Rice (Oryza sativa) is the staple food for more than 3.5 billion people worldwide. As the population continues to grow, rice yield will need to increase by 1% every year for the next 30 years to keep up with the growth. In the US, Arkansas accounts for more than 50% of rice production. Over the last 68 years, rice production has continued to grow in Mississippi, placing it in fourth place after Arkansas, Louisiana, and California. Due to increasing rice acreage, regionally and worldwide, the need to develop abiotic stress-tolerant rice has increased. Unfortunately, current rice breeding programs lack genetic diversity, and many traits have been lost through the domestication of cultivated rice. Currently, stressors stemming from the continued effects of climate change continue to impact rice. To counteract the impacts of climate change, research has shifted to evaluating wild and weedy relatives of rice to improve breeding techniques. Weedy rice (Oryza sativa ssp.) is a genetically similar, noxious weed in rice with increased competitive ability. Studies have demonstrated that weedy rice has increased genetic variability and inherent tolerance to abiotic stressors. The aims of this study were to 1) screen a weedy rice mini-germplasm for tolerance to cold, heat, and complete submergence-stress, 2) utilize simple sequence repeat (SSR) markers and single nucleotide polymorphisms to evaluate the genetic diversity of the weedy rice population, and 3) use genome-wide association (GWAS) to identify SNPs associated with candidate genes within the population.

No description available.

<p>Hypercholesterolemia is the presence of high levels of cholesterol in the blood, and it is one of the major factors for the development of long-term complications in T1D patients.</p> <p>In the thesis, we studied 1303 Caucasians with type 1 diabetes in the Diabetes Control and Complications Trial (DCCT). With the experience of diabetes study, many factors are associated with diabetes complications, they are age, gender, cohort, treatment, diabetes duration, body mass index (BMI), exercise, insulin dose, etc. We mainly focus on which factors are associated with total cholesterol (CHL) analysis in the thesis.</p> <p>Many measures were collected monthly, quarterly or yearly for average 6.5 years from 1983 to 1993. We used annually lipid measures of DCCT because of their values are sufficient and complete, and they belong to longitudinal data.</p> <p>Different methods are discussed in the study, and linear mixed effect models are the appropriate approach to the study. The details of model selection with CHL model analysis are shown, which includes fixed effect selection, random effects selection, and residual correlation structure selection. Then the SNPs were added on three models individually in GWAS. We found locus (rs7412) is not only genome-wide associated with CHL, but also genome-wide associated with LDL.</p> <p>We will assess whether these SNPs are diabetes-specific in the future, and we will add dietary data in the three models to identify locus are associated with the interaction of diet and SNPs.</p> / Master of Science (MSc)

In multiple species, genome-wide association (GWA) studies have become an increasingly prevalent method of identifying the quantitative trait loci (QTLs) that underlie complex traits. Despite this, relatively few GWA analyses using high-density genomic markers have been carried out on highly quantitative traits in wheat. We utilized single-nucleotide polymorphism (SNP) data generated via a genotyping-by-sequencing (GBS) protocol to perform GWA on multiple yield-related traits using a panel of 329 soft red winter wheat genotypes grown in four environments. In addition, the SNP data was used to examine linkage disequilibrium and population structure within the testing panel. The results indicated that an alien translocation from the species Triticum timopheevii was responsible for the majority of observed population structure. In addition, a total of 50 significant marker-trait associations were identified. However, a subsequent study cast some doubt upon the reproducibility and reliability of plant QTLs identified via GWA analyses. We used two highly-related panels of different genotypes grown in different sets of environments to attempt to identify highly stable QTLs. No QTLs were shared across panels for any trait, suggesting that QTL-by-environment and QTL-by-genetic background interaction effects are significant, even when testing across many environments. In light of the challenges involved in QTL mapping, prediction of phenotypes using whole-genome marker data is an attractive alternative. However, many evaluations of genomic prediction in crop species have utilized univariate models adapted from animal breeding. These models cannot directly account for genotype-by-environment interaction, and hence are often not suitable for use with lower-heritability traits assessed in multiple environments. We sought to test genomic prediction models capable of more ad-hoc analyses, utilizing highly unbalanced experimental designs consisting of individuals with varying degrees of relatedness. The results suggest that these designs can successfully be used to generate reasonably accurate phenotypic predictions. In addition, multivariate models can dramatically increase predictive accuracy for some traits, though this depends upon the quantity and characteristics of genotype-by-environment interaction. / Ph. D.

