The ability to make specific edits of genetic material has been a long-standing goal in molecular biology. Until recently, such DNA engineering was cumbersome, expensive, and difficult since it relied on site-specific nucleases or random insertions. CRISPR technology represents a milestone in genome editing because it makes DNA engineering highly efficient, relatively simple to use, and cost-effective through the use of endonucleases that can be flexibly programmed to cut specific sequences dictated by a guide RNA (gRNA; Moon et al., 2019; Mali et al., 2013).

The programmability of CRISPR/Cas9 systems allows for numerous potential applications (Moon et al., 2019), including cancer and disease treatment (Chen et al., 2019; Hodges and Conlon, 2019; Jo et al., 2019; Zhang et al., 2020a; Yang et al., 2017), stimuli tracking in living cells (Tang and Liu, 2018), and crop improvement (Zhang et al., 2020b). While most applications of CRISPR use this technology to engineer specific modifications in a given DNA sequence, some proposed applications take the idea one step further by integrating the CRISPR machinery itself into an organism’s genome. In that case, endonuclease activity can continue to produce genetic changes in the cells of the living organism. When present in the germline, these genetic changes might even be passed on to future generations, such as in CRISPR-based gene drives—‘selfish’ genetic elements that are engineered to rapidly spread a desired genetic trait through a population (Esvelt et al., 2014; Champer et al., 2016; Unckless et al., 2017; Noble et al., 2017; Burt, 2014).

However, major questions loom large about the technical feasibility of these proposed applications. For example, it remains unclear whether activity of CRISPR endonucleases could entail unintended and potentially harmful consequences in the transgenic organisms, for instance due to the tendency to produce non-specific DNA modifications (so-called ‘off-target effects’; Zhang et al., 2015). Such off-target cleavage could be substantially higher when Cas9 is continuously expressed from a genome and inherited by offspring, where further off-target cleavage can occur.

In this study, we seek to address this question in the context of CRISPR gene drive, an emerging technology that could be used for applications ranging from the control of vector-borne diseases to the suppression of invasive species (Esvelt et al., 2014; Unckless et al., 2017; Burt, 2014; Alphey, 2014). One class of CRISPR-based gene drives is the so-called ‘homing drives’. These genetic constructs are programmed to cleave a wild-type sister chromatid and get copied to the target site through homology-directed repair. Since ‘homing’ occurs in the germline, the drive allele will be inherited at a super-Mendelian rate and can thereby spread quickly through the population. The effectiveness of such systems has now been demonstrated in various organisms, including yeast (Roggenkamp et al., 2018; Shapiro et al., 2018; DiCarlo et al., 2015; Basgall et al., 2018), mosquitoes (Hammond et al., 2017; Gantz et al., 2015; Kyrou et al., 2018; Hammond et al., 2016), fruit flies (Oberhofer et al., 2018; Carrami et al., 2018; Gantz and Bier, 2015; Champer et al., 2017; Champer et al., 2018; Champer et al., 2019a; Champer et al., 2019b; Champer et al., 2020b), and mice (Grunwald et al., 2019). Another class of CRISPR gene drives operates by the ‘toxin-antidote’ principle (Champer et al., 2020a). Here, the drive allele serves as the ‘toxin’ by carrying a CRISPR endonuclease programmed to target and disrupt an essential wild-type gene. At the same time, the construct also contains a recoded version of that gene (the ‘antidote’), which is immune to cleavage by the drive. Over time, the drive will continuously increase in frequency and remove wild-type alleles from the population (Burt and Crisanti, 2018). Both homing and toxin-antidote drives can be ‘modification drives’, intended to spread a desired genetic payload through the population (e.g. a gene that prevents mosquitoes from transmitting malaria), or ‘suppression drives’ that seek to diminish or outright eliminate the target population (Champer et al., 2020a; Champer et al., 2021a).

A key factor in determining the expected population dynamics of any type of gene drive is the fitness cost imposed by the drive (Wedell et al., 2019). Such fitness costs could come in the form of reduced viability, fecundity, or mating success of the individuals that carry drive alleles. In suppression drives, some fitness costs are typically an intended feature of the drive, necessary to ultimately achieve population suppression. However, these costs are usually recessive to allow the drive to spread to high frequency, and there is generally a limit as to how high other costs can be before the drive will lose its ability to spread effectively (Champer et al., 2020a; Champer et al., 2021a; Champer et al., 2021b; Deredec et al., 2008). For modification drives, fitness costs tend to slow the spread of the drive and can thereby increase the chance that resistance alleles evolve, which could ultimately defeat the drive (Unckless et al., 2017). For such applications, it is therefore desirable to minimize any fitness costs. In drives with frequency-dependent invasion dynamics, such as most CRISPR toxin-antidote systems (Champer et al., 2020b; Oberhofer et al., 2019), fitness costs typically determine the frequency threshold required for the drive to spread through the population (Champer et al., 2020a; Champer et al., 2021a; Champer et al., 2021b).

We believe it is useful to distinguish between two types of fitness costs of a gene drive. The first class comprises any costs resulting from the genomic integration of the drive construct (e.g. when this disrupts a functionally important region), costs of potential ‘payload’ genes included in the drive construct, costs resulting directly from the expression of the endonuclease or other drive elements such as gRNAs, and costs due to cleavage of the intended target site. We will call these ‘direct’ fitness costs. By contrast, the second class comprises any potential fitness costs resulting from cleavage and disruption of unintended sites in the genome, so-called ‘off-target’ effects. Despite their critical importance, we still know surprisingly little about the specific types of fitness costs imposed by gene drives. Furthermore, it remains unclear whether there are certain baseline fitness costs that would be difficult to avoid in any gene drive construct, for instance, because they are inherent to the expression and activity of the CRISPR endonuclease.

In this study, we conduct a comprehensive assessment of the fitness effects resulting from the genomic expression of CRISPR/Cas9 in experimental Drosophila melanogaster populations. We specifically investigate four different transgenic constructs that allow us to disentangle direct fitness costs from those due to off-target effects. We estimate these fitness costs both through statistical inference from allele frequency trajectories in cage populations using a maximum likelihood approach, and a direct evaluation of individual fitness components using viability, fecundity, and mate choice assays.

Negative selection will tend to displace any alleles from the population that are sufficiently deleterious. This effect can be quantified by an allele’s fitness (i.e. the relative reproductive success between carriers and noncarriers of the allele). In this study, we measured the fitness of transgenic Cas9/gRNA alleles in D. melanogaster, which constitute an essential component of CRISPR gene drives. A quantitative estimate of the fitness costs imposed by such constructs is critical for predicting the performance and potential limitations of this emerging technology.

