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廣泛的區域內和跨民族的轉錄變化反映了人類的適應和自然選擇。基因表達是轉化基因組信息為功能基因產品 - 蛋白質的主要機制。異常基因的表達和疾病的發病機制有關。基因組革命提供了獨特的機會為複雜的人類轉錄組進行全面的研究。轉錄分析需要複雜的生物信息學方法。在技術角度，一個實證模型用了哺乳動物基因組中內含子長度幾何尾分佈的定律準確地確定剪接交界處和非唯一映射讀取的位置。這種方法在處理非唯一映射讀取比BWA更好。這方法還比其他工具檢測出更多已經實驗證實的剪接交界處。核糖核酸測序首先用於北京漢人和西歐之間的表達表型與的轉錄變化的詳盡研究。民族的具體剪接交界處被發現。此外，民族的具體特點體現在相對異構體的豐度差。最後，這分子表型剪接頻譜的變化在不同種族之間的不同表明了另一個描繪種族多樣性的方法，核糖核酸測序還被用於探索的一種複雜的疾病：二型糖尿病的分子異常。二型糖尿病表現在廣泛不同的基因表達。（1）這研究證實先前公佈的全基因組關聯研究;（2）改善策劃不佳的位點和（3）發現新型2型糖尿病相關的基因。本研究通過整合各種改變的信號，並在一個高度可信的基因 - 基因相互作用網絡進行解釋，增強表達異常在2型糖尿病的認識。在更廣泛的69×79的情況下，對照組的結果進行了驗證。本研究增強表達異常在2型糖尿病的認識。 / Extensive intra- and inter- ethnic transcriptome variation reflects human adaptation and natural selection. Gene expression is the primary mechanism that translates genome information into functional gene product that lead to physiological phenotypes. Aberrant gene expression has been associated to the pathogenesis of diseases. The genome revolution has offered unique opportunity for a comprehensive interrogation of the complexity of human transcriptome. Analysis of transcriptome using RNA-Seq requires sophisticated bioinformatics approach. In a technical perspective, an empirical model based on the geometric-tail distribution of intron lengths in mammalian genome was developed to accurately determine splice junctions from junction reads and locations of non-uniquely mapped reads. Such method handles non-uniquely mapped reads better than BWA. The method can also detect more experimentally confirmed splice junction than other tools. Expressional phenotyping was employed to explore global transcriptomic variation between Beijing Han Chinese and Western European. In addition to inter-ethnic variations in gene expression, ethnic specific splice juctions were found. Further, ethnic specific trait manifests in differential relative isoform abundance. Lastly, such spectrum of variations was different between different ethnic groups, suggesting alternative splicing as another molecular phenotype that delineates ethnic diversity. Expressional phenotyping was then used in a case-control study to explore the molecular abnormalities of a complex disease: Type 2 Diabetes (T2DM). T2DM manifested in wide-spread repression of gene expression. The study (1) confirmed previously reported Genome-wide Association Study (GWAS) loci; (2) curated poorly characteriezed GWAS loci and (3) discovered novel T2DM associated genes. By integrating various alteration signals and interpretation performed in a highly confident gene-gene interaction network, this study augmented the understanding of expressed abnormalities in T2DM. The results were validated in a broader 69 x 79 case-control group. / Detailed summary in vernacular field only. / Li, Jing Woei. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 118-130). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.v / 中文擇要 --- p.vi / Thesis/Assessment Committee --- p.ix / Acknowledgement --- p.ix / List of figures --- p.x / List of tables --- p.xii / List of Abbreviations --- p.xiii / Scientific contributions --- p.xv / List of Publication(s) related to this thesis --- p.xvi / Conference presentations --- p.xvii / Chapter Chapter 1: --- Introduction and Literature Reviews --- p.1 / Chapter 1.1 --- The variable human transcriptome --- p.1 / Chapter 1.2 --- Significance of variation in gene expression and transcript variants --- p.2 / Chapter 1.3 --- Transcriptomic study in a technological perspective --- p.8 / Chapter 1.3.1 --- Microarray: Probing what was