PROJECT SUMMARY Ion channels pass charged ions through lipid membranes in a regulated manner. Ion channels play major roles in regulating electrically excitable tissues, sensing and responding to the environment, and maintaining cell homeostasis. Over 70 ion channels have been linked to Mendelian “channelopathy” disorders, affecting a diverse set of organ systems. As genetic testing and genomic medicine become prominent, an important challenge is to understand the spectrum of which mutations in ion channel genes cause disease. Unfortunately, a large fraction of variants are currently annotated as “Variants of Uncertain Significance,” which limits the effectiveness and potential of genomic medicine. This proposal seeks to decipher which variants in channelopathy genes cause disease. We will first use large biobank datasets with linked genome sequencing and phenotype data. We will examine associations between genetic variants in 76 channelopathy genes and relevant disease phenotypes. Using control pathogenic variants, we will first determine which gene-phenotype pairs are associated in biobank datasets, then discover novel candidate disease-associated variants that are present in carriers with relevant disease phenotypes. Next, we will use high-throughput automated patch clamping to study hundreds of variants in ion channel genes. Our initial focus will be 5 key ion channel genes that span a range of ion types and organ systems, as well as selected variants from the biobank genetic analyses. Next, we will perform deep mutational scans (a comprehensive mutational study) of every mutation in selected ion channel genes, starting with GABRA1, a ligand- gated ion channel gene (receptor) involved in GABA sensing and linked to seizure disorders. We will generate all possible mutations with degenerate mutagenesis reactions, integrate the mutation library into cells, then measure each mutation's impact on cell surface trafficking and channel function using high-throughput sequencing. Finally, we will integrate these patient and in vitro functional datasets to learn fundamental features of ion channel biology and disease. Through an analysis of the 2D and 3D protein structures, we will decipher protein mutational hotspots. From an analysis of mutational impacts from homologous genes I will determine whether mutation information can be ported to homologous genes. Finally, we will integrate variant data into the American College of Medical Genetics classification framework to clinically reclassify variants. Overall, these experiments have great potential to help resolve the VUS problem for ion channels and decipher novel ion channel biology.