Our constructs were designed to mimic a gene drive, yet without homing or any other mechanism that would facilitate super-Mendelian inheritance. This allowed us to estimate their ‘baseline’ fitness costs by tracking allele frequencies in cage populations. Using a maximum likelihood inference approach (Liu et al., 2019), we did not detect fitness costs due to Cas9/gRNA integration, expression, and on-target activity in our experiments (which together we refer to as ‘direct’ costs). However, we inferred a moderate fitness cost possibly resulting from off-target effects. This interpretation was further corroborated by several additional results: first, fitness costs were only detected for the construct that expressed both Cas9 and gRNAs, but not for the constructs that lacked gRNAs, suggesting that these fitness costs are primarily due to cleavage activity, rather than just the expression of Cas9 or its genomic integration. Furthermore, we did not detect any fitness costs for the construct in which Cas9 was replaced with Cas9HF1, a high-fidelity version of Cas9 designed to minimize off-target cleavage (Kleinstiver et al., 2016). Finally, a model where fitness costs are primarily due to off-target cuts is also consistent with the heterogeneity we observed in the frequency trajectories of the Cas9_gRNAs construct in our cage experiments, as this could be explained by the accumulation of cut alleles at the off-target sites over the course of the experiment. It would be more difficult to reconcile this observation with models where direct fitness costs are the driving factor or where fitness costs would be due to the specific genetic background of the construct flies or the health or development-related effects of the initially released flies. While the effects of off-target cutting in cells transiently exposed to Cas9 have already been extensively studied (Kimberland et al., 2018; Palermo et al., 2019; Klein et al., 2018; Carroll, 2019), our results suggest that such cutting may have more substantial negative consequences over multiple generations when the cells are continuously expressing Cas9 from a genomic source.

By inferring fitness costs from allele frequency trajectories in a population, our approach is complementary to previous studies that sought to directly detect individual off-target mutations and predict their potential phenotypic effects, including a recent study on mosquito gene drives (Garrood et al., 2021). Our approach, by contrast, infers the overall impact that a Cas9 allele has on reproductive success across multiple generations, thereby integrating selective effects over all life stages and affected phenotypes. Such a direct measurement of the ‘fitness’ of the given construct avoids many of the complexities involved in seeking to identify all off-target mutations. For example, this would likely require time-resolved whole-genome sequencing on the population level so that also rare but potentially consequential mutations can be detected, which could be anywhere in the genome. Even when all such mutations can be accurately identified, predicting their potential effects on fitness will not always be straightforward.

These complexities are further underlined by the fact that we did not actually detect mutated sequences at any of the top six computationally predicted off-target sites for our Cas9_gRNAs construct. One potential explanation is that all these predicted sites in fact contain several nucleotide mismatches (often in the critical Protospacer Adjacent Motif (PAM)-proximal 12 nucleotides), which should make cleavage less likely. These predictions by CRISPR Optimal Target Finder are based on in vivo cleavage specificity in cell lines where off-target cutting is known to occur more frequently than in animals (Gratz et al., 2014). However, our system may be substantially different because it involves strong in vivo germline and early embryo expression across several generations, and it is unclear whether that would lead to higher or lower expected cleavage rates than in cell line studies.

At first glance, the lack of mutated alleles at this small set of computationally predicted sites may seem to contradict the interpretation that off-target cleavage is the primary driver of fitness costs for the Cas9_gRNAs construct. However, we believe it is likely that off-target cleavage did still occur, yet at other genomic sites than those we tested. Indeed, off-target cleavage can frequently occur at sites that are difficult to predict computationally (Li et al., 2019; Höijer et al., 2020), and it has been observed at sites with 5 or more mismatches (Pan et al., 2022; Tsai et al., 2015; Fu et al., 2013; Chaudhari et al., 2020), meaning there are possibly thousands of potential off-target sites in the fly genome. While the individual cleavage rates at most of these sites might be vanishingly low, the combined genome-wide rate could still be substantial. Off-target sites could also simply fail to be predicted if they occur in regions of the genome that are poorly assembled. Importantly, even rare off-target cleavage events can still have a strong impact on fitness. For example, if deleterious cleavage events occur in only 10% of drive carriers but cause them to have a 50% reduction in viability or fertility, this would still amount to a net fitness cost of 5% for the construct. Such events do not need to occur consistently at the same sites to be able to substantially alter construct frequency trajectories in aggregate. For all these reasons, we believe that our inability to detect mutated alleles at a small set of computationally predicted off-target sites does not rule out the presence of off-target cleavage at other sites. Indeed, a key benefit of our approach is that it allows us to estimate fitness costs without having to rely on the detection of specific off-target cleavage events and subsequent evaluation of their direct potential effects on fitness, which can be complicated.

Nevertheless, our study does have some important limitations. The maximum likelihood framework for fitness estimation necessarily relied on a simplistic ranking based on AICc values (a goodness-of-fit measure) of five highly idealized selection models. Although AICc values are useful for choosing the model with the best fit, we must not systematically dismiss alternative evolutionary scenarios. Thus, while our model comparison points to off-target fitness costs affecting viability as the most plausible explanation for the observed construct frequency dynamics among the models we tested, the suggested presence of off-target fitness costs in our experiment does not rule out other factors contributing to fitness costs of CRISPR endonucleases in Drosophila.

Another simplified feature of our model is that we only used a single genomic site with codominant fitness costs to represent off-target cutting. In reality, there could be many possible off-target sites, with variable types of alleles produced after cleavage, different fitness costs, dominance relationships, degrees of genetic linkage, and possibly even epistatic interactions between them; not to mention the plethora of alternative factors that could contribute to the observed fitness costs of CRISPR endonucleases in our study. For example, a detrimental mutation could have occurred, by chance, in only a subset of our transgenic fly lines carrying the Cas9_gRNAs construct. While our ‘initial off-target’ model—which assumes that the construct insertion site and the idealized off-target site are unlinked—did not fit the Cas9_gRNAs construct dynamics well, a model accounting for intermediate linkage might have resulted in a better fit. Given the limited number of data points in our cage experiments, together with the large number of conceivable models, we cannot rule out that more complex selection scenarios could ultimately provide a better fit to our data.

The same holds true for the inference of different fitness components. While we did compare models where fitness affected viability versus models where it affected fecundity and mate choice, we believe that any conclusions from these comparisons should be taken with a grain of salt, given how many assumptions still went into each model (e.g. multiplicative fitness costs, same costs in males and females, and equal costs for fecundity and mate choice). Indeed, while our maximum likelihood analysis of the cage experiments ranked the viability model higher than the fecundity/mate choice model, we did observe reduced fecundity and mating success of genotypes carrying the Cas9_gRNAs construct in our phenotypic assays, while not finding a substantial effect on viability. As explained above, this could be due to a lower power of the phenotypic assays caused either by limited sample size, or the fact that only heterozygotes were studied in the viability assay. Furthermore, it is possible that many of those individuals with reduced fecundity and mating success in the phenotypic assays would not have survived into adult stage in the cage populations due to increased larval competition at higher densities. In summary, we hypothesize that environmental-dependent fitness effects (Yang et al., 2022) and the dependencies between reduced fecundity/mate choice and viability could explain the discrepancies between our fitness estimates in complex (cage) and simple (vial) environments and should be investigated in future studies.

