Genome Wide Association Studies (GWAS) have uncovered many genetic regions which are associated with autoimmune disease risk. In this thesis, I present methods which I have developed to build upon these studies and enable the analysis of the causal variants of these diseases. Colocalization methods disentangle whether potential causal variants are shared or distinct in related diseases, and enable the discovery of novel associations below the single-trait significance threshold. However, existing approaches require independent datasets to accomplish this. I extended two methods to allow for the shared-control design; one of these extensions also enables fine mapping in the case of shared variants. My analysis of four autoimmune diseases identified 90 regions associated with at least one disease, 33 of which were associated with 2 or more disorders; 14 of these had evidence of distinct causal variants. Once associated variants have been identified, we may wish to test some aggregate property, such as enrichment within an annotation of interest. However, the null distribution of GWAS signals showing association with a trait and preserving expected correlation due to linkage disequilibrium is complicated. I present an algorithm which computes the expected output of a GWAS, given any arbitrary definition of "null", and hence can be used to simulate the null distribution required for such a test. Commonly, GWAS report only summary data, and determining which genetic variants are causal is more difficult; the strongest signal may merely be correlated with the true causal variant. I have developed a statistical method for fine mapping a region, requiring only GWAS p-values and publicly available reference datasets. I sample from the space of potential causal models, rejecting those leading to expected summary data excessively different from that observed. This removes the need for the assumption of a single causal variant. In contrast to other summary statistic methods which allow for multiple causal variants, it does not depend upon availability of effect size estimates, or the allelic direction of effect and it can infer whether the pattern of association is likely caused by a non-genotyped SNP without requiring imputation. I discuss the effect of choice of reference dataset, and the implications for other summary statistics techniques.

White mold (Sclerotinia sclerotiorum Lib. de Bary) is one of the most important diseases of common bean in the U.S. with seed yield losses up to 100%. White mold resistance is hard to incorporate into breeding materials because of low heritability, cumbersome screening methods, and few sources of resistance. A Multi-parent Advanced Generation Inter-Crosses population was created to develop resistant germplasm and map the genetics factors controlling the resistance. A seedling straw test method was used to identify resistant lines. Twenty genotypes were found to be resistant based on its response when inoculated with isolate 1980 of white mold. GWAS using quantitative, polynomial and binomial phenotypic distribution data, identified 30 genomic regions associated with resistance on Pv01, Pv02, Pv04, Pv05, Pv07, Pv08, Pv10 and Pv11. Cumulative R2 values were 57% for binomial distribution using 13 genomic intervals, 41% for polynomial using 8 intervals, and 40% for quantitative using 11 intervals.

Challenges to growing winter wheat in North Dakota include winter temperature and disease pressure. Fusarium head blight (FHB) is a devastating disease that necessitates breeding for resistance. In the NDSU breeding program FHB resistance genes are often associated with a decrease in performance. This study used single seed descent to advance lines while maintaining a near random population. Early generation (F4) selection focused heavily on yield and the presence of FHB resistance quantitative trait loci to develop winter wheat lines with FHB resistance and high yield. Stripe rust is a fungal disease that is becoming increasingly problematic in North Dakota. To assess the available stripe rust resistance in the NDSU winter wheat germplasm, two sets of diverse breeding lines were used for stripe rust resistance phenotyping and genotyping by sequencing. The phenotype and genotype data were then used to locate resistance genes through genome wide association study.

For more than half of the world’s population, rice (Oryza sativa L.), the most saltsensitive cereal, is a dietary staple. Soil salinity is a major constraint to rice production worldwide. Thus, to feed 9 billion people by 2050, we need to increase rice production while facing the challenges of rapid global environmental changes. To meet some of these challenges, there is a vital requirement to significantly increase rice production in salinized land and improve photosynthetic efficiency. Exposure of plants to soil salinity rapidly reduces their growth and transpiration rates (TRs) due to the ‘osmotic component’ of salt stress (sensu Munns and Tester), which is hypothesized to be related to sensing and signaling mechanisms. Over time, toxic concentrations of Na+ and Cl− accumulate in the cells of the shoot, known as the ‘ionic component’ of salt stress, which causes premature leaf senescence. Both osmotic and ionic components of salinity stress are likely to impact yield. Despite significant advances in our understanding of the ionic components of salinity tolerance, little is known about the early responses of plants to salinity stress. In my PhD project, the aim was to analyze naturally occurring variation in salinity tolerance of rice and identify key genes related to higher salinity tolerance using high-throughput phenomics and field trials. I used a forward genetics approach, with two rice diversity panels (indica and aus) and recently published sequencing data (McCouch et al., 2017). Indica and aus were phenotyped under controlled conditions, while the indica diversity panel was also further studied under field conditions for salinity tolerance. I also examined previously unexplored traits associated with salinity tolerance, in particular the effects of salinity on transpiration and transpiration use efficiency. The non-destructive high-throughput experiments conducted under controlled conditions gave insights into the understudied shoot ion-independent component of salinity tolerance. In parallel, the field experiments increased our understanding of the genetic control of further components of salinity tolerance, including the maintenance of yield under saline conditions. Importantly, this project also aimed to improve the current association methods of GWAS by exploring and testing novel Mixed Linear Models. One major benefit of this Ph.D. project was the development of a more holistic approach that recognizes the complexity of the genotype–phenotype interaction. The purpose of my work was to shed more light on the genetic mechanisms of salinity tolerance in rice and discover genes associated with traits contributing to higher photosynthetic activity under both controlled and field conditions. This will ultimately lead to further exploration of the genetic diversity present in the PRAY indica panel, in order to develop higher yielding rice varieties.

