A Spatial Framework for Understanding Population Structure and Admixture

Jointly representing long-range genetic similarity and spatially heterogeneous isolation-by-distance

Isolation-by-distance patterns in genetic variation are a widespread feature of the geographic structure of genetic variation in many species, and many methods have been developed to illuminate such patterns in genetic data. However, long-range genetic similarities also exist, often as a result of rare or episodic long-range gene flow. Jointly characterizing patterns of isolation-by-distance and long-range genetic similarity in genetic data is an open data analysis challenge that, if resolved, could help produce more complete representations of the geographic structure of genetic data in any given species. Here, we present a computationally tractable method that identifies long-range genetic similarities in a background of spatially heterogeneous isolation-by-distance variation. The method uses a coalescent-based framework, and models long-range genetic similarity in terms of directional events with source fractions describing the fraction of ancestry at a location tracing back to a remote source. The method produces geographic maps annotated with inferred long-range edges, as well as maps of uncertainty in the geographic location of each source of long-range gene flow. We have implemented the method in a package called FEEMSmix (an extension to FEEMS from Marcus et al., 2021), and validated its implementation using simulations representative of typical data applications. We also apply this method to two empirical data sets. In a data set of over 4,000 humans (Homo sapiens) across Afro-Eurasia, we recover many known signals of long-distance dispersal from recent centuries. Similarly, in a data set of over 100 gray wolves (Canis lupus) across North America, we identify several previously unknown long-range connections, some of which were attributable to recording errors in sampling locations. Therefore, beyond identifying genuine long-range dispersals, our approach also serves as a useful tool for quality control in spatial genetic studies.

Determining population structure from k-mer frequencies

Background

Understanding population structure within species provides information on connections among different populations and how they evolve over time. This knowledge is important for studies ranging from evolutionary biology to large-scale variant-trait association studies. Current approaches to determining population structure include model-based approaches, statistical approaches, and distance-based ancestry inference approaches.

Methods

In this work, we identify population structure from DNA sequence data using an alignment-free approach. We use the frequencies of short DNA substrings from across the genome (k-mers) with principal component analysis (PCA). K-mer frequencies can be viewed as a summary statistic of a genome and have the advantage of being easily derived from a genome by counting the number of times a k-mer occurred in a sequence. In contrast, most population structure work employing PCA uses multi-locus genotype data (SNPs, microsatellites, or haplotypes). No genetic assumptions must be met to generate k-mers, whereas current population structure approaches often depend on several genetic assumptions and can require careful selection of ancestry informative markers to identify populations. We compare our k-mer based approach to population structure estimated using SNPs with both empirical and simulated data.

Results

In this work, we show that PCA is able to determine population structure just from the frequency of k-mers found in the genome. The application of PCA and a clustering algorithm to k-mer profiles of genomes provides an easy approach to detecting the number and composition of populations (clusters) present in the dataset. Using simulations, we show that results are at least comparable to population structure estimates using SNPs. When using human genomes from populations identified by the 1000 Genomes Project, the results are better than population structure estimates using SNPs from the same samples, and comparable to those found by a model-based approach using genetic markers from larger numbers of samples.

Conclusions

This study shows that PCA, together with the clustering algorithm, is able to detect population structure from k-mer frequencies and can separate samples of admixed and non-admixed origin. Using k-mer frequencies to determine population structure has the potential to avoid some challenges of existing methods and may even improve on estimates from small samples.

The Role of Hybridization in Species Formation and Persistence

Hybridization, or interbreeding between different taxa, was traditionally considered to be rare and to have a largely detrimental impact on biodiversity, sometimes leading to the breakdown of reproductive isolation and even to the reversal of speciation. However, modern genomic and analytical methods have shown that hybridization is common in some of the most diverse clades across the tree of life, sometimes leading to rapid increase of phenotypic variability, to introgression of adaptive alleles, to the formation of hybrid species, and even to entire species radiations. In this review, we identify consensus among diverse research programs to show how the field has progressed. Hybridization is a multifaceted evolutionary process that can strongly influence species formation and facilitate adaptation and persistence of species in a rapidly changing world. Progress on testing this hypothesis will require cooperation among different subdisciplines.

Genetic characterization of cashmere goat (Capra hircus) populations in Mongolia

Objective

Characterization studies of the phenotypic and genetic diversity of Mongolian goats are limited, despite several goat breeds being registered in the country. This study aimed to evaluate the phenotypic and genetic diversity of 14 cashmere goat populations in Mongolia, consisting largely of identified goat breeds.

Methods

Body weight, cashmere quality, and coat color were the phenotypic traits considered in this study. A linear model was used to fit body weight and cashmere traits, and least squares means (LSMs) were estimated for the region and location classes. Genetic diversity and structure were assessed using a goat 50K SNP array.

Results

The studied populations exhibited greater phenotypic diversity at the regional level. A very small overall differentiation index (Fst: 0.017) was revealed by Wright's Fst and a very small overall inbreeding index (F ROH1 :0.019) was revealed based on runs of homozygosity. Genetic clustering of populations by principal components showed large variances for the two goat populations of the Russian admixture (Gobi Gurvan Saikhan and Uuliin Bor), and smaller but differentiated clusters for the remaining populations. Similar results were observed in the admixture analysis, which identified populations with the highest (Govi Gurvan Saikhan and Uuliin Bor) and lowest (Tsagaan Ovoo Khar) exotic admixtures. A genomewide association study (GWAS) of body weight and cashmere traits identified a few significant variants on chromosomes 2, 4, 5, 9, and 15, with the strongest variant for cashmere yield on chromosome 4. The GWAS on coat color yielded nine significant variants, with the strongest variants located on chromosomes 6, 13, and 18 and potential associations with KIT, ASIP, and MC1R genes. These signals were also found in other studies on coat color and patterns in goats.

Conclusion

Mongolian cashmere goats showed relatively low genetic differentiation and low inbreeding levels, possibly caused by the traditional pastoral livestock management system and the practice of trading breeding bucks across provinces, along with a recent increase in the goat population. Further investigation of cashmere traits using larger samples and alternative methods may help identify the genes or genomic regions underlying cashmere quality in goats.

