Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to investigate the causal relationship between exposure and outcome. It has become widely popular due to its versatile applications in epidemiological research. Its rising popularity is largely driven by the ease of accessing summary-level data from large consortia, making it a cost-effective choice for researchers. In this thesis, we focus on three issues in MR that result in potential bias in causal inference. We first address the “winner’s curse” in MR, which arises from selecting genetic markers based on their significance or ranking. To mitigate this bias, we adapt the bootstrap-based BR-squared method to function with summary-level data. Our findings reveal that the correction methods can effectively reduce bias, albeit with an increase in variability. We then develop a method that accounts for the correlation caused by sample overlap while addressing potential bias from weak instruments. This proposed method yields stable causal estimates, although the standard errors of causal estimates may not be precisely estimated. Lastly, we introduce a novel approach for identifying invalid instrumental variables showing signs of horizontal pleiotropy. We recommend using the bootstrap method to account for the data-driven process of IV selection. Our results indicate that the bootstrap intervals approach the nominal level of coverage rate when the proportion of invalid IVs is less than 50%. / Dissertation / Doctor of Philosophy (PhD)

An exciting new era has dawned for the prevention and management of CAD utilizing genetic risk variants. The recent identification of over 60 susceptibility loci for coronary artery disease (CAD) confirm not only the importance of established risk factors, but also the existence of many novel causal pathways that are expected to improve our understanding of the genetic basis of CAD and facilitate the development of new therapeutic agents over time. Concurrently, Mendelian randomization studies have provided intriguing insights on the causal relationship between CAD-related traits, and highlight the potential benefits of long-term modifications of risk factors. Lastly, genetic risk scores of CAD may serve not only as prognostic, but also as predictive markers and carry the potential to considerably improve the delivery of established prevention strategies. This review will summarize the evolution and discovery of genetic risk variants for CAD and their current and future clinical applications.

Indiana University-Purdue University Indianapolis (IUPUI) / Alternative RNA splicing is an important means of genetic control and transcriptome diversity. Alternative splicing events are frequently studied independently, and coordinated splicing controlled by common factors is often overlooked: The molecular mechanisms by which splicing regulators promote or repress specific pre-mRNA processing are still not yet well understood. It is well known that splicing factors can regulate splicing in a context-dependent manner, and the search for modulation of splicing factor activity via direct or indirect mechanisms is a worthwhile pursuit towards explaining context-dependent activity. We hypothesized that the combined analysis of hundreds of consortium RNA-seq datasets could identify trans-acting “modulators” whose expression is correlated with differential effects of a splicing factor on its target splice events in mRNAs. We first tested a genome-wide approach to identify relationships between RNA-binding proteins and their inferred modulators in kidney cancer. We then applied a more targeted approach to identify novel modulators of splicing factor SRSF1 function over dozens of its intron retention splicing targets in a neurological context using hundreds of dorsolateral prefrontal cortex samples. Our hypothesized model was further strengthened with the incorporation of genetic variants to impute gene expression in a Mendelian randomization-based approach. The modulators of intron retention splicing we identified may be associated with risk variants linked to Alzheimer’s Disease, among other neurological disorders, to explain disease-causing splicing mechanisms. Our strategy can be widely used to identify modulators of RNA-binding proteins involved in tissue-specific alternative splicing.

Mendelian Randomization (MR) methods use genetic variants as instrumental variables (IV) to infer causal relationships between an exposure and an outcome, which overcomes the inability to infer such a relationship in observational studies due to unobserved confounders. There are several MR methods, including the inverse variance weighted (IVW) method, which has been extended to deal with correlated IVs; the median method, which provides consistent causal estimates in the presence of pleiotropy when less than half of the genetic variants are invalid IVs but assumes independent IVs. In this dissertation, we propose two new methods to improve precision for MR analysis. In the first chapter, we extend the median method to correlated IVs: the quasi-boots median method, that accounts for IV correlation in the standard error estimation using a quasi-bootstrap method. Simulation studies show that this method outperforms existing median methods under the correlated IVs setting with and without the presence of pleiotropic effects. In the second chapter, to overcome the lack of an effective solution to account for sample overlap in current IVW methods, we propose a new overall causal effect estimator by exploring the distribution of the estimator for individual IVs under the independent IVs setting, which we name the IVW-GH method. In the final chapter, we extend the IVW-GH method to correlated IVs. In simulation studies, the IVW-GH method outperforms the existing IVW methods under the one-sample setting for independent IVs and shows reasonable results for other settings. We apply these proposed methods to genome-wide association results from the Framingham Heart Study Offspring Study and the Million Veteran Program to identify potential causal relationships between a number of proteins and lipids. All the proposed methods are able to identify some proteins known to be related to lipids. In addition, the quasi-boots median method is robust to pleiotropic effects in the real data application. Consequently, the newly proposed quasi-boots median method and IVW-GH method may provide additional insights for identifying causal relationships. / 2025-01-23T00:00:00Z

