Scalable assessment of genome editing off-targets associated with genetic variants

Abstract

Genome editing with RNA-guided DNA binding factors carries risk of off-target editing at homologous sequences. Genetic variants may introduce sequence changes that increase homology to a genome editing target, thereby increasing risk of off-target editing. Conventional methods to verify candidate off-targets rely on access to cells with genomic DNA carrying these sequences. However, for candidate off-targets associated with genetic variants, appropriate cells for experimental verification may not be available. Here we develop a method, Assessment By Stand-in Off-target LentiViral Ensemble with sequencing (ABSOLVE-seq), to integrate a set of candidate off-target sequences along with unique molecular identifiers (UMIs) in genomes of primary cells followed by clinically relevant gene editor delivery. Gene editing of dozens of candidate off-target sequences may be evaluated in a single experiment with high sensitivity, precision, and power. We provide an open-source pipeline to analyze sequencing data. This approach enables experimental assessment of the influence of human genetic diversity on specificity evaluation during gene editing therapy development.