Biology subjects

Messer, P. W.

Publications and source records attributed to Messer, P. W..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

Fitness effects of CRISPR endonucleases in Drosophila melanogaster populations

CRISPR/Cas9 systems provide a highly efficient and flexible genome editing technology with numerous potential applications in areas ranging from gene therapy to population control. Some proposed applications involve CRISPR/Cas9 endonucleases integrated into an organisms genome, which raises questions about potentially harmful effects to the transgenic individuals. One application where this is particularly relevant are CRISPR-based gene drives, which promise a mechanism for rapid genetic alteration of entire populations. The performance of such drives can strongly depend on fitness costs experienced by drive carriers, yet relatively little is known about the magnitude and causes of these costs. Here, we assess the fitness effects of genomic CRISPR/Cas9 expression in Drosophila melanogaster cage populations by tracking allele frequencies of four different transgenic constructs, designed to disentangle direct fitness costs due to the integration, expression, and target-site activity of Cas9 from costs due to potential off-target cleavage. Using a maximum likelihood framework, we find a moderate level of fitness costs due to off-target effects but do not detect significant direct costs. Costs of off-target effects are minimized for a construct with Cas9HF1, a high-fidelity version of Cas9. We further demonstrate that using Cas9HF1 instead of standard Cas9 in a homing drive achieves similar drive conversion efficiency. Our results suggest that gene drives should be designed with high-fidelity endonucleases and may have implications for other applications that involve genomic integration of CRISPR endonucleases.

evolutionary biology↗

bioRxiv2021

Predicting the Genomic Resolution of Bulk Segregant Analysis

Bulk segregant analysis (BSA) is a technique for identifying the genetic loci that underlie phenotypic trait differences. The basic approach of this method is to compare two pools of individuals from the opposing tails of the phenotypic distribution, sampled from an interbred population. Each pool is sequenced and scanned for alleles that show divergent frequencies between the pools, indicating potential association with the observed trait differences. BSA has already been successfully applied to the mapping of various quantitative trait loci in organisms ranging from yeast to maize. However, these studies have typically suffered from rather low mapping resolution, and we still lack a detailed understanding of how this resolution is affected by experimental parameters. Here, we use coalescence theory to calculate the expected genomic resolution of BSA. We first show that in an idealized interbreeding population of infinite size, the expected length of the mapped region is inversely proportional to the recombination rate, the number of generations of interbreeding, and the number of genomes sampled, as intuitively expected. In a finite population, coalescence events in the genealogy of the sample reduce the number of potentially informative recombination events during interbreeding, thereby increasing the length of the mapped region. This is incorporated into our theory by an effective population size parameter that specifies the pairwise coalescence rate of the interbreeding population. The mapping resolution predicted by our theory closely matches numerical simulations. Furthermore, we show that the approach can easily be extended to modifications of the crossing scheme. Our framework enables researchers to predict the expected power of their mapping experiments, and to evaluate how their experimental design could be tuned to optimize mapping resolution.

genetics↗

bioRxiv2020

Modeling CRISPR gene drives for suppression of invasive rodents

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.

ecology↗

bioRxiv2020

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 instead these haplotype patterns can be more easily explained by complex neutral demographic models. They also claim, confusingly, 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 the demographic models employed in such analyses are generally necessarily too simple to capture the full demographic complexity, while more realistic models are unlikely to be inferred correctly because they require fitting a very large number of free parameters.

evolutionary biology↗

bioRxiv2019

A community-maintained standard library of population genetic models

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

evolutionary biology↗

bioRxiv2019

Design and analysis of CRISPR-based underdominance toxin-antidote gene drives

CRISPR gene drive systems offer a mechanism for transmitting a desirable transgene throughout a population for purposes ranging from vector-borne disease control to invasive species suppression. In this simulation study, we assess the performance of several CRISPR-based underdominance gene drive constructs employing toxin-antidote principles. These drives disrupt the wild-type version of an essential gene using a CRISPR nuclease (the toxin) while simultaneously carrying a recoded version of the gene (the antidote). Drives of this nature allow for releases that could be potentially confined to a desired geographic location. This is because such drives have a nonzero invasion threshold frequency, referring to the critical frequency required for the drive to spread through the population. We model drives which target essential genes that are either haplosufficient or haplolethal, using nuclease promoters with expression restricted to the germline, promoters that additionally result in cleavage activity in the early embryo from maternal deposition, and promoters that have ubiquitous somatic expression. We also study several possible drive architectures, considering both "same-site" and "distant-site" systems, as well as several reciprocally targeting drives. Together, these drive variants provide a wide range of invasion threshold frequencies and options for both population modification and suppression. Our results suggest that CRISPR toxin-antidote underdominance drive systems could allow for the design of highly flexible and potentially confinable gene drive strategies.

