How admixed captive breeding populations could be rescued using local ancestry information.

This paper asks the question: can genomic information be used to recover a species that is already on the pathway to extinction due to genetic swamping from a related and more numerous population? We show that a breeding strategy in a captive breeding program can use whole genome sequencing to identify and remove segments of DNA introgressed through hybridisation. The proposed policy uses a generalized measure of kinship or heterozygosity accounting for local ancestry, that is, whether a specific genetic location was inherited from the target of conservation. We then show that optimizing these measures would minimize undesired ancestry while also controlling kinship and/or heterozygosity, in a simulated breeding population. The process is applied to real data representing the hybridized Scottish wildcat breeding population, with the result that it should be possible to breed out domestic cat ancestry. The ability to reverse introgression is a powerful tool brought about through the combination of sequencing with computational advances in ancestry estimation. Since it works best when applied early in the process, important decisions need to be made about which genetically distinct populations should benefit from it and which should be left to reform into a single population.

Genomics and conservation: Guidance from training to analyses and applications.

Environmental change is intensifying the biodiversity crisis and threatening species across the tree of life. Conservation genomics can help inform conservation actions and slow biodiversity loss. However, more training, appropriate use of novel genomic methods and communication with managers are needed. Here, we review practical guidance to improve applied conservation genomics. We share insights aimed at ensuring effectiveness of conservation actions around three themes: (1) improving pedagogy and training in conservation genomics including for online global audiences, (2) conducting rigorous population genomic analyses properly considering theory, marker types and data interpretation and (3) facilitating communication and collaboration between managers and researchers. We aim to update students and professionals and expand their conservation toolkit with genomic principles and recent approaches for conserving and managing biodiversity. The biodiversity crisis is a global problem and, as such, requires international involvement, training, collaboration and frequent reviews of the literature and workshops as we do here.

Genetic swamping of the critically endangered Scottish wildcat was recent and accelerated by disease.

The European wildcat population in Scotland is considered critically endangered as a result of hybridization with introduced domestic cats,1,2 though the time frame over which this gene flow has taken place is unknown. Here, using genome data from modern, museum, and ancient samples, we reconstructed the trajectory and dated the decline of the local wildcat population from viable to severely hybridized. We demonstrate that although domestic cats have been present in Britain for over 2,000 years,3 the onset of hybridization was only within the last 70 years. Our analyses reveal that the domestic ancestry present in modern wildcats is markedly over-represented in many parts of the genome, including the major histocompatibility complex (MHC). We hypothesize that introgression provides wildcats with protection against diseases harbored and introduced by domestic cats, and that this selection contributes to maladaptive genetic swamping through linkage drag. Using the case of the Scottish wildcat, we demonstrate the importance of local ancestry estimates to both understand the impacts of hybridization in wild populations and support conservation efforts to mitigate the consequences of anthropogenic and environmental change.

Rapid evolution of novel biotic interactions in the UK Brown Argus butterfly uses genomic variation from across its geographical range.

Understanding the rate and extent to which populations can adapt to novel environments at their ecological margins is fundamental to predicting the persistence of biological communities during ongoing and rapid global change. Recent range expansion in response to climate change in the UK butterfly Aricia agestis is associated with the evolution of novel interactions with a larval food plant, and the loss of its ability to use an ancestral host species. Using ddRAD analysis of 61,210 variable SNPs from 261 females from throughout the UK range of this species, we identify genomic regions at multiple chromosomes that are associated with evolutionary responses, and their association with demographic history and ecological variation. Gene flow appears widespread throughout the range, despite the apparently fragmented nature of the habitats used by this species. Patterns of haplotype variation between selected and neutral genomic regions suggest that evolution associated with climate adaptation is polygenic, resulting from the independent spread of alleles throughout the established range of this species, rather than the colonization of pre-adapted genotypes from coastal populations. These data suggest that rapid responses to climate change do not depend on the availability of pre-adapted genotypes. Instead, the evolution of novel forms of biotic interaction in A. agestis has occurred during range expansion, through the assembly of novel genotypes from alleles from multiple localities.

Estimating temporally variable selection intensity from ancient DNA data with the flexibility of modelling linkage and epistasis.

