Detection of hard and soft selective sweeps from Drosophila melanogaster population genomic data.

Whether hard sweeps or soft sweeps dominate adaptation has been a matter of much debate. Recently, we developed haplotype homozygosity statistics that (i) can detect both hard and soft sweeps with similar power and (ii) can classify the detected sweeps as hard or soft. The application of our method to population genomic data from a natural population of Drosophila melanogaster (DGRP) allowed us to rediscover three known cases of adaptation at the loci Ace, Cyp6g1, and CHKov1 known to be driven by soft sweeps, and detected additional candidate loci for recent and strong sweeps. Surprisingly, all of the top 50 candidates showed patterns much more consistent with soft rather than hard sweeps. Recently, Harris et al. 2018 criticized this work, suggesting that all the candidate loci detected by our haplotype statistics, including the positive controls, are unlikely to be sweeps at all and that instead these haplotype patterns can be more easily explained by complex neutral demographic models. They also claim that these neutral non-sweeps are likely to be hard instead of soft sweeps. Here, we reanalyze the DGRP data using a range of complex admixture demographic models and reconfirm our original published results suggesting that the majority of recent and strong sweeps in D. melanogaster are first likely to be true sweeps, and second, that they do appear to be soft. Furthermore, we discuss ways to take this work forward given that most demographic models employed in such analyses are necessarily too simple to capture the full demographic complexity, while more realistic models are unlikely to be inferred correctly because they require a large number of free parameters.

Incorporating ecology into gene drive modelling.

Gene drive technology, in which fast-spreading engineered drive alleles are introduced into wild populations, represents a promising new tool in the fight against vector-borne diseases, agricultural pests and invasive species. Due to the risks involved, gene drives have so far only been tested in laboratory settings while their population-level behaviour is mainly studied using mathematical and computational models. The spread of a gene drive is a rapid evolutionary process that occurs over timescales similar to many ecological processes. This can potentially generate strong eco-evolutionary feedback that could profoundly affect the dynamics and outcome of a gene drive release. We, therefore, argue for the importance of incorporating ecological features into gene drive models. We describe the key ecological features that could affect gene drive behaviour, such as population structure, life-history, environmental variation and mode of selection. We review previous gene drive modelling efforts and identify areas where further research is needed. As gene drive technology approaches the level of field experimentation, it is crucial to evaluate gene drive dynamics, potential outcomes, and risks realistically by including ecological processes.

SLiM 4: Multispecies Eco-Evolutionary Modeling.

AbstractThe SLiM software framework for genetically explicit forward simulation has been widely used in population genetics. However, it has been largely restricted to modeling only a single species, which has limited its broader utility in evolutionary biology. Indeed, to our knowledge no general-purpose, flexible modeling framework exists that provides support for simulating multiple species while also providing other key features, such as explicit genetics and continuous space. The lack of such software has limited our ability to model higher biological levels such as communities, ecosystems, coevolutionary and eco-evolutionary processes, and biodiversity, which is crucial for many purposes, from extending our basic understanding of evolutionary ecology to informing conservation and management decisions. We here announce the release of SLiM 4, which fills this important gap by adding support for multiple species, including ecological interactions between species such as predation, parasitism, and mutualism, and illustrate its new features with examples.

A CRISPR homing gene drive targeting a haplolethal gene removes resistance alleles and successfully spreads through a cage population.

Engineered gene drives are being explored as a new strategy in the fight against vector-borne diseases due to their potential for rapidly spreading genetic modifications through a population. However, CRISPR-based homing gene drives proposed for this purpose have faced a major obstacle in the formation of resistance alleles that prevent Cas9 cleavage. Here, we present a homing drive in Drosophila melanogaster that reduces the prevalence of resistance alleles below detectable levels by targeting a haplolethal gene with two guide RNAs (gRNAs) while also providing a rescue allele. Resistance alleles that form by end-joining repair typically disrupt the haplolethal target gene and are thus removed from the population because individuals that carry them are nonviable. We demonstrate that our drive is highly efficient, with 91% of the progeny of drive heterozygotes inheriting the drive allele and with no functional resistance alleles observed in the remainder. In a large cage experiment, the drive allele successfully spread to all individuals within a few generations. These results show that a haplolethal homing drive can provide an effective tool for targeted genetic modification of entire populations.

