Modeling CRISPR gene drives for suppression of invasive rodents

Abstract

Invasive rodent populations pose a threat to biodiversity across the globe. When confronted with these new invaders, native species that evolved independently are often defenseless. CRISPR gene drive systems could provide a solution to this problem by spreading transgenes among invaders that induce population collapse. Such systems might be deployed even where traditional control methods are impractical or prohibitively expensive. Here, we develop a high-fidelity model of an island population of invasive rodents that includes three types of suppression gene drive systems. The individual-based model is spatially explicit and allows for overlapping generations and a fluctuating population size. Our model includes variables for drive fitness, efficiency, resistance allele formation rate, as well as a variety of ecological parameters. The computational burden of evaluating a model with such a high number of parameters presents a substantial barrier to a comprehensive understanding of its outcome space. We therefore accompany our population model with a meta-model that utilizes supervised machine learning to approximate the outcome space of the underlying model with a high degree of accuracy. This enables us to conduct an exhaustive inquiry of the population model, including variance-based sensitivity analyses using tens of millions of evaluations. Our results suggest that sufficiently capable gene drive systems have the potential to eliminate island populations of rodents under a wide range of demographic assumptions, but only if resistance can be kept to a minimal level. This study highlights the power of supervised machine learning for identifying the key parameters and processes that determine the population dynamics of a complex evolutionary system.

Finding the strongest gene drive: Simulations reveal unexpected performance differences betweenAnopheleshoming suppression drive candidates

“…However, spatially explicit models have indicated that the outcomes of a suppression drive release can be substantially more complicated than those predicted by panmictic population models. In particular, it has been shown that population structure can substantially delay or even prevent complete population suppression for drives with a genetic load high enough to reliably induce population collapse in a panmictic population [11][12][13][14][15] . One mechanism that can prevent population collapse is "chasing", a phenomenon where wild-type individuals recolonize regions where the drive has eliminated the population.…”

“…In this manner, drive and wild-type alleles can persist indefinitely, following a chaotic pattern of local suppression and recolonization. This chasing phenomenon seems to be a common feature of spatial models for many types of suppression drive, regardless of whether the model is implemented using abstract spatial patches 14 , networks of linked demes of mosquito populations 12,13 , or continuous space with discrete generations 11,15 . Unlike panmictic models, these spatial models predict that even modest differences in efficiency between drives can potentially have large effects on the outcome of a drive release, meriting careful consideration of drive candidates to identify those with the greatest potential for success in realistic conditions.…”

Population genomics of invasive rodents on islands: Genetic consequences of colonization and prospects for localized synthetic gene drive

“…The trade‐off, however, is an increased risk of effects on nontarget populations in the event of a gene drive escape due to a higher frequency of susceptible alleles. While the gene drive is expected to rapidly be eliminated in such a scenario due to the presence of resistance alleles (Champer, Oakes, et al, 2020; Sudweeks et al, 2019), transient nontarget population impacts or even the public perception of increased risk of spread might prove unacceptable. Filtering LFA based on an intermediate “source” population allele frequency (≤0.50) resulted in a small number of sites, though several showed potential for multiplexed gRNA and occurred in genes of interest, and may therefore represent a reasonable compromise.…”

Current applications and future promise of genetic/genomic data for conservation in an Aotearoa New Zealand context

“…High-quality annotated reference genomes are required to identify target genes and enable comprehensive evaluation of the effects of existing variation on gene drive efficiency. These data can then inform predictive models assessing the effectiveness of specific gene drive systems in target populations under variable conditions (including environmental change and conservation management; Champer et al, 2020). Further, gene drive trials must be carefully designed to be representative of real-world impacts, as there may be differences in implementation and effects between captive laboratory populations and wild populations due to local behavioural adaptation or other indirect ecological effects (Mazza et al, 2020;Russell et al, 2009;Tompkins & Veltman, 2006).…”