Population-scale cellular GUIDE-seq-2 and biochemical CHANGE-seq-R profiles reveal human genetic variation frequently affects Cas9 off-target activity

Abstract

Genome editing enzymes can introduce targeted changes to DNA in living cells1-4, transforming biological research and enabling the first approved gene editing therapy for sickle cell disease5. However, their activity can be altered by genetic variation at on- or off-target sites6-8, potentially impacting both their precision and therapeutic safety. Due to a lack of scalable methods to measure genome-wide editing activity in cells from large populations and diverse target libraries, the frequency and extent of these variant effects on editing remain unknown. Here, we present the first systematic, population-scale study of how genetic variation affects the cellular genome-wide activity of CRISPR-Cas9, enabled by a novel, sensitive, and unbiased cellular assay, GUIDE-seq-2, with improved scalability and accuracy compared to the original broadly adopted method9. Analyzing Cas9 genome-wide activity at 1,115 on- and off-target sites across six guide RNAs in cells from 95 individuals spanning four genetically diverse populations, we found that genetic variants frequently overlap off-target sites, with 14% significantly altering Cas9 editing activity. To understand the effect of mismatches in more diverse sequence contexts, we developed a novel method, combinatorial high-throughput analysis of nuclease cleavage effects (CHANCE-seq), the first massively parallel biochemical approach that can quantify Cas9 activity across millions of mismatched target sites. We leveraged this large-scale CHANCE-seq dataset to train a context-aware deep neural network model, CHANCE-net, to accurately predict and interpret the effects of single-nucleotide variants on off-targets with up to six mismatches. Our deep combinatorial profiling of Cas9 off-target activity revealed frequent, context-dependent synergistic effects of mismatches. Taken together, our findings illuminate an approach to accounting for genetic variation when designing genome-editing strategies for research and therapeutics.