Genetic homogeneity and weak signatures of local adaptation in the marine mussel Mytilus chilensis

The natural populations of the marine mussel Mytilus chilensis and the associated aquaculture industry forms a sensitive social-ecological system that relies on the released propagules for cultivation in the highly heterogeneous environment (temperature, productivity, and salinity) of northern Patagonia (42-44 °S). We assessed spatial genetic structure, signals of local adaptation, and population assignment of M. chilensis analyzing 5963 SNPs from 125 individuals across six natural populations sampled over two consecutive years along the southeast Pacific coast (39° 25' to 43° 07' S, ~ 430 km). Neutral and putatively adaptive loci revealed high genetic diversity and low genetic differentiation among populations. Of the whole dataset, less than 1% (50) of loci were identified as putatively adaptive through multiple approaches, with only 0.1% detected in by all of them, and only two loci of them were correlated with environmental variables. No evidence of Isolation by Environment (IBE) was found, albeit a slight differentiation in the southern sampling location (Yaldad). These results suggest that the genetic structure observed is primarily shaped by neutral processes with weak signals of local adaptation. Gene-flow appears to be the main evolutionary force influencing the species' population genetic structure. Because of the importance for the industry, the probability of correct assignment of individuals to their population of origin using allelic frequencies was evaluated. Analyses exhibited relatively low probabilities (< 50% for four out of six sites) of accurately assigning individuals to their geographic origin, with a limited success of SNP markers the for such purposes. Likely, species' high dispersal capacity, seed translocation, and the spill-over effect of mussel aquaculture prevents population genetic differentiation through high effective gene flow, hindering local genetic adaptation.

On the connections between the spatial Lambda-Fleming-Viot model and other processes for analysing geo-referenced genetic data

The introduction of the spatial Lambda-Fleming-Viot model (ΛV) in population genetics was mainly driven by the pioneering work of Alison Etheridge, in collaboration with Nick Barton and Amandine Véber about ten years ago (Barton et al., 2010; Barton et al., 2013). The ΛV model provides a sound mathematical framework for describing the evolution of a population of related individuals along a spatial continuum. It alleviates the "pain in the torus" issue with Wright and Malécot's isolation by distance model and is sampling consistent, making it a tool of choice for statistical inference. Yet, little is known about the potential connections between the ΛV and other stochastic processes generating trees and the spatial coordinates along the corresponding lineages. This work focuses on a version of the ΛV whereby lineages move rapidly over small distances. Using simulations, we show that the induced ΛV tree-generating process is well approximated by a birth-death model. Our results also indicate that Brownian motions modelling the movements of lines of descent along birth-death trees do not generally provide a good approximation of the ΛV due to habitat boundaries effects that play an increasingly important role in the long run. Accounting for habitat boundaries through reflected Brownian motions considerably increases the similarity to the ΛV model however. Finally, we describe efficient algorithms for fast simulation of the backward and forward in time versions of the ΛV model.

Deep history of cultural and linguistic evolution among Central African hunter-gatherers

Human evolutionary history in Central Africa reflects a deep history of population connectivity. However, Central African hunter-gatherers (CAHGs) currently speak languages acquired from their neighbouring farmers. Hence it remains unclear which aspects of CAHG cultural diversity results from long-term evolution preceding agriculture and which reflect borrowing from farmers. On the basis of musical instruments, foraging tools, specialized vocabulary and genome-wide data from ten CAHG populations, we reveal evidence of large-scale cultural interconnectivity among CAHGs before and after the Bantu expansion. We also show that the distribution of hunter-gatherer musical instruments correlates with the oldest genomic segments in our sample predating farming. Music-related words are widely shared between western and eastern groups and likely precede the borrowing of Bantu languages. In contrast, subsistence tools are less frequently exchanged and may result from adaptation to local ecologies. We conclude that CAHG material culture and specialized lexicon reflect a long evolutionary history in Central Africa.

Genetic divergence and migration patterns of a galatheoid squat lobster highlight the need for deep-sea conservation

Information on genetic divergence and migration patterns of vent- and seep-endemic macrobenthos can help delimit biogeographical provinces and provide scientific guidelines for deep-sea conservation under the growing threats of anthropogenic disturbances. Nevertheless, related studies are still scarce, impeding the informed conservation of these hotspots of deep-sea biodiversity. To bridge this knowledge gap, we conducted a population connectivity study on the galatheoid squat lobster Shinkaia crosnieri - a deep-sea foundation species widely distributed in vent and seep ecosystems in the Northwest Pacific. With the application of an interdisciplinary methodology involving population genomics and oceanographic approaches, we unveiled two semi-isolated lineages of S. crosnieri with limited and asymmetrical gene flow potentially shaped by the geographic settings, habitat types, and ocean currents - one comprising vent populations in the Okinawa Trough, with those inhabiting the southern trough area likely serving as the source; the other being the Jiaolong (JR) seep population in the South China Sea. The latter might have recently experienced a pronounced demographic contraction and exhibited genetic introgression from the Okinawa Trough lineage, potentially mediated by the intrusion of the North Pacific Intermediate Water. We then compared the biogeographic patterns between S. crosnieri and two other representative and co-occurring vent- and seep-endemic species using published data. Based on their biogeographical subdivisions and source-sink dynamics, we highlighted the southern Okinawa Trough vents and the JR seep warrant imperative conservation efforts to sustain the deep-sea biodiversity in the Northwest Pacific.

The genetic legacy of the expansion of Bantu-speaking peoples in Africa

The expansion of people speaking Bantu languages is the most dramatic demographic event in Late Holocene Africa and fundamentally reshaped the linguistic, cultural and biological landscape of the continent1-7. With a comprehensive genomic dataset, including newly generated data of modern-day and ancient DNA from previously unsampled regions in Africa, we contribute insights into this expansion that started 6,000-4,000 years ago in western Africa. We genotyped 1,763 participants, including 1,526 Bantu speakers from 147 populations across 14 African countries, and generated whole-genome sequences from 12 Late Iron Age individuals8. We show that genetic diversity amongst Bantu-speaking populations declines with distance from western Africa, with current-day Zambia and the Democratic Republic of Congo as possible crossroads of interaction. Using spatially explicit methods9 and correlating genetic, linguistic and geographical data, we provide cross-disciplinary support for a serial-founder migration model. We further show that Bantu speakers received significant gene flow from local groups in regions they expanded into. Our genetic dataset provides an exhaustive modern-day African comparative dataset for ancient DNA studies10 and will be important to a wide range of disciplines from science and humanities, as well as to the medical sector studying human genetic variation and health in African and African-descendant populations.

