# Robust verification of genetic variant-associated candidate off-target sites

> **NIH AI U01** · BOSTON CHILDREN'S HOSPITAL · 2026 · $1,675,364

## Abstract

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

## Key facts

- **NIH application ID:** 11333023
- **Project number:** 4U01AI191210-02
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Daniel Evan Bauer; Danilo  Pellin; Luca  Pinello
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** AI
- **Fiscal year:** 2026
- **Award amount:** $1,675,364
- **Award type:** 4N
- **Project period:** 2025-05-19T00:00:00 → 2028-04-30T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11333023

## Citation

> US National Institutes of Health, RePORTER application 11333023, Robust verification of genetic variant-associated candidate off-target sites (4U01AI191210-02). Retrieved via AI Analytics 2026-05-20 from https://api.ai-analytics.org/grant/nih/11333023. Licensed CC0.

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