syndrome --- p.109 / Chapter 4.1.5 --- Inflammation markers --- p.110 / Chapter 4.2 --- Blood pressure response to the RAS blockers --- p.110 / Chapter 4.2.1 --- Clinic blood pressure --- p.110 / Chapter 4.2.2 --- 24-hour ambulatory blood pressure --- p.112 / Chapter 4.3 --- Biomedical characteristics --- p.118 / Chapter 4.4 --- Compliance, side effects and adverse events --- p.120 / Chapter 4.5 --- Gene expression differences between treatments --- p.121 / Chapter 4.5.1 --- Gene expression differences between placebo and ramipril --- p.121 / Chapter 4.5.1.1 --- Expression changes in genes related to regulation of transcription with ramipril --- p.122 / Chapter 4.5.1.2 --- Expression changes with ramipril in genes related to molecular mechanism of cardiovascular changes in hypertension --- p.125 / Chapter 4.5.1.3 --- Expression changes in genes related to blood pressure with ramipril --- p.128 / Chapter 4.5.1.4 --- Expression changes in genes related to fatty acid metabolism with ramipril --- p.130 / Chapter 4.5.1.5 --- Expression changes in genes related to inflammation with ramipril --- p.130 / Chapter 4.5.1.6 --- Expression changes in genes related to oxidative stress with ramipril --- p.133 / Chapter 4.5.1.7 --- Power estimation --- p.133 / Chapter 4.5.2 --- Gene expression differences between placebo and telmisartan --- p.135 / Chapter 4.5.2.1 --- Changes in regulation oftranscription with telmisartan --- p.137 / Chapter 4.5.2.2 --- Expression changes in genes related to glucose metabolism with telmisartan --- p.141 / Chapter 4.5.2.3 --- Expression changes in genes related to lipid metabolism with telmisartan --- p.143 / Chapter 4.5.2.4 --- Expression changes in genes related to inflammation with telmisartan --- p.143 / Chapter 4.5.2.5 --- Power estimation --- p.145 / Chapter 4.5.3 --- WGCNA for comparison between placebo and ramipriI --- p.147 / Chapter 4.5.3.1 --- Midnight blue module and clinical responses to ramipril --- p.152 / Chapter 4.5.3.2 --- Magenta module and blood pressure responses to ramipril --- p.154 / Chapter 4.5.3.3 --- Yellow module and clinical responses to ramipril --- p.158 / Chapter 4.5.3.4 --- Red module and clinical responses to ramipril --- p.161 / Chapter 4.5.3.5 --- Salmon module and clinical responses to ramipril --- p.163 / Chapter 4.5.4 --- WGCNA for comparison between placebo and telmisaItan --- p.168 / Chapter 4.5.4.1 --- Diastolic blood pressure load and gene coexpression modules --- p.168 / Chapter 4.5.4.2 --- Lipids, hsCRP and gene coexpression modules --- p.172 / Chapter Chapter 5 --- Discussion --- p.176 / Chapter 5.1 --- Gene expression changes related to ramipril --- p.177 / Chapter 5.1.1 --- Gene expression changes and blood pressure reduction by ramipri1 --- p.177 / Chapter 5.1.2 --- Gene expression changes and vascular protection by ramipri1 --- p.181 / Chapter 5.1.3 --- Obesity and gene expression changes by ramipril --- p.183 / Chapter 5.2 --- Gene expression changes related to telmisartan --- p.185 / Chapter 5.2.1 --- Blood pressure and coexpressed gene modules with telmisartan --- p.185 / Chapter 5.2.2 --- Lipid metabolism and gene expression changes by telmisartan --- p.187 / Chapter 5.2.3 --- Glucose metabolism and gene expression changes by telmisartan --- p.189 / Chapter 5.2.4 --- hsCRP and gene expression changes by telmisartan --- p.190 / Chapter 5.3 --- Limitations of this study and future directions of research --- p.191 / Chapter Chapter 6 --- Conclusion --- p.194 / References --- p.198 / Appendices --- p.257