Experimental demonstration of tethered gene drive systems for confined population modification or suppression.

BACKGROUND: Homing gene drives hold great promise for the genetic control of natural populations. However, current homing systems are capable of spreading uncontrollably between populations connected by even marginal levels of migration. This could represent a substantial sociopolitical barrier to the testing or deployment of such drives and may generally be undesirable when the objective is only local population control, such as suppression of an invasive species outside of its native range. Tethered drive systems, in which a locally confined gene drive provides the CRISPR nuclease needed for a homing drive, could provide a solution to this problem, offering the power of a homing drive and confinement of the supporting drive. RESULTS: Here, we demonstrate the engineering of a tethered drive system in Drosophila, using a regionally confined CRISPR Toxin-Antidote Recessive Embryo (TARE) drive to support modification and suppression homing drives. Each drive was able to bias inheritance in its favor, and the TARE drive was shown to spread only when released above a threshold frequency in experimental cage populations. After the TARE drive had established in the population, it facilitated the spread of a subsequently released split homing modification drive (to all individuals in the cage) and of a homing suppression drive (to its equilibrium frequency). CONCLUSIONS: Our results show that the tethered drive strategy is a viable and easily engineered option for providing confinement of homing drives to target populations.

Modeling CRISPR gene drives for suppression of invasive rodents using a supervised machine learning framework.

Invasive rodent populations pose a threat to biodiversity across the globe. When confronted with these 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, and could 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, allows for overlapping generations and a fluctuating population size, and 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, though only if resistance can be kept to a minimal level. This study highlights the power of supervised machine learning to identify the key parameters and processes that determine the population dynamics of a complex evolutionary system.

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

Rapid evolutionary processes can produce drastically different outcomes when studied in panmictic population models vs. spatial models. One such process is gene drive, which describes the spread of "selfish" genetic elements through a population. Engineered gene drives are being considered for the suppression of disease vectors or invasive species. While laboratory experiments and modelling in panmictic populations have shown that such drives can rapidly eliminate a population, it remains unclear if these results translate to natural environments where individuals inhabit a continuous landscape. Using spatially explicit simulations, we show that the release of a suppression drive can result in what we term "chasing" dynamics, in which wild-type individuals recolonize areas where the drive has locally eliminated the population. Despite the drive subsequently reconquering these areas, complete population suppression often fails to occur or is substantially delayed. This increases the likelihood that the drive is lost or that resistance evolves. We analyse how chasing dynamics are influenced by the type of drive, its efficiency, fitness costs, and ecological factors such as the maximal growth rate of the population and levels of dispersal and inbreeding. We find that chasing is more common for lower efficiency drives when dispersal is low and that some drive mechanisms are substantially more prone to chasing behaviour than others. Our results demonstrate that the population dynamics of suppression gene drives are determined by a complex interplay of genetic and ecological factors, highlighting the need for realistic spatial modelling to predict the outcome of drive releases in natural populations.

Reducing resistance allele formation in CRISPR gene drive.

CRISPR homing gene drives can convert heterozygous cells with one copy of the drive allele into homozygotes, thereby enabling super-Mendelian inheritance. Such a mechanism could be used, for example, to rapidly disseminate a genetic payload in a population, promising effective strategies for the control of vector-borne diseases. However, all CRISPR homing gene drives studied in insects thus far have produced significant quantities of resistance alleles that would limit their spread. In this study, we provide an experimental demonstration that multiplexing of guide RNAs can both significantly increase the drive conversion efficiency and reduce germline resistance rates of a CRISPR homing gene drive in Drosophila melanogaster We further show that an autosomal drive can achieve drive conversion in the male germline, with no subsequent formation of resistance alleles in embryos through paternal carryover of Cas9. Finally, we find that the nanos promoter significantly lowers somatic Cas9 expression compared with the vasa promoter, suggesting that nanos provides a superior choice in drive strategies where gene disruption in somatic cells could have fitness costs. Comparison of drive parameters among the different constructs developed in this study and a previous study suggests that, while drive conversion and germline resistance rates are similar between different genomic targets, embryo resistance rates can vary significantly. Taken together, our results mark an important step toward developing effective gene drives capable of functioning in natural populations and provide several possible avenues for further control of resistance rates.

Narrow thermal tolerance and low dispersal drive higher speciation in tropical mountains.