Innovations in ancient DNA (aDNA) preparation and sequencing technologies have exponentially increased the quality and quantity of aDNA data extracted from ancient biological materials. The additional temporal component from the incoming aDNA data can provide improved power to address fundamental evolutionary questions like characterizing selection processes that shape the phenotypes and genotypes of contemporary populations or species. However, utilizing aDNA to study past selection processes still involves considerable hurdles like how to eliminate the confounding factor of genetic interactions in the inference of selection. To address this issue, we extend the approach of He et al., 2023 to infer temporally variable selection from the aDNA data in the form of genotype likelihoods with the flexibility of modelling linkage and epistasis in this work. Our posterior computation is carried out by a robust adaptive version of the particle marginal Metropolis-Hastings algorithm with a coerced acceptance rate. Our extension inherits the desirable features of He et al., 2023 such as modelling sample uncertainty resulting from the damage and fragmentation of aDNA molecules and reconstructing underlying gamete frequency trajectories of the population. We evaluate its performance through extensive simulations and show its utility with an application to the aDNA data from pigmentation loci in horses.

Estimating Temporally Variable Selection Intensity from Ancient DNA Data.

Novel technologies for recovering DNA information from archaeological and historical specimens have made available an ever-increasing amount of temporally spaced genetic samples from natural populations. These genetic time series permit the direct assessment of patterns of temporal changes in allele frequencies and hold the promise of improving power for the inference of selection. Increased time resolution can further facilitate testing hypotheses regarding the drivers of past selection events such as the incidence of plant and animal domestication. However, studying past selection processes through ancient DNA (aDNA) still involves considerable obstacles such as postmortem damage, high fragmentation, low coverage, and small samples. To circumvent these challenges, we introduce a novel Bayesian framework for the inference of temporally variable selection based on genotype likelihoods instead of allele frequencies, thereby enabling us to model sample uncertainties resulting from the damage and fragmentation of aDNA molecules. Also, our approach permits the reconstruction of the underlying allele frequency trajectories of the population through time, which allows for a better understanding of the drivers of selection. We evaluate its performance through extensive simulations and demonstrate its utility with an application to the ancient horse samples genotyped at the loci for coat coloration. Our results reveal that incorporating sample uncertainties can further improve the inference of selection.

Recommendations for improving statistical inference in population genomics.

The field of population genomics has grown rapidly in response to the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population genetic insights outpaced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous nonadaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our consensus views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model fitting results, and of carefully defining addressable hypotheses and underlying uncertainties.

Inferring the timing and strength of natural selection and gene migration in the evolution of chicken from ancient DNA data.

With the rapid growth of the number of sequenced ancient genomes, there has been increasing interest in using this new information to study past and present adaptation. Such an additional temporal component has the promise of providing improved power for the estimation of natural selection. Over the last decade, statistical approaches for the detection and quantification of natural selection from ancient DNA (aDNA) data have been developed. However, most of the existing methods do not allow us to estimate the timing of natural selection along with its strength, which is key to understanding the evolution and persistence of organismal diversity. Additionally, most methods ignore the fact that natural populations are almost always structured, which can result in an overestimation of the effect of natural selection. To address these issues, we introduce a novel Bayesian framework for the inference of natural selection and gene migration from aDNA data with Markov chain Monte Carlo techniques, co-estimating both timing and strength of natural selection and gene migration. Such an advance enables us to infer drivers of natural selection and gene migration by correlating genetic evolution with potential causes such as the changes in the ecological context in which an organism has evolved. The performance of our procedure is evaluated through extensive simulations, with its utility shown with an application to ancient chicken samples.

On the use of genome-wide data to model and date the time of anthropogenic hybridisation: An example from the Scottish wildcat.

While hybridisation has long been recognised as an important natural phenomenon in evolution, the conservation of taxa subject to introgressive hybridisation from domesticated forms is a subject of intense debate. Hybridisation of Scottish wildcats and domestic cats is a good example in this regard. Here, we developed a modelling framework to determine the timescale of introgression using approximate Bayesian computation (ABC). Applying the model to ddRAD-seq data from 129 individuals, genotyped at 6546 loci, we show that a population of wildcats genetically distant from domestic cats is still present in Scotland. These individuals were found almost exclusively within the captive breeding programme. Most wild-living cats sampled were introgressed to some extent. The demographic model predicts high levels of gene-flow between domestic cats and Scottish wildcats (13% migrants per generation) over a short timeframe, the posterior mean for the onset of hybridisation (T1 ) was 3.3 generations (~10 years) before present. Although the model had limited power to detect signals of ancient admixture, we found evidence that significant recent hybridisation may have occurred subsequent to the founding of the captive breeding population (T2 ). The model consistently predicts T1 after T2 , estimated here to be 19.3 generations (~60 years) ago, highlighting the importance of this population as a resource for conservation management. Additionally, we evaluate the effectiveness of current methods to classify hybrids. We show that an optimised 35 SNP panel is a better predictor of the ddRAD-based hybrid score in comparison with a morphological method.

