In this study we build solutions to three common challenges in the fields of bioinformatics through utilizing statistical methods and developing computational approaches. First, we address a common problem in genome wide association studies, which is linking genotype features within organisms of the same species to their phenotype characteristics. We specifically studied FHA domain genes in Arabidopsis thaliana distributed within Eurasian regions by clustering those plants that share similar genotype characteristics and comparing that to the regions from which they were taken. Second, we also developed a tool for calculating transposable element density within different regions of a genome. The tool is built to utilize the information provided by other transposable element annotation tools and to provide the user with a number of options for calculating the density for various genomic elements such as genes, piRNA and miRNA or for the whole genome. It also provides a detailed calculation of densities for each family and subamily of the transposable elements. Finally, we address the problem of mapping multi reads in the genome and their effects on gene expression. To accomplish this, we implemented methods to determine the statistical significance of expression values within the genes utilizing both a unique and multi-read weighting scheme. We believe this approach provides a much more accurate measure of gene expression than existing methods such as discarding multi reads completely or assigning them randomly to a set of best assignments, while also providing a better estimation of the proper mapping locations of ambiguous reads. Overall, the solutions we built in these studies provide researchers with tools and approaches that aid in solving some of the common challenges that arise in the analysis of high throughput sequence data.

<p>Complex traits are influenced by genetic and environmental factors and their interactions. Most common human disorders such as cardiovascular, metabolic, autoimmune, and neurological diseases are complex. Understanding their genetic architecture and etiology is an important step to prevent, diagnose and treat these conditions. Genome Wide Association Studies (GWAS) have emerged as a powerful and widely used tool that can be used to explore and identify the genetic variants associated with complex traits. In this dissertation, we present some of the downstream applications of GWAS studies to analyze and understand the genetic risk and etiology of complex traits and provide important insights into the genetic architecture and background of several complex phenotypes. First, we examined whether prevalence of complex disorders around the world correlates to Polygenic Risk Scores (PRS). To do so, we determined the average PRS of 14 such complex disorders across 24 world populations using results of GWAS studies. We found variation in risk across populations and significant correlation was obtained between average disease risk and prevalence for seven of the studied disorders. Further exploring the power of PRS- based calculations, we performed a PRS - based phenome wide association study (PheWAS) for Tourette Syndrome (TS) and identified 57 phenotypic outcomes significantly associated with TS PRS. The strongest associations were found between TS PRS and mental health factors. Cross- disorder comparisons of phenotypic associations with genetic risk for other childhood-onset disorders (e.g.: attention deficit hyperactivity disorder [ADHD], autism spectrum disorder [ASD], and obsessive-compulsive disorder [OCD]) indicated an overlap in associations between TS and these disorders. Furthermore, we performed a sex specific PheWAS that highlighted differences in associations of complex disorders with TS PRS in males and females. Finally, we used large- scale GWAS results to identify causal associations between different biological markers (proteins, metabolites, and microbes) and subcortical brain structure volumes using Mendelian Randomization (MR) analysis. We identified eleven proteins and six metabolites to be significantly associated with subcortical brain volume structures. Enrichment analysis indicated that the associated proteins were enriched for proteolytic functions and regulation of apoptotic pathways. Overall, our work demonstrates the power of GWAS studies to help disentangle the genetic basis of complex diseases and also provides important insights into the etiology of the studied complex traits. </p>

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

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Conduct disorder (CD) is a psychiatric syndrome in childhood and adolescence that is one of the most common childhood disorders with continuously increasing prevalence but uncertain pathogenesis. We performed a genome-wide, family-based association study of CD using P2BAT/FBAT software. The data is gathered from Collaborative Study on the Genetics of Alcoholism (COGA) and International Multi-Center ADHD Genetics Project (IMAGE). Using COGA data, we identified 20 markers which showed suggestive associations (p<10-3) with CD. Nine of them are located in known genes. Two genes, ADAM10 and CAMK2A, which had been reported associated with Alzheimer's disease (AD), bipolar disorder, and depression, were of more concern. Using IMAGE sample, our results were well replicated. This study identified several CD associated genetic variants, especially two novel candidate genes. These findings may serve as a resource for replication in other populations to elucidate the potential role of these genetic variants in CD.

Yes / Symptoms and signs (symptoms in brief) are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM). To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. The proposed method gets reliable gene rank list with AUC (area under curve) 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms. / NSFC Project (61105055, 81230086), China 973 Program (2014CB542903), The National Key Technology R&D Program (2013BAI02B01, 2013BAI13B04), the National S&T Major Special Project on Major New Drug Innovation (2012ZX09503-001-003), and the Fundamental Research Funds for the Central Universities.

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

Background: Community-acquired pneumonia (CAP) is an acute disease condition with a high risk of rapid deteriorations. We analysed the influence of genetics on cytokine regulation to obtain a better understanding of patient’s heterogeneity. Methods: For up to N = 389 genotyped participants of the PROGRESS study of hospitalised CAP patients, we performed a genome-wide association study of ten cytokines IL-1β, IL-6, IL-8, IL-10, IL-12, MCP-1 (MCAF), MIP-1α (CCL3), VEGF, VCAM-1, and ICAM-1. Consecutive secondary analyses were performed to identify independent hits and corresponding causal variants. Results: 102 SNPs from 14 loci showed genome-wide significant associations with five of the cytokines. The most interesting associations were found at 6p21.1 for VEGF (p = 1.58 × 10−20), at 17q21.32 (p = 1.51 × 10−9) and at 10p12.1 (p = 2.76 × 10−9) for IL-1β, at 10p13 for MIP-1α (CCL3) (p = 2.28 × 10−9), and at 9q34.12 for IL-10 (p = 4.52 × 10−8). Functionally plausible genes could be assigned to the majority of loci including genes involved in cytokine secretion, granulocyte function, and cilial kinetics. Conclusion: This is the first context-specific genetic association study of blood cytokine concentrations in CAP patients revealing numerous biologically plausible candidate genes. Two of the loci were also associated with atherosclerosis with probable common or consecutive pathomechanisms.

Plant symbiosis with arbuscular mycorrhizal (AM) fungi provides many benefits for plants, including increased nutrient uptake, drought tolerance, and belowground pathogen resistance. In order to have a better understanding of the genetic architecture of mycorrhizal symbiosis, we conducted a genome-wide association study (GWAS) by phenotyping a diversity panel of cultivated sunflower (Helianthus annuus) for root colonization under inoculation with the AM fungus Rhizophagus intraradices. This mapping panel consists of 261 inbred lines that capture approximately 90% of the genetic diversity present in the cultivated sunflower germplasm. Using a mixed linear model approach with a high-density genetic map, we determined regions of the genome that are likely associated with AM colonization in sunflower. Additionally, we used a ‘core 12' set of twelve diverse lines (representing approximately 50% of the genetic diversity in the cultivated germplasm) to assess the effect that inoculation with AM fungi has on dried shoot biomass and macronutrient uptake. Colonization rate among lines in the mapping panel ranged from 0 to 70% and was not correlated with mycorrhizal growth response, shoot P response, or shoot K response among the core 12 lines. Association mapping yielded three SNPs that were significantly associated with AM colonization rate. These SNPs explained 19.0%, 14.4%, and 27.9% of the variance in three different metrics used to measure the degree of root colonization. Three genes of interest identified from the significant regions that contained these SNPs are potentially related to plant defense. Overall, our data suggests that candidate genes involved in plant defense may affect AM colonization rates within cultivated sunflower, and that these genes have a large effect size.