snpR: User friendly population genomics for SNP data sets with categorical metadata

The analysis of genomic data can be an intimidating process, particularly for researchers who are not experienced programmers. Commonly used analyses are spread across many programs, each requiring their own specific input formats, and so data must often be repeatedly reorganized and transformed into new formats. Analyses often require splitting data according to metadata variables such as population or family, which can be challenging to manage in large data sets. Here, we introduce snpR, a user-friendly data analysis package in R for processing SNP genomic data. snpR is designed to automate data subsetting and analyses across categorical metadata while also streamlining repeated analyses by integrating approaches contained in many different packages in a single ecosystem. snpR facilitates iterative and efficient analyses centred on a single R object for an entire analysis pipeline.     

Diving in uncharted waters: An updated genetics toolkit highlights the challenges of polyploidy in landscape genomics analyses

As molecular ecologists, we have by necessity become adept at working across computational platforms. A diverse community of scientists has developed a broad array of analytical resources spanning command line to graphical user interface across Linux, Mac, and Windows environments and a dizzying array of program‐specific input formats. In light of this, we often explore our data like free divers – filling our lungs with air and descending for a short period of time into one part of our data set before resurfacing, reformatting, and preparing for our next analysis. In this issue of Molecular Ecology Resources, Meirmans (2020) presents an updated version of GenoDive, a program with a toolkit that provides users with the opportunity to stay a while and delve deeper into the diverse portfolio of information provided by a genomic data set. The comprehensive nature of GenoDive coupled with its unique capability to handle both diploid and polyploid data also provides an opportunity to reflect on the unevenness of resources available for the analysis of polyploid versus diploid data. Since new updates include the addition of plug‐ins for genotype‐environment association analyses, we limit the observations presented here to the common tools used for landscape genomics analyses.     

Interpreting the flock algorithm: a reply to Anderson & Barry (2015)

Anderson & Barry (Molecular Ecology Resources, 2015, 10, 1020–1030) compared a reprogrammed version of flock (Duchesne & Turgeon , Molecular Ecology Resources, 2009, 9, 1333–1344), flockture , to a particular model of structure (Pritchard , Genetics, 2000, 155, 945–959) that they propose is equivalent to flock , a non‐MCMC, non‐Bayesian algorithm. They conclude that structure performs better than flockture at clustering individuals from simulated populations with very low level of differentiation (F_ST c. 0.008) based on 15 microsatellites or 96 SNPs. We rather consider that both algorithms failed, with proportions of correct allocations lower than 50%. The authors also noted the slightly better performance of flockture with SNPs at intermediate F_ST values (c. 0.02–0.04) but did not comment. Finally, we disagree with the way the processing time of each program was compared. When compared on the basis of a run leading to a clustering solution, the main output of any clustering algorithm, flock , is, as users can readily experience, much faster. In all, we feel that flock performs at least as well as structure as a clustering algorithm. Moreover, flock has two major assets: high speed and clear, well validated, rules to estimate K, the number of populations. It thus provides a valuable addition to the set of tools at the disposal of the many researchers dealing with real empirical data sets.     

Identifying the number of population clusters with structure: problems and solutions

The program structure has been used extensively to understand and visualize population genetic structure. It is one of the most commonly used clustering algorithms, cited over 11 500 times in Web of Science since its introduction in 2000. The method estimates ancestry proportions to assign individuals to clusters, and post hoc analyses of results may indicate the most likely number of clusters, or populations, on the landscape. However, as has been shown in this issue of Molecular Ecology Resources by Puechmaille ( 2016 ), when sampling is uneven across populations or across hierarchical levels of population structure, these post hoc analyses can be inaccurate and identify an incorrect number of population clusters. To solve this problem, Puechmaille ( 2016 ) presents strategies for subsampling and new analysis methods that are robust to uneven sampling to improve inferences of the number of population clusters.     

learnPopGen: An R package for population genetic simulation and numerical analysis

