CRISPR gene editing holds great promise to modify DNA sequences in somatic cells to treat disease. However, standard computational and biochemical methods to predict off-target potential focus on reference genomes. We developed an efficient tool called CRISPRme that considers single-nucleotide polymorphism (SNP) and indel genetic variants to nominate and prioritize off-target sites. We tested the software with a BCL11A enhancer targeting guide RNA (gRNA) showing promise in clinical trials for sickle cell disease and β-thalassemia and found that the top candidate off-target is produced by an allele common in African-ancestry populations (MAF 4.5%) that introduces a protospacer adjacent motif (PAM) sequence. We validated that SpCas9 generates strictly allele-specific indels and pericentric inversions in CD34+ hematopoietic stem and progenitor cells (HSPCs), although high-fidelity Cas9 mitigates this off-target. This report illustrates how genetic variants should be considered as modifiers of gene editing outcomes. We expect that variant-aware off-target assessment will become integral to therapeutic genome editing evaluation and provide a powerful approach for comprehensive off-target nomination.
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Sequencing data are deposited in the NCBI Sequence Read Archive database under accession number PRJNA733110. The data for 1000 G were downloaded from https://doi.org/10.12688/wellcomeopenres.15126.2. The data for the HGDP were downloaded from https://doi.org/10.1126/science.aay5012. The full CRISPRme results for NGG PAM gRNAs (including sg1617) are available at https://doi.org/10.5281/zenodo.7195706. Source data are provided with this paper.
CRISPRme source code is available at https://github.com/pinellolab/crisprme and https://github.com/InfOmics/CRISPRme. The web app is available online at http://crisprme.di.univr.it. The versions of CRISPRme (1.8.8 and v1.7.7) used to generate the results presented in this manuscript have been deposited on Zenodo: https://doi.org/10.5281/zenodo.5047489. CRISPRitz v2.6.5 used to generate the data presented in this paper have been deposited to Zenodo: https://doi.org/10.5281/zenodo.7078220.
The scripts to generate the plots presented in the manuscript have been deposited to Zenodo: https://doi.org/10.5281/zenodo.7193131.
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L.P. received support from the National Institutes of Health (NIH) (R35 HG010717 and RM1 HG009490). D.E.B. was supported by the National Heart, Lung, and Blood Institute (OT2HL154984 and P01HL053749), Burroughs Wellcome Fund, Doris Duke Charitable Foundation and the St. Jude Children’s Research Hospital Collaborative Research Consortium. R.G received support from European Union’s ERA-NET JPCOFUND2 (JPND2019-466-037). We thank S. H. Orkin, G. Lettre, J. K. Joung, V. Pattanayak, K. Petri, A. H. Shen, E. Dirupo and F. Masillo for helpful input.
L.P. has financial interests in Edilytics, Excelsior Genomics and SeQure Dx. L.P.’s interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies. The remaining authors declare no competing interests.
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Scatter plots show from left to right, the correlation of ranked targets, extracted by selecting top 10000 targets ordered by CFD and CRISTA score, respectively. The left plot shows the rank correlation of targets with 0 bulges (Pearson’s correlation: 0.57, p < 1e-10, Spearman’s correlation: 0.55, p < 1e-10), the center plot shows rank correlation of targets with 1 bulge (Pearson’s correlation: −0.16, p < 1 e-10, Spearman’s correlation: −0.33, p < 1 e-10) and the right plot shows the rank correlation of targets with 2 bulges (Pearson’s correlation: −0.55, p < 1e-10, Spearman’s correlation: −0.80, p < 1e-10). The correlation values and p-values(two-sided) were calculated using standard functions from the Python scipy library. The colors represent the lowest count of bulges for each target, because the two scoring methods may prioritize different alignments and thus different number of mismatches and bulges of the same genomic target.
HGDP variant off-targets with CFD ≥ 0.2 and increase in CFD of ≥0.1. Individual samples from each of the seven superpopulations were shuffled 100 times to calculate the mean and 95% confidence interval. First panel shows distribution within all 54 discrete populations, colored by superpopulation. Additional seven panels show distribution of discrete populations within each listed superpopulation.
Extended Data Fig. 4 Candidate transcript off-targets introduced by common genetic variants for non-CRISPR sequence-based RNA-targeting therapeutic strategies.
a) A common SNP (in blue) introduces a candidate CDS off-target site with 2 mismatches for the FDA-approved antisense oligo Nusinersen. b) Top 1000 candidate transcript off-targets ranked by mismatches and bulges for Nusinersen from a search performed with the 1000 G and HGDP genetic variant datasets. c) A common insertion variant (in red) introduces a candidate 3’UTR off-target site with 4 mismatches + bulges for the FDA-approved RNAi therapy Inclisiran. d) Top 1000 candidate transcript off-targets ranked by mismatches and bulges for Inclisiran from a search performed with the 1000 G and HGDP genetic variant datasets.
Supplementary Figures 1–13, Tables 1–5, Notes 1–9 and Data 1–4. Supplementary figures, tables and notesare included in the flat file. Supplementary data files are separate (see below).
Top candidate off-targets in CRISPRme search results for sg1617 using hg38, 1000 G and HGDP data with up to six mismatches and two bulges (including the integrated_results, all_results_with_alternative_alignments, and private_targets files).
Top candidate off-targets in CRISPRme search results for other example gRNAs with NGG PAMs.
Top candidate off-targets in CRISPRme search results for example gRNAs with non-NGG PAMs.
Top candidate off-targets in CRISPRme search results for example non-CRISPR-based, RNA-targeting strategies (antisense oligo and RNA interference).
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Cancellieri, S., Zeng, J., Lin, L.Y. et al. Human genetic diversity alters off-target outcomes of therapeutic gene editing. Nat Genet 55, 34–43 (2023). https://doi.org/10.1038/s41588-022-01257-y
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