Weak seed banks influence the signature and detectability of selective sweeps.

Seed banking (or dormancy) is a widespread bet-hedging strategy, generating a form of population overlap, which decreases the magnitude of genetic drift. The methodological complexity of integrating this trait implies it is ignored when developing tools to detect selective sweeps. But, as dormancy lengthens the ancestral recombination graph (ARG), increasing times to fixation, it can change the genomic signatures of selection. To detect genes under positive selection in seed banking species it is important to (1) determine whether the efficacy of selection is affected, and (2) predict the patterns of nucleotide diversity at and around positively selected alleles. We present the first tree sequence-based simulation program integrating a weak seed bank to examine the dynamics and genomic footprints of beneficial alleles in a finite population. We find that seed banking does not affect the probability of fixation and confirm expectations of increased times to fixation. We also confirm earlier findings that, for strong selection, the times to fixation are not scaled by the inbreeding effective population size in the presence of seed banks, but are shorter than would be expected. As seed banking increases the effective recombination rate, footprints of sweeps appear narrower around the selected sites and due to the scaling of the ARG are detectable for longer periods of time. The developed simulation tool can be used to predict the footprints of selection and draw statistical inference of past evolutionary events in plants, invertebrates, or fungi with seed banks.

Approximate Bayesian Computation applied to time series of population genetic data disentangles rapid genetic changes and demographic variations in a pathogen population.

Adaptation can occur at remarkably short timescales in natural populations, leading to drastic changes in phenotypes and genotype frequencies over a few generations only. The inference of demographic parameters can allow understanding how evolutionary forces interact and shape the genetic trajectories of populations during rapid adaptation. Here we propose a new Approximate Bayesian Computation (ABC) framework that couples a forward and individual-based model with temporal genetic data to disentangle genetic changes and demographic variations in a case of rapid adaptation. We test the accuracy of our inferential framework and evaluate the benefit of considering a dense versus sparse sampling. Theoretical investigations demonstrate high accuracy in both model and parameter estimations, even if a strong thinning is applied to time series data. Then, we apply our ABC inferential framework to empirical data describing the population genetic changes of the poplar rust pathogen following a major event of resistance overcoming. We successfully estimate key demographic and genetic parameters, including the proportion of resistant hosts deployed in the landscape and the level of standing genetic variation from which selection occurred. Inferred values are in accordance with our empirical knowledge of this biological system. This new inferential framework, which contrasts with coalescent-based ABC analyses, is promising for a better understanding of evolutionary trajectories of populations subjected to rapid adaptation.

Joint inference of evolutionary transitions to self-fertilization and demographic history using whole-genome sequences.

The evolution from outcrossing to selfing occurred recently across the eukaryote tree of life in plants, animals, fungi, and algae. Despite short-term advantages, selfing is hypothetically an evolutionary dead-end reproductive strategy. The tippy distribution on phylogenies suggests that most selfing species are of recent origin. However, dating such transitions is challenging yet central for testing this hypothesis. We build on previous theories to disentangle the differential effect of past changes in selfing rate or from that of population size on recombination probability along the genome. This allowed us to develop two methods using full-genome polymorphisms to (1) test if a transition from outcrossing to selfing occurred and (2) infer its age. The teSMC and tsABC methods use a transition matrix summarizing the distribution of times to the most recent common ancestor along the genome to estimate changes in the ratio of population recombination and mutation rates overtime. First, we demonstrate that our methods distinguish between past changes in selfing rate and demographic history. Second, we assess the accuracy of our methods to infer transitions to selfing approximately up to 2.5Ne generations ago. Third, we demonstrate that our estimates are robust to the presence of purifying selection. Finally, as a proof of principle, we apply both methods to three Arabidopsis thaliana populations, revealing a transition to selfing approximately 600,000 years ago. Our methods pave the way for studying recent transitions to self-fertilization and better accounting for variation in mating systems in demographic inferences.

Determinants of rapid adaptation in species with large variance in offspring production.