Here, I briefly present a new R package called learnPopGen that has been designed primarily for the purposes of teaching evolutionary biology, population genetics, and evolutionary theory. Functions of the package can be used to conduct simulations and numerical analyses of a wide range of evolutionary phenomena that would typically be covered in advanced undergraduate through graduate‐level curricula in population genetics or evolution. For instance, learnPopGen functions can be used to visualize gene frequency changes through time under multiple deterministic and stochastic processes, to compute and animate the changes in phenotypic trait values or distributions under natural selection, to numerically analyze and graph the outcome of simple game theory models, and to plot coalescence within a population experiencing genetic drift, along with a number of other things. Functions have been designed to be maximally didactic and frequently employ compelling animated visualizations. Furthermore, it is straightforward to export plots and animations from R in the form of flat or animated graphics, or as videos. For maximum flexibility, students working with the package can run functions directly in R; however, instructors may choose to guide students less adept in the R environment to one of various web interfaces that I have built for a number of the functions of the package and that are already available online.     

Capwire: a R package for estimating population census size from non‐invasive genetic sampling

Non‐invasive genetic sampling is an increasingly popular approach for investigating the demographics of natural populations. This has also become a useful tool for managers and conservation biologists, especially for those species for which traditional mark–recapture studies are not practical. However, the consequence of collecting DNA indirectly is that an individual may be sampled multiple times per sampling session. This requires alternative statistical approaches to those used in traditional mark–recapture studies. Here we present the R package capwire , an implementation of the population size estimators of Miller et al. (Molecular Ecology 2005; 14 : 1991), which were designed to deal specifically with this type of sampling. The aim of this project is to enable users across platforms to easily manipulate their data and interact with existing R packages. We have also provided functions to simulate data under a variety of scenarios to allow for rigorous testing of the robustness of the method and to facilitate further development of this approach.     

Mixed signals from hybrid genomes

Admixture results from interbreeding between individuals from different populations or species that were previously genetically isolated from each other (Fig.  1 ). Identifying admixture events in the genome is not always a straightforward task, because the genetic signature left behind fades with time as recombination events fragment the genomic segments introduced during the interbreeding event. Additionally, when the genetic architecture of populations or species that admix is not very different (e.g. they coalesce to a common ancestor recently), admixture signatures may be difficult to detect. Ignoring the effects of admixture can, however, pose severe problems for population genetic analyses that rely on the distribution of polymorphic markers across the genome. In this issue of Molecular Ecology, Bosse et al. ( 2014 ) analyse genomic data from modern pigs to understand hybridization processes that occurred between domestic pigs from European and Asiatic origin, and between pigs and wild boars. Their results are interesting regarding the fine‐scale distribution of admixture across the pig genome, and the way in which this admixture biases estimates of the effective population size in European domestic pigs. The implications of these results are significant, as they serve as a cautionary note on genomic analyses that depend on the distribution of polymorphic variants in potentially admixed populations.     

RAD genotyping reveals fine‐scale genetic structuring and provides powerful population assignment in a widely distributed marine species,the American lobster (Homarus americanus)

Deciphering genetic structure and inferring connectivity in marine species have been challenging due to weak genetic differentiation and limited resolution offered by traditional genotypic methods. The main goal of this study was to assess how a population genomics framework could help delineate the genetic structure of the American lobster (Homarus americanus) throughout much of the species’ range and increase the assignment success of individuals to their location of origin. We genotyped 10 156 filtered SNPs using RAD sequencing to delineate genetic structure and perform population assignment for 586 American lobsters collected in 17 locations distributed across a large portion of the species’ natural distribution range. Our results revealed the existence of a hierarchical genetic structure, first separating lobsters from the northern and southern part of the range (F_CT = 0.0011; P‐value = 0.0002) and then revealing a total of 11 genetically distinguishable populations (mean F_ST = 0.00185; CI: 0.0007–0.0021, P‐value < 0.0002), providing strong evidence for weak, albeit fine‐scale population structuring within each region. A resampling procedure showed that assignment success was highest with a subset of 3000 SNPs having the highest F_ST. Applying Anderson's (Molecular Ecology Resources, 2010, 10, 701) method to avoid ‘high‐grading bias’, 94.2% and 80.8% of individuals were correctly assigned to their region and location of origin, respectively. Lastly, we showed that assignment success was positively associated with sample size. These results demonstrate that using a large number of SNPs improves fine‐scale population structure delineation and population assignment success in a context of weak genetic structure. We discuss the implications of these findings for the conservation and management of highly connected marine species, particularly regarding the geographic scale of demographic independence.     