Though considerable uncertainty remains regarding the precise nature of the fitness effects of our constructs, the overall finding that our Cas9_gRNAs construct imposes a moderate fitness cost is robust. It remains to be seen how well these results translate to other applications that involve genomic integration of Cas9 endonucleases. If these fitness cost indeed stem from off-target cleavage, they could vary substantially depending on the specific target sequence(s), genome composition, and expression patterns of Cas9 and gRNAs (Zhang et al., 2015; Gisler et al., 2019). While we specifically selected gRNAs with a low number of predicted off-target sites to minimize such effects, this may not be possible for every application. Some applications may also require the use of different promoters with higher somatic expression rates than nanos (Gantz et al., 2015; Hammond et al., 2016; Champer et al., 2017; Champer et al., 2018), which could increase fitness costs caused by off-target cleavage. On the other hand, Cas9 expression may be lower in some organisms or at other genomic sites, and certain applications might require fewer than the four gRNAs we included in our constructs, thereby potentially reducing off-target effects.

Our results have important implications for the modeling of gene drive approaches. Thus far, only direct fitness costs have been included in such studies, arising from the CRISPR nuclease itself, a payload gene, or cleavage of the intended target site. It is well known that such direct fitness costs can reduce the power of a suppression drive (Champer et al., 2021b; Deredec et al., 2008; North et al., 2020) and reduce the persistence of a modification drive in the face of resistance alleles (Unckless et al., 2017). If these costs are in fact lower than 5%, as suggested by our study in Drosophila with the nanos promoter, then they would not be expected to substantially impede the spread of suppression drives, though they could still suffer from other forms of direct fitness costs such as haploinsufficiency of the target gene and somatic Cas9 expression and cleavage, the latter of which has proven to be a particular determinate to some drives in mosquitoes (Hammond et al., 2016). The direct fitness of modification drives would be largely determined by their cargo gene(s) and possibly their rescue efficiency if they involve use of a recoded gene (Champer et al., 2020b; Oberhofer et al., 2019; Champer et al., 2020d; Adolfi et al., 2020).

On the other hand, if off-target effects are indeed the primary cause of fitness costs of a drive with otherwise low direct fitness costs, this should result in different population dynamics. Our modeling indicates that for a modification drive, such off-target effects should only slow the drive initially. After the drive has spread through most of the population and cleaved off-target alleles had time to accumulate, resistance alleles would not be as selectively advantageous. Thus, it would take them longer to outcompete drive alleles in the long run as compared to a scenario with primarily direct fitness costs. A suppression drive still suffers from cuts at off-target sites in a manner more closely resembling direct fitness costs, because these effects come into play during the early spread of a drive, often the most critical period in determining the fate of a suppression drive (Champer et al., 2021b; Deredec et al., 2008; North et al., 2020). However, if the rate at which off-target mutations arise is sufficiently low, then mutated off-target sequences may not have a large effect on population dynamics. This has been shown in a recent study on a suppression drive with a germline-restricted promoter and a single gRNA that successfully eliminated a mosquito cage population before substantial amounts of off-target cleavage could occur (Garrood et al., 2021). Indeed, our results suggest that off-target fitness costs could potentially support a suppression drive if off-target sites are not genetically linked to the drive locus. In that case, cut off-target alleles will increasingly be found also in individuals that do not carry the drive, reducing their ability to persist in the face of an imperfect drive. In complex, spatially explicit scenarios, such drives may still be weaker, since any long-term benefits may be rendered void by their reduced rate of spread, which should make them more vulnerable to the chasing effect (Champer et al., 2021b). Though we did not model it explicitly, threshold-based drives would likely suffer more from off-target fitness costs due to the critical importance of fitness of the released individuals. Specifically, off-target fitness costs should increase the necessary introduction threshold for these drives.

We demonstrated that Cas9HF1, which largely eliminates off-target cleavage (Kleinstiver et al., 2016), does not induce substantial negative fitness effects in our cage populations when used as a replacement for standard Cas9. Furthermore, we showed that homing drives with either form of Cas9 perform similarly for at least one gRNA target site. We, therefore, recommend that gene drives, as well as other applications that require the genomic integration of CRISPR endonucleases, should consider moving from standard Streptococcus pyogenes Cas9 to higher fidelity versions that can effectively minimize off-target effects. This would have the added advantage of reducing the generation of unanticipated genetic changes in natural populations from off-target cleavage and repair. One potential drawback of these nucleases is that they tend to have a lower cleavage rate that can depend on the specific gRNA sequence employed (Casini et al., 2018; Tan et al., 2019; Chatterjee et al., 2020; Xie et al., 2020; Slaymaker et al., 2016; Lee et al., 2019). In practice, this may reduce the number of available gRNA target sites and increase the need for the initial evaluation of gRNA targets. Newer improved forms of Cas9 (Casini et al., 2018; Tan et al., 2019; Xie et al., 2020; Slaymaker et al., 2016; Lee et al., 2019), including ones with an expanded range of target sites (Chatterjee et al., 2020), promise to ameliorate this issue.

In conclusion, we have demonstrated that genomic CRISPR/Cas9 expression in D. melanogaster can impose a moderate level of fitness costs, presumably via off-target effects. Our results further indicate that these fitness costs can be minimized by using a high-fidelity endonuclease with reduced off-target cleavage. Future studies should investigate whether our conclusions hold in other experiments involving different constructs, target sites, and organisms.

All data generated or analyzed during this study are included in the manuscript and supporting files.