The speed of population adaptation to changing biotic and abiotic environments is determined by the interaction between genetic drift, positive selection and linkage effects. Many marine species (fish, crustaceans), invertebrates and pathogens of humans and crops, exhibit sweepstakes reproduction characterized by the production of a very large amount of offspring (fecundity phase) from which only a small fraction may survive to the next generation (viability phase). Using stochastic simulations, we investigate whether the occurrence of sweepstakes reproduction affects the efficiency of a positively selected unlinked locus, and thus, the speed of adaptation since fecundity and/or viability have distinguishable consequences on mutation rate, probability and fixation time of advantageous alleles. We observe that the mean number of mutations at the next generation is always the function of the population size, but the variance increases with stronger sweepstakes reproduction when mutations occur in the parents. On the one hand, stronger sweepstakes reproduction magnifies the effect of genetic drift thus increasing the probability of fixation of neutral allele and decreasing that of selected alleles. On the other hand, the time to fixation of advantageous (as well as neutral) alleles is shortened by stronger sweepstakes reproduction. Importantly, fecundity and viability selection exhibit different probabilities and times to fixation of advantageous alleles under intermediate and weak sweepstakes reproduction. Finally, alleles under both strong fecundity and viability selection display a synergistic efficiency of selection. We conclude that measuring and modelling accurately fecundity and/or viability selection are crucial to predict the adaptive potential of species with sweepstakes reproduction.

Selective sweeps linked to the colonization of novel habitats and climatic changes in a wild tomato species.

Positive selection is the driving force underpinning local adaptation and leaves footprints of selective sweeps on the underlying major genes. Quantifying the timing of selection and revealing the genetic bases of adaptation in plant species occurring in steep and varying environmental gradients are crucial to predict a species' ability to colonize new niches. We use whole-genome sequence data from six populations across three different habitats of the wild tomato species Solanum chilense to infer the past demographic history and search for genes under strong positive selection. We then correlate current and past climatic projections with the demographic history, allele frequencies, the age of selection events and distribution shifts. Several selective sweeps occur at regulatory networks involved in root-hair development in low altitude and response to photoperiod and vernalization in high-altitude populations. These sweeps appear to occur in a concerted fashion in a given regulatory gene network at particular periods of substantial climatic change. Using a unique combination of genome scans and modelling of past climatic data, we quantify the timing of selection at genes likely underpinning local adaptation to semiarid habitats.

Genome-wide characterization of the NLR gene family in tomato (Solanum lycopersicum) and their relatedness to disease resistance.

Nucleotide-binding leucine-rich-repeat receptors (NLR), the largest group of genes associated with plant disease resistance (R), have attracted attention due to their crucial role in protecting plants from pathogens. Genome-wide studies of NLRs have revealed conserved domains in the annotated tomato genome. The 321 NLR genes identified in the tomato genome have been randomly mapped to 12 chromosomes. Phylogenetic analysis and classification of NLRs have revealed that 211 genes share full-length domains categorized into three major clades (CNL, TNL, and RNL); the remaining 110 NLRs share partial domains and are classified in CN, TN, and N according to their motifs and gene structures. The cis-regulatory elements of NLRs exhibit the maximum number of these elements and are involved in response to biotic and abiotic stresses, pathogen recognition, and resistance. Analysis of the phylogenetic relationship between tomato NLRs and orthologs in other species has shown conservation among Solanaceae members and variation with A. thaliana. Synteny and Ka/Ks analyses of Solanum lycopersicum and Solanum tuberosum orthologs have underscored the importance of NLR conservation and diversification from ancestral species millions of years ago. RNA-seq data and qPCR analysis of early and late blight diseases in tomatoes revealed consistent NLR expression patterns, including upregulation in infected compared to control plants (with some exceptions), suggesting the role of NLRs as key regulators in early blight resistance. Moreover, the expression levels of NLRs associated with late blight resistance (Solyc04g007060 [NRC4] and Solyc10g008240 [RIB12]) suggested that they regulate S. lycopersicum resistance to P. infestans. These findings provide important fundamental knowledge for understanding NLR evolution and diversity and will empower the broader characterization of disease resistance genes for pyramiding through speed cloning to develop disease-tolerant varieties.