Exploring linkage disequilibrium

Linkage disequilibrium (LD, association of allelic states across loci) is poorly understood by many evolutionary biologists, but as technology for multilocus sampling improves, we ignore LD at our peril. If we sample variation at 10 loci in an organism with 20 chromosomes, we can reasonably treat them as 10 ‘independent witnesses’ of the evolutionary process. If instead, we sample variation at 1000 loci, many are bound to be close together on a chromosome. With only one or two crossovers per meiosis, associations between close neighbours decay so slowly that even LD created far in the past will not have dissipated, so we cannot treat the 1000 loci as independent witnesses (Barton 2011 ). This means that as marker density on genomes increases classic analyses assuming independent loci become mired in the problem of overconfidence: if 1000 independent witnesses are assumed, and that number should be much lower, any conclusion will be overconfident. This is of special concern because our literature suffers from a strong publication bias towards confident answers, even when they turn out to be wrong (Knowles 2008 ). In contrast, analyses that take into account associations across loci both control for overconfidence and can inform us about LD generating events far in the past, for example human/Neanderthal admixture (Fu et al. 2014 ). With increased marker density, biologists must increase their awareness of LD and, in this issue of Molecular Ecology Resources, Kemppainen et al. ( 2015 ) make software available that can only help in this process: LDna allows patterns of LD in a data set to be explored using tools borrowed from network analysis. This has great potential, but realizing that potential requires understanding LD.     

Adaptive maintenance of European alleles in the Brazilian Africanized honeybee

The Anthropocene is an epoch hallmarked by intensified human intrusion across ecosystems. One such intrusion is the movement and re‐introduction of long‐separated populations. By facilitating introgression – intraspecific genetic admixture – secondary contact can facilitate range expansion and the establishment of invasive species. The proximate mechanisms through which introgression facilitates expansion are rarely known (Bock et al., 2015 ; Rius & Darling, 2014 ), but managed species provide a useful avenue for exploration. Bee‐keepers have been interbreeding highly diverged honeybee clades for centuries, often to introduce “useful” phenotypic variation to their stocks. Across the Western honeybee's (Apis mellifera) European range, this practice has not resulted in range expansion (Moritz, Härtel, & Neumann, 2005 ). In the Americas, however, introgression of European with African subspecies resulted in a widely publicized invasive population: The Africanized honeybee (AHB). In this issue of Molecular Ecology, Nelson, Wallberg, Simões, Lawson, and Webster ( 2017 ) have made the first step towards understanding how this invasive species successfully spread across the Americas.     

driftsel: an R package for detecting signals of natural selection in quantitative traits

Approaches and tools to differentiate between natural selection and genetic drift as causes of population differentiation are of frequent demand in evolutionary biology. Based on the approach of Ovaskainen et al. (2011), we have developed an R package (driftsel ) that can be used to differentiate between stabilizing selection, diversifying selection and random genetic drift as causes of population differentiation in quantitative traits when neutral marker and quantitative genetic data are available. Apart from illustrating the use of this method and the interpretation of results using simulated data, we apply the package on data from three‐spined sticklebacks (Gasterosteus aculeatus) to highlight its virtues. driftsel can also be used to perform usual quantitative genetic analyses in common‐garden study designs.     

Population genomic analyses reveal possible drivers of population divergence

Recent advances in sequencing technology and efficiency enable new and improved methods to investigate how populations diverge and species evolve. Fungi have relatively small and simple genomes and can often be cultured in the laboratory. Fungal populations can thus be sequenced for a relatively low cost, which makes them ideal for population genomic analyses. In several recent population genomic studies, wild populations of fungal model organisms and human pathogens have been analysed, for example Neurospora crassa (Ellison et al. 2011 ), Saccharomyces uvarum (Almeida et al. 2014 ), Coccidioides spp. (Neafsey et al. 2010 ) and Cryptococcus gatti (Engelthaler et al. 2014 ). In this issue of Molecular Ecology, Branco et al. ( 2015 ) apply population genomic tools to understand population divergence and adaptation in a symbiotic (mycorrhizal) fungus. This study exemplifies the possibilities of diving deeper into the genomic features involved in population divergence and speciation, also for nonmodel organisms, and how molecular and analytical tools will improve our understanding of the patterns and mechanisms that underlie adaptation to habitats, population divergence and dispersal limitation of fungi.     