genetics↗

bioRxiv2019

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 versus spatial models where the rate of evolution is limited by dispersal. One such process is gene drive, which allows \"selfish\" genetic elements to quickly spread through a population. Engineered gene drive systems are being considered as a means for suppressing disease vector populations or invasive species. While laboratory experiments and modeling in panmictic populations have shown that such drives can rapidly eliminate a population, it is not yet clear how well these results translate to natural environments where individuals inhabit a continuous landscape. Using spatially explicit simulations, we show that instead of population elimination, release of a suppression drive can result in what we term \"chasing\" dynamics. This describes a condition in which wild-type individuals quickly recolonize areas where the drive has locally eliminated the population. Despite the drive subsequently chasing the wild-type allele into these newly re-colonized areas, complete population suppression often fails or is substantially delayed. This delay increases the likelihood that the drive becomes lost or that resistance evolves. We systematically analyze how chasing dynamics are influenced by the type of drive, its efficiency, fitness costs, as well as ecological and demographic factors such as the maximal growth rate of the population, the migration rate, and the level of inbreeding. We find that chasing is generally more common for lower efficiency drives and in populations with low dispersal. However, we further find that some drive mechanisms are substantially more prone to chasing behavior 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 modeling to predict the outcome of drive releases in natural populations.

ecology↗

bioRxiv2019

Computational and experimental performance of CRISPR homing gene drive strategies with multiplexed gRNAs

CRISPR homing gene drives potentially have the capacity for large-scale population modification or suppression. However, resistance alleles formed by the drives can prevent them from successfully spreading. Such alleles have been found to form at high rates in most studies, including those in both insects and mammals. One possible solution to this issue is the use of multiple guide RNAs (gRNAs), thus allowing cleavage by the drive even if resistance sequences are present at some of the gRNA target sequences. Here, we develop a high-fidelity model incorporating several factors affecting the performance of drives with multiple gRNAs, including timing of cleavage, reduction in homology-directed repair efficiency due to imperfect homology around the cleavage site, Cas9 activity saturation, variance in the activity level of individual gRNAs, and formation of resistance alleles due to incomplete homology-directed repair. We parameterize the model using data from homing drive experiments designed to investigate these factors and then use it to analyze several types of homing gene drives. We find that each type of drive has an optimal number of gRNAs, usually between two and eight, dependent on drive type and performance parameters. Our model indicates that utilization of multiple gRNAs is insufficient for construction of successful gene drives, but that it provides a critical boost to drive efficiency when combined with other strategies for population modification or suppression.

genetics↗

bioRxiv2019

The Effects of Quantitative Trait Architecture on Detection Power in Artificial Selection Experiments

Evolve and resequence (E&R) experiments, in which artificial selection is imposed on organisms in a controlled environment, are becoming an increasingly accessible tool for studying the genetic basis of adaptation. Previous work has assessed how different experimental design parameters affect the power to detect the quantitative trait loci (QTLs) that underlie adaptive responses in such experiments, but so far there has been little exploration of how this power varies with the genetic architecture of the evolving traits. In this study, we use forward simulation to build a realistic model of an E&R experiment in which a quantitative polygenic trait experiences a short, but strong, episode of truncation selection. We study the expected power for QTL detection in such an experiment and how this power is influenced by different aspects of trait architecture, including the number of QTLs affecting the trait, their starting frequencies, effect sizes, clustering along a chromosome, dominance, and epistasis patterns. We show that all of the above parameters can affect allele frequency dynamics at the QTLs and linked loci in complex and often unintuitive ways, and thus influence our power to detect them. One consequence of this is that existing detection methods based on models of independent selective sweeps at individual QTLs often have lower detection power than a simple measurement of allele frequency differences before and after selection. Our findings highlight the importance of taking trait architecture into account in designing studies of molecular adaptation with temporal data. We provide a customizable modeling framework that will enable researchers to easily simulate E&R experiments with different trait architectures and parameters tuned to their specific study system, allowing for assessment of expected detection power and optimization of experimental design.