designed to be probed --- p.8 / Chapter 1.3.2 --- RNA-Seq: the ab initio decoder of biological sequences --- p.9 / Chapter 1.4 --- Analysis of RNA-Seq data --- p.10 / Chapter 1.4.1 --- The bioinformatics challenges prevail --- p.10 / Chapter 1.4.2 --- Identifying changes in gene expression --- p.16 / Chapter 1.4.3 --- Identifying splice site, quantification of isoform level expression --- p.17 / Chapter 1.5 --- Conclusion --- p.19 / Chapter 1.6 --- Aims of this study --- p.20 / Chapter 1.6.1 --- Splice junction determination --- p.20 / Chapter 1.6.2 --- Expressional phenotyping in ethnical context --- p.20 / Chapter 1.6.3 --- Expressional phenotyping in a disease context --- p.20 / Chapter Chapter 2: --- Detection of splicing events --- p.21 / Chapter 2.1 --- Abstract --- p.21 / Chapter 2.2 --- Introduction --- p.22 / Chapter 2.3 --- Methods and workflow --- p.25 / Chapter 2.4 --- Algorithm --- p.29 / Chapter 2.5 --- Geometric-tail distribution --- p.32 / Chapter 2.6 --- Insert-size distribution --- p.33 / Chapter 2.7 --- Multiread analysis --- p.34 / Chapter 2.7.1 --- GT model probably places multiread more accurately than BWA --- p.35 / Chapter 2.8 --- Splice-site comparison --- p.37 / Chapter 2.8.1 --- GT model discovers more experimentally confirmed splice junction --- p.37 / Chapter 2.8.2 --- GT model is highly accurate --- p.39 / Chapter 2.9 --- Discussion --- p.40 / Chapter 2.10 --- Limitation --- p.40 / Chapter Chapter 3: --- Transcriptomic variation in a ethnicity context --- p.41 / Chapter 3.1 --- Abstract --- p.41 / Chapter 3.2 --- Introduction --- p.42 / Chapter 3.3 --- Materials and Methods --- p.46 / Chapter 3.3.1 --- HapMap lymphoblastoid cell-lines --- p.46 / Chapter 3.3.2 --- Sequenced samples --- p.48 / Chapter 3.3.3 --- Paired-end RNA-Seq, dataset and reads processing --- p.48 / Chapter 3.3.4 --- Genome reference and annotation --- p.49 / Chapter 3.3.5 --- Strategies for reads mapping --- p.49 / Chapter 3.3.6 --- Pathway and Gene Ontology analysis --- p.50 / Chapter 3.3.7 --- Differential gene expression analysis --- p.50 / Chapter 3.3.8 --- Ethnic specific splice junction --- p.51 / Chapter 3.3.9 --- Junction sites saturation analysis --- p.51 / Chapter 3.3.10 --- Ethnical novel transcribed regions --- p.52 / Chapter 3.3.11 --- Isoform dynamics and meta-analysis --- p.53 / Chapter 3.4 --- Result --- p.54 / Chapter 3.4.1 --- Paired-end RNA-Seq --- p.54 / Chapter 3.4.2 --- Differential gene expression and meta-analysis --- p.56 / Chapter 3.4.3 --- Ethnic specific splice junction is rare --- p.58 / Chapter 3.4.4 --- Saturation of discovery of highly confident annotated junctions --- p.59 / Chapter 3.4.5 --- Novel transcribed regions --- p.62 / Chapter 3.4.6 --- Isoform dynamics and meta-analysis --- p.63 / Chapter 3.5 --- Discussion --- p.66 / Chapter 3.6 --- Limitations --- p.67 / Chapter 3.6.1 --- HapMap LCLs may not reflect the entire spectrum of natural variation --- p.67 / Chapter 3.6.2 --- Sequencing depth and the usefulness of published dataset --- p.67 / Chapter 3.6.3 --- Knowledge gap in understanding of the human genome --- p.69 / Chapter Chapter 4: --- Transcriptomic investigation of complex disease: Type 2 Diabetes --- p.70 / Chapter 4.1 --- Abstract --- p.70 / Chapter 4.2 --- Introduction --- p.72 / Chapter 4.3 --- Materials and Methods --- p.75 / Chapter 4.3.1 --- Subjects --- p.75 / Chapter 4.3.2 --- Strand-specific RNA-Seq Library Construction --- p.77 / Chapter 4.3.3 --- Genome annotation sequencing reads processing --- p.81 / Chapter 4.3.4 --- Reads mapping for expression analysis --- p.82 / Chapter 4.3.5 --- Differential Gene expression analysis --- p.82 / Chapter 4.3.6 --- GWAS candidate genes --- p.83 / Chapter 4.3.7 --- Individual network, pathway and Gene Ontology analysis --- p.83 / Chapter 4.3.8 --- Alternative Splicing Variation --- p.83 / Chapter 4.3.9 --- Reads mapping and processing for expressed genomic variants discovery --- p.84 / Chapter 4.3.10 --- Expressed and functional genomic variants --- p.85 / Chapter 4.3.11 --- Screening for gene fusion --- p.86 / Chapter 4.3.12 --- Sense and Antisense analysis --- p.86 / Chapter 4.3.13 --- Integrated multi-level T2DM alternations gene interaction network --- p.87 / Chapter 4.3.14 --- Validation of selected genes --- p.87 / Chapter 4.4 --- Results --- p.88 / Chapter 4.4.1 --- High quality strand-specific pair-ended RNA-Seq facilitated downstream analyses --- p.88 / Chapter 4.4.2 --- Definition of significance --- p.91 / Chapter 4.4.3 --- Wide-spread repressed gene expression in T2DM --- p.91 / Chapter 4.4.4 --- Confirmation and curation of T2DM GWAS loci by RNA-Seq --- p.92 / Chapter 4.4.5 --- Global expression alteration on T2DM associated genes --- p.97 / Chapter 4.4.6 --- Alteration of relative splicing isoforms variations and T2DM specific isoforms --- p.100 / Chapter 4.4.7 --- Rare and deleterious SNPs --- p.100 / Chapter 4.4.8 --- Absence of alteration in Sense/Antisense ratio and expressed fusion gene --- p.101 / Chapter 4.4.9 --- T2DM manifests a broad spectrum of expressed abnormalities --- p.101 / Chapter 4.4.10 --- Pathway-based integration of multiple levels of alteration expanded the T2DM network --- p.103 / Chapter 4.4.11 --- Validation of selected genes --- p.107 / Chapter 4.5 --- Discussion --- p.108 / Chapter Chapter 5: --- Conclusions and future perspectives --- p.115 / Chapter 5.1 --- Conclusions --- p.115 / Chapter 5.2 --- Future perspective --- p.115 / Chapter 5.2.1 --- Splicing detection --- p.115 / Chapter 5.2.2 --- Studies related to ethnicity --- p.116 / Chapter 5.2.3 --- Complex diseases --- p.116 / References --- p.118 / Appendix --- p.131

Master of Public Health / OBJECTIVES: In Australia, diabetes causes significant morbidity and mortality. Whilst the need to prevent diabetes and its complications has been widely recognised, the capacity of health care systems - which organise diabetes care - to facilitate prevention has not been fully established. METHODS: A series of seven population-based case-control studies were used to examine the effectiveness of the Australian health care system and its capacity to manage diabetes. Six of the studies compared the patterns of care of patients who had developed advanced diabetes complications in 2000 (cases), to similar patients who remained free of the condition (controls) across Australia and for various risk groups. A secondary study investigated the role of treating GPs in the development of the outcome. RESULTS: A strong relationship between the patterns of care and the development of advanced diabetes complications was found and is described in Chapter 4. In Chapter 5, this same relationship was investigated for each Australian state and territory, and similar findings were made. The study in Chapter 6 investigated whether late diagnosis or the patterns of care was the stronger risk factor for advanced diabetes complications, finding that the greatest risk was associated with the latter. In Chapter 7 the influence of medical care during the pre-diagnosis period was explored, and a strong relationship between care obtained in this period and the development of advanced complications was found. In Chapter 8, which investigated the role of socio-economic status in the development of advanced complications, found that the risk of advanced diabetes complications was higher in low socio-economic groups. Chapter 9 investigated geographic isolation and the development of advanced diabetes complications and found that the risk of advanced complications was higher in geographically isolated populations. Finally, Chapter 10, which utilised a provider database, found that some GP characteristics were associated with the development of advanced diabetes complications in patients. CONCLUSION: A number of major risk factors for the development of advanced complications in Australia was found. These related to poorer diabetes management, later diagnosis, low socioeconomic status and geographic isolation. Strategies must be devised to promote effective diabetes management and the early diagnosis of diabetes across the Australian population.

Master of Public Health / OBJECTIVES: In Australia, diabetes causes significant morbidity and mortality. Whilst the need to prevent diabetes and its complications has been widely recognised, the capacity of health care systems - which organise diabetes care - to facilitate prevention has not been fully established. METHODS: A series of seven population-based case-control studies were used to examine the effectiveness of the Australian health care system and its capacity to manage diabetes. Six of the studies compared the patterns of care of patients who had developed advanced diabetes complications in 2000 (cases), to similar patients who remained free of the condition (controls) across Australia and for various risk groups. A secondary study investigated the role of treating GPs in the development of the outcome. RESULTS: A strong relationship between the patterns of care and the development of advanced diabetes complications was found and is described in Chapter 4. In Chapter 5, this same relationship was investigated for each Australian state and territory, and similar findings were made. The study in Chapter 6 investigated whether late diagnosis or the patterns of care was the stronger risk factor for advanced diabetes complications, finding that the greatest risk was associated with the latter. In Chapter 7 the influence of medical care during the pre-diagnosis period was explored, and a strong relationship between care obtained in this period and the development of advanced complications was found. In Chapter 8, which investigated the role of socio-economic status in the development of advanced complications, found that the risk of advanced diabetes complications was higher in low socio-economic groups. Chapter 9 investigated geographic isolation and the development of advanced diabetes complications and found that the risk of advanced complications was higher in geographically isolated populations. Finally, Chapter 10, which utilised a provider database, found that some GP characteristics were associated with the development of advanced diabetes complications in patients. CONCLUSION: A number of major risk factors for the development of advanced complications in Australia was found. These related to poorer diabetes management, later diagnosis, low socioeconomic status and geographic isolation. Strategies must be devised to promote effective diabetes management and the early diagnosis of diabetes across the Australian population.