Evolutionary history of Castanea sativa in the Caucasus driven by Middle and Late Pleistocene paleoenvironmental changes

Due to global climate cooling and aridification since the Paleogene, members of the Neogene flora were extirpated from the Northern Hemisphere or were confined to a few refugial areas. For some species, the final reduction/extinction came in the Pleistocene, but some others have survived climatic transformations up to the present. This has occurred in Castanea sativa, a species of high commercial value in Europe and a significant component of the Caucasian forests' biodiversity. In contrast to the European range, neither the historical biogeography nor the population genetic structure of the species in its isolated Caucasian range has been clarified. Here, based on a survey of 21 natural populations from the Caucasus and a single one from Europe, we provide a likely biogeographic reconstruction and genetic diversity details. By applying Bayesian inference, species distribution modelling and fossil pollen data, we estimated (i) the time of the Caucasian-European divergence during the Middle Pleistocene, (ii) the time of divergence among Caucasian lineages and (iii) outlined the glacial refugia for species. The climate changes related to the Early-Middle Pleistocene Transition are proposed as the major drivers of the intraspecific divergence and European-Caucasian disjunction for the species, while the impact of the last glacial cycle was of marginal importance.

Historical biogeography and local adaptation explain population genetic structure in a widespread terrestrial orchid

BACKGROUND AND AIMS

Historical changes in environmental conditions and colonization-extinction dynamics have a direct impact on the genetic structure of plant populations. However, understanding how past environmental conditions influenced the evolution of species with high gene flow is challenging when signals for genetic isolation and adaptation are swamped by gene flow. We investigated the spatial distribution and genetic structure of the widespread terrestrial orchid Epipactis helleborine to identify glacial refugia, characterize postglacial population dynamics and assess its adaptive potential.

METHODS

Ecological niche modelling was used to locate possible glacial refugia and postglacial recolonization opportunities of E. helleborine. A large single-nucleotide polymorphism (SNP) dataset obtained through genotyping by sequencing was used to define population genetic diversity and structure and to identify sources of postglacial gene flow. Outlier analyses were used to elucidate how adaptation to the local environment contributed to population divergence.

KEY RESULTS

The distribution of climatically suitable areas was restricted during the Last Glacial Maximum to the Mediterranean, south-western Europe and small areas in the Alps and Carpathians. Within-population genetic diversity was high in E. helleborine (mean expected heterozygosity, 0.373 ± 0.006; observed heterozygosity, 0.571 ± 0.012; allelic richness, 1.387 ± 0.007). Italy and central Europe are likely to have acted as important genetic sources during postglacial recolonization. Adaptive SNPs were associated with temperature, elevation and precipitation.

CONCLUSIONS

Forests in the Mediterranean and Carpathians are likely to have acted as glacial refugia for Epipactis helleborine. Postglacial migration northwards and to higher elevations resulted in the dispersal and diversification of E. helleborine in central Europe and Italy, and to geographical isolation and divergent adaptation in Greek and Italian populations. Distinguishing adaptive from neutral genetic diversity allowed us to conclude that E. helleborine has a high adaptive potential to climate change and demonstrates that signals of adaptation and historical isolation can be identified even in species with high gene flow.

Isolation by distance in populations with power-law dispersal

Limited dispersal of individuals between generations results in isolation by distance, in which individuals further apart in space tend to be less related. Classic models of isolation by distance assume that dispersal distances are drawn from a thin-tailed distribution and predict that the proportion of the genome that is identical by descent between a pair of individuals should decrease exponentially with the spatial separation between them. However, in many natural populations, individuals occasionally disperse over very long distances. In this work, we use mathematical analysis and coalescent simulations to study the effect of long-range (power-law) dispersal on patterns of isolation by distance. We find that it leads to power-law decay of identity-by-descent at large distances with the same exponent as dispersal. We also find that broad power-law dispersal produces another, shallow power-law decay of identity-by-descent at short distances. These results suggest that the distribution of long-range dispersal events could be estimated from sequencing large population samples taken from a wide range of spatial scales.

Estimating human mobility in Holocene Western Eurasia with large-scale ancient genomic data

The recent increase in openly available ancient human DNA samples allows for large-scale meta-analysis applications. Trans-generational past human mobility is one of the key aspects that ancient genomics can contribute to since changes in genetic ancestry-unlike cultural changes seen in the archaeological record-necessarily reflect movements of people. Here, we present an algorithm for spatiotemporal mapping of genetic profiles, which allow for direct estimates of past human mobility from large ancient genomic datasets. The key idea of the method is to derive a spatial probability surface of genetic similarity for each individual in its respective past. This is achieved by first creating an interpolated ancestry field through space and time based on multivariate statistics and Gaussian process regression and then using this field to map the ancient individuals into space according to their genetic profile. We apply this algorithm to a dataset of 3138 aDNA samples with genome-wide data from Western Eurasia in the last 10,000 y. Finally, we condense this sample-wise record with a simple summary statistic into a diachronic measure of mobility for subregions in Western, Central, and Southern Europe. For regions and periods with sufficient data coverage, our similarity surfaces and mobility estimates show general concordance with previous results and provide a meta-perspective of genetic changes and human mobility.

Modeling the spatiotemporal spread of beneficial alleles using ancient genomes

Ancient genome sequencing technologies now provide the opportunity to study natural selection in unprecedented detail. Rather than making inferences from indirect footprints left by selection in present-day genomes, we can directly observe whether a given allele was present or absent in a particular region of the world at almost any period of human history within the last 10,000 years. Methods for studying selection using ancient genomes often rely on partitioning individuals into discrete time periods or regions of the world. However, a complete understanding of natural selection requires more nuanced statistical methods which can explicitly model allele frequency changes in a continuum across space and time. Here we introduce a method for inferring the spread of a beneficial allele across a landscape using two-dimensional partial differential equations. Unlike previous approaches, our framework can handle time-stamped ancient samples, as well as genotype likelihoods and pseudohaploid sequences from low-coverage genomes. We apply the method to a panel of published ancient West Eurasian genomes to produce dynamic maps showcasing the inferred spread of candidate beneficial alleles over time and space. We also provide estimates for the strength of selection and diffusion rate for each of these alleles. Finally, we highlight possible avenues of improvement for accurately tracing the spread of beneficial alleles in more complex scenarios.

Neo-sex chromosome evolution and phenotypic differentiation across an elevational gradient in horned larks (Eremophila Alpestris)

Genetic structure and phenotypic variation among populations is affected by both geographic distance and environmental variation across species' distributions. Understanding the relative contributions of isolation by distance (IBD) and isolation by environment (IBE) is important for elucidating population dynamics across habitats and ecological gradients. In this study, we compared phenotypic and genetic variation among Horned Lark (Eremophila alpestris) populations from 10 sites encompassing an elevational gradient from low-elevation desert scrub in Death Valley (285 a.s.l.) to high-elevation meadows in the White Mountains of the Sierra Nevada of California (greater than 3000 m a.s.l.). Using a ddRAD dataset of 28,474 SNPs aligned to a high-quality reference genome, we compared genetic structure with elevational, environmental, and spatial distance to quantify how different aspects of the landscape drive genomic and phenotypic differentiation in Horned Larks. We found larger-bodied birds were associated with sites that had less seasonality and higher annual precipitation, and longer spurs occurred in soils with more clay and silt content, less sand, and finer fragments. Larks have large neo-sex chromosomes, and we found that associations with elevation and environmental variation were much stronger among neo-sex chromosomes compared to autosomes. Furthermore, we found that putative chromosomal translocations, fusions, and inversions were associated with elevation and may underlie local adaptation across an elevational gradient in Horned Larks. Our results suggest that genetic variation in Horned Larks is affected more by IBD than IBE, but specific phenotypes and genomic regions-particually on neo-sex chromosomes-bear stronger associations with the environment.

