Browsing by Subject "genomics"
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Item Applications of Genomewide Selection in a New Plant Breeding Program(2019-07) Neyhart, JeffreyNewly established breeding programs must undergo population improvement and determine superior germplasm for deployment in diverse growing environments. More rapid progress towards these goals may be made by incorporating genomewide selection, or the use of genomewide molecular markers to predict the merit of unphenotyped individuals. Within the context of a new two-row barley (Hordeum vulgare L.) breeding program, my objectives were to i) investigate various methods of updating training population data and their impact on long-term genomewide recurrent selection, ii) assess genomewide prediction accuracy with informed subsetting of data across diverse environments, and iii) validate genomewide predictions of the mean, genetic variance, and superior progeny mean of potential breeding crossses. My first study relied on simulations to examine the impact on prediction accuracy and response to selection when updating the training population each cycle with lines selected based on predictions (best, worst, both best and worst), model criteria (PEVmean and CDmean), random sampling, or no selections. In the short-term, we found that updating with the best or both best and worst predicted lines resulted in high prediction accuracy and genetic gain; in the long-term, all methods (besides not updating) performed similarly. In an actual breeding program, a breeder may want phenotypic data on lines predicted to be the best and our results suggest that this method may be effective for long-term genomewide selection and practical for a breeder. In my second study, a 183-line training population and 50-line offspring validation population were phenotyped in 29 location-year environments for grain yield, heading date, and plant height. Environmental relationships were measured using phenotypic data, geographic distance, or environmental covariables. When adding data from increasingly distant environments to a training set, we observed diminishing gains in prediction accuracy; in some cases, accuracy declined with additional data. Clustering environments led to a small, but non-significant gain in prediction accuracy compared to simply using data from all environments. Our results suggest that informative environmental subsets may improve genomewide selection within a single population, but not when predicting a new generation under realistic breeding circumstances. Finally, my third study used genomewide marker effects from the same training population above to predict the mean (μ), genetic variance (VG), and superior progeny mean (μSP ; mean of the best 10% of lines) of 330,078 possible crosses for Fusarium head blight (FHB) severity, heading date, and plant height. Twenty-seven of these crosses were developed as validation populations. Predictions of μ and μSP were moderate to high in accuracy (rMP = 0.46 – 0.69), while predictions of VG were less accurate (rMP = 0.01 – 0.48). Predictive ability was likely a function of trait heritability, as rMP estimates for heading date (the most heritable) were highest and rMP estimates for FHB severity (the least heritable) were lowest. Accurate predictions of VG and μ are feasible, but, like any implementation of genomewide selection, reliable phenotypic data is critical.Item Characterization of Tissue-Specific Functional Networks and Genome-Wide Association Study Genes(2016-01) Kuriger-Laber, JacquelynPresent-day biological research has generated a vast body of data related to variation in the human genome, but in many cases the biological role of this variation is unknown or only partially understood. In order to integrate the diverse body of experimental genetic and genomic data, systems biologists pioneered computational approaches to infer functional networks. These networks provide a powerful platform to investigate genomic findings at a functional level. Recently, systems biologists designed a second generation of functional networks that reflect tissue-specificity in gene functional interactions. We examine both characteristics of these tissue-specific functional networks and the topology of genome-wide association study (GWAS) variant-related genes in these networks. We find significant variation in network quality and suggest metrics to identify well-performing networks. Finally, we show GWAS trait-associated genes have non-random topology in tissue-specific networks and that this must be taken into account when applying network-enabled methods to genomic data.Item Crossbred Genotype Admixture(2022-04-27) Heins, Bradley; Huson, Heather; Dechow, Chad; Azwan Jaafar, Mohd; hein0106@umn.edu; Heins, Bradley; University of Minnesota West Central Research and Outreach Center Dairy HerdOur study evaluated two 3-breed rotational crossbred dairy cattle populations. ProCROSS is a product of crossbreeding Viking Red, Holstein, and Montbeliarde whereas Grazecross consists of Viking Red, Normande, and Jersey. The rotational crossbreeding system capitalizes on heterosis with each generation and the specific breeds used target trait improvement and development for certain environments.The effect of ancestry and use of reference populations in determining ancestry in ProCROSS and Grazecross were