Breast cancer susceptibility is influenced not only by common genetic variation but also by rare, protein-altering variants with potentially large effects that are 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. Using ICD-9/10 codes for case/control selection, functional variant annotation, and multiple burden masks within a 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 rare variant association results to the SIMPLEXO breast cancer project, supporting gene-level discovery in large biobank cohorts