Breast cancer susceptibility is not only influenced by common genetic variation but also by rare, protein-altering variants not well captured by traditional genome-wide association studies. Whole-exome sequencing in large, clinically linked biobanks enables systematic interrogation of these rare variants at scale. In this study, we use whole-exome sequencing data from the Penn Medicine Biobank to perform an exome-wide association study of breast cancer risk using both single-variant and gene-based aggregation approaches. Employing ICD-9/10 codes for case/control selection, functional variant annotation, and multiple burden masks within the REGENIE mixed-model framework, we assess the contribution of rare coding variation to breast cancer susceptibility. This work establishes a scalable and reproducible exome analysis pipeline and contributes to the SIMPLEXO breast cancer project, supporting gene-level discovery in large biobank cohorts.