analyzed using genomic and pedigree-based estimations.Our study provides evidence of the importance of identifying the most appropriate reference animals and the effect of ancestry composition on ProCROSS and Grazecross dairy cattle performance. This work is supported by Organic Agriculture Research and Extension Initiative [grant no. 2016-51300-25862/project accession no. 1010366] from the USDA National Institute of Food and Agriculture.Item Developing genomic tools to breed for climate-adapted plant varieties(2023-03) Della Coletta, RafaelClimate change is a major threat to global food security, as current plant varieties used by farmers may not adapt to new growing environments. To mitigate this problem, plant breeders must use all available tools to speed up the development and release of new climate-adapted varieties. In this dissertation, I discuss how the recent advances in crop genomics due to improvements in sequencing technology, genome assembly methods, and computational resources are revolutionizing plant breeding. Particularly, I argue that the analysis of the complete catalog of genetic variation of a crop can provide useful information for plant breeders. I demonstrate that modeling this pan-genome information can increase the accuracy of multi- environment genomic prediction models, a tool widely used by breeders to develop new plant varieties. I also show how utilizing prior information on genetic variants associated with certain phenotypes can help simulate traits that are more realistic and relevant for breeders using digital breeding, a tool where breeders can test many different experiments before deployment in their breeding programs. Finally, I developed a new tool that identifies genetic variants associated with specific environmental factors via network analysis of common datasets available to plant breeders.Item Dictionary-based methods and their applications in biology and medicine(2021-05) Lenskaia, TatianaThis study proposes methods to explore genome organization and identify genome interactions that do not rely on annotations and aim to work on whole genome data. These methods use string matching between collections of dictionaries that depict genomes with different levels of resolution. Each dictionary represents a mapping of the complete genome data into a set of unique fixed-length segments. The methods are inspired by biological mechanisms including restriction-modification systems and CRISPR-Cas defenses that use exact matching. The use of this string-oriented approach might help researchers better understand biological mechanisms and avoid many of the drawbacks associated with annotations. These methods shift the computational paradigm from looking for specific instances such as genes and other elements within a genome to "full-search" analysis without preconceived targets. We hypothesize that the development of efficient dictionary-based screening methods will lead to a better understanding of genome organization and genome interactions. The results of this study indicate that these methods can capture many biologically significant relationships not easily captured by traditional approaches. The results of this study contribute to (a) changing a computational paradigm for processing genome data; (b) developing new methods for analyzing genome organization and relationships between genomes; and, (c) identifying and evaluating potential genome interactions at a broader scale for biological and medical applications.Item Integrating Genomics and Metabolomics to Inform Breeding for Powdery Mildew Resistance in Grapevine(2018-08) Teh, Soon LiTwo powdery mildew resistance loci have been identified using pedigree-connected F1 mapping families at the University of Minnesota grape breeding program. A consensus linkage map of the resistant parent (MN1264) was developed for genetic mapping. The resistance loci were mapped on chromosomes 2 and 15, with additive effects accounting for over 30% phenotypic variation. Marker haplotypes, hap+chr2 and hap+chr15, were constructed to trace the inheritance of resistance loci in grandparent-parent-progeny relationships. Both hap+chr2 and hap+chr15 in the resistant F1 progeny were inherited from parent MN1264, that originated from grandparent ‘Seyval blanc’. Additionally, two microsatellites markers (i.e., UDV-015b and VViv67) were identified to be associated with hap+chr15, and can be applied for marker-assisted selection. In a follow-up study to characterize metabolic changes attributed to hap+chr2 and hap+chr15, a metabolomic experiment was conducted on whole-plant propagated grapes in a time-course response to in vivo inoculation. The use of several multivariate analyses systematically identified 52 biomarkers that were associated with hap+chr2, and 12 biomarkers with hap+chr15. In a temporal assessment of biomarkers, the discriminating metabolic changes distinguishing resistant and susceptible individuals appeared to be occurring from 24 to 48 hours after inoculation.Item Leveraging High Throughput Sequencing For Fine Fescue (Festuca Spp.) Breeding And Genetics(2020-03) Qiu, YinjieFine fescues (Festuca L., Poaceae) are turfgrass species that perform well in low-input environments. Improvement of these grass species through breeding and genetics have been limited due to the difficulty of species identification and lack of genomic