Wednesday, February 5, 2025
2/5/2025
PROJECT SUMMARY Parent award summary: Mayo Clinic is one of the largest and top-ranked academic health systems in the US. The Mayo Clinic Center for Clinical and Translational Sciences (CCaTS) was created to be an engine of translational science and innovations, envisioning to enable high-quality multidisciplinary research to accelerate clinical trials, facilitate digital health transformation, and partner with stakeholders and communities to improve patient care and health for all. The CCaTS was awarded a UL-1 grant (UL1TR002377-07) with the following specific aims: simplify and accelerate the work of translation to improve health for all by advancing clinical trial innovations and digital health transformations, streamlining methods and processes, and developing novel informatics solutions that increase efficiency and drive implementation of discoveries that improve health and promote health equity (Aim 1); enhance education programs through the expanded reach of competency- based, learner-focused solutions, training a diverse, inclusive, multidisciplinary CTS workforce to be prepared to address the urgent health care needs of all communities in a rapidly changing environment (Aim 2); engage local community members and patients to be active partners in translational teams, expanding research capabilities of underserved communities (Aim 3); expand national and regional partnerships and strengthen collaborative CTS networks in all aspects of CTS and education, focusing on sharing innovative approaches and prioritizing diversity, equity, and inclusion (Aim 4). Diversity supplement summary: Chronic kidney disease (CKD) is a growing burden in public health and has a significant genetic component that remains largely under investigated. Prior studies have identified genes associated with CKD, but their effects are relatively small, and a large proportion of the remaining genetic risk is expected to be related to rare gene variants yet to be investigated. In addition, the penetrance of pathogenic variants (percentage of a clinical trait in carriers of pathogenic genetic variants) is not well determined, limiting out ability to interpret the significance of a positive finding. Investigations into new genes and on penetrance of CKD-associated genes are an unmet need to promote individualized medicine, expected to accelerate innovations in care by allowing the identification of people at risk, mechanisms of disease, and potential new targets for treatment and prevention. New genomic and digital resources are now available enabling us to fulfil this gap in knowledge. By specifically aligning with the Parent Aim 1 (facilitation of digital health transformation) and Parent Aim 2 (preparation of a diverse workforce), this Diversity Supplement intends to: Aim 1. To investigate rare genetic variants associated to CKD using meta-analysis of burden tests for rare variants from four large repositories with genomic data. I will do meta-analysis of the association of rare variants (MAF<10-3) and various CKD phenotypes in the UK-Biobank, All of Us Study, Mayo Clinic Tapestry Study, and Mayo Biobank Study. Phenotypes tested will be estimated glomerular filtration rate, all- cause and specific causes of CKD, end-stage kidney disease, albuminuria, and renal cysts. I will generate computational algorithms to perform data extraction and harmonization of variables between studies, creating an integrated system able to facilitate similar analysis. In addition, computational algorithms linking selected variants to a set of secondary data sources will be developed to generate a map of associations to other related phenotypes and potential therapeutic targets for drug development or repurposing. Aim 2. To define the penetrance of pathogenic genetic variants for renal phenotypes in a panel of CKD-related genes using four large repositories with genomic data. I will first identify pathogenic or likely- pathogenic variants related to CKD, including those from Aim 1. For each gene, observed penetrance in UK- Biobank, All of Us Study, Mayo Clinic Tapestry Study, and Mayo-Biobank Study will be computed. For the genes with sample size amenable to investigation, modulators of penetrance will be investigated by comparing within the group harboring pathogenic variants those with or without the phenotype of interest.
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