Origin matters: Using a local reference genome improves measures in population genomics

Genome sequencing enables answering fundamental questions about the genetic basis of adaptation, population structure and epigenetic mechanisms. Yet, we usually need a suitable reference genome for mapping population-level resequencing data. In some model systems, multiple reference genomes are available, giving the challenging task of determining which reference genome best suits the data. Here, we compared the use of two different reference genomes for the three-spined stickleback (Gasterosteus aculeatus), one novel genome derived from a European gynogenetic individual and the published reference genome of a North American individual. Specifically, we investigated the impact of using a local reference versus one generated from a distinct lineage on several common population genomics analyses. Through mapping genome resequencing data of 60 sticklebacks from across Europe and North America, we demonstrate that genetic distance among samples and the reference genomes impacts downstream analyses. Using a local reference genome increased mapping efficiency and genotyping accuracy, effectively retaining more and better data. Despite comparable distributions of the metrics generated across the genome using SNP data (i.e. π, Tajima's D and F_ST), window-based statistics using different references resulted in different outlier genes and enriched gene functions. A marker-based analysis of DNA methylation distributions had a comparably high overlap in outlier genes and functions, yet with distinct differences depending on the reference genome. Overall, our results highlight how using a local reference genome decreases reference bias to increase confidence in downstream analyses of the data. Such results have significant implications in all reference-genome-based population genomic analyses.     

Making use of the social network in conservation genomics: Integrating kinship and network analyses to understand connectivity

Inferring and quantifying recent barriers to connectivity is increasingly important for conservation and management in a world undergoing rapid environmental change. Traditional measures of genetic differentiation can take many generations to reflect a new barrier to connectivity. Although methods that use the linkage disequilibrium signal in mixed genetic samples are able to reflect recent levels of gene flow, they are not suitable for use in situations with low levels of genetic differentiation. Kinship‐based methods, those that assess the spatio‐temporal distribution of related individuals, have been used in this context, but a formal statistical framework for such approaches has been lacking. In this issue of Molecular Ecology Resources, Escoda, et al. adapt the assortativity coefficient, AC, to analyse the networks of kin relationships in the Pyrenean desman (Galemys pyrenaicus) across potential barriers to dispersal. Their modified AC quantifies the proportion of missing kin relationships across putative dispersal barriers with respect to the expected proportion if there was no barrier. This application highlights that AC can be used to test the null hypothesis that a putative barrier has no effect on gene flow, in which case AC is not significantly different from 0. The method represents a useful step forward in conservation genomics by developing and adapting tools to assess contemporary connectivity using genomic data.     

Ecological genomics in full colour

Colour patterns in animals have long offered an opportunity to observe adaptive traits in natural populations. Colour plays myriad roles in interactions within and among species, from reproductive signalling to predator avoidance, leading to multiple targets of natural and sexual selection and opportunities for diversification. Understanding the genetic and developmental underpinnings of variation in colour promises a fuller understanding of these evolutionary processes, but the path to unravelling these connections can be arduous. The advent of genomic techniques suitable for nonmodel organisms is now beginning to light the way. Two new studies in this issue of Molecular Ecology use genomic sequencing of laboratory crosses to map colour traits in cichlid fishes, a remarkably diverse group in which coloration has played a major role in diversification. They illustrate how genomic approaches, specifically RAD sequencing, can rapidly identify both simple and more complex genetic variation underlying ecologically important traits. In the first, Henning et al. ( 2014 ) detect a single locus that appears to control in a Mendelian fashion the presence of horizontal stripes, a trait that has evolved in numerous cichlid lineages. In the second, Albertson et al. ( 2014 ) identify several genes and epistatic interactions affecting multiple colour traits, as well as a novel metric describing integration across colour traits. Albertson et al. ( 2014 ) go further, by quantifying differential expression of parental alleles at a candidate locus and by relating differentiation among natural populations at mapped loci to trait divergence. Herein lies the promise of ecological genomics – efficiently integrating genetic mapping of phenotypes with population genomic data to both identify functional genes and unravel their evolutionary history. These studies offer guidance on how genomic techniques can be tailored to a research question or study system, and they also add to the growing body of empirical examples addressing basic questions about how ecologically important traits evolve in natural populations.     

