Rare diseases affect 30 million Americans, many of whom remain undiagnosed due to limited functional characterization of DNA variants. Propionic acidemia is caused by variants in PCCA or PCCB that impair enzyme function, leading to severe metabolic dysfunction, often presenting in early infancy. While the Wisconsin newborn screening panel tests for this disorder, screening is neither 100% effective nor does it identify the cause of propionic acidemia in each patient. There are 979 reported DNA variants of uncertain significance or conflicting classification in PCCA and PCCB, meaning that it is unclear if these mutations cause the disease: thus, identification of one of these variants in a patient does not equal clear diagnosis. To address this gap, our lab uses a minigene system to examine whether variants have functional effects. Although we can effectively assess individual variants with this system, it is a relatively low-throughput method. We present our efforts at optimizing this system through improved sample processing, next-generation sequencing (NGS), and development of efficient R scripts. An improved pipeline should accelerate the resolution of variants of uncertain significance associated both with propionic acidemia and across rare genetic diseases.