1. 1. Adolfi A
   2. Gantz VM
   3. Jasinskiene N
   4. Lee H-F
   5. Hwang K
   6. Terradas G
   7. Bulger EA
   8. Ramaiah A
   9. Bennett JB
   10. Emerson JJ
   11. Marshall JM
   12. Bier E
   13. James AA

   (2020) Efficient population modification gene-drive rescue system in the malaria mosquito Anopheles stephensi

   Nature Communications 11:5553.

   https://doi.org/10.1038/s41467-020-19426-0

   • PubMed
   • Google Scholar
2. Book

   1. Akaike H

   (1998) Information theory and an extension of the maximum likelihood principle

   In: Parzen E, Tanabe K, Kitagawa G, editors. Selected Papers of Hirotugu Akaike. Springer. pp. 199–213.

   https://doi.org/10.1007/978-1-4612-1694-0_15

   • Google Scholar
3. 1. Alphey L

   (2014) Genetic control of mosquitoes

   Annual Review of Entomology 59:205–224.

   https://doi.org/10.1146/annurev-ento-011613-162002

   • PubMed
   • Google Scholar
4. 1. Basgall EM
   2. Goetting SC
   3. Goeckel ME
   4. Giersch RM
   5. Roggenkamp E
   6. Schrock MN
   7. Halloran M
   8. Finnigan GC

   (2018) Gene drive inhibition by the anti-CRISPR proteins acriia2 and acriia4 in Saccharomyces cerevisiae

   Microbiology 164:464–474.

   https://doi.org/10.1099/mic.0.000635

   • PubMed
   • Google Scholar
5. 1. Burt A

   (2003) Site-Specific selfish genes as tools for the control and genetic engineering of natural populations

   Proceedings. Biological Sciences 270:921–928.

   https://doi.org/10.1098/rspb.2002.2319

   • PubMed
   • Google Scholar
6. 1. Burt A

   (2014) Heritable strategies for controlling insect vectors of disease

   Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 369:20130432.

   https://doi.org/10.1098/rstb.2013.0432

   • PubMed
   • Google Scholar
7. 1. Burt A
   2. Crisanti A

   (2018) Gene drive: evolved and synthetic

   ACS Chemical Biology 13:343–346.

   https://doi.org/10.1021/acschembio.7b01031

   • PubMed
   • Google Scholar
8. 1. Carrami EM
   2. Eckermann KN
   3. Ahmed HMM
   4. Sánchez C HM
   5. Dippel S
   6. Marshall JM
   7. Wimmer EA

   (2018) Consequences of resistance evolution in a cas9-based sex conversion-suppression gene drive for insect pest management

   PNAS 115:6189–6194.

   https://doi.org/10.1073/pnas.1713825115

   • PubMed
   • Google Scholar
9. 1. Carroll D

   (2019) Collateral damage: benchmarking off-target effects in genome editing

   Genome Biology 20:114.

   https://doi.org/10.1186/s13059-019-1725-0

   • PubMed
   • Google Scholar
10. 1. Casini A
    2. Olivieri M
    3. Petris G
    4. Montagna C
    5. Reginato G
    6. Maule G
    7. Lorenzin F
    8. Prandi D
    9. Romanel A
    10. Demichelis F
    11. Inga A
    12. Cereseto A

    (2018) A highly specific spcas9 variant is identified by in vivo screening in yeast

    Nature Biotechnology 36:265–271.

    https://doi.org/10.1038/nbt.4066

    • PubMed
    • Google Scholar
11. 1. Champer J
    2. Buchman A
    3. Akbari OS

    (2016) Cheating evolution: engineering gene drives to manipulate the fate of wild populations

    Nature Reviews. Genetics 17:146–159.

    https://doi.org/10.1038/nrg.2015.34

    • PubMed
    • Google Scholar
12. 1. Champer J
    2. Reeves R
    3. Oh SY
    4. Liu C
    5. Liu J
    6. Clark AG
    7. Messer PW

    (2017) Novel CRISPR/Cas9 gene drive constructs reveal insights into mechanisms of resistance allele formation and drive efficiency in genetically diverse populations

    PLOS Genetics 13:e1006796.

    https://doi.org/10.1371/journal.pgen.1006796

    • PubMed
    • Google Scholar
13. 1. Champer J
    2. Liu J
    3. Oh SY
    4. Reeves R
    5. Luthra A
    6. Oakes N
    7. Clark AG
    8. Messer PW

    (2018) Reducing resistance allele formation in CRISPR gene drive

    PNAS 115:5522–5527.

    https://doi.org/10.1073/pnas.1720354115

    • PubMed
    • Google Scholar
14. Software

    1. Champer J

    (2019) Binomial analysis tool, version 3d24659

    GitHub.

    https://github.com/MesserLab/Binomial-Analysis
15. 1. Champer J
    2. Chung J
    3. Lee YL
    4. Liu C
    5. Yang E
    6. Wen Z
    7. Clark AG
    8. Messer PW

    (2019a) Molecular Safeguarding of CRISPR gene drive experiments

    eLife 8:e41439.

    https://doi.org/10.7554/eLife.41439

    • PubMed
    • Google Scholar
16. 1. Champer J
    2. Wen Z
    3. Luthra A
    4. Reeves R
    5. Chung J
    6. Liu C
    7. Lee YL
    8. Liu J
    9. Yang E
    10. Messer PW
    11. Clark AG

    (2019b) Crispr gene drive efficiency and resistance rate is highly heritable with no common genetic loci of large effect

    Genetics 212:333–341.

    https://doi.org/10.1534/genetics.119.302037

    • PubMed
    • Google Scholar
17. 1. Champer J
    2. Kim IK
    3. Champer SE
    4. Clark AG
    5. Messer PW

    (2020a) Performance analysis of novel toxin-antidote CRISPR gene drive systems

    BMC Biology 18:27.

    https://doi.org/10.1186/s12915-020-0761-2

    • PubMed
    • Google Scholar
18. 1. Champer J
    2. Lee E
    3. Yang E
    4. Liu C
    5. Clark AG
    6. Messer PW

    (2020b) A toxin-antidote CRISPR gene drive system for regional population modification

    Nature Communications 11:1082.

    https://doi.org/10.1038/s41467-020-14960-3

    • PubMed
    • Google Scholar
19. 1. Champer SE
    2. Oh SY
    3. Liu C
    4. Wen Z
    5. Clark AG
    6. Messer PW
    7. Champer J

    (2020c) Computational and experimental performance of CRISPR homing gene drive strategies with multiplexed gRNAs

    Science Advances 6:eaaz0525.

    https://doi.org/10.1126/sciadv.aaz0525

    • PubMed
    • Google Scholar
20. 1. Champer J
    2. Yang E
    3. Lee E
    4. Liu J
    5. Clark AG
    6. Messer PW

    (2020d) A CRISPR homing gene drive targeting a haplolethal gene removes resistance alleles and successfully spreads through a cage population

    PNAS 117:24377–24383.

    https://doi.org/10.1073/pnas.2004373117

    • PubMed
    • Google Scholar
21. 1. Champer J
    2. Champer SE
    3. Kim IK
    4. Clark AG
    5. Messer PW

    (2021a) Design and analysis of CRISPR-based underdominance toxin-antidote gene drives

    Evolutionary Applications 14:1052–1069.

    https://doi.org/10.1111/eva.13180

    • PubMed
    • Google Scholar
22. 1. Champer J
    2. Kim IK
    3. Champer SE
    4. Clark AG
    5. Messer PW