Echo chambers and opinion dynamics explain the occurrence of vaccination hesitancy.

Vaccination hesitancy is a major obstacle to achieving and maintaining herd immunity. Therefore, public health authorities need to understand the dynamics of an anti-vaccine opinion in the population. We introduce a spatially structured mathematical model of opinion dynamics with reinforcement. The model allows as an emergent property for the occurrence of echo chambers, i.e. opinion bubbles in which information that is incompatible with one's entrenched worldview, is probably disregarded. We scale the model both to a deterministic limit and to a weak-effects limit, and obtain bifurcations, phase transitions and the invariant measure. Fitting the model to measles and meningococci vaccination coverage across Germany, reveals that the emergence of echo chambers dynamics explains the occurrence and persistence of the anti-vaccination opinion in allowing anti-vaxxers to isolate and to ignore pro-vaccination facts. We predict and compare the effectiveness of different policies aimed at influencing opinion dynamics in order to increase vaccination uptake. According to our model, measures aiming at reducing the salience of partisan anti-vaccine information sources would have the largest effect on enhancing vaccination uptake. By contrast, measures aiming at reducing the reinforcement of vaccination deniers are predicted to have the smallest impact.

Unraveling coevolutionary dynamics using ecological genomics.

Coevolutionary interactions, from the delicate co-dependency in mutualistic interactions to the antagonistic relationship of hosts and parasites, are a ubiquitous driver of adaptation. Surprisingly, little is known about the genomic processes underlying coevolution in an ecological context. However, species comprise genetically differentiated populations that interact with temporally variable abiotic and biotic environments. We discuss the recent advances in coevolutionary theory and genomics as well as shortcomings, to identify coevolving genes that take into account this spatial and temporal variability of coevolution, and propose a practical guide to understand the dynamic of coevolution using an ecological genomics lens.

Life-History traits and the replicator equation.

Due to the relevance for conservation biology, there is an increasing interest to extend evolutionary genomics models to plant, animal or microbial species. However, this requires to understand the effect of life-history traits absent in humans on genomic evolution. In this context, it is fundamentally of interest to generalize the replicator equation, which is at the heart of most population genomics models. However, as the inclusion of life-history traits generates models with a large state space, the analysis becomes involving. We focus, here, on quiescence and seed banks, two features common to many plant, invertebrate and microbial species. We develop a method to obtain a low-dimensional replicator equation in the context of evolutionary game theory, based on two assumptions: (1) the life-history traits are per se neutral, and (2) frequency-dependent selection is weak. We use the results to investigate the evolution and maintenance of cooperation based on the Prisoner's dilemma and the snowdrift game. We first consider the generalized replicator equation, and then refine the investigation using adaptive dynamics. It turns out that, depending on the structure and timing of the quiescence/dormancy life-history trait, cooperation in a homogeneous population can be stabilized. We finally discuss and highlight the relevance of these results for plant, invertebrate and microbial communities.

Approximate Bayesian Computation Untangles Signatures of Contemporary and Historical Hybridization between Two Endangered Species.

Contemporary gene flow, when resumed after a period of isolation, can have crucial consequences for endangered species, as it can both increase the supply of adaptive alleles and erode local adaptation. Determining the history of gene flow and thus the importance of contemporary hybridization, however, is notoriously difficult. Here, we focus on two endangered plant species, Arabis nemorensis and A. sagittata, which hybridize naturally in a sympatric population located on the banks of the Rhine. Using reduced genome sequencing, we determined the phylogeography of the two taxa but report only a unique sympatric population. Molecular variation in chloroplast DNA indicated that A. sagittata is the principal receiver of gene flow. Applying classical D-statistics and its derivatives to whole-genome data of 35 accessions, we detect gene flow not only in the sympatric population but also among allopatric populations. Using an Approximate Bayesian computation approach, we identify the model that best describes the history of gene flow between these taxa. This model shows that low levels of gene flow have persisted long after speciation. Around 10 000 years ago, gene flow stopped and a period of complete isolation began. Eventually, a hotspot of contemporary hybridization was formed in the unique sympatric population. Occasional sympatry may have helped protect these lineages from extinction in spite of their extremely low diversity.