evolutionary biology↗

bioRxiv2019

Resistance is futile: A CRISPR homing gene drive targeting a haplolethal gene

Engineered gene drives are being explored as a potential strategy for the control of vector-borne diseases due to their ability to rapidly spread genetic modifications through a population. While an effective CRISPR homing gene drive for population suppression has recently been demonstrated in mosquitoes, formation of resistance alleles that prevent Cas9 cleavage remains the major obstacle for drive strategies aiming at population modification, rather than elimination. Here, we present a homing drive in Drosophila melanogaster that reduces resistance allele formation below detectable levels by targeting a haplolethal gene with two gRNAs while also providing a rescue allele. This is because any resistance alleles that form by end-joining repair will typically disrupt the haplolethal target gene, rendering the individuals carrying them nonviable. We demonstrate that our drive is highly efficient, with 91% of the progeny of drive heterozygotes inheriting the drive allele and with no resistance alleles observed in the remainder. In a large cage experiment, the drive allele successfully spread to all individuals. These results show that a haplolethal homing drive can be a highly effective tool for population modification.

genetics↗

bioRxiv2019

A toxin-antidote CRISPR gene drive system for regional population modification

Engineered gene drives have been suggested as a mechanism for rapidly spreading genetic alterations through a population. One promising type of drive is the CRISPR homing drive, which has recently been demonstrated in several organisms. However, such drives face a major obstacle in the form of resistance against the drive that typically evolves rapidly. In addition, homing-type drives are generally self-sustaining, meaning that a drive would likely spread to all individuals of a species even when introduced at low frequency in a single location. Here, we develop a new form of CRISPR gene drive, the Toxin-Antidote Recessive Embryo (TARE) drive, which successfully limits resistance by targeting a recessive lethal gene while providing a recoded sequence to rescue only drive-carrying individuals. Our computational modeling shows that such a drive will have threshold-dependent dynamics, spreading only when introduced above a frequency threshold that depends on the fitness cost of the drive. We demonstrate such a drive in Drosophila with 88-95% transmission to the progeny of female drive heterozygotes. This drive was able to spread through a large cage population in just six generations following introduction at 24% frequency without any apparent evolution of resistance. Our results suggest that TARE drives constitute promising candidates for the development of effective, regionally confined population modification drives.

genetics↗

bioRxiv2019

Performance analysis of novel toxin-antidote CRISPR gene drive systems

Gene drives can potentially fixate in a population by biasing inheritance in their favor, opening up a variety of potential applications in areas such as disease-vector control and conservation. CRISPR homing gene drives have shown much promise for providing an effective drive mechanism, but they typically suffer from the rapid formation of resistance alleles. Even if the problem of resistance can be overcome, the utility of such drives would still be limited by their tendency to spread into all areas of a population. To provide additional options for gene drive applications that are substantially less prone to the formation of resistance alleles and could potentially remain confined to a target area, we developed several designs for CRISPR-based gene drives utilizing toxin-antidote (TA) principles. These drives target and disrupt an essential gene with the drive providing rescue. Here, we assess the performance of several types of TA gene drive systems using modeling and individual-based simulations. We show that Toxin-Antidote Recessive Embryo (TARE) drive should allow for the design of robust, regionally confined, population modification strategies with high flexibility in choosing drive promoters and recessive lethal targets. Toxin-Antidote Dominant Embryo (TADE) drive requires a haplolethal target gene and a germline-restricted promoter but should enable the design of both faster regional population modification drives and even regionally-confined population suppression drives. Toxin-antidote dominant sperm (TADS) drive can be used for population modification or suppression. It spreads nearly as quickly as a homing drive and can flexibly use a variety of promoters, but unlike the other TA systems, it is not regionally confined and requires highly specific target genes. Overall, our results suggest that CRISPR-based TA gene drives provide promising candidates for further development in a variety of organisms and may allow for flexible ecological engineering strategies.

genetics↗

bioRxiv2018

Population dynamics of underdominance gene drive systems in continuous space

Underdominance gene drive systems promise a mechanism for rapidly spreading payload alleles through a local population while otherwise remaining confined, unable to spread into neighboring populations due to their frequency-dependent dynamics. Such systems could provide a new tool in the fight against vector-borne diseases by disseminating transgenic payloads through vector populations. If local confinement can indeed be achieved, the decision-making process for the release of such constructs would likely be considerably simpler compared to other gene drive mechanisms such as CRISPR homing drives. So far, the confinement ability of underdominance systems has only been demonstrated in models of panmictic populations linked by migration. How such systems would behave in realistic populations where individuals move over continuous space remains largely unknown. Here, we study several underdominance systems in continuous-space population models and show that their dynamics are drastically altered from those in panmictic populations. Specifically, we find that all underdominance systems we studied can fail to persist in such environments, even after successful local establishment. At the same time, we find that a two-locus two-toxin-antitoxin system can still successfully invade neighboring populations in many scenarios even under weak migration. This suggests that the parameter space for underdominance systems to both establish in a given region and remain confined to that region would likely be highly limited. Overall, these results indicate that spatial context must be considered when assessing strategies for the deployment of underdominance systems.

