This dissertation seeks to explore the genetic and phenotypic diversity across Cucurbita pepo market classes and elucidate the genetic architecture of key fruit quality and seed quality traits in the context of applied breeding strategies. Chapter one evaluates the genetic distances between economically important market classes and the phenotypic variation for glucose (mg/g), fructose (mg/g), sucrose (mg/g), starch (mg/g), °Brix, and dry matter content, and identifies single nucleotide polymorphisms (SNPs) significantly associated with the phenotypic variation by leveraging mixed models. Significant associations were identified for sucrose (mg/g dry weight), starch (mg/g dry weight), fructose (mg/g dry weight), glucose (mg/g dry weight), dry matter content, starch (mg/g fresh weight), glucose (mg/g fresh weight), sucrose (mg/g fresh weight), and total sugars (mg/g fresh weight). Market class as a grouping variable was found to explain a limited amount of the genetic distance between market classes (>5%) discerned from an Adonis test of a Nei’s genetic distance matrix. Chapter two identifies a proximal and biological candidate NAC transcription factor gene responsible for the major reduction in lignin deposition in the seed coat, commonly observed in pepita pumpkin seeds. Like chapter one, an association analysis was conducted using genetic markers and mixed models; however, differential expression analysis was also performed to further support the hypothesis that the NAC transcription factor is indeed a strong candidate. Chapter three evaluates genomic prediction efficacy utilizing a genomic best linear unbiased prediction (GBLUP) model for important seed quality and agronomic traits: seed size, residual seed coat, and number of seeds per pumpkin. Additionally, a marker panel for targeted genotyping assays was developed alongside an image analysis pipeline for the rapid phenotyping of thousands of seeds. Ultimately, chapter three provides a wholistic view of contemporary considerations for pepita breeding efforts and shows that genomic prediction is a valuable methodology in an applied breeding program with limited resources.