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.

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.

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.

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.

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.

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.

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.

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.

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.

The shaping of genetic variation in edge-of-range populations under past and future climate change.

With rates of climate change exceeding the rate at which many species are able to shift their range or adapt, it is important to understand how future changes are likely to affect biodiversity at all levels of organisation. Understanding past responses and extent of niche conservatism in climatic tolerance can help predict future consequences. We use an integrated approach to determine the genetic consequences of past and future climate changes on a bat species, Plecotus austriacus. Glacial refugia predicted by palaeo-modelling match those identified from analyses of extant genetic diversity and model-based inference of demographic history. Former refugial populations currently contain disproportionately high genetic diversity, but niche conservatism, shifts in suitable areas and barriers to migration mean that these hotspots of genetic diversity are under threat from future climate change. Evidence of population decline despite recent northward migration highlights the need to conserve leading-edge populations for spearheading future range shifts.

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.

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.

Mcheza: a workbench to detect selection using dominant markers.

MOTIVATION: Dominant markers (DArTs and AFLPs) are commonly used for genetic analysis in the fields of evolutionary genetics, ecology and conservation of genetic resources. The recent prominence of these markers has coincided with renewed interest in detecting the effects of local selection and adaptation at the level of the genome. RESULTS: We present Mcheza, an application for detecting loci under selection based on a well-evaluated F(ST)-outlier method. The application allows robust estimates to be made of model parameters (e.g. genome-wide average, neutral F(ST)), provides data import and export functions, iterative contour smoothing and generation of graphics in an easy to use graphical user interface with a computation engine that supports multicore processors for enhanced performance. Mcheza also provides functionality to mitigate common analytical errors when scanning for loci under selection. AVAILABILITY: Mcheza is freely available under GPL version 3 from http://popgen.eu/soft/mcheza.

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.

Multiple approaches to detect outliers in a genome scan for selection in ocellated lizards (Lacerta lepida) along an environmental gradient.

Identification of loci with adaptive importance is a key step to understand the speciation process in natural populations, because those loci are responsible for phenotypic variation that affects fitness in different environments. We conducted an AFLP genome scan in populations of ocellated lizards (Lacerta lepida) to search for candidate loci influenced by selection along an environmental gradient in the Iberian Peninsula. This gradient is strongly influenced by climatic variables, and two subspecies can be recognized at the opposite extremes: L. lepida iberica in the northwest and L. lepida nevadensis in the southeast. Both subspecies show substantial morphological differences that may be involved in their local adaptation to the climatic extremes. To investigate how the use of a particular outlier detection method can influence the results, a frequentist method, DFDIST, and a Bayesian method, BayeScan, were used to search for outliers influenced by selection. Additionally, the spatial analysis method was used to test for associations of AFLP marker band frequencies with 54 climatic variables by logistic regression. Results obtained with each method highlight differences in their sensitivity. DFDIST and BayeScan detected a similar proportion of outliers (3-4%), but only a few loci were simultaneously detected by both methods. Several loci detected as outliers were also associated with temperature, insolation or precipitation according to spatial analysis method. These results are in accordance with reported data in the literature about morphological and life-history variation of L. lepida subspecies along the environmental gradient.

Evaluating nested clade phylogeographic analysis under models of restricted gene flow.

Nested clade phylogeographic analysis (NCPA) is a widely used method that aims to identify past demographic events that have shaped the history of a population. In an earlier study, NCPA has been fully automated, allowing it to be tested with simulated data sets generated under a null model in which samples simulated from a panmictic population are geographically distributed. It was noted that NCPA was prone to inferring false positives, corroborating earlier findings. The present study aims to evaluate both single-locus and multilocus NCPA under the scenario of restricted gene flow among spatially distributed populations. We have developed a new program, ANeCA-ML, which implements multilocus NCPA. Data were simulated under 3 models of gene flow: a stepping stone model, an island model, and a stepping stone model with some long-distance dispersal. Results indicate that single-locus NCPA tends to give a high frequency of false positives, but, unlike the random-mating scenario presented previously, inferences are not limited to restricted gene flow with isolation by distance or contiguous range expansion. The proportion of single-locus data sets that contained false inferences was 76% for the panmictic case, 87% for the stepping stone model, 79% for the stepping stone model with long-distance dispersal, and more than 99% for the island model. The frequency of inferences is inversely related to the amount of gene flow between demes. We performed multilocus NCPA by grouping the simulated loci into data sets of 5 loci. The false-positive rate was reduced in multilocus NCPA for some inferences but remained high for others. The proportion of multilocus data sets that contained false inferences was 17% for the panmictic case, 30% for the stepping stone model, 4% for the stepping stone model with long-distance dispersal, and 54% for the island model. Multilocus NCPA reduces the false-positive rate by restricting the sensitivity of the method but does not appear to increase the accuracy of the approach. Three classical tests-the analysis of molecular variance method, Fu's Fs, and the Mantel test-show that there is information in the data that gives rise to explicable results using these standard approaches. In conclusion, for the scenarios that we have examined, our simulation study suggests that the NCPA method is unreliable and its inferences may be misleading. We suggest that the NCPA method should not be used without objective simulation-based testing by independent researchers.

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.

PopABC: a program to infer historical demographic parameters.

UNLABELLED: PopABC is a computer package for inferring the pattern of demographic divergence of closely related populations and species. The software performs coalescent simulation in the framework of approximate Bayesian computation (ABC). PopABC can also be used to perform Bayesian model choice to discriminate between different demographic scenarios. The program can be used either for research or for education and teaching purposes. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available at http://www.reading.ac.uk/ approximately sar05sal/software.htm. The program was implemented in C and can run on UNIX, MacOSX and Windows operating systems.

A novel parallel approach to the likelihood-based estimation of admixture in population genetics.

SUMMARY: Inferring population admixture from genetic data and quantifying it is a difficult but crucial task in evolutionary and conservation biology. Unfortunately state-of-the-art probabilistic approaches are computationally demanding. Effectively exploiting the computational power of modern multiprocessor systems can thus have a positive impact to Monte Carlo-based simulation of admixture modeling. A novel parallel approach is briefly described and promising results on its message passing interface (MPI)-based C++ implementation are reported. AVAILABILITY: The software package parLEA is freely available at (http://dm.unife.it/parlea).

Approximate bayesian computation without summary statistics: the case of admixture.

In recent years approximate Bayesian computation (ABC) methods have become popular in population genetics as an alternative to full-likelihood methods to make inferences under complex demographic models. Most ABC methods rely on the choice of a set of summary statistics to extract information from the data. In this article we tested the use of the full allelic distribution directly in an ABC framework. Although the ABC techniques are becoming more widely used, there is still uncertainty over how they perform in comparison with full-likelihood methods. We thus conducted a simulation study and provide a detailed examination of ABC in comparison with full likelihood in the case of a model of admixture. This model assumes that two parental populations mixed at a certain time in the past, creating a hybrid population, and that the three populations then evolve under pure drift. Several aspects of ABC methodology were investigated, such as the effect of the distance metric chosen to measure the similarity between simulated and observed data sets. Results show that in general ABC provides good approximations to the posterior distributions obtained with the full-likelihood method. This suggests that it is possible to apply ABC using allele frequencies to make inferences in cases where it is difficult to select a set of suitable summary statistics and when the complexity of the model or the size of the data set makes it computationally prohibitive to use full-likelihood methods.