Fusarium head blight (FHB) is a serious disease of wheat (Triticum aestivum) and other small grains; disease severity is affected by temperature and rainfall. This research comprised three studies: an artificially warmed experiment during 2016-2017, a morphology study and an FHB resistance screening study in 2015-2016, using approximately 250 wheat cultivars and breeding lines from programs in the eastern US. The location was the University of Kentucky Spindletop Research Farm in Lexington, KY. Higher levels of Fusarium damaged kernels and the toxin deoxynivalenol (DON) were observed in the warmed treatment compared to the control, and plant development was accelerated. In the FHB resistance screen, significant (p < 0.05) genotype differences for all traits were observed. A GWAS identified 16 SNPs associated with resistance and susceptibility, ranging from -2.14 to 4.01%. Three DON-associated SNPs reduced toxin levels by 3.2, 2.1, and 1.5 ppm. In the morphology study, negative correlations were observed among morphological and disease traits. Small effect SNPs were identified for all morphological traits, which might be useful in genomic selection; traits like spike length, spikelet number and inclination could be used in phenotyping. Response to warming indicates that existing resistance sources may be less effective in a warming climate.

Alcohol use disorder is a global health issue that affects a significant portion of the population, with affects including both negative mental and physical consequences. Currently, there are few treatment options available to those who suffer from alcohol use disorder, alcohol abuse, or alcohol dependence. Identifying candidate genes or environmental influences would therefore improve the means for possible treatments or identification of those people at risk for alcohol use disorder. Previous studies in humans have demonstrated an inverse association between initial sensitivity and risk for alcohol abuse. This connection allows investigators, and our laboratory, to investigate genetic and environmental factors that may influence initial ethanol sedation. Our laboratory utilizes Drosophila melanogaster (flies) as a model organism to identify these such factors influencing acute behavioral responses to alcohol. Our lab has found evidence for both environmental and genetic factors that influence initial alcohol sensitivity in flies. In one study, flies that are fed increased amounts of dietary yeast are resistant to ethanol. We have found that this ethanol resistance is related to the amount of nutrients that is consumed, which then affects alcohol uptake/metabolism, to influence initial alcohol sensitivity. Importantly, we found that serotonergic neuron function is essential for regulating the consumption of high dietary yeast media for the increased nutrient intake to occur. In two separate projects, we identified a role for myocyte enhancer factor 2 (Mef2) and nitric oxide synthase (Nos) in initial alcohol sensitivity. Mef2 was obtained via a GWAS study identifying genes with an association with initial sensitivity in humans. We found that decreasing or altering Mef2 expression, using mutants or Mef2 RNAi, resulted in flies having decreased sensitivity to alcohol. The gene Nos, came out of a previous genetic interaction screen in the laboratory. Multiple reagents to assess Nos’s role in alcohol behavior were obtained and consistent evidence from three piggyBac transposon insertion flies and, importantly, a Nos null fly, demonstrate that decreased Nos expression results in increased ethanol sensitivity. Other preliminary results suggest that Nos expression during adulthood, as well as the mechanism of S-nitrosation, may be important for ethanol sedation in Drosophila.

Orientador: Ana Fabrícia Braga Magalhães / Resumo: ABSTRACT – Preweaning calf mortality is one of main causes of economic losses in beef cattle, since most of economic incomes are represented by the number of weaned calves available for sale. It is also detrimental for genetic progress due to the reduction of young candidates for selection. The inclusion of molecular markers in genetic analysis allows a better understanding of genetic mechanisms underlying calf mortality. The objectives of this study were: i) to estimate direct and maternal heritability of preweaning calf mortality in Nellore cattle and ii) to seek for genomic regions and candidate genes affecting direct and maternal effects of preweaning calf mortality in Nellore cattle. Variance components were estimated via Bayesian Inference using a threshold animal model, that included the systematic effects of contemporary group, birth weight as linear covariate, and age of dam at calving as linear and quadratic covariates. The direct and maternal genetics, and the residual were fitted as random effects. The final dataset used contained phenotypic records on 67,196 animals, offspring of 1,469 sires and 30,970 dams. The SNPs effects were estimated based on the weighted single-step GBLUP approach, using information of 8,443 genotyped animals with 410,936 SNPs. Direct and maternal heritability estimates were of 0.2143±0.0348 and 0.0137±0.0066, respectively. The top 10 genomic regions accounted for 13.61 and 14.23% of direct and maternal additive genetic variance and harbor... (Resumo completo, clicar acesso eletrônico abaixo) / Mestre