resources. The objectives of this dissertation were to develop an improved method for fine fescue species identification, generate the first reference transcriptome for hard fescue, and use the reference transcriptome for transcriptome studies. In my first project, I used flow cytometry, chloroplast genome sequencing, and molecular marker development to provide new fine fescue identification methodology. Next, I used flow cytometry to characterize ploidy level in the USDA F. ovina collection. My third project used PacBio Isoform sequencing to develop the reference transcriptome using four tissue types for hard fescue; by using a phylotranscriptomic approach, the reference transcriptome provided information of the allopolyploid origin of the hexaploid species. Finally, the reference transcriptome was used to study how hard fescue responded to propiconazole fungicide application; in addition, untargeted metabolomics was used to study changes in metabolites caused by fungicide application. This dissertation developed methods for fine fescue by a combination use of flow cytometry and molecular markers. Methods and genomics resources developed in this dissertation will benefit fine fescue breeding and genetics programs.Item Multi-omics of host-microbiome interactions in human diseases(2021-10) Priya, SambhawaWhile host genetics and gut microbiome have separately been identified as contributing factors to human health and disease, it is unclear how interactions between the two might drive disease risk. The modulation of host gene expression by the gut microbiome has been demonstrated as a potential mechanism by which microbes can affect host physiology. Therefore, understanding the molecular interactions between the microbiome and host gene regulation is critical for unravelling their contribution to the etiology of human diseases. Here, we comprehensively characterize functional interactions between the gut microbiome and host gene regulation across diverse human diseases to understand how these complex interactions might contribute to host pathophysiology. First, we characterized interactions between the gut mucosal microbiome and host gene expression in the colon of patients with cystic fibrosis to elucidate the potential role of host-microbiome interactions in the etiology of colorectal cancer in cystic fibrosis. Next, we developed a machine learning-based framework to jointly analyze host transcriptomic and microbiome profiles from colonic mucosal samples of patients with colorectal cancer, inflammatory bowel disease, and irritable bowel syndrome. We identified potential interactions between gut microbes and host genes that are disease-specific, as well as interactions that are shared across the three diseases, involving host genes and gut microbes previously implicated in gastrointestinal inflammation, gut barrier protection, energy metabolism, and tumorigenesis. We further adapted this integration framework to characterize multi-omic interactions between host gene expression, gut microbiome, and gut metabolome in irritable bowel syndrome. We also developed and applied supervised learning models to characterize patterns of host-microbiota interactions in diverse contexts to reveal microbial mediators of ethnic health disparities in the United States, and bacterial modulators of susceptibility to konzo in the Democratic Republic of the Congo. By identifying the host-microbiome interactions associated with human health and disease, results from our work can facilitate new insights into the molecular mechanisms by which microbiota impacts host health, and potentially lead to biomarkers for diagnostic and therapeutic interventions.Item Pleiotropy and epistasis in trans-acting expression quantitative loci hotspots(2023-11) Van Dyke, KrisnaDifferences in non-coding regions of genomes explain the majority of heritable differences between individuals such as disease heritability. These non-coding differences are thought to largely act by altering gene expression, positioning regulatory variation as a key bridge between DNA variation and differences in traits. Expression quantitative trait loci (eQTLs) are regions of the genome containing one or more variants that alter the expression of a gene. In a cross between two strains of Saccharomyces cerevisiae, most heritable variation in gene expression acted in trans, with 90% of these trans-eQTLs overlapping only 102 “hotspot” loci. The large amount of heritable variation in gene expression that hotspots account for, and their discovery across the tree of life suggest they are a critical and ubiquitous feature of genome architecture. Classifying the structure of genetic variation underlying hotspots and learning what mechanisms allow hotspots to affect such large numbers of genes is critical to understanding how genetic variation gives rise to phenotypic variation. The following chapters describe a dissection of the variation underlying a hotspot and the uncovering of a new framework for how hotspots affect such numerous genes. Chapter II details how hotspots can co-opt the cellular mechanisms that cause adjacent genes to be coexpressed to extend their effect in cis. Chapter III dissects a hotspot with a complex epistatic basis to demonstrate how variants and groups of variants within a gene can interact to have wide-reaching impacts on the expression of many genes.