Wheat is the most widely grown crop in the world and as such, is an essential source of energy and nutrition. The challenges that breeders presently face is to increase production to feed the rising population of the world, while also accounting for climate change, pollution, water and environmental stresses. As genetic uniformity of modern cultivars has increased vulnerability to pests and diseases, the wild relatives of wheat offer a rich source of genetic diversity and stress tolerance traits, that can be harnessed and transferred in to modern wheat. In this study, we used array-based genotyping to explore genetic diversity in 385 domesticated and non-domesticated lines of wheat and their wild relatives. Genetic characterization using the wheat 90K array, and subsequent filtering and validation mapped 9,570 single nucleotide polymorphic markers onto the wheat reference genome. Phylogenetic analyses illustrated four major clades, clearly separating the wild species from the domesticated, and the ancient Triticum turgidum species from modern T. turgidum cultivars. Using this diverse germplasm, a genome-wide association study (GWAS) was performed for leaf rust, the most widespread rust disease of wheat. Identification of novel sources of resistance is necessary to maintain disease resistance and stay ahead in the plant-pathogen evolutionary arms race. GWAS was conducted using eight statistical models for infection types against six leaf rust isolates and leaf rust severity rated in field trials for 3-4 years at 2-3 locations in Canada. Functional annotation of genes containing significant quantitative trait nucleotides (QTNs) identified 96 disease-related nucleotide associated with leaf rust resistance. A total of 21 QTNs were in haplotype blocks or within flanking markers of at least 16 known leaf rust (Lr) resistance genes. The remaining significant QTNs were considered loci that putatively harbor new Lr resistance genes. Future efforts to validate these loci will help understand their role in disease resistance and promote their utility for marker-assisted selection in pre-breeding.

The genome-wide association study (GWAS) approach has identified novel loci for a variety of complex diseases. However, for most of these disorder much of the heritability is not explained by this approach, which focuses on identifying common variants that are associated with disease risk. The unexplained heritability may be due to genetic or phenotypic heterogeneity or the influence of rare variants. The motivation behind this thesis was to uncover the unexplained heritability by applying joint analyses of sets of variants (gene-based association test) and multiple disease-related phenotypes (called multivariate gene-based association test). First, we evaluated multivariate gene-based methods for detecting association of common genetic variants with correlated phenotypes. An extensive simulation study showed that the method combining the MultiPhen and GATES software performed best for most tested scenarios especially when correlations among phenotypes are relatively low. We developed a new multivariate gene-based test using rare variants called VEMPHAS. A simulation study using VEMPHAS showed that this method correctly controls for type I error in all tested scenarios. We applied VEMPHAS to analysis of various phenotypes related to Alzheimer disease (AD) and found suggestive association (P < 4.15x10-6) with the gene TRIM22, which has been identified in a previous sequencing study of AD onset in PSEN1/2 mutation carriers. We also developed software with a graphical user interface which is designed for integrating information from different types of data sources including genetic data (from GWAS or sequencing), expression data (from RNA-Seq), and protein structures (from protein data banks). This software has several features including 1) testing associations between genetic variants and gene expressions; 2) locating amino acids, encoded by the variants, in a protein structure; and 3) retrieving genetic locations (chromosome and base pair positions) of amino acids of interest in the protein structure. The last feature can be applied for prioritizing coding variants for gene-based association testing. The methods and strategies developed for this dissertation project can effectively uncover a portion of the remaining heritability of complex diseases that is unexplained by traditional GWAS approaches.

Indiana University-Purdue University Indianapolis (IUPUI) / Recent genome-wide association studies (GWAS) have identified many common genetic variants that are associated with women’s reproductive timing characteristics including ages at menarche and at natural menopause. However, the associations of these variants with other human health related phenotypes such as blood pressure, cancer, diabetes, obesity, and body height growth have not been well studied. No published studies to our knowledge have directly assessed the genetic influence of reproductive timing related variants on the aforementioned common traits. A better understanding of pleiotropic effects of these variants is important because it will help elucidate the precise mechanisms of common traits/diseases such as hypertension which have not been fully understood so far, and give clues for developing better solutions for disease prevention and treatment. We, therefore, conducted three studies to explore genetic variant effects on blood pressure and body height growth. In the first study, we analyzed data from a local cohort of 601 healthy adolescents from Indianapolis schools. Mixed effect model analysis revealed that 11 reproductive related single nucleotide polymorphisms (SNPs) were significantly associated with blood pressure in the study subjects. In order to assess if these genetic effects extended to the adult blood pressure, we performed the second study to investigate the genetic effect on blood pressure in adults. We used the summary statistics obtained from the two large international GWAS consortia, the Blood Pressure Consortium and the ReproGen Consortium. Bivariate analyses showed that more than 100 SNPs were associated with both blood pressure and reproductive timing. As the blood pressure development is closely related to somatic growth, we conducted the third study to exam the genetic effect of reproductive-timing related variants on the linear growth from the aforementioned local cohort. We identified 8 genetic variants significantly associated with the catch-up of linear growth in the study subjects. In conclusion, these three studies collectively provided evidence in support of the pleiotropic effects of the reproductive timing variants, suggesting the common genetic basis underlying the correlated traits. Future research is needed to validate the findings. / 2 years

