# Advancing the implementation of variant-level functional data into clinical databases and clinical practice > **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2024 · $793,971 ## Abstract SUMMARY One of the major challenges limiting the potential of genomic medicine to revolutionize clinical care is the current lack of knowledge about the function of most human genetic variants. Consequently, the majority of clinically encountered genetic variants are classified as variants of uncertain significance (VUS), which cannot be used for clinical decision making given their unknown relationship to disease. The VUS problem is particularly acute for individuals from populations historically excluded from research, compounding existing healthcare inequities when implementing genomic medicine. Variant-level functional data has the potential to overcome many of these challenges by providing pathogenicity information for variants from diverse populations. For example, we and others have demonstrated that multiplexed assays of variant effect (MAVEs) can resolve a large fraction of VUS in clinically important genes (e.g., 49% of BRCA1 VUS, 69% of TP53 VUS, and 93% of DDX3X VUS), and several U.S. and international consortia are currently using MAVEs to produce functional data for all possible coding and non-coding variants associated with all genes that have been linked to human disease. Given the potential of functional information to augment the implementation of genomic medicine, clinical guidelines have recently been updated to recommend the use of variant-level functional data when interpreting variant pathogenicity. However, despite the potential clinical utility of large-scale functional datasets, how best to implement them such that clinicians can appropriately incorporate variant functional data into clinical practice remains unknown. In this proposal we aim to address this unmet need in genomic medicine using the following approach: ● First, we will generate a framework for standardizing and disseminating curated large-scale functional data into clinician-facing resources, and implement this framework into ClinVar (Aim 1) ● Second, we will perform a proof-of-concept integration of variant level functional data in ClinVar into two large clinical practices to evaluate the clinical uptake and impact of variant-level functional data (Aim 2). ● Finally, we will build and disseminate resources for training clinicians on best practices for integrating functional data into clinical practice (Aim 3). Overall, this proposal has the potential to significantly advance the implementation of genomic data into clinical practice by enabling clinicians to appropriately leverage emerging variant-level functional data to resolve VUS, thereby making genomic medicine more equitable and impactful. ## Key facts - **NIH application ID:** 10850825 - **Project number:** 5R01HG013025-02 - **Recipient organization:** UNIVERSITY OF WASHINGTON - **Principal Investigator:** Lea Starita - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $793,971 - **Award type:** 5 - **Project period:** 2023-06-01 → 2028-03-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10850825 ## Citation > US National Institutes of Health, RePORTER application 10850825, Advancing the implementation of variant-level functional data into clinical databases and clinical practice (5R01HG013025-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10850825. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*