Inorganic arsenic is a potent carcinogen and toxicant associated with numerous adverse health outcomes, and is number one on the Agency for Toxic Substances and Disease Registry Substance Priority List. Uranium is also a carcinogen and nephrotoxicant, however health effects at levels experienced by general populations is unclear. Chronic exposure to inorganic arsenic (As) and uranium (U) in the United States (US) occurs from unregulated private wells and federally regulated community water systems (CWSs). Geogenic arsenic contamination typically occurs in groundwater as opposed to surface water supplies. Groundwater is a major source for many CWSs in the US. Although the US Environmental Protection Agency sets the maximum contaminant level (MCL enforceable since 2006: 10 µg/L) for arsenic in CWSs, private wells are not federally regulated. The contribution of drinking water from private wells and regulated CWSs to total inorganic arsenic and uranium exposure is not clear.In the United States (US), type 2 diabetes (T2D) affects approximately 37.3 million people (11.3% of the population), with the highest burden in American Indian communities. Toxic metal exposures have been identified as risk factors of T2D. Most studies rely on biomarkers, which could be affected by early disease processes. Studies directly measuring metals in drinking water in US populations have been limited. In Chapter 2, we evaluated county-level associations between modeled values of the probability of private well arsenic exceeding 10 µg/L and CWS arsenic concentrations for 2,231 counties in the conterminous US, using time invariant private well arsenic estimates and CWS arsenic estimates for two time periods. Nationwide, county-level CWS arsenic concentrations increased by 8.4 µg/L per 100% increase in the probability of private well arsenic exceeding 10 µg/L for 2006 – 2008 (the initial compliance monitoring period after MCL implementation), and by 7.3 µg/L for 2009 – 2011 (the second monitoring period following MCL implementation) (1.1 µg/L mean decline over time). Regional differences in this temporal decline suggest that interventions to implement the MCL were more pronounced in regions served primarily by groundwater. The strong association between private well and CWS arsenic in Rural, American Indian, and Semi Urban, Hispanic counties suggests that future research and regulatory support are needed to reduce water arsenic exposures in these vulnerable subpopulations. This comparison of arsenic exposure values from major private and public drinking water sources nationwide is critical to future assessments of drinking water arsenic exposure and health outcomes. In Chapter 3, we aimed to determine the association between drinking water arsenic estimates and urinary arsenic concentrations in the 2003-2014 National Health and Nutrition Examination Survey (NHANES). We evaluated 11,088 participants from the 2003-2014 NHANES cycles. For each participant, we assigned private well and CWS arsenic levels according to county of residence using estimates previously derived by the U.S. Environmental Protection Agency and U.S. Geological Survey. We used recalibrated urinary dimethylarsinate (rDMA) to reflect the internal dose of estimated water arsenic by applying a previously validated, residual-based method that removes the contribution of dietary arsenic sources. We compared the adjusted geometric mean ratios and corresponding percent change of urinary rDMA across tertiles of private well and CWS arsenic levels, with the lowest tertile as the reference. Comparisons were made overall and stratified by census region and race/ethnicity. Overall, the geometric mean of urinary rDMA was 2.52 (2.30, 2.77) µg/L among private well users and 2.64 (2.57, 2.72) µg/L among CWS users. Urinary rDMA was highest among participants in the West and South, and among Mexican American, Other Hispanic, and Non-Hispanic Other participants. Urinary rDMA levels were 25% (95% confidence interval (CI): 17-34%) and 20% (95% CI: 12-29%) higher comparing the highest to the lowest tertile of CWS and private well arsenic, respectively. The strongest associations between water arsenic and urinary rDMA were observed among participants in the South, West, and among Mexican American and Non-Hispanic White and Black participants. Both private wells and regulated CWSs are associated with inorganic arsenic internal dose as reflected in urine in the general U.S. population. In Chapter 4, our objective was to evaluate regional and sociodemographic inequalities in water arsenic exposure reductions associated with the US Environmental Protection Agency’s Final Arsenic Rule, which lowered the arsenic maximum contaminant level to 10 µg/L in public water systems. We analyzed 8,544 participants