Indiana University-Purdue University Indianapolis (IUPUI) / Purpose. In the US, melanoma and non-melanoma skin cancer (NMSC) rates have increased substantially in recent decades. While many skin cancer risk factors have been established, the impact of dietary citrus, which is naturally abundant in photocarcinogenic psoralens, remains enigmatic. The purpose of this research was to investigate associations between citrus consumption and risks of melanoma and NMSC, and to conduct a genome-wide study to identify genetic variants that may modify this association. Methods. Participants from the UK Biobank were leveraged for these analyses. Citrus consumption was collected via five rounds of 24-hour recall questionnaires, with complete citrus data available for n=210,126 participants. Ascertainment of melanoma and NMSC cases were identified by international classification of disease codes via linkage with national registries. Logistic regression was used to estimate odds ratios and 95% confidence intervals for the associations between citrus consumption and skin cancer outcomes. Individual citrus products were assessed for independent associations with skin cancer risk, and established skin cancer risk factors were tested for interaction. Joint 2-degree-of-freedom (df) and 1-df tests were used to assess interaction between total citrus consumption and genetic variants. Results. After controlling for covariates, high total citrus consumption was significantly associated with increased melanoma risk, an association primarily driven by orange and orange juice consumption. Skin color was found to be a significant effect modifier for the association between total citrus consumption and melanoma risk, but only before adjusting for multiple comparisons. No significant associations were observed for high total citrus consumption or consumption of any individual citrus products and NMSC risk. Significant associations for half a serving of citrus consumption and NMSC risk were likely due to chance or confounding. Index SNPs on chromosomes 3, 9, and 16 were significant according to the joint 2-df test, and 7 SNPs on chromosome 16 displayed evidence of a citrus-gene interaction. Conclusion. My analyses provide evidence in support of high citrus consumption significantly increasing risk of melanoma, but not NMSC. I also identified SNPs on AFG3L1P that may modify this association. Future research should further explore these associations, particularly for NMSC and to confirm my genetic findings.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第18847号 / 医博第3958号 / 新制||医||1007(附属図書館) / 31798 / 京都大学大学院医学研究科医学専攻 / (主査)教授 小川 誠司, 教授 小泉 昭夫, 教授 藤渕 航 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Many plants are highly sensitive to salt in the soil, and their growth and yield can be greatly hindered by as little as less than 1% salt concentration in the soil. Additionally, soil salinity is a growing issue globally and affects significant areas in Utah. Halophytes are plants that are adapted to grow in saline soils and have been widely studied for their physiological and molecular characteristics, but little is known about their associated microbiomes. Bacteria were isolated from the rhizosphere and as root endophytes of Salicornia rubra, Sarcocornia utahensis, and Allenrolfea occidentalis, three native Utah halophytes. Several strains of halophilic bacteria have been isolated and screened for the ability to stimulate plant growth in saline conditions despite the high salt concentrations. Halomonas, Bacillus, and Kushneria species were consistently isolated both from the soil and as endophytes from roots of all three plant species at all collection times. Of the isolates tested for the ability to stimulate growth of alfalfa under saline conditions, Halomonas and Kushneria strains stimulated plant growth in the presence of 1% NaCl. The same bacteria used in the inoculation were recovered from surface sterilized alfalfa roots, indicating the ability of the inoculum to become established as an endophyte. This raises the question of whether these plant associated halophilic isolates contain genes that aid in plant growth promotion. We are interested in genomic sequencing of our Halomonas and Kushneria strains and performing genomic analysis to determine if there is a difference in genes between plant associated and non-plant associated halophilic isolates. We explored the hypothesis that certain bacterial properties have been selected for to aid plant growth. This was accomplished by performing whole genome sequencing of 26 Kushneria and Halomonas strains, both plant and non-plant associated. These strains came from freezer stocks of previously collected isolates as well as field trips to collect more samples. Halophilic bacteria were isolated from bulk soil, rhizosphere, and halophyte tissues (root and shoot tissues). The non-plant associated (bulk soil) halophilic Kushneria and Halomonas strains aided in determining if there are specific bacterial genes that are expressed in plant associated strains. Whole genome sequencing of the isolates was performed on the Oxford Nanopore platform. The sequence data was then assembled and annotated. The genomes were then included in a genome wide association study was performed. The results from the GWAS show that there is not a significant difference between plant and non-plant associated isolates, disproving our hypothesis. The results also show that few known genes for phytohormone synthesis were present in the pangenome, highlighting the need for further research to determine how these halophilic isolates aid in plant growth promotion in saline soils.