Novel genomic approaches to study antagonistic coevolution between hosts and parasites.

Host-parasite coevolution is ubiquitous, shaping genetic and phenotypic diversity and the evolutionary trajectory of interacting species. With the advances of high throughput sequencing technologies applicable to model and non-model organisms alike, it is now feasible to study in greater detail (a) the genetic underpinnings of coevolution, (b) the speed and type of dynamics at coevolving loci, and (c) the genomic consequences of coevolution. This review focuses on three recently developed approaches that leverage information from host and parasite full genome data simultaneously to pinpoint coevolving loci and draw inference on the coevolutionary history. First, co-genome-wide association study (co-GWAS) methods allow pinpointing the loci underlying host-parasite interactions. These methods focus on detecting associations between genetic variants and the outcome of experimental infection tests or on correlations between genomes of naturally infected hosts and their infecting parasites. Second, extensions to population genomics methods can detect genes under coevolution and infer the coevolutionary history, such as fitness costs. Third, correlations between host and parasite population size in time are indicative of coevolution, and polymorphism levels across independent spatially distributed populations of hosts and parasites can reveal coevolutionary loci and infer coevolutionary history. We describe the principles of these three approaches and discuss their advantages and limitations based on coevolutionary theory. We present recommendations for their application to various host (prokaryotes, fungi, plants, and animals) and parasite (viruses, bacteria, fungi, and macroparasites) species. We conclude by pointing out methodological and theoretical gaps to be filled to extract maximum information from full genome data and thereby to shed light on the molecular underpinnings of coevolution.

Limits and convergence properties of the sequentially Markovian coalescent.

Several methods based on the sequentially Markovian coalescent (SMC) make use of full genome sequence data from samples to infer population demographic history including past changes in population size, admixture, migration events and population structure. More recently, the original theoretical framework has been extended to allow the simultaneous estimation of population size changes along with other life history traits such as selfing or seed banking. The latter developments enhance the applicability of SMC methods to nonmodel species. Although convergence proofs have been given using simulated data in a few specific cases, an in-depth investigation of the limitations of SMC methods is lacking. In order to explore such limits, we first develop a tool inferring the best case convergence of SMC methods assuming the true underlying coalescent genealogies are known. This tool can be used to quantify the amount and type of information that can be confidently retrieved from given data sets prior to the analysis of the real data. Second, we assess the inference accuracy when the assumptions of SMC approaches are violated due to departures from the model, namely the presence of transposable elements, variable recombination and mutation rates along the sequence, and SNP calling errors. Third, we deliver a new interpretation of SMC methods by highlighting the importance of the transition matrix, which we argue can be used as a set of summary statistics in other statistical inference methods, uncoupling the SMC from hidden Markov models (HMMs). We finally offer recommendations to better apply SMC methods and build adequate data sets under budget constraints.

Correction: Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data.

[This corrects the article DOI: 10.1371/journal.pgen.1008698.].

Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data.

Several methods based on the Sequential Markovian coalescence (SMC) have been developed that make use of genome sequence data to uncover population demographic history, which is of interest in its own right and is a key requirement to generate a null model for selection tests. While these methods can be applied to all possible kind of species, the underlying assumptions are sexual reproduction in each generation and non-overlapping generations. However, in many plants, invertebrates, fungi and other taxa, those assumptions are often violated due to different ecological and life history traits, such as self-fertilization or long term dormant structures (seed or egg-banking). We develop a novel SMC-based method to infer 1) the rates/parameters of dormancy and of self-fertilization, and 2) the populations' past demographic history. Using simulated data sets, we demonstrate the accuracy of our method for a wide range of demographic scenarios and for sequence lengths from one to 30 Mb using four sampled genomes. Finally, we apply our method to a Swedish and a German population of Arabidopsis thaliana demonstrating a selfing rate of ca. 0.87 and the absence of any detectable seed-bank. In contrast, we show that the water flea Daphnia pulex exhibits a long lived egg-bank of three to 18 generations. In conclusion, we here present a novel method to infer accurate demographies and life-history traits for species with selfing and/or seed/egg-banks. Finally, we provide recommendations for the use of SMC-based methods for non-model organisms, highlighting the importance of the per site and the effective ratios of recombination over mutation.

