Analysis | Published:

Implications of human genetic variation in CRISPR-based therapeutic genome editing

Nature Medicine volume 23, pages 10951101 (2017) | Download Citation

Abstract

CRISPR–Cas genome-editing methods hold immense potential as therapeutic tools to fix disease-causing mutations at the level of DNA. In contrast to typical drug development strategies aimed at targets that are highly conserved among individual patients, treatment at the genomic level must contend with substantial inter-individual natural genetic variation. Here we analyze the recently released ExAC and 1000 Genomes data sets to determine how human genetic variation impacts target choice for Cas endonucleases in the context of therapeutic genome editing. We find that this genetic variation confounds the target sites of certain Cas endonucleases more than others, and we provide a compendium of guide RNAs predicted to have high efficacy in diverse patient populations. For further analysis, we focus on 12 therapeutically relevant genes and consider how genetic variation affects off-target candidates for these loci. Our analysis suggests that, in large populations of individuals, most candidate off-target sites will be rare, underscoring the need for prescreening of patients through whole-genome sequencing to ensure safety. This information can be integrated with empirical methods for guide RNA selection into a framework for designing CRISPR-based therapeutics that maximizes efficacy and safety across patient populations.

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References

  1. 1.

    et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

  2. 2.

    et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

  3. 3.

    et al. Cpf1 is a single RNA-guided endonuclease of a class 2 CRISPR–Cas system. Cell 163, 759–771 (2015).

  4. 4.

    et al. Targeted and genome-wide sequencing reveal single nucleotide variations impacting specificity of Cas9 in human stem cells. Nat. Commun. 5, 5507 (2014).

  5. 5.

    et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

  6. 6.

    1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

  7. 7.

    1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

  8. 8.

    1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  9. 9.

    et al. In vivo genome editing using Staphylococcus aureus Cas9. Nature 520, 186–191 (2015).

  10. 10.

    et al. Engineered CRISPR–Cas9 nucleases with altered PAM specificities. Nature 523, 481–485 (2015).

  11. 11.

    et al. An updated evolutionary classification of CRISPR–Cas systems. Nat. Rev. Microbiol. 13, 722–736 (2015).

  12. 12.

    et al. The CRISPR/Cas bacterial immune system cleaves bacteriophage and plasmid DNA. Nature 468, 67–71 (2010).

  13. 13.

    et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31, 827–832 (2013).

  14. 14.

    et al. High-throughput profiling of off-target DNA cleavage reveals RNA-programmed Cas9 nuclease specificity. Nat. Biotechnol. 31, 839–843 (2013).

  15. 15.

    et al. High-frequency off-target mutagenesis induced by CRISPR–Cas nucleases in human cells. Nat. Biotechnol. 31, 822–826 (2013).

  16. 16.

    , , , & RNA-guided editing of bacterial genomes using CRISPR–Cas systems. Nat. Biotechnol. 31, 233–239 (2013).

  17. 17.

    et al. Rationally engineered Cas9 nucleases with improved specificity. Science 351, 84–88 (2016).

  18. 18.

    et al. High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects. Nature 529, 490–495 (2016).

  19. 19.

    et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR–Cas nucleases. Nat. Biotechnol. 33, 187–197 (2015).

  20. 20.

    et al. Genome-wide detection of DNA double-stranded breaks induced by engineered nucleases. Nat. Biotechnol. 33, 179–186 (2015).

  21. 21.

    et al. Digenome-seq: genome-wide profiling of CRISPR–Cas9 off-target effects in human cells. Nat. Methods 12, 237–243, 1, 243 (2015).

  22. 22.

    et al. CRISPR/Cas9 systems have off-target activity with insertions or deletions between target DNA and guide RNA sequences. Nucleic Acids Res. 42, 7473–7485 (2014).

  23. 23.

    et al. Genome-wide specificities of CRISPR–Cas Cpf1 nucleases in human cells. Nat. Biotechnol. 34, 869–874 (2016).

  24. 24.

    et al. Genome-wide analysis reveals specificities of Cpf1 endonucleases in human cells. Nat. Biotechnol. 34, 863–868 (2016).

  25. 25.

    , , & Personalized medicine and human genetic diversity. Cold Spring Harb. Perspect. Med. 4, a008581 (2014).

  26. 26.

    et al. SITE-Seq: a genome-wide method to measure Cas9 cleavage. Protocol Exchange (2017).

  27. 27.

    et al. CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR–Cas9 nuclease off-targets. Nat. Methods 14, 607–614 (2017).

  28. 28.

    et al. BLISS is a versatile and quantitative method for genome-wide profiling of DNA double-strand breaks. Nat. Commun. 8, 15058 (2017).

  29. 29.

    , , , & Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016).

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Acknowledgements

We would like to thank R. Macrae, L. Francioli, S. Jones, J. Strecker, D. Cox, I. Slaymaker, and W. Yan for helpful discussions and insights. F.Z. is a New York Stem Cell Foundation–Robertson Investigator. F.Z. is supported by the US National Institutes of Health through the National Institute of Mental Health (5DP1-MH100706 and 1R01-MH110049); the National Science Foundation; the New York Stem Cell Foundation; the Howard Hughes Medical Institute; the Simons Foundation; the Paul G. Allen Family Foundation; the Vallee Foundation; the Skoltech–MIT Next-Generation Program; James and Patricia Poitras; Robert Metcalfe; and David Cheng. The computer code and resources related to this work are available through the Zhang laboratory website (http://www.genome-engineering.org/) and GitHub (http://github.com/fengzhanglab).

Author information

Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

    • David A Scott
    •  & Feng Zhang
  2. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • David A Scott
    •  & Feng Zhang
  3. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • David A Scott
    •  & Feng Zhang
  4. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Feng Zhang

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Contributions

D.A.S. and F.Z. conceived the study; D.A.S. performed all experiments and analyses; D.A.S. and F.Z. wrote the manuscript.

Competing interests

F.Z. is a founder of Editas Medicine and a scientific advisor for Editas Medicine and Horizon Discovery.

Corresponding author

Correspondence to Feng Zhang.

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DOI

https://doi.org/10.1038/nm.4377

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