Testing for evolutionary change in restoration: A genomic comparison between ex situ, native, and commercial seed sources of Helianthus maximiliani

Globally imperiled ecosystems often depend upon collection, propagation, and storage of seed material for use in restoration. However, during the restoration process demographic changes, population bottlenecks, and selection can alter the genetic composition of seed material, with potential impacts for restoration success. The evolutionary outcomes associated with these processes have been demonstrated using theoretical and experimental frameworks, but no study to date has examined their impact on the seed material maintained for conservation and restoration. In this study, we compare genomic variation across seed sources used in conservation and restoration for the perennial prairie plant Helianthus maximiliani, a key component of restorations across North American grasslands. We compare individuals sourced from contemporary wild populations, ex situ conservation collections, commercially produced restoration material, and two populations selected for agronomic traits. Overall, we observed that ex situ and contemporary wild populations exhibited similar genomic composition, while four of five commercial populations and selected lines were differentiated from each other and other seed source populations. Genomic differences across seed sources could not be explained solely by isolation by distance nor directional selection. We did find evidence of sampling effects for ex situ collections, which exhibited significantly increased coancestry relative to commercial populations, suggesting increased relatedness. Interestingly, commercially sourced seed appeared to maintain an increased number of rare alleles relative to ex situ and wild contemporary seed sources. However, while commercial seed populations were not genetically depauperate, the genomic distance between wild and commercially produced seed suggests differentiation in the genomic composition could impact restoration success. Our results point toward the importance of genetic monitoring of seed sources used for conservation and restoration as they are expected to be influenced by the evolutionary processes that contribute to divergence during the restoration process.

Fast and flexible estimation of effective migration surfaces

Spatial population genetic data often exhibits ‘isolation-by-distance,’ where genetic similarity tends to decrease as individuals become more geographically distant. The rate at which genetic similarity decays with distance is often spatially heterogeneous due to variable population processes like genetic drift, gene flow, and natural selection. Petkova et al., 2016 developed a statistical method called Estimating Effective Migration Surfaces (EEMS) for visualizing spatially heterogeneous isolation-by-distance on a geographic map. While EEMS is a powerful tool for depicting spatial population structure, it can suffer from slow runtimes. Here, we develop a related method called Fast Estimation of Effective Migration Surfaces (FEEMS). FEEMS uses a Gaussian Markov Random Field model in a penalized likelihood framework that allows for efficient optimization and output of effective migration surfaces. Further, the efficient optimization facilitates the inference of migration parameters per edge in the graph, rather than per node (as in EEMS). With simulations, we show conditions under which FEEMS can accurately recover effective migration surfaces with complex gene-flow histories, including those with anisotropy. We apply FEEMS to population genetic data from North American gray wolves and show it performs favorably in comparison to EEMS, with solutions obtained orders of magnitude faster. Overall, FEEMS expands the ability of users to quickly visualize and interpret spatial structure in their data.

An overview of current population genomics methods for the analysis of whole-genome resequencing data in eukaryotes

Characterizing the population history of a species and identifying loci underlying local adaptation is crucial in functional ecology, evolutionary biology, conservation and agronomy. The constant improvement of high-throughput sequencing techniques has facilitated the production of whole genome data in a wide range of species. Population genomics now provides tools to better integrate selection into a historical framework, and take into account selection when reconstructing demographic history. However, this improvement has come with a profusion of analytical tools that can confuse and discourage users. Such confusion limits the amount of information effectively retrieved from complex genomic data sets, and impairs the diffusion of the most recent analytical tools into fields such as conservation biology. It may also lead to redundancy among methods. To address these isssues, we propose an overview of more than 100 state-of-the-art methods that can deal with whole genome data. We summarize the strategies they use to infer demographic history and selection, and discuss some of their limitations. A website listing these methods is available at www.methodspopgen.com.

Human genetic admixture

Throughout human history, large-scale migrations have facilitated the formation of populations with ancestry from multiple previously separated populations. This process leads to subsequent shuffling of genetic ancestry through recombination, producing variation in ancestry between populations, among individuals in a population, and along the genome within an individual. Recent methodological and empirical developments have elucidated the genomic signatures of this admixture process, bringing previously understudied admixed populations to the forefront of population and medical genetics. Under this theme, we present a collection of recent PLOS Genetics publications that exemplify recent progress in human genetic admixture studies, and we discuss potential areas for future work.

Model-based genotype and ancestry estimation for potential hybrids with mixed-ploidy

Non-random mating among individuals can lead to spatial clustering of genetically similar individuals and population stratification. This deviation from panmixia is commonly observed in natural populations. Consequently, individuals can have parentage in single populations or involving hybridization between differentiated populations. Accounting for this mixture and structure is important when mapping the genetics of traits and learning about the formative evolutionary processes that shape genetic variation among individuals and populations. Stratified genetic relatedness among individuals is commonly quantified using estimates of ancestry that are derived from a statistical model. Development of these models for polyploid and mixed-ploidy individuals and populations has lagged behind those for diploids. Here, we extend and test a hierarchical Bayesian model, called entropy, which can use low-depth sequence data to estimate genotype and ancestry parameters in autopolyploid and mixed-ploidy individuals (including sex chromosomes and autosomes within individuals). Our analysis of simulated data illustrated the trade-off between sequencing depth and genome coverage and found lower error associated with low-depth sequencing across a larger fraction of the genome than with high-depth sequencing across a smaller fraction of the genome. The model has high accuracy and sensitivity as verified with simulated data and through analysis of admixture among populations of diploid and tetraploid Arabidopsis arenosa.

Mixed logistic regression in genome-wide association studies

Background

Mixed linear models (MLM) have been widely used to account for population structure in case-control genome-wide association studies, the status being analyzed as a quantitative phenotype. Chen et al. proved in 2016 that this method is inappropriate in some situations and proposed GMMAT, a score test for the mixed logistic regression (MLR). However, this test does not produces an estimation of the variants’ effects. We propose two computationally efficient methods to estimate the variants’ effects. Their properties and those of other methods (MLM, logistic regression) are evaluated using both simulated and real genomic data from a recent GWAS in two geographically close population in West Africa.