Schizophrenia is a complex mental disorder that is commonly characterized as deterioration of intellectual process and emotional responses and affects 1% of any given population. SNPs are single nucleotide changes that take place in DNA sequences and establish the major percentage of genomic variations. In this study, our goal was to identify SNPs as genomic markers that are related with schizophrenia and investigate the genes and pathways that are identified through the analysis of SNPs. Genome wide association studies (GWAS) analyse the whole genome of case and control groups to identify genetic variations and search for related markers, like SNPs. GWASs are the most common method to investigate genetic causes of a complex disease such as v schizophrenia because regular linkage studies are not sufficient. Out of 909,622 SNPs analysis of the dbGAP Schizophrenia genotyping data identified 25,555 SNPs with a p-value 5x10-5. Next, combined p-value approach to identify associated genes and pathways and AHP based prioritization to select biologically relevant SNPs with high statistical association are used through METU-SNP software. 6,000 SNPs had an AHP score above 0.4, which mapped to 2,500 genes suggested to be associated with schizophrenia and related conditions. In addition to previously described neurological pathways, pathway and network analysis showed enrichment of two pathways. Melanogenesis and vascular smooth muscle contraction pathways were found to be highly associated with schizophrenia. We have also shown that these pathways can be organized in one biological network, which might have a role in the molecular etiology of schizophrenia. Overall analysis results revealed two novel candidate genes SOS1 and GUCY1B3 that have a possible relation with schizophrenia.

During the last two decades, genotyping technology has advanced rapidly, which enabled the tremendous success of genome-wide association studies (GWAS) in the search of disease susceptibility loci (DSLs). However, only a small fraction of the overall predicted heritability can be explained by the DSLs discovered. One possible explanation for this ”missing heritability” phenomenon is that many causal variants are rare. The recent development of high-throughput next-generation sequencing (NGS) technology provides the instrument to look closely at these rare variants with precision and efficiency. However, new approaches for both the storage and analysis of sequencing data are in imminent needs. In this thesis, we introduce three methods that could be utilized in the management and analysis of sequencing data. In Chapter 1, we propose a novel and simple algorithm for compressing sequencing data that leverages on the scarcity of rare variant data, which enables the storage and analysis of sequencing data efficiently in current hardware environment. We also provide a C++ implementation that supports direct and parallel loading of the compressed format without requiring extra time for decompression. Chapter 2 and 3 focus on the association analysis of sequencing data in population-based design. In Chapter 2, we present a statistical methodology that allows the identification of genetic outliers to obtain a genetically homogeneous subpopulation, which reduces the false positives due to population substructure. Our approach is computationally efficient that can be applied to all the genetic loci in the data and does not require pruning of variants in linkage disequilibrium (LD). In Chapter 3, we propose a general analysis framework in which thousands of genetic loci can be tested simultaneously for association with complex phenotypes. The approach is built on spatial-clustering methodology, assuming that genetic loci that are associated with the target phenotype cluster in certain genomic regions. In contrast to standard methodology for multi-loci analysis, which has focused on the dimension reduction of data, the proposed approach profits from the availability of large numbers of genetic loci. Thus it will be especially relevant for whole-genome sequencing studies which commonly record several thousand loci per gene.

We first show theoretically and in simulation how power varies as a function of SNP correlation structure with currently-implemented gene-based testing methods. We propose alternative testing methods whose power does not vary with the correlation structure. We then propose hypothesis tests for detecting prevalence-incidence bias in case-control studies, a bias perhaps overrepresented in GWAS due to currently used study designs. Lastly, we hypothesize how different incentive structures used to keep clinical trial participants in studies may interact with a background of dependent censoring and result in variation in the bias of the Kaplan-Meier survival curve estimator.

The malaria parasite, Plasmodium falciparum, has a demonstrated history of adaptation to antimalarials and host immune pressure. This ability unraveled global eradication programs fifty years ago and seriously threatens renewed efforts today. Despite the magnitude of the global health problem, little is known about the genetic mechanisms by which the parasite evades control efforts. Population genomic methods provide a new way to identify the mutations and genes responsible for drug resistance and other clinically important traits.

