Holistic and growth stage-specific screening is needed for identifying tolerant genotypes and for formulating strategies to mitigate the negative effects of abiotic stresses on crops. The objectives of this study were to characterize the genetic variability of 100 rice lines for early-season vigor, growth and physiological plasticity, and drought and temperature tolerance. Five studies were conducted to accomplish these objectives. In study 1 and 2, 100 rice genotypes consisting of several cultivars and experimental breeding lines were characterized for early-season vigor using several shoot and root morphological, physiological, and yield related traits. In study 3, low- and high-temperature tolerance assessed on select rice cultivars/hybrids during early-season. In study 4, genotypic variability in response to drought stress tolerance using morpo-physiological traits including roots was assessed under pot-culture conditions in a mini-greenhouse conditions. In study 5, the 100 rice genotypes were used to identify and validate SNP markers, and genome-wide association study (GWAS) to generate genotypic and phenotypic data with the objective of identifying new genetic loci controlling drought stress traits. Significant variability was recorded among rice genotypes and treatments for many traits measured. Early-season cumulative vigor response indices (CVRI) developed by summing individual responses indices for each trait varied among the rice genotypes, 21.36 (RU1404196) to 36.17 (N-22). Based on means and standard deviation of the CVRI, rice genotypes were classified as low- (43) and moderately low- (33), high- (16), and very high-vigor (5) groups. Total low-temperature response index values ranged from 18.48 to 23.15 whereas total high-temperature responses index values ranged from 42.01 to 48.82. Antonio, CLXL 745, and Mermentau were identified as sensitive to cold- and heat, and XL 753 was highly cold and heat tolerant genotypes tested. A cumulative drought stress response index (CDSRI) values varied between 14.7 (CHENIERE) and 27.9 (RU1402174) among the genotypes tested. This preliminary analysis of GWA indicated that substantial phenotypic and genotypic diversity exists in the 100 rice genotypes, despite their narrow genetic pool. The stress tolerant and high vigor rice genotypes will be valuable for rice breeders for developing new genotypes best suited under growing environments prone to early-season drought and temperature.

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Background: South Asians are at an increased risk of developing dysglycemia during and after pregnancy. In pregnant women, dysglycemia often develops in the form of gestational diabetes mellitus (GDM), which may predispose their newborns to adverse health outcomes through abnormal cord blood insulin levels. However, reasons for the elevated risk of dysglycemia in South Asians have not been extensively studied. Genetic factors may contribute to the heritability of GDM and abnormal cord blood insulin levels in South Asians. Objectives: The objectives of this thesis were to test the association of: 1) A type 2 diabetes polygenic risk score with GDM in South Asian pregnant women from the South Asian Birth Cohort (START); 2) maternal and newborn insulin-based polygenic risk scores with cord blood insulin and glucose/insulin ratio in South Asian newborns from START Methods: Three polygenic risk scores were created to test their association with participant data (N=1012) from START. GDM was defined using cut-offs established by the Born in Bradford cohort of South Asian women. The type 2 diabetes polygenic risk score was created in 832 START mothers and included 35,274 independent variants. The maternal and newborn insulin-based polygenic risk scores were created in 604 START newborns and included 1128017 independent variants. Univariate and multiple logistic and linear regression models were used to test the associations between the polygenic risk scores and dysglycemia outcomes. Results: The type 2 diabetes polygenic risk score was associated with GDM in both univariate (OR: 2.00, 95% CI: 1.46-2.75, P<0.001), and multivariable models (OR: 1.81, 95% CI: 1.30-2.53, P<0.001). The maternal insulin-based polygenic risk score was not associated with cord blood insulin or cord glucose/insulin ratio. However, the newborn insulin-based polygenic risk score was associated with cord blood insulin in a multivariable model adjusted for maternal insulin-based polygenic risk score (β = 0.036, 95% CI: 0.002 – 0.069; P=0.038 among other factors. Conclusion: A type 2 diabetes polygenic risk score and a newborn insulin-based polygenic risk score may be associated with maternal and newborn dysglycemia. / Thesis / Master of Science (MSc) / Background: South Asians are approximately two times more at risk for developing gestational diabetes mellitus (GDM) compared to white Caucasians. Genetic factors may contribute to this elevated risk. Polygenic risk scores (PRSs), which combine the effects of multiple disease loci and variants associated with the disease into one variable could be useful in further understanding how GDM develops in South Asians. Methods: Data from the South Asian Birth Cohort (START) was used to test the association of three PRSs with the outcomes of interest. Results: The type 2 diabetes PRS was independently associated with GDM. The insulin-based maternal PRS was not associated with cord blood insulin but the insulin-based newborn PRS was independently associated with cord blood insulin. However, neither the insulin-based maternal nor newborn PRS was associated with cord blood glucose/insulin ratio. Conclusion: The PRSs suggests a possible genetic component, which contributes to abnormal glycemic status development in South Asian mothers and their newborns.

