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Evolution of resistance is a major barrier to successful deployment of gene drive systems to suppress natural populations, which could greatly reduce the burden of many vector borne diseases. Multiplexed guide RNAs that require resistance mutations in all target cut sites is a promising anti-resistance strategy, since in principle resistance would only arise in unrealistically large populations. Using novel stochastic simulations that accurately model evolution at very large population sizes, we explore the probability of resistance due to three important mechanisms: 1) non-homologous end-joining mutations, 2) single nucleotide mutants arising de novo or, 3) single nucleotide polymorphisms pre-existing as standing variation. Our results explore the relative importance of these mechanisms and highlight a complexity of the mutation-selection-drift balance between haplotypes with complete resistance and those with an incomplete number of resistant alleles. We find this leads to a qualitatively new phenomenon where weakly deleterious naturally occurring variants greatly amplify the probability of multi-site resistance. This challenges the intuition that many target sites would guarantee prevention of resistance, where in the face of standing genetic variation, it can be probable even in not very large populations. This result has broad application to resistance arising in many multi-site evolutionary scenarios including multi-drug resistance to antibiotics, antivirals and cancer treatments, as well as the evolution of vaccine escape mutations in large populations.

DNA sequences that are exactly conserved over long evolutionary time scales have been observed in a variety of taxa. Such sequences are likely under strong functional constraint and they have been useful in the field of comparative genomics for identifying genome regions with regulatory function. A potential new application for these ultra-conserved elements has emerged in the development of gene drives to control mosquito populations. Many gene drives work by recognising and inserting at a specific target sequence in the genome, often imposing a reproductive load as a consequence. They can therefore select for target sequence variants that provide resistance to the drive. Focusing on highly conserved, highly constrained sequences lowers the probability that variant, gene drive-resistant alleles can be tolerated.Here we search for conserved sequences of 18bp and over in an alignment of 21Anophelesgenomes, spanning an evolutionary timescale of 100 million years, and characterise the resulting sequences according to their location and function. Over 8000 ultra-conserved elements were found across the alignment, with a maximum length of 164 bp. Length-corrected gene ontology analysis revealed that genes containingAnophelesultra-conserved elements were over-represented in categories with structural or nucleotide binding functions. Known insect transcription factor binding sites were found in 48% of intergenicAnophelesultra-conserved elements. When we looked at the genome sequences of 1142 wild-caught mosquitoes we found that 15% of theAnophelesultra-conserved elements contained no polymorphisms. Our list ofAnophelesultra-conserved elements should provide a valuable starting point for the selection and testing of new targets for gene-drive modification in the mosquitoes that transmit malaria.

Estimating recent effective population size is of great importance in characterising and predicting the evolution of natural populations. Methods based on nucleotide diversity may underestimate current day effective population sizes due to historical bottlenecks, whilst methods that reconstruct demographic history typically only detect long-term variations. However, soft selective sweeps, which leave a fingerprint of mutational history by recurrent mutations on independent haplotype backgrounds, holds promise of an estimate more representative of recent population history. Here we present a simple and robust method of estimation based only on knowledge of the number of independent recurrent origins and the current frequency of the beneficial allele in a population sample, independent of the strength of selection and age of the mutation. Using a forward time theoretical framework, we show the mean number of origins is a function ofθ= 2Nμand current allele frequency, through a simple equation, and the distribution is approximately Poisson. This estimate is robust to whether mutants pre-existed before selection arose, and is equally accurate for diploid populations with incomplete dominance. For fast (e.g., seasonal) demographic changes compared to time scale for fixation of the mutant allele, and for moderate peak-to-trough ratios, we show our constant population size estimate can be used to bound the maximum and minimum population size. Applied to the Vgsc gene ofAnopheles gambiae, we estimate an effective population size of roughly 6 × 107, and including seasonal demographic oscillations, a minimum effective population size greater than 6 × 106and a maximum less than 3 × 109.

CRISPR-Cas9 nuclease-based gene drives rely on inducing chromosomal breaks in the germline that are repaired in ways that lead to a biased inheritance of the drive. Gene drives designed to impair female fertility can suppress populations of the mosquito vector of malaria. However, strong unintended fitness costs, due to ectopic nuclease expression, and high levels of resistant mutations, limited the potential of the first generation of gene drives to spread.Here we show that changes to regulatory sequences in the drive element, designed to contain nuclease expression to the germline, confer improved fecundity over previous versions and generate drastically lower rates of target site resistance. We employed a genetic screen to show that this effect is explained by reduced rates of end-joining repair of DNA breaks at the target site caused by deposited nuclease in the embryo.Highlighting the impact of deposited Cas9, many of the mutations arising from this source of nuclease activity in the embryo are heritable, thereby having the potential to generate resistant target sites that reduce the penetrance of the gene drive.Finally, in cage invasion experiments these gene drives show improved invasion dynamics compared to first generation drives, resulting in greater than 90% suppression of the reproductive output and a delay in the emergence of target site resistance, even at a resistance-prone target sequence. We shed light on the dynamics of generation and selection of resistant alleles in a population by tracking, longitudinally, the frequency of resistant alleles in the face of an invading gene drive. Our results illustrate important considerations for future gene drive design and should expedite the development of gene drives robust to resistance.

The sustainability of malaria control in Africa is threatened by rising levels of insecticide resistance, and new tools to prevent malaria transmission are urgently needed. To gain a better understanding of the mosquito populations that transmit malaria, we sequenced the genomes of 765 wild specimens ofAnopheles gambiaeandAnopheles coluzziisampled from 15 locations across Africa. The data reveal high levels of genetic diversity, with over 50 million single nucleotide polymorphisms across the 230 Mbp genome. We observe complex patterns of population structure and marked variations in local population size, some of which may be due at least in part to malaria control interventions. Insecticide resistance genes show strong signatures of recent selection associated with multiple independent mutations spreading over large geographical distances and between species. The genetic variability of natural populations substantially reduces the target space for novel gene-drive strategies for mosquito control. This large dataset provides a foundation for tracking the emergence and spread of insecticide resistance and developing new vector control tools.

Mosquito control remains a central pillar of efforts to reduce malaria burden in sub-Saharan Africa. However, insecticide resistance is entrenched in malaria vector populations, and countries with a high malaria burden face a daunting challenge to sustain malaria control with a limited set of surveillance and intervention tools. Here we report on the second phase of a project to build an open resource of high-quality data on genome variation among natural populations of the major African malaria vector species Anopheles gambiae and Anopheles coluzzii. We analyzed whole genomes of 1142 individual mosquitoes sampled from the wild in 13 African countries, as well as a further 234 individuals comprising parents and progeny of 11 laboratory crosses. The data resource includes high-confidence single-nucleotide polymorphism (SNP) calls at 57 million variable sites, genome-wide copy number variation (CNV) calls, and haplotypes phased at biallelic SNPs. We use these data to analyze genetic population structure and characterize genetic diversity within and between populations. We illustrate the utility of these data by investigating species differences in isolation by distance, genetic variation within proposed gene drive target sequences, and patterns of resistance to pyrethroid insecticides. This data resource provides a foundation for developing new operational systems for molecular surveillance and for accelerating research and development of new vector control tools. It also provides a unique resource for the study of population genomics and evolutionary biology in eukaryotic species with high levels of genetic diversity under strong anthropogenic evolutionary pressures.