The U.S. is the largest maize producer in the world with a production of 300 million tons in 2012. Approximately 86% of the maize production is focused on the Midwestern states. The rest of the production is focused in the Southern states, where Texas is the largest maize producer. Grain yield in Texas ranges from 18 tons/ha in the irrigated production zones to 3 tons/ha in the dryland production zones. As a result, grain yield has increased slowly because of the poor production in the non-irrigated acres. Methods to improve the grain yield in Texas is to breed for maize varieties adapted to Texas growing conditions, including mapping genes that can be incorporated into germplasm through marker assisted selection. This dissertation includes two separate projects that exploit historical data and maize diversity to increase grain yield in Texas. For the first project, a large dataset collected by Texas AgriLife program was analyzed to elucidate past trends and future hints on how to improve maize yield within Texas. This study confirmed previous reports that the rate of increase for grain yield in Texas is less than the rate observed in the Midwestern US. For the second project, a candidate gene and whole genome association mapping analysis was performed for drought and aflatoxin resistance in maize. In order to do so, maize inbred lines from a diversity panel were testcrossed to isogenic versions of Tx714. The hybrids were evaluated under irrigated and non-irrigated conditions. The irrigated trials were inoculated with Aspergillus flavus and the aflatoxin level was quantified. This study found that the gene ZmLOX4 was associated with days to silk, and the gene ZmLOX5 gene was associated with plant and ear height. In addition, this study identified 13 QTL variants for grain yield, plant height, days to anthesis and days to silk. Furthermore, this study shows that diverse maize inbred lines can make hybrids that out yield commercial hybrids under heat and drought stress. Therefore, there are useful genes present in these diverse lines that can be exploited in maize breeding programs

No description available.

Many of the patterns of genetic variation we observe today have arisen via the complex dynamics of interactions and isolation of historic human populations. In this thesis, we focus on two important features of the genetics of populations that can be used to learn about human history: population structure and admixture. The Iberian peninsula has a complex demographic history, as well as rich linguistic and cultural diversity. However, previous studies using small genomic regions (such as Y-chromosome and mtDNA) as well as genome-wide data have so far detected limited genetic structure in Iberia. Larger datasets and powerful new statistical methods that exploit information in the correlation structure of nearby genetic markers have made it possible to detect and characterise genetic differentiation at fine geographic scales. We performed the largest and most comprehensive study of Spanish population structure to date by analysing genotyping array data for ~1,400 Spanish individuals genotyped at ~700,000 polymorphic loci. We show that at broad scales, the major axis of genetic differentiation in Spain runs from west to east, while there is remarkable genetic similarity in the north-south direction. Our analysis also reveals striking patterns of geographically-localised and subtle population structure within Spain at scales down to tens of kilometres. We developed and applied new approaches to show how this structure has arisen from a complex and regionally-varying mix of genetic isolation and recent gene-flow within and from outside of Iberia. To further explore the genetic impact of historical migrations and invasions of Iberia, we assembled a data set of 2,920 individuals (~300,000 markers) from Iberia and the surrounding regions of north Africa, Europe, and sub-Saharan Africa. Our admixture analysis implies that north African-like DNA in Iberia was mainly introduced in the earlier half (860 - 1120 CE) of the period of Muslim rule in Iberia, and we estimate that the closest modern-day equivalents to the initial migrants are located in Western Sahara. We also find that north African-like DNA in Iberia shows striking regional variation, with near-zero contributions in the Basque regions, low amounts (~3%) in the north east of Iberia, and as high as (~11%) in Galicia and Portugal. The UK Biobank project is a large prospective cohort study of ~500,000 individuals from across the United Kingdom, aged between 40-69 at recruitment. A rich variety of phenotypic and health-related information is available on each participant, making the resource unprecedented in its size and scope. Understanding the role that genetics plays in phenotypic variation, and its potential interactions with other factors, provides a critical route to a better understanding of human biology and population health. As such, a key component of the UK Biobank resource has been the collection of genome-wide genetic data (~805,000 markers) on every participant using purpose-designed genotyping arrays. These data are the focus of the second part of this thesis. In particular, we designed and implemented a quality control (QC) pipeline on behalf of the current and future use of this multi-purpose resource. Genotype data on this scale offers novel opportunities for assessing quality issues, although the wide range of ancestral backgrounds in the cohort also creates particular challenges. We also conducted a set of analyses that reveal properties of the genetic data, including population structure and familial relatedness, that can be important for downstream analyses. We find that cryptic relatedness is common among UK Biobank participants (~30% have at least one first cousin relative or closer), and a full range of human population structure is present in this cohort: from world-wide ancestral diversity to subtle population structure at sub-national geographic scales. Finally, we performed a genome-wide association scan on a well-studied and highly polygenic phenotype: standing height. This provided a further test of the effectiveness of our QC, as well as highlighting the potential of the resource to uncover novel regions of association.