Species richness is greatest in the tropics, and much of this diversity is concentrated in mountains. Janzen proposed that reduced seasonal temperature variation selects for narrower thermal tolerances and limited dispersal along tropical elevation gradients [Janzen DH (1967) Am Nat 101:233-249]. These locally adapted traits should, in turn, promote reproductive isolation and higher speciation rates in tropical mountains compared with temperate ones. Here, we show that tropical and temperate montane stream insects have diverged in thermal tolerance and dispersal capacity, two key traits that are drivers of isolation in montane populations. Tropical species in each of three insect clades have markedly narrower thermal tolerances and lower dispersal than temperate species, resulting in significantly greater population divergence, higher cryptic diversity, higher tropical speciation rates, and greater accumulation of species over time. Our study also indicates that tropical montane species, with narrower thermal tolerance and reduced dispersal ability, will be especially vulnerable to rapid climate change.

Performance analysis of novel toxin-antidote CRISPR gene drive systems.

BACKGROUND: CRISPR gene drive systems allow the rapid spread of a genetic construct throughout a population. Such systems promise novel strategies for the management of vector-borne diseases and invasive species by suppressing a target population or modifying it with a desired trait. However, current homing-type drives have two potential shortcomings. First, they can be thwarted by the rapid evolution of resistance. Second, they lack any mechanism for confinement to a specific target population. In this study, we conduct a comprehensive performance assessment of several new types of CRISPR-based gene drive systems employing toxin-antidote (TA) principles, which should be less prone to resistance and allow for the confinement of drives to a target population due to invasion frequency thresholds. RESULTS: The underlying principle of the proposed CRISPR toxin-antidote gene drives is to disrupt an essential target gene while also providing rescue by a recoded version of the target as part of the drive allele. Thus, drive alleles tend to remain viable, while wild-type targets are disrupted and often rendered nonviable, thereby increasing the relative frequency of the drive allele. Using individual-based simulations, we show that Toxin-Antidote Recessive Embryo (TARE) drives targeting an haplosufficient but essential gene (lethal when both copies are disrupted) can enable the design of robust, regionally confined population modification strategies with high flexibility in choosing promoters and targets. Toxin-Antidote Dominant Embryo (TADE) drives require a haplolethal target gene and a germline-restricted promoter, but they could permit faster regional population modification and even regionally confined population suppression. Toxin-Antidote Dominant Sperm (TADS) drives can be used for population modification or suppression. These drives are expected to spread rapidly and could employ a variety of promoters, but unlike TARE and TADE, they would not be regionally confined and also require highly specific target genes. CONCLUSIONS: Overall, our results suggest that CRISPR-based TA gene drives provide promising candidates for flexible ecological engineering strategies in a variety of organisms.

SLiM 3: Forward Genetic Simulations Beyond the Wright-Fisher Model.

With the desire to model population genetic processes under increasingly realistic scenarios, forward genetic simulations have become a critical part of the toolbox of modern evolutionary biology. The SLiM forward genetic simulation framework is one of the most powerful and widely used tools in this area. However, its foundation in the Wright-Fisher model has been found to pose an obstacle to implementing many types of models; it is difficult to adapt the Wright-Fisher model, with its many assumptions, to modeling ecologically realistic scenarios such as explicit space, overlapping generations, individual variation in reproduction, density-dependent population regulation, individual variation in dispersal or migration, local extinction and recolonization, mating between subpopulations, age structure, fitness-based survival and hard selection, emergent sex ratios, and so forth. In response to this need, we here introduce SLiM 3, which contains two key advancements aimed at abolishing these limitations. First, the new non-Wright-Fisher or "nonWF" model type provides a much more flexible foundation that allows the easy implementation of all of the above scenarios and many more. Second, SLiM 3 adds support for continuous space, including spatial interactions and spatial maps of environmental variables. We provide a conceptual overview of these new features, and present several example models to illustrate their use.

Evolutionary Modeling in SLiM 3 for Beginners.

The SLiM forward genetic simulation framework has proved to be a powerful and flexible tool for population genetic modeling. However, as a complex piece of software with many features that allow simulating a diverse assortment of evolutionary models, its initial learning curve can be difficult. Here we provide a step-by-step demonstration of how to build a simple evolutionary model in SLiM 3, to help new users get started. We will begin with a panmictic neutral model, and build up to a model of the evolution of a polygenic quantitative trait under selection for an environmental phenotypic optimum.

