Genome-wide association (GWA) studies have been successfully applied in detection of susceptibility loci for complex diseases, but most of the identified variants have a large to moderate effect, and explain only a limited proportion of the heritability of the diseases. It is believed that the majority of the latent risk alleles have very small risk effects that are difficult to be identified and GWA study may have inadequate power in dealing with those small effect variants. Researchers will often collect other phenotypic information in addition to disease status to maximize the output from the study. Some of the phenotypes can be on the pathway to the disease, i.e., intermediate phenotype. Statistical methods based on both the disease status and intermediate phenotype should be more powerful than a case-control study as it incorporates more information. Meta-analysis has been used in genetic association analysis for many years to combine information from multiple populations, but never been used in a single population GWA study. In this study, simulations were conducted and the results show that when an intermediate phenotype is available, the meta-analysis incorporating the disease status and intermediate phenotype information from a single population has more power than a case-control study only in GWA study of complex diseases, especially for identification of those loci that have a very small effect. And compared with Fisher's method, the modified inverse variance weighted meta-analysis method is more robust as it is more powerful and has a lower type I error rate at the same time, which provides a potent approach in detecting the susceptibility loci associated with complex diseases, especially for those latent loci whose effect are very small. In the meta-analysis of lung cancer with smoking data, the results replicate the signal in \emph{CHRNA3} and \emph{CHRNA5} genes on chromosome 15q25. Some new signals in \emph{CYP2F1} on chromosome 19, \emph{SUMF1} on chromosome 3, and \emph{ARHGAP10} on chromosome 4 are also detected. And the \emph{CYP2F1} gene, close to the already known cigarette-induced lung cancer gene \emph{CYP2A6}, is highly possible another cytochrome P450 (CYP) gene that is related to the smoking-involved lung cancer. The meta-analysis of rheumatoid arthritis with anti-cyclic citrullinated peptide (anti-CCP) data identified new signals on 9q24 and 16q12. There are evidences these two regions are involved in other autoimmune diseases and different autoimmune/inflammatory diseases may share same genetic susceptibility loci. Both the theoretical and empirical studies show that the modified variance weighted meta-analysis method is a robust method and is a potent approach in detecting the susceptibility loci associated with complex diseases when an intermediate phenotype is available.

In der Arbeit werden Fehlstellen in der Silizium (2x1)-(111)-Oberfläche mit den ab-initio-Methoden DFT-LDA und GdW betrachtet. Zunächst werden dabei die bei dieser Oberfläche auftretenden Pandeyketten innerhalb der DFT-LDA näher betrachtet und der Einfluss zwischen und innerhalb der Ketten untersucht. Um den Einfluss der Phosphorfehlstelle zu betrachten wird ein System erstellt, das groß genug ist, um einzelne Defekte in der Oberfläche untersuchen zu können. Die Ergebnisse der DFT werden durch Berechnungen der Vielteilchenstörungstheorie GdW erweitert, wodurch gleichzeitig gezeigt werden konnte, dass sich die GdW auf Systeme mit 600 Elektronen anwenden lässt. Weiterhin wird durch Betrachtung der ortsaufgelösten DOS die Wechselwirkung der Phosphordotierung mit dem Siliziumkristall untersucht.

Dans le contexte des changements globaux, un des enjeux majeurs en génomique écologique est d'estimer le potentiel adaptatif des populations naturelles. Répondre à cet enjeu nécessite 3 étapes: identification des agents sélectifs et de leurs échelles spatiales de variation, identification des bases génétiques de l'adaptation et étude de la dynamique adaptative sur une courte échelle de temps. Durant ma thèse, je me suis intéressé à étudier le potentiel adaptatif de la plante modèle Arabidopsis thaliana. A partir de 168 populations naturelles d'A. thaliana caractérisées pour 24 traits phénotypiques et 60 facteurs abiotiques (climat, sol) et biotiques (communautés végétales et microbiote), j'ai pu mettre en évidence que les communautés végétales étaient les principaux agents sélectifs associés à la fitness. Après avoir séquencé le génome de ces 168 populations (~ 4.8 millions de SNPs), j'ai effectué des analyses de type 'association génome-environnement' couplé à des scans génomiques de différenciation génétique spatiale. Ces analyses ont confirmé l'importance de considérer les interactions plante-plante dans l'étude de l'adaptation chez A. thaliana. Afin d'étudier le potentiel adaptatif d'A. thaliana sur le court terme dans le contexte d'un réchauffement climatique, j'ai combiné une étude de résurrection in situ avec une étude de Genome Wide Association mapping, à partir de 195 accessions locales caractérisées pour 29 traits phénotypiques et pour environ 1.9 million de SNPs. J'ai identifié une architecture originale de l'adaptation vers un nouvel optimum phénotypique combinant (i) de rares QTLs avec des degrés de pléiotropie intermédiaires fortement sélectionnés et (ii) de très nombreux QTLs spécifiques d'un micro-habitat et faiblement sélectionnés. A travers les différents projets abordés pendant ma thèse, j'ai pu suggérer qu'une architecture génétique flexible pouvait permettre à A. thaliana de s'adapter rapidement aux changements globaux, tout en maintenant de la diversité génétique au sein des populations naturelles d'A. thaliana. / In the context of global changes, one of the challenges in ecological genomics is to estimate the adaptive potential of natural populations. Three steps are requested to address this challenge: identification of the selective agents and their associated spatial grains, identification of the genetic bases of adaptation and monitoring the adaptive dynamics of natural population over a short time period. Here, I aimed at studying the adaptive potential of the model plant Arabidopsis thaliana. Based on 168 natural populations of A. thaliana characterized for 24 phenotypic traits and 60 abiotic (climate, soil) and biotic (plant communities and microbiota) factors, plant communities were found to be the main selective agents. Based on 4.8 million SNPs, I combined Genome Environment Association analysis with genome scans for signatures of selection. I confirmed the importance to consider plant-plant interactions when studying adaptation in A. thaliana. To monitor the adaptive dynamics of a natural population in the context of global warming, I combined an in situ resurrection study with an approach of GWA mapping based on 195 local accessions characterized for 29 phenotypic traits and 1.9 million SNPs. Adaptive evolutionary changes were largely driven by rare QTLs with intermediate degrees of pleiotropy under strong selection. In addition to these rare pleiotropic QTLs, weak selection was detected for frequent small micro-habitat-specific QTLs that shape single traits. Overall, I suggest that a rapid adaptive phenotypic evolution can be rapidly achieved in A. thaliana, while still maintaining genetic variation in natural populations.

