PROJECT SUMMARY Therapeutic genome editing holds immense promise for treating genetic diseases, but off-target effects remain a critical safety concern, particularly when considering human genetic diversity. Most existing methods for identifying off-target sites do not account for genetic variants, potentially missing important risks for individual patients. We have developed CRISPRme, a computational tool that nominates off-target sites based on genetic variants, and ABSOLVE-seq, an experimental method to verify these sites in relevant cellular contexts. This proposal aims to optimize and integrate these approaches into a comprehensive pipeline for assessing variant- associated off-target risks in genome editing therapeutics to support IND applications. Aim 1 focuses on optimizing CRISPRme by incorporating larger variant datasets, integrating advanced prediction tools, and developing standardized protocols for off-target nomination. We will extend the search space to include genetic variants from the All of Us Research Program and implement a risk assessment framework that considers functional annotations and cancer-related somatic mutations. Aim 2 will optimize the ABSOLVE-seq experimental methodology and analysis pipeline. We will improve vector design, plasmid fidelity, and transduction protocols to enhance sensitivity and scalability. The analysis pipeline will be refined to provide robust statistical modeling of editing outcomes across various editing modalities, including nucleases, base editors, and prime editors. Throughout the project, we will collaborate with SCGE Consortium members to generate benchmark datasets for lead gRNAs being explored for clinical development. We will work to extend the methods to diverse cell types and tissues relevant to in vivo gene therapy of the liver, eye, and central nervous system. The deliverables will include standardized software modules, SOPs, and comprehensive data packages suitable for regulatory submissions. Our use