京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第22335号 / 医博第4576号 / 新制||医||1041(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 山下 潤, 教授 岩田 想, 教授 木村 剛 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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.

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Philosophiae Doctor - PhD / Tuberculosis (TB) remains a global epidemic despite the fact that treatment has been available since the 1950’s. This disease is highly contagious and spreads via transmission of the Mycobacterium Tuberculosis (MTB) tubercle via coughing, sneezing and spitting. The disease has various side effects including weight loss, fatigue and even death. To date no cure has been found for TB and thus optimisation of treatment is a constant focus in health related research. TB is highly prevalent in South Africa due to the increased level of patients who are co-infected with HIV. Treatment for TB consists of first line drugs including isoniazid (INH), ethambutol (ETH), pyrazinamide (PYR) and rifampicin (RIF). These drugs are highly effective but also produce many adverse drug reactions (ADR’s) over the 6-month course of treatment. These reactions lead to patients not completing the course, losing quality of life and ultimately adding to the development of drug resistant strains of TB. A method of minimising these ADR’s is the development of a phenotype sensor, which is able to determine the metabolic profile of patients. Metabolic profiles play a huge role in the efficacy of treatment by tailoring treatment in order for patients to stay within the therapeutic range of treatment. This would in turn minimise both toxicity and ineffective treatment. Various methods for the quantification of drugs have been developed such as high performance liquid chromatography (HPLC), mass spectrometry (MS) and ultra-violet visible spectroscopy (UV-vis). / 2023-12-01

Vascular smooth muscle cells display a certain level of phenotype plasticity. Under specific conditions fully differentiated cells are able to undergo dedifferentiation and to restart growth and proliferation. An organ culture method is a useful technique for the analysis of dedifferentiation of vascular smooth muscle cells, because it provides an opportunity for studying the changes in cell phenotype. The aim of this study was to investigate the basic contractile characteristics in rat femoral arteries cultured for different time periods (from one to three days). In addition, the effects of fetal bovine serum (FBS), that contains various growth factors and other biological active molecules, on contractile function were studied. We also tried to attenuate cell dedifferentiation by lowering the calcium influx, because calcium is an important second messenger participating in cell growth and proliferation. To achieve this goal we used cultivation with nifedipine, a voltage-dependent calcium channel inhibitor. The cultivation without FBS slightly decreased arterial contractility, whereas the cultivation with FBS decreased arterial contractility considerably. The major change in contractility of arteries cultivated with FBS occurred approximately within 24 hours of cultivation. The cultivation with...

The concept of a fitness landscape arose in theoretical biology, while that of effective fitness has its origin in evolutionary computation. Both have emerged as useful conceptual tools with which to understand the dynamics of evolutionary processes, especially in the presence of complex genotype-phenotype relations. In this contribution we attempt to provide a unified discussion of these two approaches, discussing both their advantages and disadvantages in the context of some simple models. We also discuss how fitness and effective fitness change under various transformations of the configuration space of the underlying genetic model, concentrating on coarse-graining transformations and on a particular coordinate transformation that provides an appropriate basis for illuminating the structure and consequences of recombination.

