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Evaluating strategies for reversing CRISPR-Cas9 gene drives

View ORCID ProfileMichael R. Vella, View ORCID ProfileChristian E. Gunning, View ORCID ProfileAlun L. Lloyd, Fred Gould
doi: https://doi.org/10.1101/144097
Michael R. Vella
1North Carolina State University, Biomathematics Graduate Program, Department of Mathematics, Raleigh, 27695, USA
2North Carolina State University, Genetic Engineering and Society Center, Raleigh, 27695, USA
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Christian E. Gunning
3North Carolina State University, Department of Entomology and Plant Pathology, Raleigh, 27695, USA
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Alun L. Lloyd
1North Carolina State University, Biomathematics Graduate Program, Department of Mathematics, Raleigh, 27695, USA
2North Carolina State University, Genetic Engineering and Society Center, Raleigh, 27695, USA
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Fred Gould
2North Carolina State University, Genetic Engineering and Society Center, Raleigh, 27695, USA
3North Carolina State University, Department of Entomology and Plant Pathology, Raleigh, 27695, USA
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Abstract

A gene drive biases inheritance of a gene so that it increases in frequency within a population even when the gene confers no fitness benefit. There has been renewed interest in environmental releases of engineered gene drives due to recent proof of principle experiments with the CRISPR-Cas9 system as a drive mechanism. Release of modified organisms, however, is controversial, especially when the drive mechanism could theoretically alter all individuals of a species. Thus, it is desirable to have countermeasures to reverse a drive if a problem arises. Several genetic mechanisms for limiting or eliminating gene drives have been proposed and/or developed, including synthetic resistance, reversal drives, and immunizing reversal drives. While predictions about efficacy of these mechanisms have been optimistic, we lack detailed analyses of their expected dynamics. We develop a discrete time model for population genetics of a drive and proposed genetic countermeasures. Efficacy of drive reversal varies between countermeasures. For some parameter values, the model predicts unexpected behavior including polymorphic equilibria and oscillatory dynamics. The timing and number of released individuals containing a genetic countermeasure can substantially impact outcomes. The choice among countermeasures by researchers and regulators will depend on specific goals and population parameters of target populations.

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Posted August 10, 2017.
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Evaluating strategies for reversing CRISPR-Cas9 gene drives
Michael R. Vella, Christian E. Gunning, Alun L. Lloyd, Fred Gould
bioRxiv 144097; doi: https://doi.org/10.1101/144097
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Evaluating strategies for reversing CRISPR-Cas9 gene drives
Michael R. Vella, Christian E. Gunning, Alun L. Lloyd, Fred Gould
bioRxiv 144097; doi: https://doi.org/10.1101/144097

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