Results

We show that, when the disease prevalence differs between population strata, MLM is inappropriate to analyze binary traits. MLR performs the best in all circumstances. The variants’ effects are well evaluated by our methods, with a moderate bias when the effect sizes are large. Additionally, we propose a stratified QQ-plot, enhancing the diagnosis of p values inflation or deflation when population strata are not clearly identified in the sample.

Conclusion

The two proposed methods are implemented in the R package milorGWAS available on the CRAN. Both methods scale up to at least 10,000 individuals. The same computational strategies could be applied to other models (e.g. mixed Cox model for survival analysis).

Divergence, gene flow, and the origin of leapfrog geographic distributions: The history of colour pattern variation in Phyllobates poison-dart frogs

The geographic distribution of phenotypic variation among closely related populations is a valuable source of information about the evolutionary processes that generate and maintain biodiversity. Leapfrog distributions, in which phenotypically similar populations are disjunctly distributed and separated by one or more phenotypically distinct populations, represent geographic replicates for the existence of a phenotype, and are therefore especially informative. These geographic patterns have mostly been studied from phylogenetic perspectives to understand how common ancestry and divergent evolution drive their formation. Other processes, such as gene flow between populations, have not received as much attention. Here, we investigate the roles of divergence and gene flow between populations in the origin and maintenance of a leapfrog distribution in Phyllobates poison frogs. We found evidence for high levels of gene flow between neighbouring populations but not over long distances, indicating that gene flow between populations exhibiting the central phenotype may have a homogenizing effect that maintains their similarity, and that introgression between 'leapfroging' taxa has not played a prominent role as a driver of phenotypic diversity in Phyllobates. Although phylogenetic analyses suggest that the leapfrog distribution was formed through independent evolution of the peripheral (i.e. leapfrogging) populations, the elevated levels of gene flow between geographically close populations poise alternative scenarios, such as the history of phenotypic change becoming decoupled from genome-averaged patterns of divergence, which we cannot rule out. These results highlight the importance of incorporating gene flow between populations into the study of geographic variation in phenotypes, both as a driver of phenotypic diversity and as a confounding factor of phylogeographic inferences.

Multi-level patterns of genetic structure and isolation by distance in the widespread plant Mimulus guttatus

An understanding of genetic structure is essential for answering many questions in population genetics. However, complex population dynamics and scale-dependent processes can make it difficult to detect if there are distinct genetic clusters present in natural populations. Inferring discrete population structure is particularly challenging in the presence of continuous genetic variation such as isolation by distance. Here, we use the plant species Mimulus guttatus as a case study for understanding genetic structure at three spatial scales. We use reduced-representation sequencing and marker-based genotyping to understand dispersal dynamics and to characterise genetic structure. Our results provide insight into the spatial scale of genetic structure in a widespread plant species, and demonstrate how dispersal affects spatial genetic variation at the local, regional, and range-wide scale. At a fine-spatial scale, we show dispersal is rampant with little evidence of spatial genetic structure within populations. At a regional-scale, we show continuous differentiation driven by isolation by distance over hundreds of kilometres, with broad geographic genetic clusters that span major barriers to dispersal. Across Western North America, we observe geographic genetic structure and the genetic signature of multiple postglacial recolonisation events, with historical gene flow linking isolated populations. Our genetic analyses show M. guttatus is highly dispersive and maintains large metapopulations with high intrapopulation variation. This high diversity and dispersal confounds the inference of genetic structure, with multi-level sampling and spatially-explicit analyses required to understand population history.

Genetic Landscapes Reveal How Human Genetic Diversity Aligns with Geography

Geographic patterns in human genetic diversity carry footprints of population history and provide insights for genetic medicine and its application across human populations. Summarizing and visually representing these patterns of diversity has been a persistent goal for human geneticists, and has revealed that genetic differentiation is frequently correlated with geographic distance. However, most analytical methods to represent population structure do not incorporate geography directly, and it must be considered post hoc alongside a visual summary of the genetic structure. Here, we estimate "effective migration" surfaces to visualize how human genetic diversity is geographically structured. The results reveal local patterns of differentiation in detail and emphasize that while genetic similarity generally decays with geographic distance, the relationship is often subtly distorted. Overall, the visualizations provide a new perspective on genetics and geography in humans and insight to the geographic distribution of human genetic variation.

When are populations not connected like a circuit? Identifying biases in gene flow from coalescent times

Connectivity and movement patterns of populations are influenced by past and present environmental and biotic factors, which are reflected in genetic relatedness among populations. Methods that estimate the "commute time" between populations based on electrical resistance (i.e., isolation-by-resistance [IBR]) have been widely applied to either infer movement patterns directly from environmental factors or detect possible barriers to gene flow given empirical genetic relatedness. Yet, the commute time is only equivalent to the coalescence time between populations under symmetric migration with isotropic landscapes. Asymmetric gene flow is relatively common when populations are expanding, retreating, or with source-sink dynamics. In a From the Cover paper in this issue of Molecular Ecology Resources, Lundgren and Ralph (Molecular Ecology Resources, 19, 2019) describe a Bayesian method to infer bidirectional gene flow rates and population sizes without the assumption of symmetry. The method shows great accuracy in connectivity estimations under symmetric, as well as asymmetric gene flow scenarios where resistance methods fail. However, computational complexity limits the method to a few populations, preventing its application to finescale environmental maps. Also, as a discrete-deme static model, the inferred differences in gene flow rates are sensitive to population discretization and cannot be directly used to differentiate among processes (e.g., past expansion vs. current barrier). Here, we discuss scenarios where the new method can best be utilized and provide potential directions to identify the underlying processes causing deviations in gene flow patterns.

Toward the Definition of Efficacy and Safety Criteria for Advancing Gene Drive-Modified Mosquitoes to Field Testing

Mosquitoes containing gene drive systems are being developed as complementary tools to prevent transmission of malaria and other mosquito-borne diseases. As with any new tool, decision makers and other stakeholders will need to balance risks (safety) and benefits (efficacy) when considering the rationale for testing and deploying gene drive-modified mosquito products. Developers will benefit from standards for judging whether an investigational gene drive product meets acceptability criteria for advancing to field trials. Such standards may be formalized as preferred product characteristics and target product profiles, which describe the desired attributes of the product category and of a particular product, respectively. This report summarizes discussions from two scientific workshops aimed at identifying efficacy and safety characteristics that must be minimally met for an investigational gene drive-modified mosquito product to be deemed viable to move from contained testing to field release and the data that will be needed to support an application for first field release.