The role of gene flow and chromosomal instability in shaping the bread wheat genome.

Bread wheat (Triticum aestivum) is one of the world's most important crops; however, a low level of genetic diversity within commercial breeding accessions can significantly limit breeding potential. In contrast, wheat relatives exhibit considerable genetic variation and so potentially provide a valuable source of novel alleles for use in breeding new cultivars. Historically, gene flow between wheat and its relatives may have contributed novel alleles to the bread wheat pangenome. To assess the contribution made by wheat relatives to genetic diversity in bread wheat, we used markers based on single nucleotide polymorphisms to compare bread wheat accessions, created in the past 150 years, with 45 related species. We show that many bread wheat accessions share near-identical haplotype blocks with close relatives of wheat's diploid and tetraploid progenitors, while some show evidence of introgressions from more distant species and structural variation between accessions. Hence, introgressions and chromosomal rearrangements appear to have made a major contribution to genetic diversity in cultivar collections. As gene flow from relatives to bread wheat is an ongoing process, we assess the impact that introgressions might have on future breeding strategies.

Rapid homoploid hybrid speciation in British gardens: The origin of Oxford ragwort (Senecio squalidus).

Hybridisation can lead to homoploid hybrid speciation, i.e., the origin of new species without change in chromosome number between parents and offspring. Central to homoploid hybrid speciation is the role of hybridisation in the establishment of reproductive isolation between the hybrid and the parental species in the early stages of speciation, when typically all species occur at least partly in sympatry. In this work we analyse genome-wide polymorphism data obtained by transcriptome sequencing of the British hybrid species Oxford ragwort (Senecio squalidus, Asteraceae), its two Italian parental species (S. aethnensis and S. chrysanthemifolius) and their naturally occurring hybrids on Mt Etna (Italy). We show that Oxford ragwort most likely originated from de novo hybridisation between its two Italian parental species whilst they were in cultivation in British gardens at the turn of the 18th century. Reproductive isolation between the new hybrid species and its parental species probably resulted from inheritance of genetic incompatibilities between the two parental species and subsequent ecological segregation - both of which have been shown in previous studies. Our results imply that S. squalidus meets the most stringent criteria set forth to identify homoploid hybrid speciation, and call attention to the creative role of hybridisation in responding to novel environmental conditions.

Detecting and Quantifying Natural Selection at Two Linked Loci from Time Series Data of Allele Frequencies with Forward-in-Time Simulations.

Recent advances in DNA sequencing techniques have made it possible to monitor genomes in great detail over time. This improvement provides an opportunity for us to study natural selection based on time serial samples of genomes while accounting for genetic recombination effect and local linkage information. Such time series genomic data allow for more accurate estimation of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel Bayesian statistical framework for inferring natural selection at a pair of linked loci by capitalising on the temporal aspect of DNA data with the additional flexibility of modeling the sampled chromosomes that contain unknown alleles. Our approach is built on a hidden Markov model where the underlying process is a two-locus Wright-Fisher diffusion with selection, which enables us to explicitly model genetic recombination and local linkage. The posterior probability distribution for selection coefficients is computed by applying the particle marginal Metropolis-Hastings algorithm, which allows us to efficiently calculate the likelihood. We evaluate the performance of our Bayesian inference procedure through extensive simulations, showing that our approach can deliver accurate estimates of selection coefficients, and the addition of genetic recombination and local linkage brings about significant improvement in the inference of natural selection. We also illustrate the utility of our method on real data with an application to ancient DNA data associated with white spotting patterns in horses.

Estimation of Natural Selection and Allele Age from Time Series Allele Frequency Data Using a Novel Likelihood-Based Approach.