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

A functioning gene drive system could fundamentally change our strategies for the control of vector-borne diseases by facilitating rapid dissemination of transgenes that prevent pathogen transmission or reduce vector capacity. CRISPR/Cas9 gene drive promises such a mechanism, which works by converting cells that are heterozygous for the drive construct into homozygotes, thereby enabling super-Mendelian inheritance. Although CRISPR gene drive activity has already been demonstrated, a key obstacle for current systems is their propensity to generate resistance alleles, which cannot be converted to drive alleles. In this study, we developed two CRISPR gene drive constructs based on the nanos and vasa promoters that allowed us to illuminate the different mechanisms by which resistance alleles are formed in the model organism Drosophila melanogaster. We observed resistance allele formation at high rates both prior to fertilization in the germline and post-fertilization in the embryo due to maternally deposited Cas9. Assessment of drive activity in genetically diverse backgrounds further revealed substantial differences in conversion efficiency and resistance rates. Our results demonstrate that the evolution of resistance will likely impose a severe limitation to the effectiveness of current CRISPR gene drive approaches, especially when applied to diverse natural populations.

Can Population Genetics Adapt to Rapid Evolution?

Population genetics largely rests on a 'standard model' in which random genetic drift is the dominant force, selective sweeps occur infrequently, and deleterious mutations are purged from the population by purifying selection. Studies of phenotypic evolution in nature reveal a very different picture, with strong selection and rapid heritable trait changes being common. The time-rate scaling of phenotypic evolution suggests that selection on phenotypes is often fluctuating in direction, allowing phenotypes to respond rapidly to environmental fluctuations while remaining within relatively constant bounds over longer periods. Whether such rapid phenotypic evolution undermines the standard model will depend on how many genomic loci typically contribute to strongly selected traits and how phenotypic evolution impacts the dynamics of genetic variation in a population. Population-level sequencing will allow us to dissect the genetic basis of phenotypic evolution and study the evolutionary dynamics of genetic variation through direct measurement of polymorphism trajectories over time.

SLiM 2: Flexible, Interactive Forward Genetic Simulations.

Modern population genomic datasets hold immense promise for revealing the evolutionary processes operating in natural populations, but a crucial prerequisite for this goal is the ability to model realistic evolutionary scenarios and predict their expected patterns in genomic data. To that end, we present SLiM 2: an evolutionary simulation framework that combines a powerful, fast engine for forward population genetic simulations with the capability of modeling a wide variety of complex evolutionary scenarios. SLiM achieves this flexibility through scriptability, which provides control over most aspects of the simulated evolutionary scenarios with a simple R-like scripting language called Eidos. An example SLiM simulation is presented to illustrate the power of this approach. SLiM 2 also includes a graphical user interface for simulation construction, interactive runtime control, and dynamic visualization of simulation output, facilitating easy and fast model development with quick prototyping and visual debugging. We conclude with a performance comparison between SLiM and two other popular forward genetic simulation packages.

Quantification of GC-biased gene conversion in the human genome.

Much evidence indicates that GC-biased gene conversion (gBGC) has a major impact on the evolution of mammalian genomes. However, a detailed quantification of the process is still lacking. The strength of gBGC can be measured from the analysis of derived allele frequency spectra (DAF), but this approach is sensitive to a number of confounding factors. In particular, we show by simulations that the inference is pervasively affected by polymorphism polarization errors and by spatial heterogeneity in gBGC strength. We propose a new general method to quantify gBGC from DAF spectra, incorporating polarization errors, taking spatial heterogeneity into account, and jointly estimating mutation bias. Applying it to human polymorphism data from the 1000 Genomes Project, we show that the strength of gBGC does not differ between hypermutable CpG sites and non-CpG sites, suggesting that in humans gBGC is not caused by the base-excision repair machinery. Genome-wide, the intensity of gBGC is in the nearly neutral area. However, given that recombination occurs primarily within recombination hotspots, 1%-2% of the human genome is subject to strong gBGC. On average, gBGC is stronger in African than in non-African populations, reflecting differences in effective population sizes. However, due to more heterogeneous recombination landscapes, the fraction of the genome affected by strong gBGC is larger in non-African than in African populations. Given that the location of recombination hotspots evolves very rapidly, our analysis predicts that, in the long term, a large fraction of the genome is affected by short episodes of strong gBGC.