Inference of coevolutionary dynamics and parameters from host and parasite polymorphism data of repeated experiments.

There is a long-standing interest in understanding host-parasite coevolutionary dynamics and associated fitness effects. Increasing amounts of genomic data for both interacting species offer a promising source to identify candidate loci and to infer the main parameters of the past coevolutionary history. However, so far no method exists to perform the latter. By coupling a gene-for-gene model with coalescent simulations, we first show that three types of biological costs, namely, resistance, infectivity and infection, define the allele frequencies at the internal equilibrium point of the coevolution model. These in return determine the strength of selective signatures at the coevolving host and parasite loci. We apply an Approximate Bayesian Computation (ABC) approach on simulated datasets to infer these costs by jointly integrating host and parasite polymorphism data at the coevolving loci. To control for the effect of genetic drift on coevolutionary dynamics, we assume that 10 or 30 repetitions are available from controlled experiments or several natural populations. We study two scenarios: 1) the cost of infection and population sizes (host and parasite) are unknown while costs of infectivity and resistance are known, and 2) all three costs are unknown while populations sizes are known. Using the ABC model choice procedure, we show that for both scenarios, we can distinguish with high accuracy pairs of coevolving host and parasite loci from pairs of neutrally evolving loci, though the statistical power decreases with higher cost of infection. The accuracy of parameter inference is high under both scenarios especially when using both host and parasite data because parasite polymorphism data do inform on costs applying to the host and vice-versa. As the false positive rate to detect pairs of genes under coevolution is small, we suggest that our method complements recently developed methods to identify host and parasite candidate loci for functional studies.

Neutral genomic signatures of host-parasite coevolution.

BACKGROUND: Coevolution is a selective process of reciprocal adaptation in hosts and parasites or in mutualistic symbionts. Classic population genetics theory predicts the signatures of selection at the interacting loci of both species, but not the neutral genome-wide polymorphism patterns. To bridge this gap, we build an eco-evolutionary model, where neutral genomic changes over time are driven by a single selected locus in hosts and parasites via a simple biallelic gene-for-gene or matching-allele interaction. This coevolutionary process may lead to cyclic changes in the sizes of the interacting populations. RESULTS: We investigate if and when these changes can be observed in the site frequency spectrum of neutral polymorphisms from host and parasite full genome data. We show that changes of the host population size are too smooth to be observable in its polymorphism pattern over the course of time. Conversely, the parasite population may undergo a series of strong bottlenecks occurring on a slower relative time scale, which may lead to observable changes in a time series sample. We also extend our results to cases with 1) several parasites per host accelerating relative time, and 2) multiple parasite generations per host generation slowing down rescaled time. CONCLUSIONS: Our results show that time series sampling of host and parasite populations with full genome data are crucial to understand if and how coevolution occurs. This model provides therefore a framework to interpret and draw inference from genome-wide polymorphism data of interacting species.

Are the better cooperators dormant or quiescent?