Genetic Affinities among Southern Africa Hunter-Gatherers and the Impact of Admixing Farmer and Herder Populations

Southern African indigenous groups, traditionally hunter-gatherers (San) and herders (Khoekhoe), are commonly referred to as “Khoe-San” populations and have a long history in southern Africa. Their ancestors were largely isolated up until ∼2,000 years ago before the arrival of pastoralists and farmers in southern Africa. Assessing relationships among regional Khoe-San groups has been challenging due to admixture with immigrant populations that obscure past population affinities and gene flow among these autochthonous communities. We re-evaluate a combined genome-wide data set of previously published southern Africa Khoe-San populations in conjunction with novel data from Khoe-San individuals collected in Xade (Central Kalahari Game Reserve, Botswana) prior to their resettlement outside the reserve. After excluding regions in the genome that trace their ancestry to recent migrant groups, the genetic diversity of 20 Khoe-San groups fitted an isolation-by-distance model. Even though isolation-by-distance explained most genetic affinities between the different autochthonous groups, additional signals of contact between Khoe-San groups could be detected. For instance, we found stronger genetic affinities, than what would be explained by isolation-by-distance gene flow, between the two geographically separated Khoe-San groups, who speak branches of the Kx’a-language family (ǂHoan and Ju). We also scanned the genome-wide data for signals of adaptive gene flow from farmers/herders into Khoe-San groups and identified a number of genomic regions potentially introduced by the arrival of the new groups. This study provides a comprehensive picture of affinities among Khoe-San groups, prior to the arrival of recent migrants, and found that these affinities are primarily determined by the geographic landscape.

Ancestral and neo-sex chromosomes contribute to population divergence in a dioecious plant

Empirical evidence from several animal groups suggests sex chromosomes disproportionately contribute to reproductive isolation. This effect may be enhanced when sex chromosomes are associated with turnover of sex determination systems resulting from structural rearrangements to the chromosomes. We investigated these predictions in the dioecious plant Rumex hastatulus, which is composed of populations of two different sex chromosome cytotypes caused by an X-autosome fusion. Using population genomic analyses, we investigated the demographic history of R. hastatulus and explored the contributions of ancestral and neo-sex chromosomes to population genetic divergence. Our study revealed that the cytotypes represent genetically divergent populations with evidence for historical but not contemporary gene flow between them. In agreement with classical predictions, we found that the ancestral X chromosome was disproportionately divergent compared with the rest of the genome. Excess differentiation was also observed on the Y chromosome, even when we used measures of differentiation that control for differences in effective population size. Our estimates of the timing of the origin of neo-sex chromosomes in R. hastatulus are coincident with cessation of gene flow, suggesting that the chromosomal fusion event that gave rise to the origin of the XYY cytotype may have also contributed to reproductive isolation.

Transcontinental dispersal of Anopheles gambiae occurred from West African origin via serial founder events

The mosquito Anopheles gambiae s.s. is distributed across most of sub-Saharan Africa and is of major scientific and public health interest for being an African malaria vector. Here we present population genomic analyses of 111 specimens sampled from west to east Africa, including the first whole genome sequences from oceanic islands, the Comoros. Genetic distances between populations of A. gambiae are discordant with geographic distances but are consistent with a stepwise migration scenario in which the species increases its range from west to east Africa through consecutive founder events over the last ~200,000 years. Geological barriers like the Congo River basin and the East African rift seem to play an important role in shaping this process. Moreover, we find a high degree of genetic isolation of populations on the Comoros, confirming the potential of these islands as candidate sites for potential field trials of genetically engineered mosquitoes for malaria control.

ResDisMapper: An r package for fine-scale mapping of resistance to dispersal

Management of biological invasions and conservation activity in the fight against habitat fragmentation both require information on how ongoing dispersal of organisms is affected by the environment. However, there are few landscape genetic computer programs that map resistance to dispersal at small spatiotemporal scales. To facilitate such analyses, we present an r package named ResDisMapper for the mapping of resistance to dispersal at small spatiotemporal scales, without the need for prior knowledge on environmental features or intensive computation. Based on the concept of isolation by distance (IBD), ResDisMapper calculates resistance using deviations of each pair of samples from the general IBD trend (IBD residuals). The IBD residuals are projected onto the studied area, which allows construction and visualization of a fine-scale map of resistance based on spatial accumulation of positive or negative IBD residuals. In this study, we tested ResDisMapper with both simulated and empirical data sets and compared its performance with two other popular landscape genetic programs. Overall, we found that ResDisMapper can map resistance with relatively high accuracy. The latest version of the package and associated documentation are available on Github (https://github.com/takfung/ResDisMapper).

Population Structure and Genetic Diversity among Isolates of Coccidioides posadasii in Venezuela and Surrounding Regions

Coccidioides posadasii is a pathogenic fungus that causes coccidioidomycosis in many arid regions of the Americas. One of these regions is bordered by the Caribbean Sea, and the surrounding landscape may play an important role in the dispersion of C. posadasii across South America through southeastern Mexico, Honduras, Guatemala, and Venezuela. Comparative phylogenomic analyses of C. posadasii reveal that clinical strains from Venezuela are genetically distinct from the North American populations found in (i) Arizona and (ii) Texas, Mexico, and the rest of South America (TX/MX/SA). We find evidence for admixture between the Venezuela and the North American populations of C. posadasii in Central America. Additionally, the proportion of Venezuelan alleles in the admixed population decreases as latitude (and distance from Venezuela) increases. Our results indicate that the population in Venezuela may have been subjected to a recent bottleneck and shows a strong population structure. This analysis provides insight into potential for Coccidioides spp. to invade new regions.IMPORTANCE Valley Fever is a fungal disease caused by two species of fungi: Coccidioides immitis and C. posadasii These fungi are found throughout the arid regions of North and South America; however, our understanding of genetic diversity and disease in South America is limited. In this report, we analyze 10 new genomes of Coccidioides posadasii from regions bordering the Caribbean Sea. We show that these populations are distinct and that isolates from Venezuela are likely a result of a recent bottleneck. These data point to patterns that might be observed when investigating recently established populations.

Spatial Population Genetics: It's About Time

Many important questions about the history and dynamics of organisms have a geographical component: How many are there, and where do they live? How do they move and interbreed across the landscape? How were they moving a thousand years ago, and where were the ancestors of a particular individual alive today? Answers to these questions can have profound consequences for our understanding of history, ecology, and the evolutionary process. In this review, we discuss how geographic aspects of the distribution, movement, and reproduction of organisms are reflected in their pedigree across space and time. Because the structure of the pedigree is what determines patterns of relatedness in modern genetic variation, our aim is to thus provide intuition for how these processes leave an imprint in genetic data. We also highlight some current methods and gaps in the statistical toolbox of spatial population genetics.