genetics↗

bioRxiv2018

Multiple loci of small effect confer wide variability in efficiency and resistance rate of CRISPR gene drive

Gene drives could allow for control of vector-borne diseases by directly suppressing vector populations or spreading genetic payloads designed to reduce pathogen transmission. CRISPR homing gene drives work by cleaving wild-type alleles, which are then converted to drive alleles by homology-directed repair, increasing the frequency of the drive in a population. However, resistance alleles can form when end-joining repair takes place in lieu of homology-directed repair. Such alleles cannot be converted to drive alleles, which would halt the spread of a drive through a population. To investigate the effects of natural genetic variation on resistance formation, we developed a CRISPR homing gene drive in Drosophila melanogaster and crossed it into the genetically diverse Drosophila Genetic Reference Panel (DGRP) lines, measuring several performance parameters. Most strikingly, resistance allele formation post-fertilization in the early embryo ranged from 7% to 79% among lines and averaged 42{+/-}18%. We performed a Genome-Wide Association Study (GWAS) using our results in the DGRP lines and found that the resistance and conversion rates were polygenic, with several genetic polymorphisms showing relatively weak association. RNAi knockdown of several of these genes confirmed their effect, but their small effect sizes implies that their manipulation will yield only modest improvements to the efficacy of gene drives.

genetics↗

bioRxiv2018

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. These two key features allow SLiM 3 models to go beyond the Wright-Fisher model, opening up new horizons for forward genetic modeling.

evolutionary biology↗

bioRxiv2018

Molecular safeguarding of CRISPR gene drive experiments

CRISPR-based gene drives have sparked both enthusiasm and deep concerns due to their potential for genetically altering entire species. This raises the question about our ability to prevent the unintended spread of such drives from the laboratory into a natural population. Here, we experimentally demonstrate the suitability of synthetic target sites and split drives as flexible safeguarding strategies for gene drive experiments.

genetics↗

bioRxiv2018

Tree-sequence recording in SLiM opens new horizons for forward-time simulation of whole genomes

There is an increasing demand for evolutionary models to incorporate relatively realistic dynamics, ranging from selection at many genomic sites to complex demography, population structure, and ecological interactions. Such models can generally be implemented as individual-based forward simulations, but the large computational overhead of these models often makes simulation of whole chromosome sequences in large populations infeasible. This situation presents an important obstacle to the field that requires conceptual advances to overcome. The recently developed tree-sequence recording method (Kelleher et al., 2018), which stores the genealogical history of all genomes in the simulated population, could provide such an advance. This method has several benefits: (1) it allows neutral mutations to be omitted entirely from forward-time simulations and added later, thereby dramatically improving computational efficiency; (2) it allows neutral burn-in to be constructed extremely efficiently after the fact, using \"recapitation\"; (3) it allows direct examination and analysis of the genealogical trees along the genome; and (4) it provides a compact representation of a populations genealogy that can be analyzed in Python using the msprime package. We have implemented the tree-sequence recording method in SLiM 3 (a free, open-source evolutionary simulation software package) and extended it to allow the recording of non-neutral mutations, greatly broadening the utility of this method. To demonstrate the versatility and performance of this approach, we showcase several practical applications that would have been beyond the reach of previously existing methods, opening up new horizons for the modeling and exploration of evolutionary processes.

evolutionary biology↗

bioRxiv2018

Maximum likelihood estimation of fitness components in experimental evolution

Estimating fitness differences between allelic variants is a central goal of experimental evolution. Current methods for inferring selection from allele frequency time series typically assume that evolutionary dynamics at the locus of interest can be described by a fixed selection coefficient. However, fitness is an aggregate of several components including mating success, fecundity, and viability, and distinguishing between these components could be critical in many scenarios. Here we develop a flexible maximum likelihood framework that can disentangle different components of fitness and estimate them individually in males and females from genotype frequency data. As a proof-of-principle, we apply our method to experimentally-evolved cage populations of Drosophila melanogaster, in which we tracked the relative frequencies of a loss-of-function and wild-type allele of yellow. This X-linked gene produces a recessive yellow phenotype when disrupted and is involved in male courtship ability. We find that the fitness costs of the yellow phenotype take the form of substantially reduced mating preference of wild-type females for yellow males, together with a modest reduction in the viability of yellow males and females. Our framework should be generally applicable to situations where it is important to quantify fitness components of specific genetic variants, including quantitative characterization of the population dynamics of CRISPR gene drives.

evolutionary biology↗