Despite the wealth of empirical and theoretical studies, the origin and maintenance of cooperation is still an evolutionary riddle. In this context, ecological life-history traits which affect the efficiency of selection may play a role despite being often ignored. We consider here species such as bacteria, fungi, invertebrates and plants which exhibit resting stages in the form of a quiescent state or a seed bank. When quiescent, individuals are inactive and reproduce upon activation, while under seed bank parents produce offspring remaining dormant for different amount of time. We assume weak frequency-dependent selection modeled using game-theory and the prisoner's dilemma (cooperation/defect) as payoff matrix. The cooperators and defectors are allowed to evolve different quiescence or dormancy times. By means of singular perturbation theory we reduce the model to a one-dimensional equation resembling the well known replicator equation, in which the gain functions are scaled with lumped parameters reflecting the time scale of the resting state of the cooperators and defectors. If both time scales are identical cooperation cannot persist in a homogeneous population. If, however, the time scale of the cooperator is distinctively different from that of the defector, cooperation may become a locally asymptotically stable strategy. Interestingly enough, in the seed bank case the cooperator needs to become active faster than the defector, while in the quiescent case the cooperator has to be slower. We use adaptive dynamics to identify situations where cooperation may evolve and form a convergent stable ESS. We conclude by highlighting the relevance of these results for many non-model species and the maintenance of cooperation in microbial, invertebrate or plant populations.

The de Novo Reference Genome and Transcriptome Assemblies of the Wild Tomato Species Solanum chilense Highlights Birth and Death of NLR Genes Between Tomato Species.

Wild tomato species, like Solanum chilense, are important germplasm resources for enhanced biotic and abiotic stress resistance in tomato breeding. S. chilense also serves as a model to study adaptation of plants to drought and the evolution of seed banks. The absence of a well-annotated reference genome in this compulsory outcrossing, very diverse species limits in-depth studies on the genes involved.We generated ∼134 Gb of DNA and 157 Gb of RNA sequence data for S chilense, which yielded a draft genome with an estimated length of 914 Mb, encoding 25,885 high-confidence predicted gene models, which show homology to known protein-coding genes of other tomato species. Approximately 71% of these gene models are supported by RNA-seq data derived from leaf tissue samples. Benchmarking with Universal Single-Copy Orthologs (BUSCO) analysis of predicted gene models retrieved 93.3% of BUSCO genes. To further verify the genome annotation completeness and accuracy, we manually inspected the NLR resistance gene family and assessed its assembly quality. We find subfamilies of NLRs unique to S. chilense Synteny analysis suggests significant degree of the gene order conservation between the S. chilense, S. lycopersicum and S. pennellii genomesWe generated the first genome and transcriptome sequence assemblies for the wild tomato species Solanum chilense and demonstrated their value in comparative genomics analyses. These data are an important resource for studies on adaptation to biotic and abiotic stress in Solanaceae, on evolution of self-incompatibility and for tomato breeding.

Strengths and potential pitfalls of hay transfer for ecological restoration revealed by RAD-seq analysis in floodplain Arabis species.

Achieving high intraspecific genetic diversity is a critical goal in ecological restoration as it increases the adaptive potential and long-term resilience of populations. Thus, we investigated genetic diversity within and between pristine sites in a fossil floodplain and compared it to sites restored by hay transfer between 1997 and 2014. RAD-seq genotyping revealed that the stenoecious floodplain species Arabis nemorensis is co-occurring with individuals that, based on ploidy, ITS-sequencing and morphology, probably belong to the close relative Arabis sagittata, which has a documented preference for dry calcareous grasslands but has not been reported in floodplain meadows. We show that hay transfer maintains genetic diversity for both species. Additionally, in A. sagittata, transfer from multiple genetically isolated pristine sites resulted in restored sites with increased diversity and admixed local genotypes. In A. nemorensis, transfer did not create novel admixture dynamics because genetic diversity between pristine sites was less differentiated. Thus, the effects of hay transfer on genetic diversity also depend on the genetic make-up of the donor communities of each species, especially when local material is mixed. Our results demonstrate the efficiency of hay transfer for habitat restoration and emphasize the importance of prerestoration characterization of microgeographic patterns of intraspecific diversity of the community to guarantee that restoration practices reach their goal, that is maximize the adaptive potential of the entire restored plant community. Overlooking these patterns may alter the balance between species in the community. Additionally, our comparison of summary statistics obtained from de novo- and reference-based RAD-seq pipelines shows that the genomic impact of restoration can be reliably monitored in species lacking prior genomic knowledge.