An individualized medicine approach to urinary stone disease

PROJECT SUMMARY/ABSTRACT Urinary stone disease (USD) is a nephropathy affecting up to 12% of individuals that is associated with significant morbidity and health care costs. Although lifestyle, diet, and water intake contribute to the development of USD, a strong genetic component has also been identified with heritability estimates up to 36%. Screening for common genetic risk factors by genomewide association studies (GWAS) and exome-based rare variant studies (RVAS) has identified more than 100 risk genes for USD. In addition, family studies have revealed >40 genes associated with monogenic stone disease (MSD), many overlapping with the findings from association studies. Identifying the genetic cause of MSD, has provided a firm diagnosis which has aided understanding pathogenesis and increasingly highlighted novel treatment options. For USD, identifying genetic risk factors identifies patients at high-risk for recurrent disease, potentially suggesting treatment options and lifestyle changes. MSD with incomplete penetrance is increasingly blurring the separation of MSD and idiopathic USD. The premise of this proposal is that improving understanding of the etiology and risk-factors of MSD and USD will positively impact patient management/treatment and clinical trial recruitment. This MPI grant brings together expertise in USD/MSD, genetics, bioinformatics, and cell biology of transporters, with a large MSD population collected by the Rare Kidney Stone Consortium (RKSC) as well as characterized general populations available at Mayo Clinic and Geisinger Health System in which USD cohorts have been identified. The proposal has three aims to address the overall purpose of the proposal: Aim 1: Determine the genetic causes of MSD and identify disease modifying factors; Aim 2: Determine genetic risk factors of idiopathic USD; and Aim 3: Determine the functional significance of variants in MSD/USD genes. The focus of Aim 1 is MSD and will include patient recruitment, genetic screening, and variant evaluation to genetically resolve patients, including identifying new MSD genes and improving genotype/phenotype studies. We will also screen genomewide for modifiers of disease severity of specific MSD. Aim 2 describes improved identification of USD patients from population cohorts and quantifying the phenotype employing AI-driven health record screening and analyses of radiological images. Employing GWAS and RVAS in the refined USD vs. control populations, we will identify genetic risk factors for USD. Employing this data, we will develop/refine a polygenic risk score for USD. Aim 3 will employ Xenopus oocytes to assess channel function and mammalian cells to assess trafficking of known or candidate MSD proteins with specific nontruncating variants to assess their pathogenicity and test treatment options. Overall, these studies will provide a comprehensive genetic understanding of MSD and USD through sequencing, population and functional studies, improving diagnostics, prognostics, and treatment options.