The past two decades have witnessed rapid growth in the amount of data available to us. Many fields, including physics, biology, and medical studies, generate enormous datasets with a large sample size, a high number of dimensions, or both. For example, some datasets in physics contains millions of records. It is forecasted by Statista Survey that in 2022, there will be over 86 millions users of health apps in United States, which will generate massive mHealth data. In addition, more and more large studies have been carried out, such as the UK Biobank study. This gives us unprecedented access to data and allows us to extract and infer vital information. Meanwhile, it also poses new challenges for statistical methodologies and computational algorithms. For increasingly large datasets, computation can be a big hurdle for valid analysis. Conventional statistical methods lack the scalability to handle such large sample size. In addition, data storage and processing might be beyond usual computer capacity. The UK Biobank genotypes and phenotypes dataset contains about 500,000 individuals and more than 800,000 genotyped single nucleotide polymorphism (SNP) measurements per person, the size of which may well exceed a computer's physical memory. Further, the high dimensionality combined with the large sample size could lead to heavy computational cost and algorithmic instability. The aim of this dissertation is to provide some statistical approaches to address the issues. Chapter 1 provides a review on existing literature. In Chapter 2, a novel perturbation subsampling approach is developed based on independent and identically distributed stochastic weights for the analysis of large scale data. The method is justified based on optimizing convex criterion functions by establishing asymptotic consistency and normality for the resulting estimators. The method can provide consistent point estimator and variance estimator simultaneously. The method is also feasible for a distributed framework. The finite sample performance of the proposed method is examined through simulation studies and real data analysis. In Chapter 3, a repeated block perturbation subsampling is developed for the analysis of large scale longitudinal data using generalized estimating equation (GEE) approach. The GEE approach is a general method for the analysis of longitudinal data by fitting marginal models. The proposed method can provide consistent point estimator and variance estimator simultaneously. The asymptotic properties of the resulting subsample estimators are also studied. The finite sample performances of the proposed methods are evaluated through simulation studies and mHealth data analysis. With the development of technology, large scale high dimensional data is also increasingly prevailing. Conventional statistical methods for high dimensional data such as adaptive lasso (AL) lack the scalability to handle processing of such large sample size. Chapter 4 introduces the repeated perturbation subsampling adaptive lasso (RPAL), a new procedure which incorporates features of both perturbation and subsampling to yield a robust, computationally efficient estimator for variable selection, statistical inference and finite sample false discovery control in the analysis of big data. RPAL is well suited to modern parallel and distributed computing architectures and furthermore retains the generic applicability and statistical efficiency. The theoretical properties of RPAL are studied and simulation studies are carried out by comparing the proposed estimator to the full data estimator and traditional subsampling estimators. The proposed method is also illustrated with the analysis of omics datasets.

The theme of this dissertation is to improve the exploration of patient subgroups with a precision medicine lens, specifically using repeated measures data to evaluate longitudinal trajectories of clinical, biological, and lifestyle measures. Our proposed methodological contributions fall into two branches of statistical methodology: undirected graphical models and functional data analysis. In the first part of this dissertation, our goal was to study longitudinal networks of brain imaging biomarkers and clinical symptoms during the time leading up to manifest Huntington's disease diagnosis among patients with known genetic risk of disease. Understanding the interrelationships between measures may improve our ability to identify patients who are nearing disease onset and who therefore might be ideal patients for clinical trial recruitment. Gaussian graphical models are a powerful approach for network modeling, and several extensions to these models have been developed to estimate time-varying networks. We propose a time-varying Gaussian graphical model specifically for a time scale that is centered on an anchoring event such as disease diagnosis. Our method contains several novel components intended to 1) reduce bias known to stem from 𝑙₁ penalization, and 2) improve temporal smoothness in network edge strength and structure. These novel components include time-varying adaptive lasso weights, as well as a combination of 𝑙₁, 𝑙₂, and 𝑙₀ penalization. We demonstrated via simulation studies that our proposed approach, as well as more computationally efficient subsets of our full proposed approach, have superior performance compared to existing methods. We applied our proposed approach to the PREDICT-HD study and found that the network edges did change with time leading up to and beyond diagnosis, with change points occurring at different times for different edges. For clinical symptoms, bradykinesia became well-connected with symptoms from several other domains. For imaging measures, we observed a loss of connection over time among gray matter regions, white matter regions, and the hippocampus. In the second part of this dissertation, we consider time-varying network models for settings in which data are not all Gaussian. We sought to compare longitudinal clinical symptom networks between patients with neuropathologically-defined Alzheimer's disease (AD) vs. neuropathologically-defined Lewy body dementia (LBD), two common types of dementia which can often be clinically misdiagnosed. Given that the clinical measures of interest were largely non-Gaussian, we examined the literature for undirected graphical models for mixed data types. We then proposed an extension to the existing time-varying mixed graphical model by adding time-varying adaptive lasso weights, modeling time in reverse in order to treat neuropathological diagnoses as baseline covariates. The proposed adaptive lasso extension serves a two-fold purpose: they alleviate well-known bias of 𝑙₁ penalization and they encourage temporal smoothness in edge estimation. We demonstrated the improved performance of our extension in simulations studies. Applying our method to the National Alzheimer's Coordinating Center database, we found that the edge structure surrounding the Wechsler Memory Scale Revised (WMS-R) Logical Memory parts IA (immediate recall) and IIA (delayed recall) may contain important markers for discriminant analysis of AD and LBD populations. In the third part of this dissertation, we explored a methodologically distinct area of research from the first two parts, moving from graphical models to functional data analysis. Our goal was to extract meaningful chronotypes, or phenotypes of circadian rhythms, from activity count data collected from accelerometers. Existing approaches for analyzing diurnal patterns using these data, including the cosinor model and functional principal components analysis, have revealed and quantified population-level diurnal patterns, but considerable subject-level variability remained uncaptured in features such as wake/sleep times and activity intensity. This remaining informative variability could provide a better understanding of chronotypes, or behavioral manifestations of one’s underlying 24-hour rhythm. Curve registration, or alignment, is a technique in functional data analysis that separates "vertical" variability in activity intensity from "horizontal" variability in time-dependent markers like wake and sleep times. We developed a parametric registration framework for 24-hour accelerometric rest-activity profiles that are represented as dichotomized into epoch-level states of activity or rest. Specifically, we estimated subject-specific piecewise linear time-warping functions parametrized with a small set of parameters. We applied this method to data from the Baltimore Longitudinal Study of Aging and illustrated how estimated parameters can give a more flexible quantification of chronotypes compared to traditional approaches.

