京都大学 / 0048 / 新制・課程博士 / 博士(人間・環境学) / 甲第22791号 / 人博第962号 / 新制||人||228(附属図書館) / 2020||人博||962(吉田南総合図書館) / 京都大学大学院人間・環境学研究科相関環境学専攻 / (主査)教授 瀬戸口 浩彰, 教授 市岡 孝朗, 教授 宮下 英明 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DFAM

Fusarium head blight (FHB) is a devastating wheat (Triticum aestivum L.) disease worldwide. Presently, there is insufficient FHB resistance in the Canadian wheat germplasm. Genome-wide association study (GWAS) and genomic selection (GS) can be utilized to identify sources of resistance that could benefit wheat breeding. To define the genetic architecture of FHB resistance, association panels from a spring and a winter collection were evaluated using the Wheat Illumina Infinium 90K array. A total of 206 accessions from the spring panel and 73 from the winter panel were evaluated in field trials for 3-4 years at two locations, namely Morden (Manitoba) and Ottawa (Ontario). These accessions were phenotyped for FHB incidence (INC), severity (SEV), visual rating index (VRI), and deoxynivalenol (DON) content. Significant (p < 0.05) differences among genotypes for all traits were found. Genetic characterization using the wheat 90K array identified a set of 20,501 single nucleotide polymorphisms (SNPs). The probe sequences (~100 bp) of these SNPs were mapped to the Chinese Spring reference genome v2.0 to identify 13,760 SNPs in the spring panel, and 10,421 SNPs in the winter panel covering all 21 wheat chromosomes. GWAS was performed to identify novel FHB resistance loci for INC, SEV, VRI and DON content for the spring and the combined panels separately using these 13,760 SNPs and for the winter panel using 10,421 SNPs. A total of 107, 157, 174 unique quantitative trait loci (QTNs) were identified for the four traits using two single-locus and seven multi-locus GWAS models for the spring, winter, and combined panels, respectively. These QTNs represent a valuable genetic resource for the improvement of FHB resistance in commercially grown wheat cultivars. In addition, these GWAS-defined QTNs were further used for GS to determine the breeding value (BV) of individuals as outlined below. In order to understand the role of the model and that of the marker type and density in trait prediction modelling, a GS study was conducted. GS is considered as an important tool for increasing genetic gain for economically important traits such as FHB resistance. GS uses genome-wide molecular markers to develop statistical models that predict genomic estimated breeding values (GEBVs) of an individual. Our results support genomic prediction (GP) as an alternative to phenotypic selection to predict the BVs of individuals for this trait. GS accounts for minor effect QTNs, which is beneficial when breeding for quantitative traits. Moderate to high GP accuracies can be achieved for FHB resistance-related traits when implemented in a breeding program. The correlation between the estimate of the missing phenotypic value and the observed phenotype is known as predictive ability (r). Overall, the predictive ability increased significantly using a QTN-based GP approach for FHB traits in wheat and its wild relatives. DON content had the highest predictive ability among all FHB traits, and that was in the winter panel, highlighting the importance of objectively measured traits in breeding for disease resistant genotypes. Interestingly, the winter panel contained several wild relative species that may harbor genes of interest to prevent the accumulation of mycotoxins in the grain. This study showed the usability of genomic prediction by improving the predictive ability of the FHB traits, which can be applied in early generation selection to accelerate the improvement of FHB resistance in wheat. The results show that GS can be successfully implemented in wheat breeding programs over multiple breeding cycles and can be effective for economically important traits. It is anticipated that GS will play a substantial role in the future of wheat breeding.

Indiana University-Purdue University Indianapolis (IUPUI) / In the age of big data, better use of large, real-world datasets is needed, especially ultra-large databases that leverage health information exchange (HIE) systems to gather data from multiple sources. Promising as this process is, there have been challenges analyzing big data in healthcare due to big data attributes, mainly regarding volume, variety, and velocity. Thus, these health data require not only computational approaches but also context-based controls.In this research, we systematically examined associations among select neural and dermal conditions in an ultra-large healthcare database derived from an HIE, in which computational approaches with epidemiological measures were used. After a systematic cleaning, a binary logistic model-based methodology was used to search for associations, controlling for race and gender. Age groups were chosen using an algorithm to find the highest incidence rates for each condition pair. A binomial test was conducted to check for significant temporal direction among conditions to infer cause and effect. Gene-disease association data were used to evaluate the association among the conditions and assess the shared genetic background. The results were adjusted for multiple testing, and network graphs of significant associations were created. Findings among methodologies were compared to each other and with prior studies in the literature. In the results, seemingly distant neural and dermal conditions had an extensive number of associations. Controlling for race and gender tightened these associations, especially for racial factors affecting dermal conditions, like melanoma, and gender differences on conditions like migraine. Temporal and gene associations helped explain some of the results, but not all. Network visualizations summarized results, highlighting central conditions and stronger associations. Healthcare data are confounded by many factors that hide associations of interest. Triangulating associations with separate analyses helped with the interpretation of results. There are still numerous confounders in these data that bias associations. Aside from what is known, our approach with limited variables may inform hypothesis generation. Using additional variables with controlled-computational methods that require minimal external input may provide results that can guide healthcare, health policy, and further research.

