Grown on nearly 200 million hectares worldwide, Zea mays (maize) serves as a symbol of cultural resistance to many Indigenous communities and a fantastic model for understanding the genetic basis of quantitative traits. Here, we explore three projects that shed light on the genetic architecture, regulation, and translatability of discoveries to the field by investigating the patterns of pleiotropy, the impact of transposable elements on gene expression, and the development of yield testing standards. To investigate the patterns of pleiotropy, we remapped 120,597 traits from two maize association mapping populations and estimated that only 1.56–32.3% of intervals show some degree of pleiotropy. However, we found no relationship between observed pleiotropy and gene expression, chromatin accessibility, or sequence conservation compared to permuted pleiotropy. We hypothesize that pleiotropy of common alleles is highly impacted by noise terms imparted through association mapping and not widespread, as selection on standing natural variation would target wide and large effect variants, leaving the prevalence of pleiotropy relatively low. To understand the impact of transposable elements (TEs) on gene expression, we turn to the ~85% of the maize genome made of TEs, which has been shown to impact gene expression by silencing, rearranging, adding, or disrupting endogenous promoters. By modeling the impact of TE base pairs in a panel of inbreds and hybrids, we show a larger negative effect of TEs in known regulatory regions upstream of genes. We also found that smaller TE families that primarily insert near genes have a larger effect on gene expression than larger families that rarely insert near genes. These patterns could guide future machine-learning models that typically ignore TE sequence and impact. The final project reviews a growing body of research claiming that one or a few genes can impart extraordinary crop yield increases (>10%); however, as testing scales, these effects disappear. To ensure robust results are delivered to farmer fields, we developed a set of yield testing standards that emphasize matching on-farm conditions, utilizing replicated experimental designs, and testing genotype by environment interactions.