Gene‐drive into insect populations with age and spatial structure: a theoretical assessment

Abstract

The potential benefits and risks of genetically engineered gene-drive systems for replacing wild pest strains with more benign strains must be assessed prior to any field releases. We develop a computer simulation model to assess the feasibility of using engineered underdominance constructs to drive transgenes into age- and spatially structured mosquito populations. Our practical criterion for success is the achievement of a transgene frequency of at least 0.80 within 3 years of release. The impacts of a number of parameters that may affect the success of gene-drive, such as the release area, release age, density-dependent larval survival, fitness cost of the engineered genes, and migration probability of adults, are examined. Results show that patchy release generally requires the release of fewer engineered insects to achieve success than central release. When the fitness cost is very low, central release covering 25% of the total area can be more effective than a completely uniform release over the whole area. This study demonstrates that to determine the best method of spatial release, and the total number of engineered insects that must be released, it is important to take into account the age and sex of the released insects and spatial structure of the population.

Spatial gene drives and pushed genetic waves

“…11, 2017; 3 raises biosafety concerns [9], and calls for confinement strategies to prevent unintentional escape and spread of the gene drive constructs [22]. While various genetic design or containment strategies have been discussed [9,20,23,24], and a few computational simulations were conducted [17,18,25], the spatial spreading of the gene drive alleles has received less attention.To understand such phenomena in a spatial context, we will exploit a methodology developed by N. Barton and collaborators, originally in an e↵ort to understand adaptation and speciation of diploid sexually reproducing organisms in genetic hybrid zones [26][27][28].We apply these techniques to a spatial generalization of a model of diploid CRISPR/Cas9 population genetics proposed by Unckless et al [29], and highlight two distinct ways in which gene drive alleles can spread spatially. The non-Mendelian (or "super-Mendelian" [30]) population genetics of gene drives are remarkable because individuals homozygous for a gene drive can in fact spread into wild-type populations even if they carry a positive selective disadvantage s. First, for small selective disadvantages (0 < s < 0.5 in our case), the spatial spreading proceeds via a well-known Fisher-Kolmogorov-Petrovsky-Piskunov wave [31,32].…”

“…11, 2017; 3 raises biosafety concerns [9], and calls for confinement strategies to prevent unintentional escape and spread of the gene drive constructs [22]. While various genetic design or containment strategies have been discussed [9,20,23,24], and a few computational simulations were conducted [17,18,25], the spatial spreading of the gene drive alleles has received less attention.…”

“…Remarkably, high conversion rates have already been achieved with the mutagenic chain reaction ("MCR") realized by the CRISPR/Cas9 system [1][2][3] for yeast (c yeast > 0.995) [19], fruit flies (c flies = 0.97) [20] and malaria vector mosquito, Anopheles stephensi with engineered malaria resistance (c raises biosafety concerns [9], and calls for confinement strategies to prevent unintentional escape and spread of the gene drive constructs [22]. While various genetic design or containment strategies have been discussed [9,20,23,24], and a few computational simulations were conducted [17,18,25], the spatial spreading of the gene drive alleles has received less attention.…”

Dynamics of a combined medea-underdominant population transformation system

“…Considering asymmetries in population sizes, migration needs to be dealt with separately, as in [26]. Also migration dynamics with an explicitly set spatial system has been recently assessed [27] (albeit not for a combined system). In population i the expected genotype frequency of genotype k after migration is $g_{k,i}^{\prime }=(1-m)g_{k,i}+m{g}_{k,j}$, where g k , i is the frequency of the k t h genotype in population i and g k , j is the k t h genotype frequency in population j .…”

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

“…Panmictic population models have been useful in identifying optimal gene drive parameters and studying the effects of phenomena such as the evolution of resistance [16][17][18][19] . Yet it has also become clear that to accurately understand the full range of outcomes of a gene drive release, spatial factors must be explicitly considered [20][21][22][23][24][25] . Panmictic models typically predict, for instance, that a suppression drive will either successfully eliminate a population or be quickly lost.…”