Die Methoden der Netzwerktheorie erfreuen sich wachsender Beliebtheit, da sie die Darstellung von komplexen Systemen durch Netzwerke erlauben. Diese werden nur mit einer Menge von Knoten erfasst, die durch Kanten verbunden werden. Derzeit verfügbare Methoden beschränken sich hauptsächlich auf die deskriptive Analyse der Netzwerkstruktur. In der hier vorliegenden Arbeit werden verschiedene Ansätze für die Inferenz über Prozessen in komplexen Netzwerken vorgestellt. Diese Prozesse beeinflussen messbare Größen in Netzwerkknoten und werden durch eine Menge von Zufallszahlen beschrieben. Alle vorgestellten Methoden sind durch praktische Anwendungen motiviert, wie die Übertragung von Lebensmittelinfektionen, die Verbreitung von Zugverspätungen, oder auch die Regulierung von genetischen Effekten. Zunächst wird ein allgemeines dynamisches Metapopulationsmodell für die Verbreitung von Lebensmittelinfektionen vorgestellt, welches die lokalen Infektionsdynamiken mit den netzwerkbasierten Transportwegen von kontaminierten Lebensmitteln zusammenführt. Dieses Modell ermöglicht die effiziente Simulationen verschiedener realistischer Lebensmittelinfektionsepidemien. Zweitens wird ein explorativer Ansatz zur Ursprungsbestimmung von Verbreitungsprozessen entwickelt. Auf Grundlage einer netzwerkbasierten Redefinition der geodätischen Distanz können komplexe Verbreitungsmuster in ein systematisches, kreisrundes Ausbreitungsschema projiziert werden. Dies gilt genau dann, wenn der Ursprungsnetzwerkknoten als Bezugspunkt gewählt wird. Die Methode wird erfolgreich auf den EHEC/HUS Epidemie 2011 in Deutschland angewandt. Die Ergebnisse legen nahe, dass die Methode die aufwändigen Standarduntersuchungen bei Lebensmittelinfektionsepidemien sinnvoll ergänzen kann. Zudem kann dieser explorative Ansatz zur Identifikation von Ursprungsverspätungen in Transportnetzwerken angewandt werden. Die Ergebnisse von umfangreichen Simulationsstudien mit verschiedenstensten Übertragungsmechanismen lassen auf eine allgemeine Anwendbarkeit des Ansatzes bei der Ursprungsbestimmung von Verbreitungsprozessen in vielfältigen Bereichen hoffen. Schließlich wird gezeigt, dass kernelbasierte Methoden eine Alternative für die statistische Analyse von Prozessen in Netzwerken darstellen können. Es wurde ein netzwerkbasierter Kern für den logistischen Kernel Machine Test entwickelt, welcher die nahtlose Integration von biologischem Wissen in die Analyse von Daten aus genomweiten Assoziationsstudien erlaubt. Die Methode wird erfolgreich bei der Analyse genetischer Ursachen für rheumatische Arthritis und Lungenkrebs getestet. Zusammenfassend machen die Ergebnisse der vorgestellten Methoden deutlich, dass die Netzwerk-theoretische Analyse von Verbreitungsprozessen einen wesentlichen Beitrag zur Beantwortung verschiedenster Fragestellungen in unterschiedlichen Anwendungen liefern kann.

Recent technological advances and remarkable successes have led to genome-wide association studies (GWAS) becoming a tool of choice for investigating the genetic basis of common complex human diseases. These studies typically involve samples from thousands of individuals, scanning their DNA at up to a million loci along the genome to discover genetic variants that affect disease risk. Hundreds of such variants are now known for common diseases, nearly all discovered by GWAS over the last three years. As a result, many new studies are planned for the future or are already underway. In this thesis, I present analysis results from actual studies and some developments in theory and methodology. The Wellcome Trust Case Control Consortium (WTCCC) published one of the first large-scale GWAS in 2007. I describe my contribution to this study and present the results from some of my follow-up analyses. I also present results from a GWAS of a bipolar disorder sub-phenotype, and a recent and on-going fine mapping experiment. Building on methods developed as part of the WTCCC, I describe a Bayesian approach to GWAS analysis and compare it to widely used frequentist approaches. I do so both theoretically, by interpreting each approach from the perspective of the other, and empirically, by comparing their performance in the context of replicated GWAS findings. I discuss the implications of these comparisons on the interpretation and analysis of GWAS generally, highlighting the advantages of the Bayesian approach. Finally, I examine the effect of linkage disequilibrium on the detection and estimation of various types of genetic effects, particularly non-additive effects. I derive a theoretical result showing how the power to detect a departure from an additive model at a marker locus decays faster than the power to detect an association.