The metritis complex (MC), characterizing post-partum uterine diseases in dairy cattle has negative implications on animal welfare, production efficiency, and the economic stability of the dairy industry. The studies in this thesis aimed to investigate the genetic architecture of the metritis complex and identify genomic regions and metabolites associated with the development of MC. Thereby enhancing our understanding of the biological pathways and molecular mechanisms involved in the pathophysiology of MC during the transition period in Jersey and Holstein dairy cattle. Chapter 2 sheds light on the previous work done on MC. The goals of this review were to (1) provide an updated epidemiological profile of uterine ailments, (2) integrate results from genomics, transcriptomics, metabolomics, and proteomics (OMICs) studies to reveal insights on the identified biological pathways modulated during the transitional period and the onset of metritis, and (3) discuss the commonly detected molecular mechanisms in OMICs studies. Chapter 3 utilized genomic profiles to identify genetic variants, genes, and biological pathways that modulate MC development. A genome-wide association study (GWAS) was performed using a single locus mixed linear model on 1,967 Holstein and Jersey cow genotypes (624,460 SNPs), and MC records from three dairy herds. Following this, in-silico functional and gene network analyses were performed to detect biological mechanisms and pathways linked to the development of endometritis, metritis, and pyometra, diseases defined under the metritis complex development. Potential genes were significantly (P ≤ 0.0001) associated with MC and located on chromosomes 12, 10, and 21. These genes are involved in potential metabolic pathways which are directly associated with the mode of transmission for well-known pathogens in the metritis complex. Chapter 4 followed the GWAS with a high-throughput liquid chromatography-mass spectrometry (LC-MS) metabolomic study. The goals of this study were to 1) to identify metabolites associated with the development of MC in multi-parous Jersey and Holstein cows, 2) to detect the molecular pathways linked to the identified metabolites for MC, and 3) and to identify potential metabolomic biomarkers for early detection of uterine disease development in dairy cattle following parturition. A case-control design was employed on transitioning dairy cattle (n=28), at three time points (week 1, 2, and 3 post-calving). The study identified 48 significant (at false discovery rate adjusted P≤0.05) metabolic deviations for MC during the second week post-partum using single point t-test model. Using repeated measurement, 50 metabolites were identified as significant across all three time points. The results from the studies done revealed mechanisms contributing to the development of uterine disease in Jersey and Holstein breeds. These results should be validated and may be used as genomic selection or management tool to decrease the incidence of metritis complex in dairy cattle.

The missing heritability in genome-wide association studies (GWAS) is an intriguing open scientific problem which has attracted great recent interest. The interaction effects among risk factors, both genetic and environmental, are hypothesized to be one of the main missing heritability sources. Moreover, detection of multilocus interaction effect may also have great implications for revealing disease/biological mechanisms, for accurate risk prediction, personalized clinical management, and targeted drug design. However, current analysis of GWAS largely ignores interaction effects, partly due to the lack of tools that meet the statistical and computational challenges posed by taking into account interaction effects. Here, we propose a novel statistically-based framework (Significant Conditional Association) for systematically exploring, assessing significance, and detecting interaction effect. Further, our SCA work has also revealed new theoretical results and insights on interaction detection, as well as theoretical performance bounds. Using in silico data, we show that the new approach has detection power significantly better than that of peer methods, while controlling the running time within a permissible range. More importantly, we applied our methods on several real data sets, confirming well-validated interactions with more convincing evidence (generating smaller p-values and requiring fewer samples) than those obtained through conventional methods, eliminating inconsistent results in the original reports, and observing novel discoveries that are otherwise undetectable. The proposed methods provide a useful tool to mine new knowledge from existing GWAS and generate new hypotheses for further research. Microarray gene expression studies provide new opportunities for the molecular characterization of heterogeneous diseases. Multiclass gene selection is an imperative task for identifying phenotype-associated mechanistic genes and achieving accurate diagnostic classification. Most existing multiclass gene selection methods heavily rely on the direct extension of two-class gene selection methods. However, simple extensions of binary discriminant analysis to multiclass gene selection are suboptimal and not well-matched to the unique characteristics of the multi-category classification problem. We report a simpler and yet more accurate strategy than previous works for multicategory classification of heterogeneous diseases. Our method selects the union of one-versus-everyone phenotypic up-regulated genes (OVEPUGs) and matches this gene selection with a one-versus-rest support vector machine. Our approach provides even-handed gene resources for discriminating both neighboring and well-separated classes, and intends to assure the statistical reproducibility and biological plausibility of the selected genes. We evaluated the fold changes of OVEPUGs and found that only a small number of high-ranked genes were required to achieve superior accuracy for multicategory classification. We tested the proposed OVEPUG method on six real microarray gene expression data sets (five public benchmarks and one in-house data set) and two simulation data sets, observing significantly improved performance with lower error rates, fewer marker genes, and higher performance sustainability, as compared to several widely-adopted gene selection and classification methods. / Ph. D.