Background and Objectives: Pharmacogenetics has the potential to maximize drug efficacy and minimize adverse effects of cardiovascular disease (CVD) but its translation into clinical practice been slow. However, recent advancements in genotyping and statistical methodologies have now provided robust evidence in the support of personalized medicine. This thesis addresses how the advancements in pharmacogenetics may help to gain novel insights into existing drug targets, inform and guide clinical decision-making and validate potential disease target pathways. Methods: This was achieved by exploring whether the COX-2 genetic variant (rs20417) is associated with a decreased risk of CVD outcomes, assessing whether bile acid sequestrants (BAS) are associated with a reduced the risk of coronary artery disease (CAD) using the principles of Mendelian Randomization and investigating whether genetic variants associated with dysglycaemia are associated with an increased risk of CAD. Results: We demonstrated that COX-2 carrier status was associated with a decreased risk of major cardiovascular outcomes. Furthermore, we also showed that BAS appear to be associated with a reduced risk of CAD and genetic variants associated with HbA1c and diabetes were associated with an increased risk of CAD. Conclusions: The convergence of technological and statistical advancements in pharmacogenetics have led to a more high-quality and cost-effective means of assessing the effect of CVD therapeutic agents. / Thesis / Doctor of Philosophy (PhD)

Many current drugs for stroke act by targeting circulating molecules, yet these have not been exhaustively evaluated for therapeutic potential. A central challenge is that while many molecules correlate with stroke risk, only a subset cause stroke. To disentangle causality from association, a statistical genetics framework called “Mendelian Randomization” can be used by integrating genetic, biomarker, and phenotypic information. In Study 1, we screened 653 circulating proteins using this technique and found evidence supporting causal roles for seven proteins, two of which (SCARA5 and TNFSF12) were not previously implicated in stroke pathogenesis. We also characterized potential side-effects of targeting these molecules for stroke prevention and did not identify any adverse effects for SCARA5. The remaining two studies focused on investigating the role of an emerging marker of mitochondrial activity, leukocyte mitochondrial DNA copy number (mtDNA-CN). Mitochondria have long been known to play a protective role in stroke recovery; however, a mitochondrial basis for stroke protection has not been extensively studied in humans. In Study 2, we first sought to better understand the genetic basis of mtDNA-CN in a series of genetic association studies involving 395,781 UK residents. We identified 71 loci which represents a 40% increase in our knowledge. In Study 3, epidemiological analyses of 3,498 acute stroke demonstrated that low mtDNA-CN was associated with higher risk of subsequent mortality and worse functional outcome 1-month after stroke. Furthermore, Mendelian Randomization analyses corroborated a causative relationship for the first time, implying that interventions that increase mtDNA-CN levels in stroke patients may represent a novel strategy for mitigating post-stroke complications. Ultimately, this work uncovered several novel therapeutic leads for preventing stroke onset and ameliorating its progression. Future investigations are necessary to better understand the underlying biological mechanisms connecting these molecules to stroke and to further interrogate their validity as potential drug targets. / Thesis / Doctor of Philosophy (PhD) / Current stroke medications work by targeting circulating molecules. Our aim was to discover new drug candidates by combining genetic and circulating biomarker data using a technique called “Mendelian Randomization”. In Study 1, we screened 653 circulating proteins and found evidence supporting causal roles for two novel candidates, SCARA5 and TNFSF12. Prior experimental studies suggest an important role for mitochondria in stroke recovery. Accordingly, in Study 2, we characterized the genetic basis of an emerging biomarker, mitochondrial DNA copy number (mtDNA-CN). Analyses of 395,781 participants revealed 71 associated genetic regions, representing a 40% increase in our knowledge. In Study 3, we measured mtDNA-CN in 3,498 acute patients and observed that lower levels predicted elevated risk of worse post-stroke functional outcomes. Furthermore, Mendelian Randomization analysis suggested a likely causal relationship. Overall, this work uncovered several novel therapeutic leads for preventing stroke onset and progression that warrant further investigation to verify therapeutic utility.