from the 2003-14 National Health and Nutrition Examination Survey (NHANES) reliant on community water systems (CWSs). We estimated arsenic exposure from water by recalibrating urinary dimethylarsinate (rDMA) to remove smoking and dietary contributions. We evaluated mean differences and corresponding percent reductions of urinary rDMA comparing subsequent survey cycles to 2003-04 (baseline), stratified by region, race/ethnicity, educational attainment, and tertile of CWS arsenic assigned at the county level. The overall difference (percent reduction) in urine rDMA was 0.32 µg/L (9%) among participants with the highest tertile of CWS arsenic, comparing 2013-14 to 2003-04. Declines in urinary rDMA were largest in regions with the highest water arsenic: the South [0.57 µg/L (16%)] and West [0.46 µg/L, (14%)]. Declines in urinary rDMA levels were significant and largest among Mexican American [0.99 µg/L (26%)] and Non-Hispanic White [0.25 µg/L (10%)] participants. Reductions in rDMA following the Final Arsenic Rule were highest among participants with the highest CWS arsenic concentrations, supporting legislation can benefit those who need it the most, although additional efforts are still needed to address remaining inequalities in CWS arsenic exposure. In Chapter 5, we examined the contribution of water As and U to urinary biomarkers in the Strong Heart Family Study (SHFS), a prospective study of American Indian communities, and the Multi-Ethnic Study of Atherosclerosis (MESA), a prospective study of racially/ethnically diverse urban US communities. We assigned residential zip code-level estimates in CWSs (µg/L) and private wells (90th percentile probability of As >10 µg/L) to up to 1,485 and 6,722 participants with dietary information and urinary biomarkers in the SHFS (2001-2003) and MESA (2000-2002; 2010-2011), respectively. Total inorganic As exposure was estimated as the sum of inorganic and methylated species in urine (urine As). We used linear mixed-effects models to account for participant clustering and removed the effect of dietary sources of As and U via regression adjustment. The median (interquartile range) urine As was 5.32 (3.29, 8.53) and 6.32 (3.34, 12.48) µg/L for SHFS and MESA, respectively, and urine U was 0.037 (0.014, 0.071) and 0.007 (0.003, 0.018) µg/L. In a mixed-effects meta-analysis of pooled effects across the SHFS and MESA, urine As was 11% (95% CI: 3, 20%) higher and urine U was 35% (5, 73%) higher per 2-fold higher CWS As and U, respectively. In the SHFS, CWS and private well As explained >40% of variability in urine As and CWS U explained >20% of urine U. In MESA, CWS As and U explained >50% of urine As and U. Water from public water supplies and private wells represents a major contributor to inorganic As and U exposure in diverse US populations. In Chapter 6, we examined the association of arsenic exposures in community water systems (CWS) and private wells with T2D incidence in the Strong Heart Family Study (SHFS), a prospective cohort of American Indian communities, and the Multi-Ethnic Study of Atherosclerosis (MESA), a prospective study of racially/ethnically diverse urban US communities, to evaluate direct associations between drinking water metal exposures and T2D risk. We evaluated adults in the SHFS free of T2D at baseline (2001-2003) and followed through 2010, with available private well and CWS arsenic (N=1,791) estimates assigned by residential zip code. We also evaluated adults in the MESA free of T2D at baseline (2000-2002) and followed through 2019, with available zip code level CWS arsenic (N=5,577) estimates. We used mixed effects Cox models to account for clustering by family and residential zip code, with adjustment for sex, baseline age, body mass index (BMI), smoking status, and education. T2D incidence in the SHFS was 24.4 cases per 1,000 people (mean follow-up 5.6 years) and T2D incidence in MESA was 11.2 per 1,000 people (mean follow-up 6.0 years). In a meta-analysis of pooled effects across the SHFS and MESA, the corresponding hazard ratio (95% confidence interval) per 2-fold increase in water arsenic was 1.09 (1.01, 1.16). Differences were observed by BMI category and sex; positive associations were observed among participants with BMI <25 kg/m2 and among female participants. In categorical analyses, >10% probability of private well arsenic (<10% reference) in the SHFS and >1 µg/L of CWS arsenic (<1 µg/L reference) in MESA were associated with increased diabetes risk. Low to moderate water arsenic levels in unregulated private wells and federally regulated CWSs were associated with T2D incidence in the SHFS and MESA. In supplementary analyses, we also observed that CWS uranium was associated with T2D risk among SHFS and MESA participants with BMI<25 kg/m2.