The population level bias towards right-handedness in humans implies left-hemisphere dominance for fine motor control. Left-handedness and reduced cerebral asymmetry have been linked to neurodevelopmental disorders such as dyslexia. Understanding the biology of these traits at a genetic level is crucial for understanding the relationship between handedness and neurodevelopmental disorders. Here I present genome-wide association study (GWAS) meta-analyses for both relative hand skill (handedness, n = 728) and reading-related traits (n = 548) in individuals with dyslexia. I uncovered a genome-wide significant association in an intron of PCSK6 associated with relative hand skill. PCSK6 is a protease that cleaves NODAL proprotein into an active form, and NODAL determines the development of left/right (LR) asymmetry in bilaterians. I performed pathway analyses of the GWAS data that revealed handedness is determined in part by the mechanisms that establish left/right (LR) asymmetry early in development, such as NODAL signalling and ciliogenesis. This finding replicated in a general population cohort unaffected with neurodevelopmental disorders (n = 2,666). A key stage in LR asymmetry development is the rotation of cilia that creates a leftward flow of NODAL. Candidate genes for dyslexia are involved in both neuronal migration and ciliogenesis. Ciliopathies can cause not only LR body asymmetry phenotypes, but also cerebral midline phenotypes such as an absent corpus callosum. Furthermore, I identified a genome-wide significant association with non-word reading located in an intron of MAP1B, a gene involved in neuronal migration that causes an absent corpus callosum when disrupted in mice. However, this finding did not replicate in two independent cohorts with dyslexia (n = 156 & 199), or in the general population cohort (n = 2,359). Though these cohorts had inadequate reading measures and poorly matched ascertainment for dyslexia. I also performed copy number variation (CNV) pathway and burden analyses of 920 individuals with dyslexia and 1,366 unselected controls, but did not find that rare CNVs play a major role in the etiology of dyslexia. Based on these results I propose that common variants in genes responsible for ciliogenesis and corpus callosum development influence traits such as handedness and reading ability.

The post human genome sequence era has begun to explore various aspects of the functional genome in relation to disease including gene expression, genetic variation and epigenetics. The genetic determinants of common and complex phenotypes are difficult to resolve even though their heritability is recognised. Recent genome-wide association studies (GWAS) for common diseases has identified many new disease susceptibility associated loci. These loci often lie in non-coding regions of the genome and disease associated genetic variants are proposed to act by modulating gene expression. This thesis investigated genetic variation as determinants of gene expression in the context of the immune system especially focused on the innate immune and inflammatory responses. Different primary human immune cell types were collected from healthy volunteers of European ancestry to achieve this. In order to identify genetic variants associating with gene expression, expression quantitative trait loci (eQTL) mapping was conducted in a cell type specific manner. The primary dataset (n=288) consists of CD19⁺ B-cells from the adaptive immune system and CD14⁺ monocytes from the innate immune system. 78% of the total cis eQTL were found to be cell type specific and include genes relating to their roles in the immune response. Trans eQTL showed greater cell type specificity and include master regulatory eQTL on the LYZ locus at chromosome 12q15 in monocytes and the KLF4 (9p31) in B-cells. The identified eQTL are implicated in association with autoimmune disease susceptibility including inflammatory bowel disease, diabetes and rheumatoid arthritis. The second analysed dataset (n=64) consists of CD14+ monocytes and macrophages differentiated ex vivo. Macrophages are involved in many inflammatory diseases as well as in the innate immune response. The differential gene expression and eQTL mapping analyses were conducted to investigate macrophages specific gene expression signatures and associations to genetic variants. Macrophage eQTL are involved in signal transduction for the inflammatory response (IL1RN and STAT4) and lipid metabolism (PPARG) with implication for metabolic disease association. The eQTL analyses using primary immune cell types provide insights into genetic variation in association to gene expression which is involved in autoimmunity and disease susceptibility.

The fundamental theme my thesis explores is the relationship between genetic variation and phenotypic variation. It addresses three main questions. What is the genetic architecture of traits in the HS? How can sequence information help identifying the sequence variants and genes responsible for phenotypic variation? Are the genetic factors contributing to phenotypic variation in the rat homologous to those contributing to variation in the same phenotype in the mouse? To address these questions, I analysed data collected by the EURATRANS consortium on 1,407 Heterogeneous Stock (HS) rats descended from eight inbred strains through sixty generations of outbreeding. The HS rats were genotyped at 803,485 SNPs and 160 measures relevant to a number of models of disease (e.g. anxiety, type 2 diabetes, multiple sclerosis) were collected. The eight founders of the Stock were genotyped and sequenced. I identified loci in the genome that contribute to phenotypic variation (Quantitative Trait Loci, QTLs), and integrated sequence information with the mapping results to identify the genetic variants underlying the QTLs. I made some important observations about the nature of genetic architecture in rats, and how this compares to mice and humans. I also showed how sequence information can be used to improve mapping resolution, and in some cases to identify causal variants. However, I report an unexpected observation: at the majority of QTLs, the genetic effect cannot be accounted for by a single variant. This finding suggests that genetic variation cannot be reduced to sequence variation. This complexity will need to be taken into account by studies that aim at unravelling the genetic basis of complex traits.

We developed a pipeline to identify the copy number polymorphisms (CNPs) in the Northern Swedish population using whole genome sequencing (WGS) data. Two different methodologies were applied to discover CNPs in more than 1,000 individuals. We also studied the association between the identified CNPs with the expression level of 438 plasma proteins collected in the same population. The identified CNPs were summarized and filtered as a population copy number matrix for 1,021 individuals in 243,987 non-overlapping CNP loci. For the 872 individuals with both WGS and plasma protein biomarkers data, we conducted linear regression analyses with age and sex as covariance. From the analyses, we detected 382 CNP loci, clustered in 30 collapsed copy number variable regions (CNVRs) that were significantly associated with the levels of 17 plasma protein biomarkers (p &lt; 4.68×10-10).