Despite soybean's widespread recognition as a versatile and valuable crop due to many end-use purposes, breeders seek to develop varieties with improved nutritional and functional components that capture added-value for producers. Additionally, producers seek to maximize profits by utilizing field practices to augment crop value. Therefore, this dissertation had two main objectives of maximizing soybean value: 1) to evaluate accelerated selection methods by soybean breeders for methionine content and test weight, and 2) to identify sulfur fertilization impact on soybean seed composition including amino and fatty acid profiles. First, a genome-wide association study (GWAS) analyzed genomic influence on proteinogenic methionine in soybean seeds which identified 23 single nucleotide polymorphisms (SNPs). Utilizing a SNPs subset identified by GWAS, genomic selection (GS) exhibited average prediction accuracies ranging from 0.41-0.62. Secondly, a novel phenomic selection (PS) method using near-infrared reflectance spectroscopy (NIRS) was evaluated for predictive ability of soybean test weight. PS cross-validations exhibited average predictive accuracies of 0.75, 0.59, and 0.16 when incorporating all environments, between locations, and between years, respectively. Finally, sulfur fertilizer rates and sources were assessed across two years and six locations in relation to seed composition. Notably, ammonium sulfate (AMS) was found to have a significant impact (P < 0.05) on methionine content in soybean seed. These outcomes will have positive impacts on plant breeding and soybean production for seed composition and quality traits using contemporary breeding and fertilization. / Doctor of Philosophy / Despite soybean's widespread recognition as a versatile and valuable crop due to a myriad of end-use purposes, breeders seek to develop varieties with improved nutritional and functional components that captured value for producers. Additionally, producers seek to maximize their profits by utilizing field practices that increase crop value. Therefore, this dissertation had two main objectives of maximizing soybean value: 1) to evaluate accelerated selection methods by soybean breeders for methionine content and test weight, and 2) to identify sulfur fertilization impact on soybean seed protein and oil composition. The overall objective was to create a comprehensive toolset for soybean breeders to develop Mid-Atlantic soybean varieties with improved seed composition traits and to determine fertilization impacts for use by producers. Genetic controls for protein-bound methionine in soybean seed were identified and could be used for variety development. Additionally, a new prediction method that uses light reflectance to represent genetic information and environmental effects was shown to have high accuracy for soybean test weight. It was also found that sulfur fertilizer with high availability in the soil positively impacted methionine content. These outcomes will have positive impacts on plant breeding and soybean production for seed composition and quality traits using contemporary breeding and fertilization.

The data from genome-wide association studies (GWAS) in humans are still predominantly analyzed using single marker association methods. As an alternative to Single Marker Analysis (SMA), all or subsets of markers can be tested simultaneously. This approach requires a form of Penalized Regression (PR) as the number of SNPs is much larger than the sample size. Here we review PR methods in the context of GWAS, extend them to perform penalty parameter and SNP selection by False Discovery Rate (FDR) control, and assess their performance (including penalties incorporating linkage disequilibrium) in comparison with SMA. PR methods were compared with SMA on realistically simulated GWAS data consisting of genotype data from single and multiple chromosomes and a continuous phenotype and on real data. Based on our comparisons our analytic FDR criterion may currently be the best approach to SNP selection using PR for GWAS. We found that PR with FDR control provides substantially more power than SMA with genome-wide type-I error control but somewhat less power than SMA with Benjamini-Hochberg FDR control. PR controlled the FDR conservatively while SMA-BH may not achieve FDR control in all situations. Differences among PR methods seem quite small when the focus is on variable selection with FDR control. Incorporating LD into PR by adapting penalties developed for covariates measured on graphs can improve power but also generate morel false positives or wider regions for follow-up. We recommend using the Elastic Net with a mixing weight for the Lasso penalty near 0.5 as the best method. / Ph. D.