Temporally spaced genetic data allow for more accurate inference of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel likelihood-based method for jointly estimating selection coefficient and allele age from time series data of allele frequencies. Our approach is based on a hidden Markov model where the underlying process is a Wright-Fisher diffusion conditioned to survive until the time of the most recent sample. This formulation circumvents the assumption required in existing methods that the allele is created by mutation at a certain low frequency. We calculate the likelihood by numerically solving the resulting Kolmogorov backward equation backward in time while reweighting the solution with the emission probabilities of the observation at each sampling time point. This procedure reduces the two-dimensional numerical search for the maximum of the likelihood surface, for both the selection coefficient and the allele age, to a one-dimensional search over the selection coefficient only. We illustrate through extensive simulations that our method can produce accurate estimates of the selection coefficient and the allele age under both constant and nonconstant demographic histories. We apply our approach to reanalyze ancient DNA data associated with horse base coat colors. We find that ignoring demographic histories or grouping raw samples can significantly bias the inference results.

Performance of A Priori and A Posteriori Calibration Strategies in Divergence Time Estimation.

Relaxed molecular clock methods allow the use of genomic data to estimate divergence times across the tree of life. This is most commonly achieved in Bayesian analyses where the molecular clock is calibrated a priori through the integration of fossil information. Alternatively, fossil calibrations can be used a posteriori, to transform previously estimated relative divergence times that were inferred without considering fossil information, into absolute divergence times. However, as branch length is the product of the rate of evolution and the duration in time of the considered branch, the extent to which a posteriori calibrated, relative divergence time methods can disambiguate time and rate, is unclear. Here, we use forward evolutionary simulations and compare a priori and a posteriori calibration strategies using different molecular clock methods and models. Specifically, we compare three Bayesian methods, the strict clock, uncorrelated clock and autocorrelated clock, and the non-Bayesian algorithm implemented in RelTime. We simulate phylogenies with multiple, independent substitution rate changes and show that correct timescales cannot be inferred without the use of calibrations. Under our simulation conditions, a posteriori calibration strategies almost invariably inferred incorrect rate changes and divergence times. The a priori integration of fossil calibrations is fundamental in these cases to improve the accuracy of the estimated divergence times. Relative divergence times and absolute timescales derived by calibrating relative timescales to geological time a posteriori appear to be less reliable than a priori calibrated, timescales.

Climate-induced phenology shifts linked to range expansions in species with multiple reproductive cycles per year.

Advances in phenology (the annual timing of species' life-cycles) in response to climate change are generally viewed as bioindicators of climate change, but have not been considered as predictors of range expansions. Here, we show that phenology advances combine with the number of reproductive cycles per year (voltinism) to shape abundance and distribution trends in 130 species of British Lepidoptera, in response to ~0.5 °C spring-temperature warming between 1995 and 2014. Early adult emergence in warm years resulted in increased within- and between-year population growth for species with multiple reproductive cycles per year (n = 39 multivoltine species). By contrast, early emergence had neutral or negative consequences for species with a single annual reproductive cycle (n = 91 univoltine species), depending on habitat specialisation. We conclude that phenology advances facilitate polewards range expansions in species exhibiting plasticity for both phenology and voltinism, but may inhibit expansion by less flexible species.

Looking for Local Adaptation: Convergent Microevolution in Aleppo Pine (Pinus halepensis).

Finding outlier loci underlying local adaptation is challenging and is best approached by suitable sampling design and rigorous method selection. In this study, we aimed to detect outlier loci (single nucleotide polymorphisms, SNPs) at the local scale by using Aleppo pine (Pinus halepensis), a drought resistant conifer that has colonized many habitats in the Mediterranean Basin, as the model species. We used a nested sampling approach that considered replicated altitudinal gradients for three contrasting sites. We genotyped samples at 294 SNPs located in genomic regions selected to maximize outlier detection. We then applied three different statistical methodologies-Two Bayesian outlier methods and one latent factor principal component method-To identify outlier loci. No SNP was an outlier for all three methods, while eight SNPs were detected by at least two methods and 17 were detected only by one method. From the intersection of outlier SNPs, only one presented an allelic frequency pattern associated with the elevational gradient across the three sites. In a context of multiple populations under similar selective pressures, our results underline the need for careful examination of outliers detected in genomic scans before considering them as candidates for convergent adaptation.

Dominant words rise to the top by positive frequency-dependent selection.