In this paper we have compared and evaluated two different representations used in search based procedural content generation (PCG). The comparison was based on the differences in performance, quality of the generated content and the complexity of the final artifacts. This was accomplished by creating two artifacts, each of which used one of the representations in combination with a genetic algorithm. This was followed up with individual testing sessions in which 21 test subjects participated. The evaluated results were then presented in a manner of relevance for both search based PCG as a whole, and for further exploration within the area of representations used in this field.

L’identification de l’information génétique comme support de l’hérédité a accordé aux gènes une importance majeure dans l’étude de l’évolution et des mécanismes permettant la mise en place des caractères. Cependant, les processus permettant à une variation génétique de se traduire en variation phénotypique sont complexes et leur identification est centrale pour la compréhension de l’évolution.On parle de relation génotype-phénotype pour désigner la fonction qui relie l’espace des gènes à celui des caractères. Étudier les propriétés de cette relation permet d’identifier des mécanismes pouvant altérer les trajectoires évolutives et améliorer notre compréhension de l’évolution de systèmes vivants. Je défends notamment l’intérêt d’étudier mécanistiquement les processus par lesquels une variation génétique donne naissance à une variation phénotypique, et emploie, pour ce faire, un modèle de réseau de régulation transcriptionnelle.Ici, j’étudie les effets d’une information environnementale sur la relation génotype-phénotype et ses propriétés (notamment sa canalisation, sa robustesse à des perturbations génétiques ou environnementales). Pour ce faire, l’évolution de réseaux de régulation simulés est étudiée en présence d’un gène senseur de l’environnement ou d’une forme d’hérédité non génétique.Ce manuscrit débute par une discussion générale de l’intérêt des approches par modélisation, notamment pour l’étude de phénomènes complexes. Enfin, les résultats obtenus sont présentés en regard des discussions sur la nécessité d’une « synthèse évolutive étendue » pour décrire le processus évolutif d’une manière difficilement accessible par une approche gène-centrée. / The identification of genetic information as the as a physical basis for heredity put genes in the spotlight for the study of evolution and of the mechanisms shaping characters. However, the processes allowing genetic variation to translate into phenotypic variation are complex and their identification is crucial for the study of evolution.Genotype-phenotype relationship designates the function connecting the genotype and the phenotype spaces. Studying its properties will shed the light on mechanisms able to alter evolutionary trajectories and improve our understanding of the evolutionary process. I defend the importance of a mechanistic study of the processes translating genetic variation into a phenotypic one and use a model of transcriptional regulation networks to do so.This study tackles the topic of the effects of an environmental information on the genotype-phenotype relationship and its properties (especially canalization, the robustness of a phenotype to genetic or environmental disturbances). To do so, I studied the evolution of simulated regulatory networks in presence of a gene acting as an environmental sensor as well as in presence of non genetic inheritance.This document begins with a general discussion on the purpose of modelling approaches and the insights they bring on the study of complex phenomena. The results are discussed in the light of the debates on the necessity of an « evolutionary extended synthesis » to describe the evolutionary processes in a way hardly available with the gene-centered approach