    (2021b) Suppression gene drive in continuous space can result in unstable persistence of both drive and wild-type alleles

    Molecular Ecology 30:1086–1101.

    https://doi.org/10.1111/mec.15788

    • PubMed
    • Google Scholar
23. 1. Chatterjee P
    2. Jakimo N
    3. Lee J
    4. Amrani N
    5. Rodríguez T
    6. Koseki SRT
    7. Tysinger E
    8. Qing R
    9. Hao S
    10. Sontheimer EJ
    11. Jacobson J

    (2020) An engineered sccas9 with broad PAM range and high specificity and activity

    Nature Biotechnology 38:1154–1158.

    https://doi.org/10.1038/s41587-020-0517-0

    • PubMed
    • Google Scholar
24. 1. Chaudhari HG
    2. Penterman J
    3. Whitton HJ
    4. Spencer SJ
    5. Flanagan N
    6. Lei Zhang MC
    7. Huang E
    8. Khedkar AS
    9. Toomey JM
    10. Shearer CA
    11. Needham AW
    12. Ho TW
    13. Kulman JD
    14. Cradick TJ
    15. Kernytsky A

    (2020) Evaluation of homology-independent CRISPR-Cas9 off-target assessment methods

    The CRISPR Journal 3:440–453.

    https://doi.org/10.1089/crispr.2020.0053

    • PubMed
    • Google Scholar
25. 1. Chen M
    2. Mao A
    3. Xu M
    4. Weng Q
    5. Mao J
    6. Ji J

    (2019) Crispr-Cas9 for cancer therapy: opportunities and challenges

    Cancer Letters 447:48–55.

    https://doi.org/10.1016/j.canlet.2019.01.017

    • PubMed
    • Google Scholar
26. 1. Cong L
    2. Ran FA
    3. Cox D
    4. Lin S
    5. Barretto R
    6. Habib N
    7. Hsu PD
    8. Wu X
    9. Jiang W
    10. Marraffini LA
    11. Zhang F

    (2013) Multiplex genome engineering using CRISPR/Cas systems

    Science 339:819–823.

    https://doi.org/10.1126/science.1231143

    • PubMed
    • Google Scholar
27. 1. Deredec A
    2. Burt A
    3. Godfray HCJ

    (2008) The population genetics of using homing endonuclease genes in vector and pest management

    Genetics 179:2013–2026.

    https://doi.org/10.1534/genetics.108.089037

    • PubMed
    • Google Scholar
28. 1. DiCarlo JE
    2. Chavez A
    3. Dietz SL
    4. Esvelt KM
    5. Church GM

    (2015) Safeguarding CRISPR-Cas9 gene drives in yeast

    Nature Biotechnology 33:1250–1255.

    https://doi.org/10.1038/nbt.3412

    • PubMed
    • Google Scholar
29. 1. Esvelt KM
    2. Smidler AL
    3. Catteruccia F
    4. Church GM

    (2014) Concerning RNA-guided gene drives for the alteration of wild populations

    eLife 3:e03401.

    https://doi.org/10.7554/eLife.03401

    • PubMed
    • Google Scholar
30. 1. Fox J
    2. Monette G

    (1992) Generalized collinearity diagnostics

    Journal of the American Statistical Association 87:178–183.

    https://doi.org/10.1080/01621459.1992.10475190

    • Google Scholar
31. Book

    1. Fox J
    2. Weisberg S

    (2019)

    An R Companion to Applied Regression (Third)

    Sage.

    • Google Scholar
32. 1. Fu Y
    2. Foden JA
    3. Khayter C
    4. Maeder ML
    5. Reyon D
    6. Joung JK
    7. Sander JD

    (2013) High-Frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells

    Nature Biotechnology 31:822–826.

    https://doi.org/10.1038/nbt.2623

    • PubMed
    • Google Scholar
33. 1. Gantz VM
    2. Bier E

    (2015) Genome editing: the mutagenic chain reaction: a method for converting heterozygous to homozygous mutations

    Science 348:442–444.

    https://doi.org/10.1126/science.aaa5945

    • PubMed
    • Google Scholar
34. 1. Gantz VM
    2. Jasinskiene N
    3. Tatarenkova O
    4. Fazekas A
    5. Macias VM
    6. Bier E
    7. James AA

    (2015) Highly efficient cas9-mediated gene drive for population modification of the malaria vector mosquito Anopheles stephensi

    PNAS 112:E6736–E6743.

    https://doi.org/10.1073/pnas.1521077112

    • PubMed
    • Google Scholar
35. 1. Garrood WT
    2. Kranjc N
    3. Petri K
    4. Kim DY
    5. Guo JA
    6. Hammond AM
    7. Morianou I
    8. Pattanayak V
    9. Joung JK
    10. Crisanti A
    11. Simoni A

    (2021) Analysis of off-target effects in CRISPR-based gene drives in the human malaria mosquito

    PNAS 118:e2004838117.

    https://doi.org/10.1073/pnas.2004838117

    • PubMed
    • Google Scholar
36. 1. Gisler S
    2. Gonçalves JP
    3. Akhtar W
    4. de Jong J
    5. Pindyurin AV
    6. Wessels LFA
    7. van Lohuizen M

    (2019) Multiplexed Cas9 targeting reveals genomic location effects and grna-based staggered breaks influencing mutation efficiency

    Nature Communications 10:1598.

    https://doi.org/10.1038/s41467-019-09551-w

    • PubMed
    • Google Scholar
37. 1. Gratz SJ
    2. Ukken FP
    3. Rubinstein CD
    4. Thiede G
    5. Donohue LK
    6. Cummings AM
    7. O’Connor-Giles KM

    (2014) Highly specific and efficient CRISPR/Cas9-catalyzed homology-directed repair in Drosophila

    Genetics 196:961–971.

    https://doi.org/10.1534/genetics.113.160713

    • PubMed
    • Google Scholar
38. Software

    1. Gross J
    2. Ligges U

    (2015) Nortest: tests for normality, version 1.0-4

    R Package.

    https://rdrr.io/cran/nortest/
39. 1. Grunwald HA
    2. Gantz VM
    3. Poplawski G
    4. Xu X-RS
    5. Bier E
    6. Cooper KL

    (2019) Super-mendelian inheritance mediated by CRISPR-Cas9 in the female mouse germline

    Nature 566:105–109.

    https://doi.org/10.1038/s41586-019-0875-2

    • PubMed
    • Google Scholar
40. 1. Hammond A
    2. Galizi R
    3. Kyrou K
    4. Simoni A
    5. Siniscalchi C
    6. Katsanos D
    7. Gribble M
    8. Baker D
    9. Marois E
    10. Russell S
    11. Burt A
    12. Windbichler N
    13. Crisanti A
    14. Nolan T

