<p dir="ltr">Genome-wide association studies (GWAS) have uncovered numerous variants linked to a wide range of complex traits. However, understanding the mechanisms underlying these associations remains a challenge. To determine genetically regulated mechanisms, additional layers of gene regulation, such as transcriptome and proteome, need to be assayed. Transcriptome-wide association studies (TWAS) and Proteome-wide association studies (PWAS) offer a gene-centered approach to illuminate these mechanisms by examining how variants influence transcript expression and protein expression, thereby inferring their impact on complex traits. In the introductory chapter of this dissertation, I discuss the methodology of TWAS and PWAS, exploring the assumptions they make in estimating SNP-gene effect sizes, their applications, and their limitations. In Chapter 2, I undertake an integrative analysis of TWAS and PWAS using the largest cohort of individuals affected with Tourette’s Syndrome within the Psychiatric Genomics Consortium (PGC) – Tourette’s Syndrome working group. I identified genomic regions containing multiple TWAS and PWAS signals and integrated these results using the computational colocalization method to gain insights into genetically regulated genes implicated in the disorder. In Chapter 3, I conduct an extensive TWAS of the Myasthenia Gravis phenotype, uncovering novel genes associated with the disorder. Utilizing two distinct methodologies, I performed individual tissue-based and cross-tissue-based imputation to assess the genetic influence on transcript expression. A secondary TWAS analysis was conducted after removing SNPs from the major histocompatibility complex (MHC) region to identify significant genes outside this region. Finally, in Chapter 4, I present the conclusions drawn from both studies, offering a comprehensive understanding of the genetic architecture underlying these traits. I also discuss future directions aimed at advancing the mechanistic understanding of complex non-Mendelian disorders.</p>

<p dir="ltr">Metabolic pathways are shaped by dynamic biotic interactions. My research delves into coevolution exemplified through two distinct projects that investigate the specialized metabolism of organisms as a consequence of biotic interactions. The first project focused on the remarkable metabolic adaptations of <i>Elysia crispata</i> morphotype clarki. This sea slug possesses the extraordinary ability to sequester and maintain functional chloroplasts (kleptoplasts) from the algae it consumes, allowing it to sustain photosynthetically active kleptoplasts for several months without feeding. To better understand the underlying molecular mechanism of this phenomenon, I generated a comprehensive 786 Mbp draft genome of <i>E. crispata</i> using a combination of ONT long reads and Illumina short reads. The resulting assembly provided a foundational resource for phylogenetic, gene family and gene expression analyses. This work advanced our understanding of the genetic underpinnings of kleptoplasty, shedding light on the evolution and maintenance of this unique metabolic strategy in sacoglossan sea slugs. I next investigated the transcriptional impacts of herbivory on maize (<i>Zea mays</i>) and green foxtail (<i>Setaria viridis</i>), induced by fall armyworm (<i>Spodoptera frugiperda</i>) and beet armyworm (<i>Spodoptera exigua</i>) feeding. This study aimed to contrast the defensive mechanisms of these grasses in response to each herbivore, and determined that green foxtail transcriptionally differentiates its responses to fall armyworm and beet armyworm herbivory. The fall armyworm has evolved a counter adaptation to lessen plant secondary metabolite production by producing a salivary protein (SFRP1) that suppresses jasmonate signaling. Investigation of the combinatorial effects of SFRP1 and beet armyworm herbivory determined the addition of endogenous SFRP1 during beet armyworm feeding is sufficient to reduce green foxtail defense responses. Results of this research shed light on host-pest reciprocal adaptations and the role of SFRP1 as an oral secretory protein. Coexpression analysis of maize and green foxtail transcriptomic responses to herbivory also identified putative genes involved in specialized metabolic pathways in green foxtail, providing insights into plant-insect interactions and potential solutions to herbivory in wild plant species. These findings highlight how gene diversification can contribute to pest resistance in grasses. Together, these seemingly unconnected projects underscore how biotic interactions influence metabolic processes across diverse organisms and reveal the fascinating intricacies of their adaptations to environmental challenges.</p>

<p dir="ltr"> Improving feed efficiency in beef cattle is essential to meet rising global beef demand while reducing costs and environmental impacts. Genetic selection plays a significant role in identifying and breeding more feed-efficient animals, with multi-population data integration enhancing prediction accuracy. To optimize animals for selection or breeding programs related to efficiency, it is crucial to understand the traits associated with mature cow size and the genetic relationships between them. Mature cow size, defined by mature cow weight (MWT), height (MHT), and body condition score (BCS), is pivotal to cow-calf profitability, maintenance efficiency, and reproductive performance.<br><br> The objectives of the first study were to: 1) estimate variance components and genetic parameters for MWT, MHT, and BCS in the United States (US) and Australia (AUS); 2) estimate genetic correlations between mature cow size traits and early growth and carcass traits; and 3) estimate the genetic correlations among these traits across the two countries. The datasets provided by the American Angus Association and Angus Australia included 434,746 and 206,003 records for MWT, 213,875 and 15,379 records for MHT, and 382,156 and 36,184 records for BCS, respectively. Single-trait repeatability models were used to estimate heritabilities and variance components. Multiple-trait models were used to estimate phenotypic and genetic correlations between traits and across countries. Heritability estimates for MWT (US: 0.45; AUS: 0.40), MHT (US: 0.57; AUS: 0.63), and BCS (US: 0.18; AUS: 0.18) highlighted moderate-to-high genetic control. Genetic correlations within the US and Australian datasets between MWT and MHT, and MWT and BCS were > 0.50, and < 0.20 between MHT and BCS. Genetic correlations between MWT, MHT and early growth traits were generally positive and moderate-to-high, ranging from 0.51(0.01) to 0.92(0.003) in the US and 0.41(0.03) to 0.79(0.05) in Australia. Genetic correlations between the traits in the two countries were high for MWT = 0.91 (0.02) and MHT = 0.98 (0.02); and moderate for BCS = 0.65 (0.08). The results suggested that optimizing selection for mature cow traits is feasible, and that a joint evaluation between the US and Australia could be beneficial. </p><p dir="ltr"><br></p><p dir="ltr"> The objective of the second study was to investigate the impact of different modeling approaches on the estimation of breeding values for MWT, with a focus on how BCS was treated across models. The dataset provided by American Angus Association comprised 382,156 MWT and BCS records from 209,491 cows. Four modeling approaches were evaluated: Model 1 did not consider BCS; Model 2 treated BCS as a categorical fixed effect; Model 3 used pre-adjusted records standardized for BCS and age; and Model 4 used a recursive model to assess MWT as a genetically independent trait from BCS. Spearman correlations between models ranged from 0.78 to 0.95, with model choice influencing sire rankings by 5–22% and top 10% concordance differing by up to 40%. Model selection can significantly affect rankings, highlighting the importance of carefully selecting the model that aligns best with the breeding goals. The recursive approach appears to effectively derive MWT that is genetically independent of BCS. </p><p dir="ltr"> This thesis analyzes genetic relationships among mature cow size traits; mature cow weight, height, and body condition score, providing insights for selection programs aimed at optimizing cow’s efficiency. Through variance analysis and genetic correlation studies across North American and Australian Angus populations, it highlights the potential of joint evaluations across countries. It also assesses how different modeling approaches for estimating breeding values for mature cow weight can affects sire rankings and selection decisions, underscoring the importance of model alignment with breeding objectives. Ultimately, this work contributes to the goal of a more sustainable beef industry, where mature cow size is optimized.</p>