Mendelian randomization is an epidemiological method for using genetic variationto estimate the causal effect of the change in a modifiable phenotype onan outcome from observational data. A genetic variant satisfying the assumptionsof an instrumental variable for the phenotype of interest can be usedto divide a population into subgroups which differ systematically only in thephenotype. This gives a causal estimate which is asymptotically free of biasfrom confounding and reverse causation. However, the variance of the causalestimate is large compared to traditional regression methods, requiring largeamounts of data and necessitating methods for efficient data synthesis. Additionally,if the association between the genetic variant and the phenotype is notstrong, then the causal estimates will be biased due to the “weak instrument”in finite samples in the direction of the observational association. This biasmay convince a researcher that an observed association is causal. If the causalparameter estimated is an odds ratio, then the parameter of association willdiffer depending on whether viewed as a population-averaged causal effect ora personal causal effect conditional on covariates. We introduce a Bayesian framework for instrumental variable analysis, whichis less susceptible to weak instrument bias than traditional two-stage methods,has correct coverage with weak instruments, and is able to efficiently combinegene–phenotype–outcome data from multiple heterogeneous sources. Methodsfor imputing missing genetic data are developed, allowing multiple genetic variantsto be used without reduction in sample size. We focus on the question ofa binary outcome, illustrating how the collapsing of the odds ratio over heterogeneousstrata in the population means that the two-stage and the Bayesianmethods estimate a population-averaged marginal causal effect similar to thatestimated by a randomized trial, but which typically differs from the conditionaleffect estimated by standard regression methods. We show how thesemethods can be adjusted to give an estimate closer to the conditional effect. We apply the methods and techniques discussed to data on the causal effect ofC-reactive protein on fibrinogen and coronary heart disease, concluding withan overall estimate of causal association based on the totality of available datafrom 42 studies.

<div><div><div><p>Transcriptomic regulation of human liver is a tightly controlled and highly dynamic process. Genetic and environmental exposures to this process play pivotal roles in the development of multiple liver disorders. Despite accumulating knowledge have gained through large-scale genomics studies in the developed adult livers, the contributing factors to the interindividual variability in the pediatric livers remain largely uninvestigated. In the first two chapters of the present study, we addressed this question through an integrative analysis of both genetic variations and transcriptome-wide RNA expression profiles in a pediatric human liver cohort with different developmental stages ranging from embryonic to adulthood. Our systematic analysis revealed a transcriptome-wide transition from stem-cell-like to liver-specific profiles during the course of human liver development. Moreover, for the first time, we observed different genetic control of hepatic gene expression in different developmental stages. Motivated by the critical roles of genetics variations and development in regulating hepatic gene expression, we constructed robust predictive models to impute the virtual liver gene expression using easily available genotype and demographic information. Our model is promising in improving both PK/PD modeling and disease diagnosis for pediatric patients. In the last two chapters of the study, we analyzed the genomics data in a more liver disease- related context. Specifically, in the third chapter, we identified Macrophage migration inhibitory factor (MIF) and its related pathways as potential targets underlying human liver fibrosis through an integrative omics analysis. In the last chapter, utilizing the largest-to-date publicly available GWAS summary data, we dissected the causal relationships among three important and clinically related metabolic diseases: non-alcoholic fatty liver disease (NAFLD), type 2 diabetes (T2D), and obesity. Our analysis suggested new subtypes and provided insights into the precision treatment or prevention for the three complex diseases. Taken together, through integrative analysis of multiple levels of genomics information, we improved the current understanding of human liver development, the pathogenesis of liver disorders, and provided implications to precision medicine.</p></div></div></div>

No description available.

京都大学 / マギル大学 / 新制・課程博士 / 博士(ゲノム医学) / 甲第24203号 / 医博JD第1号 / 新制||医||JD1(附属図書館) / 京都大学大学院医学研究科京都大学マギル大学ゲノム医学国際連携専攻 / (主査)教授 稲垣 暢也, 教授 YOUSSEFIAN Shohab, 准教授 Majewski Jacek (マギル大学), 准教授 Gravel Simon (マギル大学), 教授 Gagneur Julien (ミュンヘン工科大学) / 学位規則第4条第1項該当 / Doctor of Philosophy in Human Genetics / Kyoto University / McGill University / DFAM