    (2016) A CRISPR-Cas9 gene drive system targeting female reproduction in the malaria mosquito vector Anopheles gambiae

    Nature Biotechnology 34:78–83.

    https://doi.org/10.1038/nbt.3439

    • PubMed
    • Google Scholar
41. 1. Hammond AM
    2. Kyrou K
    3. Bruttini M
    4. North A
    5. Galizi R
    6. Karlsson X
    7. Kranjc N
    8. Carpi FM
    9. D’Aurizio R
    10. Crisanti A
    11. Nolan T

    (2017) The creation and selection of mutations resistant to a gene drive over multiple generations in the malaria mosquito

    PLOS Genetics 13:e1007039.

    https://doi.org/10.1371/journal.pgen.1007039

    • PubMed
    • Google Scholar
42. 1. Hodges CA
    2. Conlon RA

    (2019) Delivering on the promise of gene editing for cystic fibrosis

    Genes & Diseases 6:97–108.

    https://doi.org/10.1016/j.gendis.2018.11.005

    • PubMed
    • Google Scholar
43. 1. Höijer I
    2. Johansson J
    3. Gudmundsson S
    4. Chin C-S
    5. Bunikis I
    6. Häggqvist S
    7. Emmanouilidou A
    8. Wilbe M
    9. den Hoed M
    10. Bondeson M-L
    11. Feuk L
    12. Gyllensten U
    13. Ameur A

    (2020) Amplification-free long-read sequencing reveals unforeseen CRISPR-Cas9 off-target activity

    Genome Biology 21:290.

    https://doi.org/10.1186/s13059-020-02206-w

    • PubMed
    • Google Scholar
44. 1. Jinek M
    2. Chylinski K
    3. Fonfara I
    4. Hauer M
    5. Doudna JA
    6. Charpentier E

    (2012) A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity

    Science 337:816–821.

    https://doi.org/10.1126/science.1225829

    • PubMed
    • Google Scholar
45. 1. Jo DH
    2. Song DW
    3. Cho CS
    4. Kim UG
    5. Lee KJ
    6. Lee K
    7. Park SW
    8. Kim D
    9. Kim JH
    10. Kim J-S
    11. Kim S
    12. Kim JH
    13. Lee JM

    (2019) CRISPR-cas9-mediated therapeutic editing of RPE65 ameliorates the disease phenotypes in a mouse model of Leber congenital amaurosis

    Science Advances 5:eaax1210.

    https://doi.org/10.1126/sciadv.aax1210

    • PubMed
    • Google Scholar
46. 1. Kapur JN
    2. Sahoo PK
    3. Wong AKC

    (1985) A new method for gray-level picture thresholding using the entropy of the histogram

    Computer Vision, Graphics, and Image Processing 29:273–285.

    https://doi.org/10.1016/0734-189X(85)90125-2

    • Google Scholar
47. 1. Kimberland ML
    2. Hou W
    3. Alfonso-Pecchio A
    4. Wilson S
    5. Rao Y
    6. Zhang S
    7. Lu Q

    (2018) Strategies for controlling CRISPR/Cas9 off-target effects and biological variations in mammalian genome editing experiments

    Journal of Biotechnology 284:91–101.

    https://doi.org/10.1016/j.jbiotec.2018.08.007

    • PubMed
    • Google Scholar
48. 1. Klein M
    2. Eslami-Mossallam B
    3. Arroyo DG
    4. Depken M

    (2018) Hybridization kinetics explains CRISPR-Cas off-targeting rules

    Cell Reports 22:1413–1423.

    https://doi.org/10.1016/j.celrep.2018.01.045

    • PubMed
    • Google Scholar
49. 1. Kleinstiver BP
    2. Pattanayak V
    3. Prew MS
    4. Tsai SQ
    5. Nguyen NT
    6. Zheng Z
    7. Joung JK

    (2016) High-Fidelity CRISPR-Cas9 nucleases with no detectable genome-wide off-target effects

    Nature 529:490–495.

    https://doi.org/10.1038/nature16526

    • PubMed
    • Google Scholar
50. 1. Kyrou K
    2. Hammond AM
    3. Galizi R
    4. Kranjc N
    5. Burt A
    6. Beaghton AK
    7. Nolan T
    8. Crisanti A

    (2018) A CRISPR-Cas9 gene drive targeting doublesex causes complete population suppression in caged Anopheles gambiae mosquitoes

    Nature Biotechnology 36:1062–1066.

    https://doi.org/10.1038/nbt.4245

    • PubMed
    • Google Scholar
51. Software

    1. Langmüller AM

    (2022) CRISPR-cas9-fitness-effects, version swh:1:rev:3b4fec78e1678470fb51530b22fffd848dc7ce00

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:e7f7fd8a1ebd7b933aefc183f734fd86bcda72aa;origin=https://github.com/MesserLab/CRISPR-Cas9-fitness-effects;visit=swh:1:snp:1d734f44406ebfe74fe7266ddce7c462098fa83c;anchor=swh:1:rev:3b4fec78e1678470fb51530b22fffd848dc7ce00
52. 1. Lee J
    2. Jung MH
    3. Jeong E
    4. Lee JK

    (2019) Using sniper-cas9 to minimize off-target effects of CRISPR-Cas9 without the loss of on-target activity via directed evolution

    Journal of Visualized Experiments 144:e59202.

    https://doi.org/10.3791/59202

    • PubMed
    • Google Scholar
53. Software

    1. Lenth R

    (2020) Emmeans: estimated marginal means, AKA least-squares means, version 1.8.1-1

    R Package.

    https://cran.r-project.org/web/packages/emmeans/emmeans.pdf
54. 1. Li J
    2. Manghwar H
    3. Sun L
    4. Wang P
    5. Wang G
    6. Sheng H
    7. Zhang J
    8. Liu H
    9. Qin L
    10. Rui H
    11. Li B
    12. Lindsey K
    13. Daniell H
    14. Jin S
    15. Zhang X

    (2019) Whole genome sequencing reveals rare off-target mutations and considerable inherent genetic or/and somaclonal variations in CRISPR/cas9-edited cotton plants

    Plant Biotechnology Journal 17:858–868.

    https://doi.org/10.1111/pbi.13020

    • PubMed
    • Google Scholar
55. 1. Liu J
    2. Champer J
    3. Langmüller AM
    4. Liu C
    5. Chung J
    6. Reeves R
    7. Luthra A
    8. Lee YL
    9. Vaughn AH
    10. Clark AG
    11. Messer PW

    (2019) Maximum likelihood estimation of fitness components in experimental evolution

    Genetics 211:1005–1017.