Population genetic structure and migration patterns of Dendrothrips minowai (Thysanoptera: Thripidae) in Guizhou,China

Dendrothrips minowai Priesner (Thysanoptera: Thripidae) is one of the most destructive insect pests on tea plants. Although outbreaks of this pest occur annually in South China, especially in Guizhou Province, little is known about its population genetics, such as genetic diversity and gene flow. To investigate its population genetic structure and migration routes in Guizhou Province, we analyzed 24 D. minowai populations across Guizhou using six microsatellite loci. We detected the moderate genetic diversity and the population genetic structure of this thrip species. Neighbor‐joining (NJ) phylogenetic tree and STRUCTURE analyses recognized two clusters within the studied populations. No correlation between genetic and geographical distances (r = 0.0139, P = 0.5830) was detected and more than 89% of the variation occurred among samples within populations. Gene flow analysis revealed high migration rates (74.0 – 894.1) among D. minowai populations. Overall, the trend of asymmetrical gene flow was from northeast to southwest. Our analyses demonstrated that D. minowai derived or originated from multiple sites and could be eventually divided into two groups in Guizhou.     

Testing evolutionary hypotheses for DNA barcoding failure in willows

The goal of DNA barcoding is to enable the rapid identification of taxa from short diagnostic DNA sequence profiles. But how feasible is this objective when many evolutionary processes, such as hybridization and selective sweeps, cause alleles to be shared among related taxa? In this issue of Molecular Ecology, Percy et al. (2014) test the full suite of seven candidate plant barcoding loci in a broad geographic sample of willow species. They show exceptional plastid haplotype sharing between species across continents, with most taxa not possessing a unique barcode sequence. Using population genetic and molecular dating analyses, they implicate hybridization and selective sweeps, but not incomplete lineage sorting, as the historical processes causing widespread haplotype sharing among willow taxa. This study represents an exceptional case of how poorly barcoding can perform, and highlights methodological issues using universal organellar regions for species identification.     

Population genomics: a new generation of genome scans to bridge the gap with functional genomics

Population genomics is an increasingly popular approach to investigate the genetic basis of adaptation and speciation at the genome scale. However, it has so far largely failed to go beyond the mere identification of anonymous markers displaying selection signatures. Will population genomics ever be up to our expectations and able to really pinpoint genes underlying adaptation and speciation processes? In this issue of Molecular Ecology, Namroud et al. use population genomics to investigate local adaptation in natural populations of a conifer tree, the white spruce (Picea glauca). They show how population and functional genomics can finally converge with the deployment of the next generation of genome scans, which target gene‐rich regions rather than the whole genome.     

Genomics of the divergence continuum in an African plant biodiversity hotspot,I: drivers of population divergence in Restio capensis (Restionaceae)

Understanding the drivers of population divergence, speciation and species persistence is of great interest to molecular ecology, especially for species‐rich radiations inhabiting the world's biodiversity hotspots. The toolbox of population genomics holds great promise for addressing these key issues, especially if genomic data are analysed within a spatially and ecologically explicit context. We have studied the earliest stages of the divergence continuum in the Restionaceae, a species‐rich and ecologically important plant family of the Cape Floristic Region (CFR) of South Africa, using the widespread CFR endemic Restio capensis (L.) H.P. Linder & C.R. Hardy as an example. We studied diverging populations of this morphotaxon for plastid DNA sequences and >14 400 nuclear DNA polymorphisms from Restriction site Associated DNA (RAD) sequencing and analysed the results jointly with spatial, climatic and phytogeographic data, using a Bayesian generalized linear mixed modelling (GLMM) approach. The results indicate that population divergence across the extreme environmental mosaic of the CFR is mostly driven by isolation by environment (IBE) rather than isolation by distance (IBD) for both neutral and non‐neutral markers, consistent with genome hitchhiking or coupling effects during early stages of divergence. Mixed modelling of plastid DNA and single divergent outlier loci from a Bayesian genome scan confirmed the predominant role of climate and pointed to additional drivers of divergence, such as drift and ecological agents of selection captured by phytogeographic zones. Our study demonstrates the usefulness of population genomics for disentangling the effects of IBD and IBE along the divergence continuum often found in species radiations across heterogeneous ecological landscapes.