A puzzle of language is how speakers come to use the same words for particular meanings, given that there are often many competing alternatives (e.g., "sofa," "couch," "settee"), and there is seldom a necessary connection between a word and its meaning. The well-known process of random drift-roughly corresponding in this context to "say what you hear"-can cause the frequencies of alternative words to fluctuate over time, and it is even possible for one of the words to replace all others, without any form of selection being involved. However, is drift alone an adequate explanation of a shared vocabulary? Darwin thought not. Here, we apply models of neutral drift, directional selection, and positive frequency-dependent selection to explain over 417,000 word-use choices for 418 meanings in two natural populations of speakers. We find that neutral drift does not in general explain word use. Instead, some form of selection governs word choice in over 91% of the meanings we studied. In cases where one word dominates all others for a particular meaning-such as is typical of the words in the core lexicon of a language-word choice is guided by positive frequency-dependent selection-a bias that makes speakers disproportionately likely to use the words that most others use. This bias grants an increasing advantage to the common form as it becomes more popular and provides a mechanism to explain how a shared vocabulary can spontaneously self-organize and then be maintained for centuries or even millennia, despite new words continually entering the lexicon.

Effects of the Ordering of Natural Selection and Population Regulation Mechanisms on Wright-Fisher Models.

We explore the effect of different mechanisms of natural selection on the evolution of populations for one- and two-locus systems. We compare the effect of viability and fecundity selection in the context of the Wright-Fisher model with selection under the assumption of multiplicative fitness. We show that these two modes of natural selection correspond to different orderings of the processes of population regulation and natural selection in the Wright-Fisher model. We find that under the Wright-Fisher model these two different orderings can affect the distribution of trajectories of haplotype frequencies evolving with genetic recombination. However, the difference in the distribution of trajectories is only appreciable when the population is in significant linkage disequilibrium. We find that as linkage disequilibrium decays the trajectories for the two different models rapidly become indistinguishable. We discuss the significance of these findings in terms of biological examples of viability and fecundity selection, and speculate that the effect may be significant when factors such as gene migration maintain a degree of linkage disequilibrium.

CodABC: a computational framework to coestimate recombination, substitution, and molecular adaptation rates by approximate Bayesian computation.

The estimation of substitution and recombination rates can provide important insights into the molecular evolution of protein-coding sequences. Here, we present a new computational framework, called "CodABC," to jointly estimate recombination, substitution and synonymous and nonsynonymous rates from coding data. CodABC uses approximate Bayesian computation with and without regression adjustment and implements a variety of codon models, intracodon recombination, and longitudinal sampling. CodABC can provide accurate joint parameter estimates from recombining coding sequences, often outperforming maximum-likelihood methods based on more approximate models. In addition, CodABC allows for the inclusion of several nuisance parameters such as those representing codon frequencies, transition matrices, heterogeneity across sites or invariable sites. CodABC is freely available from http://code.google.com/p/codabc/, includes a GUI, extensive documentation and ready-to-use examples, and can run in parallel on multicore machines.

Detecting and measuring selection from gene frequency data.

The recent advent of high-throughput sequencing and genotyping technologies makes it possible to produce, easily and cost effectively, large amounts of detailed data on the genotype composition of populations. Detecting locus-specific effects may help identify those genes that have been, or are currently, targeted by natural selection. How best to identify these selected regions, loci, or single nucleotides remains a challenging issue. Here, we introduce a new model-based method, called SelEstim, to distinguish putative selected polymorphisms from the background of neutral (or nearly neutral) ones and to estimate the intensity of selection at the former. The underlying population genetic model is a diffusion approximation for the distribution of allele frequency in a population subdivided into a number of demes that exchange migrants. We use a Markov chain Monte Carlo algorithm for sampling from the joint posterior distribution of the model parameters, in a hierarchical Bayesian framework. We present evidence from stochastic simulations, which demonstrates the good power of SelEstim to identify loci targeted by selection and to estimate the strength of selection acting on these loci, within each deme. We also reanalyze a subset of SNP data from the Stanford HGDP-CEPH Human Genome Diversity Cell Line Panel to illustrate the performance of SelEstim on real data. In agreement with previous studies, our analyses point to a very strong signal of positive selection upstream of the LCT gene, which encodes for the enzyme lactase-phlorizin hydrolase and is associated with adult-type hypolactasia. The geographical distribution of the strength of positive selection across the Old World matches the interpolated map of lactase persistence phenotype frequencies, with the strongest selection coefficients in Europe and in the Indus Valley.