    https://doi.org/10.1534/genetics.118.301893

    • PubMed
    • Google Scholar
56. 1. Mali P
    2. Esvelt KM
    3. Church GM

    (2013) Cas9 as a versatile tool for engineering biology

    Nature Methods 10:957–963.

    https://doi.org/10.1038/nmeth.2649

    • PubMed
    • Google Scholar
57. 1. Moon SB
    2. Kim DY
    3. Ko JH
    4. Kim YS

    (2019) Recent advances in the CRISPR genome editing tool set

    Experimental & Molecular Medicine 51:1–11.

    https://doi.org/10.1038/s12276-019-0339-7

    • PubMed
    • Google Scholar
58. 1. Moya A
    2. González-Candelas F
    3. Ménsua JL

    (1988) Larval competition in Drosophila melanogaster: frequency-dependence of viability

    Theoretical and Applied Genetics 75:366–377.

    https://doi.org/10.1007/BF00303979

    • Google Scholar
59. 1. Noble C
    2. Olejarz J
    3. Esvelt KM
    4. Church GM
    5. Nowak MA

    (2017) Evolutionary dynamics of CRISPR gene drives

    Science Advances 3:e1601964.

    https://doi.org/10.1126/sciadv.1601964

    • PubMed
    • Google Scholar
60. 1. North AR
    2. Burt A
    3. Godfray HCJ

    (2020) Modelling the suppression of a malaria vector using a CRISPR-Cas9 gene drive to reduce female fertility

    BMC Biology 18:98.

    https://doi.org/10.1186/s12915-020-00834-z

    • PubMed
    • Google Scholar
61. 1. Nouhaud P
    2. Mallard F
    3. Poupardin R
    4. Barghi N
    5. Schlötterer C

    (2018) High-Throughput fecundity measurements in Drosophila

    Scientific Reports 8:4469.

    https://doi.org/10.1038/s41598-018-22777-w

    • PubMed
    • Google Scholar
62. 1. Oberhofer G
    2. Ivy T
    3. Hay BA

    (2018) Behavior of homing endonuclease gene drives targeting genes required for viability or female fertility with multiplexed guide RNAs

    PNAS 115:E9343–E9352.

    https://doi.org/10.1073/pnas.1805278115

    • PubMed
    • Google Scholar
63. 1. Oberhofer G
    2. Ivy T
    3. Hay BA

    (2019) Cleave and rescue, a novel selfish genetic element and general strategy for gene drive

    PNAS 116:6250–6259.

    https://doi.org/10.1073/pnas.1816928116

    • PubMed
    • Google Scholar
64. 1. Palermo G
    2. Gravina Ricci C
    3. Chen JS
    4. Miao Y
    5. Jinek M
    6. Doudna JA
    7. McCammon JA

    (2019) Molecular mechanism of off-target effects in CRISPR-Cas9

    Biophysical Journal 116:319a.

    https://doi.org/10.1016/j.bpj.2018.11.1731

    • Google Scholar
65. 1. Pan X
    2. Qu K
    3. Yuan H
    4. Xiang X
    5. Anthon C
    6. Pashkova L
    7. Liang X
    8. Han P
    9. Corsi GI
    10. Xu F
    11. Liu P
    12. Zhong J
    13. Zhou Y
    14. Ma T
    15. Jiang H
    16. Liu J
    17. Wang J
    18. Jessen N
    19. Bolund L
    20. Yang H
    21. Xu X
    22. Church GM
    23. Gorodkin J
    24. Lin L
    25. Luo Y

    (2022) Massively targeted evaluation of therapeutic CRISPR off-targets in cells

    Nature Communications 13:4049.

    https://doi.org/10.1038/s41467-022-31543-6

    • PubMed
    • Google Scholar
66. 1. Port F
    2. Chen HM
    3. Lee T
    4. Bullock SL

    (2014) Optimized CRISPR/Cas tools for efficient germline and somatic genome engineering in Drosophila

    PNAS 111:E2967–E2976.

    https://doi.org/10.1073/pnas.1405500111

    • PubMed
    • Google Scholar
67. Software

    1. R Development Core Team

    (2019) R: a language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.r-project.org/index.html
68. 1. Reeves RG
    2. Tautz D

    (2017) Automated phenotyping indicates pupal size in Drosophila is a highly heritable trait with an apparent polygenic basis

    G3: Genes, Genomes, Genetics 7:1277–1286.

    https://doi.org/10.1534/g3.117.039883

    • PubMed
    • Google Scholar
69. 1. Roggenkamp E
    2. Giersch RM
    3. Schrock MN
    4. Turnquist E
    5. Halloran M
    6. Finnigan GC

    (2018) Tuning CRISPR-Cas9 gene drives in Saccharomyces cerevisiae

    G3: Genes, Genomes, Genetics 8:999–1018.

    https://doi.org/10.1534/g3.117.300557

    • PubMed
    • Google Scholar
70. 1. Schindelin J
    2. Arganda-Carreras I
    3. Frise E
    4. Kaynig V
    5. Longair M
    6. Pietzsch T
    7. Preibisch S
    8. Rueden C
    9. Saalfeld S
    10. Schmid B
    11. Tinevez J-Y
    12. White DJ
    13. Hartenstein V
    14. Eliceiri K
    15. Tomancak P
    16. Cardona A

    (2012) Fiji: an open-source platform for biological-image analysis

    Nature Methods 9:676–682.

    https://doi.org/10.1038/nmeth.2019

    • PubMed
    • Google Scholar
71. 1. Schindelin J
    2. Rueden CT
    3. Hiner MC
    4. Eliceiri KW

    (2015) The imagej ecosystem: an open platform for biomedical image analysis

    Molecular Reproduction and Development 82:518–529.

    https://doi.org/10.1002/mrd.22489

    • PubMed
    • Google Scholar
72. 1. Shapiro RS
    2. Chavez A
    3. Porter CBM
    4. Hamblin M
    5. Kaas CS
    6. DiCarlo JE
    7. Zeng G
    8. Xu X
    9. Revtovich AV
    10. Kirienko NV
    11. Wang Y
    12. Church GM
    13. Collins JJ

    (2018) A CRISPR-cas9-based gene drive platform for genetic interaction analysis in Candida albicans

    Nature Microbiology 3:73–82.

    https://doi.org/10.1038/s41564-017-0043-0

    • PubMed
    • Google Scholar
73. 1. Slaymaker IM
    2. Gao L
    3. Zetsche B
    4. Scott DA
    5. Yan WX
    6. Zhang F

    (2016) Rationally engineered Cas9 nucleases with improved specificity

    Science 351:84–88.

    https://doi.org/10.1126/science.aad5227

    • PubMed
    • Google Scholar
74. 1. Tan Y
    2. Chu AHY
    3. Bao S
    4. Hoang DA
    5. Kebede FT
    6. Xiong W
    7. Ji M
    8. Shi J
    9. Zheng Z