Evolutionary Dynamics in Structured Populations Under Strong Population Genetic Forces

In the long-term neutral equilibrium, high rates of migration between subpopulations result in little population differentiation. However, in the short-term, even very abundant migration may not be enough for subpopulations to equilibrate immediately. In this study, we investigate dynamical patterns of short-term population differentiation in adapting populations via stochastic and analytical modeling through time. We characterize a regime in which selection and migration interact to create non-monotonic patterns of population differentiation over time when migration is weaker than selection, but stronger than drift. We demonstrate how these patterns can be leveraged to estimate high migration rates using approximate Bayesian computation. We apply this approach to estimate fast migration in a rapidly adapting intra-host Simian-HIV population sampled from different anatomical locations. We find differences in estimated migration rates between different compartments, even though all are above [Formula: see text] = 1. This work demonstrates how studying demographic processes on the timescale of selective sweeps illuminates processes too fast to leave signatures on neutral timescales.

The demographic history of Atlantic salmon (Salmo salar) across its distribution range reconstructed from approximate Bayesian computations

Understanding the dual roles of demographic and selective processes in the buildup of population divergence is one of the most challenging tasks in evolutionary biology. Here, we investigated the demographic history of Atlantic salmon across the entire species range using 2035 anadromous individuals from North America and Eurasia. By combining results from admixture graphs, geo-genetic maps, and an Approximate Bayesian Computation (ABC) framework, we validated previous hypotheses pertaining to secondary contact between European and Northern American populations, but also identified secondary contacts in European populations from different glacial refugia. We further identified the major sources of admixture from the southern range of North America into more northern populations along with a strong signal of secondary gene flow between genetic regional groups. We hypothesize that these patterns reflect the spatial redistribution of ancestral variation across the entire North American range. Results also support a role for linked selection and differential introgression that likely played an underappreciated role in shaping the genomic landscape of species in the Northern hemisphere. We conclude that studies between partially isolated populations should systematically include heterogeneity in selective and introgressive effects among loci to perform more rigorous demographic inferences of the divergence process.

Genomic structure and diversity of Plasmodium falciparum in Southeast Asia reveal recent parasite migration patterns

Estimates of Plasmodium falciparum migration may inform strategies for malaria elimination. Here we elucidate fine-scale parasite population structure and infer recent migration across Southeast Asia using identity-by-descent (IBD) approaches based on genome-wide single nucleotide polymorphisms called in 1722 samples from 54 districts. IBD estimates are consistent with isolation-by-distance. We observe greater sharing of larger IBD segments between artemisinin-resistant parasites versus sensitive parasites, which is consistent with the recent spread of drug resistance. Our IBD analyses reveal actionable patterns, including isolated parasite populations, which may be prioritized for malaria elimination, as well as asymmetrical migration identifying potential sources and sinks of migrating parasites.

Spatially explicit analysis reveals complex human genetic gradients in the Iberian Peninsula

The Iberian Peninsula is a well-delimited geographic region with a rich and complex human history. However, the causes of its genetic structure and past migratory dynamics are not yet fully understood. In order to shed light on them, here we evaluated the gene flow and genetic structure throughout the Iberian Peninsula with spatially explicit modelling applied to a georeferenced genetic dataset composed of genome-wide SNPs from 746 individuals belonging to 17 different regions of the Peninsula. We found contrasting patterns of genetic structure throughout Iberia. In particular, we identified strong patterns of genetic differentiation caused by relevant barriers to gene flow in northern regions and, on the other hand, a large genetic similarity in central and southern regions. In addition, our results showed a preferential north to south migratory dynamics and suggest a sex-biased dispersal in Mediterranean and southern regions. The estimated genetic patterns did not fit with the geographical relief of the Iberian landscape and they rather seem to follow political and linguistic territorial boundaries.

Genomics, environment and balancing selection in behaviourally bimodal populations: The caribou case

Selection forces that favour different phenotypes in different environments can change frequencies of genes between populations along environmental clines. Clines are also compatible with balancing forces, such as negative frequency-dependent selection (NFDS), which maintains phenotypic polymorphisms within populations. For example, NFDS is hypothesized to maintain partial migration, a dimorphic behavioural trait prominent in species where only a fraction of the population seasonally migrates. Overall, NFDS is believed to be a common phenomenon in nature, yet a scarcity of studies were published linking naturally occurring allelic variation with bimodal or multimodal phenotypes and balancing selection. We applied a Pool-seq approach and detected selection on alleles associated with environmental variables along a North-South gradient in western North American caribou, a species displaying partially migratory behaviour. On 51 loci, we found a signature of balancing selection, which could be related to NFDS and ultimately the maintenance of the phenotypic polymorphisms known within these populations. Yet, remarkably, we detected directional selection on a locus when our sample was divided into two behaviourally distinctive groups regardless of geographic provenance (a subset of GPS-collared migratory or sedentary individuals), indicating that, within populations, phenotypically homogeneous groups were genetically distinctive. Loci under selection were linked to functional genes involved in oxidative stress response, body development and taste perception. Overall, results indicated genetic differentiation along an environmental gradient of caribou populations, which we found characterized by genes potentially undergoing balancing selection. We suggest that the underlining balancing force, NFDS, plays a strong role within populations harbouring multiple haplotypes and phenotypes, as it is the norm in animals, plants and humans too.

Neutral Variation in the Context of Selection

In its initial formulation by Motoo Kimura, the neutral theory was concerned solely with the level of variability maintained by random genetic drift of selectively neutral mutations, and the rate of molecular evolution caused by the fixation of such mutations. The original theory considered events at a single genetic locus in isolation from the rest of the genome. It did not take long, however, for theoreticians to wonder whether selection at one or more loci might influence neutral variability at linked sites. Once DNA sequence variability could be studied, and especially when resequencing of whole genomes became possible, it became clear that patterns of neutral variability in genomes are affected by selection at linked sites, and that these patterns could advance our understanding of natural selection, and can be used to detect the action of selection in genomic regions, including selection much weaker than could be detected by direct measurements of the relative fitnesses of different genotypes. We outline the different types of processes that have been studied, in approximate order of their historical development.