Recent selective sweeps in North American Drosophila melanogaster show signatures of soft sweeps.

Adaptation from standing genetic variation or recurrent de novo mutation in large populations should commonly generate soft rather than hard selective sweeps. In contrast to a hard selective sweep, in which a single adaptive haplotype rises to high population frequency, in a soft selective sweep multiple adaptive haplotypes sweep through the population simultaneously, producing distinct patterns of genetic variation in the vicinity of the adaptive site. Current statistical methods were expressly designed to detect hard sweeps and most lack power to detect soft sweeps. This is particularly unfortunate for the study of adaptation in species such as Drosophila melanogaster, where all three confirmed cases of recent adaptation resulted in soft selective sweeps and where there is evidence that the effective population size relevant for recent and strong adaptation is large enough to generate soft sweeps even when adaptation requires mutation at a specific single site at a locus. Here, we develop a statistical test based on a measure of haplotype homozygosity (H12) that is capable of detecting both hard and soft sweeps with similar power. We use H12 to identify multiple genomic regions that have undergone recent and strong adaptation in a large population sample of fully sequenced Drosophila melanogaster strains from the Drosophila Genetic Reference Panel (DGRP). Visual inspection of the top 50 candidates reveals that in all cases multiple haplotypes are present at high frequencies, consistent with signatures of soft sweeps. We further develop a second haplotype homozygosity statistic (H2/H1) that, in combination with H12, is capable of differentiating hard from soft sweeps. Surprisingly, we find that the H12 and H2/H1 values for all top 50 peaks are much more easily generated by soft rather than hard sweeps. We discuss the implications of these results for the study of adaptation in Drosophila and in species with large census population sizes.

Genome-wide signals of positive selection in human evolution.

The role of positive selection in human evolution remains controversial. On the one hand, scans for positive selection have identified hundreds of candidate loci, and the genome-wide patterns of polymorphism show signatures consistent with frequent positive selection. On the other hand, recent studies have argued that many of the candidate loci are false positives and that most genome-wide signatures of adaptation are in fact due to reduction of neutral diversity by linked deleterious mutations, known as background selection. Here we analyze human polymorphism data from the 1000 Genomes Project and detect signatures of positive selection once we correct for the effects of background selection. We show that levels of neutral polymorphism are lower near amino acid substitutions, with the strongest reduction observed specifically near functionally consequential amino acid substitutions. Furthermore, amino acid substitutions are associated with signatures of recent adaptation that should not be generated by background selection, such as unusually long and frequent haplotypes and specific distortions in the site frequency spectrum. We use forward simulations to argue that the observed signatures require a high rate of strongly adaptive substitutions near amino acid changes. We further demonstrate that the observed signatures of positive selection correlate better with the presence of regulatory sequences, as predicted by the ENCODE Project Consortium, than with the positions of amino acid substitutions. Our results suggest that adaptation was frequent in human evolution and provide support for the hypothesis of King and Wilson that adaptive divergence is primarily driven by regulatory changes.

Evaluating the performance of selection scans to detect selective sweeps in domestic dogs.

Selective breeding of dogs has resulted in repeated artificial selection on breed-specific morphological phenotypes. A number of quantitative trait loci associated with these phenotypes have been identified in genetic mapping studies. We analysed the population genomic signatures observed around the causal mutations for 12 of these loci in 25 dog breeds, for which we genotyped 25 individuals in each breed. By measuring the population frequencies of the causal mutations in each breed, we identified those breeds in which specific mutations most likely experienced positive selection. These instances were then used as positive controls for assessing the performance of popular statistics to detect selection from population genomic data. We found that artificial selection during dog domestication has left characteristic signatures in the haplotype and nucleotide polymorphism patterns around selected loci that can be detected in the genotype data from a single population sample. However, the sensitivity and accuracy at which such signatures were detected varied widely between loci, the particular statistic used and the choice of analysis parameters. We observed examples of both hard and soft selective sweeps and detected strong selective events that removed genetic diversity almost entirely over regions >10 Mbp. Our study demonstrates the power and limitations of selection scans in populations with high levels of linkage disequilibrium due to severe founder effects and recent population bottlenecks.