    (2019) Rationally engineered Staphylococcus aureus Cas9 nucleases with high genome-wide specificity

    PNAS 116:20969–20976.

    https://doi.org/10.1073/pnas.1906843116

    • PubMed
    • Google Scholar
75. 1. Tang W
    2. Liu DR

    (2018) Rewritable multi-event analog recording in bacterial and mammalian cells

    Science 360:eaap8992.

    https://doi.org/10.1126/science.aap8992

    • PubMed
    • Google Scholar
76. 1. Tsai SQ
    2. Zheng Z
    3. Nguyen NT
    4. Liebers M
    5. Topkar VV
    6. Thapar V
    7. Wyvekens N
    8. Khayter C
    9. Iafrate AJ
    10. Le LP
    11. Aryee MJ
    12. Joung JK

    (2015) GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases

    Nature Biotechnology 33:187–197.

    https://doi.org/10.1038/nbt.3117

    • PubMed
    • Google Scholar
77. Software

    1. Tseng Q

    (2018) Template matching and slice alignment--- imagej plugins

    ImageJ Plugins by Qingzong Tseng.

    http://sites.google.com/site/qingzongtseng/template-matching-ij-plugin
78. 1. Unckless RL
    2. Clark AG
    3. Messer PW

    (2017) Evolution of resistance against CRISPR/Cas9 gene drive

    Genetics 205:827–841.

    https://doi.org/10.1534/genetics.116.197285

    • PubMed
    • Google Scholar
79. 1. Wedell N
    2. Price TAR
    3. Lindholm AK

    (2019) Gene drive: progress and prospects

    Proceedings. Biological Sciences 286:20192709.

    https://doi.org/10.1098/rspb.2019.2709

    • PubMed
    • Google Scholar
80. 1. Xie H
    2. Ge X
    3. Yang F
    4. Wang B
    5. Li S
    6. Duan J
    7. Lv X
    8. Cheng C
    9. Song Z
    10. Liu C
    11. Zhao J
    12. Zhang Y
    13. Wu J
    14. Gao C
    15. Zhang J
    16. Gu F

    (2020) High-Fidelity sacas9 identified by directional screening in human cells

    PLOS Biology 18:e3000747.

    https://doi.org/10.1371/journal.pbio.3000747

    • PubMed
    • Google Scholar
81. 1. Yang S
    2. Chang R
    3. Yang H
    4. Zhao T
    5. Hong Y
    6. Kong HE
    7. Sun X
    8. Qin Z
    9. Jin P
    10. Li S
    11. Li X-J

    (2017) Crispr/Cas9-Mediated gene editing ameliorates neurotoxicity in mouse model of Huntington ’ S disease

    The Journal of Clinical Investigation 127:2719–2724.

    https://doi.org/10.1172/JCI92087

    • PubMed
    • Google Scholar
82. 1. Yang E
    2. Metzloff M
    3. Langmüller AM
    4. Xu X
    5. Clark AG
    6. Messer PW
    7. Champer J

    (2022) A homing suppression gene drive with multiplexed gRNAs maintains high drive conversion efficiency and avoids functional resistance alleles

    G3: Genes, Genomes, Genetics 12:jkac081.

    https://doi.org/10.1093/g3journal/jkac081

    • PubMed
    • Google Scholar
83. 1. Zhang XH
    2. Tee LY
    3. Wang XG
    4. Huang QS
    5. Yang SH

    (2015) Off-Target effects in CRISPR/Cas9-mediated genome engineering

    Molecular Therapy. Nucleic Acids 4:e264.

    https://doi.org/10.1038/mtna.2015.37

    • PubMed
    • Google Scholar
84. 1. Zhang Y
    2. Li H
    3. Min YL
    4. Sanchez-Ortiz E
    5. Huang J
    6. Mireault AA
    7. Shelton JM
    8. Kim J
    9. Mammen PPA
    10. Bassel-Duby R
    11. Olson EN

    (2020a) Enhanced CRISPR-Cas9 correction of Duchenne muscular dystrophy in mice by a self-complementary AAV delivery system

    Science Advances 6:eaay6812.

    https://doi.org/10.1126/sciadv.aay6812

    • PubMed
    • Google Scholar
85. 1. Zhang Y
    2. Pribil M
    3. Palmgren M
    4. Gao C

    (2020b) A CRISPR way for accelerating improvement of food crops

    Nature Food 1:200–205.

    https://doi.org/10.1038/s43016-020-0051-8

    • Google Scholar

Author details

1. Anna M Langmüller

   1. Department of Computational Biology, Cornell University, Ithaca, United States
   2. Institut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria
   3. Vienna Graduate School of Population Genetics, Vetmeduni Vienna, Vienna, Austria

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Jackson Champer

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6102-8862

2. Jackson Champer

   1. Department of Computational Biology, Cornell University, Ithaca, United States
   2. Department of Molecular Biology and Genetics, Cornell University, Ithaca, United States
   3. Center for Bioinformatics, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Anna M Langmüller

   For correspondence

   jchamper@pku.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3814-3774

3. Sandra Lapinska

   1. Department of Computational Biology, Cornell University, Ithaca, United States
   2. Department of Molecular Biology and Genetics, Cornell University, Ithaca, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

4. Lin Xie

   1. Department of Computational Biology, Cornell University, Ithaca, United States
   2. Department of Molecular Biology and Genetics, Cornell University, Ithaca, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Matthew Metzloff

   1. Department of Computational Biology, Cornell University, Ithaca, United States
   2. Department of Molecular Biology and Genetics, Cornell University, Ithaca, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6108-5031

6. Samuel E Champer

   Department of Computational Biology, Cornell University, Ithaca, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4559-7627

7. Jingxian Liu

   1. Department of Computational Biology, Cornell University, Ithaca, United States
   2. Department of Molecular Biology and Genetics, Cornell University, Ithaca, United States

   Contribution

   Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2172-3297

8. Yineng Xu

   1. Department of Computational Biology, Cornell University, Ithaca, United States
   2. Department of Molecular Biology and Genetics, Cornell University, Ithaca, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4473-4052

9. Jie Du

   Center for Bioinformatics, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

10. Andrew G Clark

    1. Department of Computational Biology, Cornell University, Ithaca, United States
    2. Department of Molecular Biology and Genetics, Cornell University, Ithaca, United States

    Contribution

    Resources, Supervision, Funding acquisition, Writing – review and editing

    Competing interests

    No competing interests declared

11. Philipp W Messer

    Department of Computational Biology, Cornell University, Ithaca, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    messer@cornell.edu

    Competing interests

    Reviewing editor, eLife

    "This ORCID iD identifies the author of this article:" 0000-0001-8453-9377

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