Estimating recent migration and population-size surfaces

In many species a fundamental feature of genetic diversity is that genetic similarity decays with geographic distance; however, this relationship is often complex, and may vary across space and time. Methods to uncover and visualize such relationships have widespread use for analyses in molecular ecology, conservation genetics, evolutionary genetics, and human genetics. While several frameworks exist, a promising approach is to infer maps of how migration rates vary across geographic space. Such maps could, in principle, be estimated across time to reveal the full complexity of population histories. Here, we take a step in this direction: we present a method to infer maps of population sizes and migration rates associated with different time periods from a matrix of genetic similarity between every pair of individuals. Specifically, genetic similarity is measured by counting the number of long segments of haplotype sharing (also known as identity-by-descent tracts). By varying the length of these segments we obtain parameter estimates associated with different time periods. Using simulations, we show that the method can reveal time-varying migration rates and population sizes, including changes that are not detectable when using a similar method that ignores haplotypic structure. We apply the method to a dataset of contemporary European individuals (POPRES), and provide an integrated analysis of recent population structure and growth over the last ∼3,000 years in Europe.

We introduce a novel statistical method to infer migration rates and population sizes across space in recent time periods. Our approach builds upon the previously developed EEMS method, which infers effective migration rates under a dense lattice. Similarly, we infer demographic parameters under a lattice and use a (Voronoi) prior to regularize parameters of the model. However, our method differs from EEMS in a few key respects. First, we use the coalescent model parameterized by migration rates and population sizes while EEMS uses a resistance model. As another key difference, our method uses haplotype data while EEMS uses the average genetic distance. A consequence of using haplotype data is that our method can separately estimate migration rates and population sizes, which in essence is done by using a recombination rate map to calibrate the decay of haplotypes over time. An additional useful feature of haplotype data is that, by varying the lengths analyzed, we can infer demography associated with different recent time periods. We call our method MAPS for estimating Migration And Population-size Surfaces. To illustrate MAPS on real data, we analyze a genome-wide SNP dataset on 2224 individuals of European ancestry.

Integrating Networks, Phylogenomics, and Population Genomics for the Study of Polyploidy

Duplication events are regarded as sources of evolutionary novelty, but our understanding of general trends for the long-term trajectory of additional genomic material is still lacking. Organisms with a history of whole genome duplication (WGD) offer a unique opportunity to study potential trends in the context of gene retention and/or loss, gene and network dosage, and changes in gene expression. In this review, we discuss the prevalence of polyploidy across the tree of life, followed by an overview of studies investigating genome evolution and gene expression. We then provide an overview of methods in network biology, phylogenomics, and population genomics that are critical for advancing our understanding of evolution post-WGD, highlighting the need for models that can accommodate polyploids. Finally, we close with a brief note on the importance of random processes in the evolution of polyploids with respect to neutral versus selective forces, ancestral polymorphisms, and the formation of autopolyploids versus allopolyploids.

Genomic data recover previously undetectable fragmentation effects in an endangered amphibian

A critical consideration when using molecular ecological methods to detect trends and parameterize models at very fine spatial and temporal scales has always been the technical limits of resolution. Key landscape features, including most anthropogenic modifications, can cause biologically important, but very recent changes in gene flow that require substantial statistical power to detect. The problem is one of temporal scale: Human change is rapid and recent, while genetic changes accumulate slowly. We generated SNPs from thousands of nuclear loci to characterize the population structure of New York-endangered eastern tiger salamanders (Ambystoma tigrinum) on Long Island and quantify the impacts of roads on population fragmentation. In stark contrast to a recent microsatellite study, we uncovered highly structured populations over an extremely small spatial scale (approximately 40 km² ) in an increasingly human-modified landscape. Geographic distance and the presence of roads between ponds were both strong predictors of genetic divergence, suggesting that both natural and anthropogenic factors contribute to the observed patterns of genetic variation. All ponds supported small to modest effective breeding populations, and pond surface area showed a strong positive correlation with population size. None of these patterns emerged in an earlier study of the same system using microsatellite loci, and we determined that at least 300-400 SNPs were needed to recover the fine-scale population structure present in this system. Conservation assessments using earlier genetic techniques in other species may similarly lack the statistical power for small-scale inferences and benefit from reassessments using genomic tools.

Selecting among Alternative Scenarios of Human Evolution by Simulated Genetic Gradients

Selecting among alternative scenarios of human evolution is nowadays a common methodology to investigate the history of our species. This strategy is usually based on computer simulations of genetic data under different evolutionary scenarios, followed by a fitting of the simulated data with the real data. A recent trend in the investigation of ancestral evolutionary processes of modern humans is the application of genetic gradients as a measure of fitting, since evolutionary processes such as range expansions, range contractions, and population admixture (among others) can lead to different genetic gradients. In addition, this strategy allows the analysis of the genetic causes of the observed genetic gradients. Here, we review recent findings on the selection among alternative scenarios of human evolution based on simulated genetic gradients, including pros and cons. First, we describe common methodologies to simulate genetic gradients and apply them to select among alternative scenarios of human evolution. Next, we review previous studies on the influence of range expansions, population admixture, last glacial period, and migration with long-distance dispersal on genetic gradients for some regions of the world. Finally, we discuss this analytical approach, including technical limitations, required improvements, and advice. Although here we focus on human evolution, this approach could be extended to study other species.

Statistical methods for detecting admixture

The increasing availability of large-scale autosomal genetic variation data sampled from world-wide geographic areas, coupled with advances in the statistical methodology to analyse these data, is showcasing the power of DNA as a major tool to gain insights into the demographic history of humans and other organisms. Here we review statistical techniques that shed light on a specific aspect of demography: the detection and description of admixture events where two or more genetically distinct groups intermixed at one or more times in the past. In particular we give an overview of some of the widely used methods to identify and describe admixture events using autosomal DNA from unrelated individuals, with a particular focus on analysing biallelic Single-Nucleotide-Polymorphsim (SNP) markers.

Inferring Continuous and Discrete Population Genetic Structure Across Space

A classic problem in population genetics is the characterization of discrete population structure in the presence of continuous patterns of genetic differentiation. Especially when sampling is discontinuous, the use of clustering or assignment methods may incorrectly ascribe differentiation due to continuous processes (e.g., geographic isolation by distance) to discrete processes, such as geographic, ecological, or reproductive barriers between populations. This reflects a shortcoming of current methods for inferring and visualizing population structure when applied to genetic data deriving from geographically distributed populations. Here, we present a statistical framework for the simultaneous inference of continuous and discrete patterns of population structure. The method estimates ancestry proportions for each sample from a set of two-dimensional population layers, and, within each layer, estimates a rate at which relatedness decays with distance. This thereby explicitly addresses the "clines versus clusters" problem in modeling population genetic variation, and remedies some of the overfitting to which nonspatial models are prone. The method produces useful descriptions of structure in genetic relatedness in situations where separated, geographically distributed populations interact, as after a range expansion or secondary contact. We demonstrate the utility of this approach using simulations and by applying it to empirical datasets of poplars and black bears in North America.