A comprehensive understanding about how genetic variants and mutations contribute to phenotypic variations and alterations entails experimental technologies and analytical methodologies that are able to detect genetic variants/mutations from various biological samples in a timely and accurate manner. High-throughput sequencing technology represents the latest achievement in a series of efforts to facilitate genetic variants discovery and genotyping and promises to transform the way we tackle healthcare and biomedical problems. The tremendous amount of data generated by this new technology, however, needs to be processed and analyzed in an accurate and efficient way in order to fully harness its potential. Structural variation (SV) encompasses a wide range of genetic variations with different sizes and generated by diverse mechanisms. Due to the technical difficulties of reliably detecting SVs, their characterization lags behind that of SNPs and indels. In this dissertation I presented two novel computational methods: one for detecting transposable element (TE) transpositions and the other for detecting SVs in general using a local assembly approach. Both methods are able to pinpoint breakpoint junctions at single-nucleotide resolution and estimate variant allele frequencies in the sample. I also applied those methods to study the impact of TE transpositions on the genomic stability, the inheritance patterns of TE insertions in the population and the molecular mechanisms and potential functional consequences of somatic SVs in cancer genomes.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19402号 / 医博第4053号 / 新制||医||1012(附属図書館) / 32427 / 京都大学大学院医学研究科医学専攻 / (主査)教授 佐藤 俊哉, 教授 小川 誠司, 教授 横出 正之 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第20278号 / 医博第4237号 / 新制||医||1021(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 森田 智視, 教授 佐藤 俊哉, 教授 中山 健夫 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

No description available.

Here we present the genome-wide association study of body weight at 8 weeks of age based onthe advanced intercross pedigree of two chicken lines gone through bi-directional selection.With improved marker density (~3M SNPs) and larger sample size (2667 individuals from F2-F15), 34 loci with suggestive significance are detected, of which 18 loci are novel, and the rest17 loci are consistent with the results of previous quantitative trait locus mapping studies onthis trait with smaller number of genetic markers and fewer individuals. The component traits,referring to traits related to body weight and possibly contributing to the body weight as well,are also measured and analysed. The combined result showed that one locus with significantmarginal effect on BW8 is associated with early growth, breast muscle development and shankdevelopment, while another locus with late development and bursa development.

The Norway spruce is of great importance from both an ecological- and economic standpoint. Information about which genes that causes certain phenotypic traits in the species is therefore highly valuable. The purpose of this project was to apply machine learning to find such genotype-phenotype correlations. The purpose was also to compare the results from different machine learning algorithms to a more traditional linear mixed model GWAS (where correlation to the phenotype is estimated for each SNP one by one) to find which is the better method for GWAS. The machine learning algorithms tested were decision tree, support vector machine and support vector regression. The phenotypes analyzed were wood density and initiation frequency of zygotic embryogenesis (ZE). The latter is related to a new method for cloning. The genetic data consisted of single-nucleotide polymorphisms (SNPs). Due to the large genome size of Norway spruce and due to limitations in the packages used in R two different approaches were taken to reduce the sample size. The first approach used Kendall’s rank correlation coefficient to remove redundant SNPs and the second used an iterative approach to the machine learning model. The iterative approach was proven to be the best and support vector machine/regression was found to be better than decision tree for both phenotypes. Support vector regression from the iterative approach resulted in a squared correlation coefficient of 0.83 for density and 0.94 for ZE initiation frequency. Note that these very high values should be interpreted with caution, as it is possible that some of the significant correlations are only due to random chance. Even a small chance for random correlations will result in findings when the number of SNPs are this large (1908552 SNPs). The significant SNPs identified by the machine learning models were compared to SNPs identified by the linear mixed model GWAS. This indeed showed some overlaps of significant SNPs, which increases the credibility of my results. However, further investigation of the identified significant SNPs is needed to determine their functional mode of action. My conclusion is that using machine learning to predict phenotypic traits from SNP data can be a good choice. However, the model might not use all correlated SNPs, just enough to get a good prediction. Therefore, for the purpose of finding significant SNPs, the linear mixed model approach might be better. In other words, the method used should be determined by the purpose of the study.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24494号 / 医博第4936号 / 新制||医||1063(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 村川 泰裕, 教授 小杉 眞司, 教授 松田 文彦 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM