Nature MethodsMolecular BiologyPeer-reviewed journal article
"Unexpected mutations after CRISPR-Cas9 editing in vivo" are most likely pre-existing sequence variants and not nuclease-induced mutations
Caleb A. LareauiD, Kendell ClementiD, Jonathan Y. HsuiD, Vikram PattanayakiD, J. Keith JoungiD, Martin J. AryeeiD, Luca PinelloiD
Abstract
Schaefer et al. recently advanced the provocative conclusion that CRISPR-Cas9 nuclease can induce off-target alterations at genomic loci that do not resemble the intended on-target site.1 Using high-coverage whole genome sequencing (WGS), these authors reported finding SNPs and indels in two CRISPR-Cas9-treated mice that were not present in a single untreated control mouse. On the basis of this association, Schaefer et al. concluded that these sequence variants were caused by CRISPR-Cas9. This new proposed CRISPR-Cas9 off-target activity runs contrary to previously published work2-8 and, if the authors are correct, could have profound implications for research and therapeutic applications. Here, we demonstrate that the simplest interpretation of Schaefer et al.s data is that the two CRISPR-Cas9-treated mice are actually more closely related genetically to each other than to the control mouse. This strongly suggests that the so-called \"unexpected mutations\" simply represent SNPs and indels shared in common by these mice prior to nuclease treatment. In addition, given the genomic and sequence distribution profiles of these variants, we show that it is challenging to explain how CRISPR-Cas9 might be expected to induce such changes. Finally, we argue that the lack of appropriate controls in Schaefer et al.s experimental design precludes assignment of causality to CRISPR-Cas9. Given these substantial issues, we urge Schaefer et al. to revise or re-state the original conclusions of their published work so as to avoid leaving misleading and unsupported statements to persist in the literature.
- organism: mouse
- assay: whole-genome sequencing
- research focus: benchmarking
- approach: statistical model
- Citations
- 34
- FWCI
- 2.40
- Field percentile
- top 10%
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