京都大学 / 新制・課程博士 / 博士(生命科学) / 甲第24943号 / 生博第505号 / 新制||生||67(附属図書館) / 京都大学大学院生命科学研究科統合生命科学専攻 / (主査)教授 上村 匡, 教授 今吉 格, 教授 鈴木 淳 / 学位規則第4条第1項該当 / Doctor of Philosophy in Life Sciences / Kyoto University / DFAM

No description available.

<p> Obesity is a growing concern as 42.3% of people in the U.S were considered obese in the years 2017- 2018. Little is known about the genetic components that contribute to weight gain. In humans, the hormone glucagon is a major contributor to the body’s energy demand as it helps break down lipids. Therefore, learning more about this pathway could enable a range of therapeutics. In fact, studies have shown that glucagon treatments have helped patients with both weight loss and appetite suppression. In this project, we analyzed candidate genes that modify the glucagon pathway in <em>Drosophila melanogaster.</em> We reduced the expression of the fly version of the glucagon receptor (AKHR) in our model. This induces fat retention in the L3 larvae, which mimics obesity in humans. We then crossed our model to the DGRP and looked for natural variation in fat content using a density assay. The density assay examines the relative fat levels of the larvae by slowly increasing the amount of sucrose in water. This enables us to observe whether we have lean larvae which float later or fat larvae which float early on. We used the variation in floating concentration to identify candidate modifier genes through GWA or genome-wide association study. We crossed our <em>AKHR</em> RNAi model to RNAi for various candidate modifier genes that may enhance or suppress fat retention. We screened these candidates initially with the same density assay used in the original study. This resulted in four candidate genes that significantly impacted the density of the larvae: <em>THADA</em>, <em>AmyD</em>, <em>GluRIIC</em>, and <em>CG9826</em>. We further characterized these candidates using biochemical assays to analyze stored metabolites such as triglycerides, glucose, glycogen, and protein. These have been further analyzed under control, high sugar, and high fat conditions to see if the larvae are resistant to environmental changes. <em>CG9826</em> showed significant increase in stored fats across all environments. <em>THADA</em> RNAi showed an increase in fat in the high fat environment. Overexpression of <em>THADA</em> showed a decrease in fat storage in the high fat environment. Our goal is to advance our understanding of the glucagon signaling pathway, obesity, and lipid metabolism. We are also hopeful to provide candidate genes that can be regarded as future therapeutic targets. </p>

<p> Despite the continuous breeding efforts towards improving yield, seed quality, and yield-related traits, there is still little understanding of several aspects of soybean breeding; however, crop breeding is ever-evolving, and plant breeding technologies offer immense potential for accelerating genetic improvement in soybeans. This thesis explores different frameworks to further characterize tradeoffs among seed quality traits, soybean maturity's genetic architecture, and selections for yield. We explored the interactions of carbohydrate traits with other seed traits, flowering, and maturity using data from a large panel of <em>G. max </em>accessions from the USDA soybean germplasm collection. We found a negative correlation between sucrose and protein and a negative correlation between protein and oil, representing a significant challenge for improving seed quality. In contrast to other well-documented correlations, such as protein and oil, correlations between raffinose and oil content seem more specific to populations and environments and are unlikely to generalize to the whole specie; however, the correlations of sucrose with protein and seed size appears to be more stable. In addition, we performed a genome-wide association analysis (GWA) to detect novel QTLs for flowering (R1) time, maturity (R8) time, and reproductive length (RL) using a soybean panel with the same genotype for major <em>E </em>genes (<em>e1-as/E2/E3). </em>While major maturity <em>E</em> genes are known to have pleiotropic effects on R1 and R8, we found two QTLs associated with R8 and RL that do not control R1, suggesting minor-effect, trait-specific loci are also involved in controlling R1 and R8. In addition, w<em>e identified six genes that may play essential roles in regulating R1, R8, and RL; however, further validation of the QTLs and f</em>ine mapping and map-based cloning studies of the candidate genes are necessary before they can be used in breeding programs. Lastly, we conducted a selection experiment in progeny row (PR) populations of four breeding programs to compare the agronomic performance of lines selected by breeders using their usual selection methods to lines selected through prediction of yield performance using new sources of data and information. Our results suggest that aerial average canopy coverage (ACC) used as a secondary trait in combination with field spatial variation adjustment is an efficient high throughput methodology to effectively select high-yielding lines from non-replicated experiments at the PR stage. </p>