Identifying the environmental factors that determine the genetic structure of populations

Landscape genetics highlights the role of bank vole metapopulation dynamics in the epidemiology of Puumala hantavirus

Rodent host dynamics and dispersal are thought to be critical for hantavirus epidemiology as they determine pathogen persistence and transmission within and between host populations. We used landscape genetics to investigate how the population dynamics of the bank vole Myodes glareolus, the host of Puumala hantavirus (PUUV), vary with forest fragmentation and influence PUUV epidemiology. We sampled vole populations within the Ardennes, a French PUUV endemic area. We inferred demographic features such as population size, isolation and migration with regard to landscape configuration. We next analysed the influence of M. glareolus population dynamics on PUUV spatial distribution. Our results revealed that the global metapopulation dynamics of bank voles were strongly shaped by landscape features, including suitable patch size and connectivity. Large effective size in forest might therefore contribute to the higher observed levels of PUUV prevalence. By contrast, populations from hedge networks highly suffered from genetic drift and appeared strongly isolated from all other populations. This might result in high probabilities of local extinction for both M. glareolus and PUUV. Besides, we detected signatures of asymmetric bank vole migration from forests to hedges. These movements were likely to sustain PUUV in fragmented landscapes. In conclusion, our study provided arguments in favour of source-sink dynamics shaping PUUV persistence and spread in heterogeneous, Western European temperate landscapes. It illustrated the potential contribution of landscape genetics to the understanding of the epidemiological processes occurring at this local scale.

Molecular adaptation in flowering and symbiotic recognition pathways: insights from patterns of polymorphism in the legume Medicago truncatula

Background

We studied patterns of molecular adaptation in the wild Mediterranean legume Medicago truncatula. We focused on two phenotypic traits that are not functionally linked: flowering time and perception of symbiotic microbes. Phenology is an important fitness component, especially for annual plants, and many instances of molecular adaptation have been reported for genes involved in flowering pathways. While perception of symbiotic microbes is also integral to adaptation in many plant species, very few reports of molecular adaptation exist for symbiotic genes. Here we used data from 57 individuals and 53 gene fragments to quantify the overall strength of both positive and purifying selection in M. truncatula and asked if footprints of positive selection can be detected at key genes of rhizobia recognition pathways.

Results

We examined nucleotide variation among 57 accessions from natural populations in 53 gene fragments: 5 genes involved in nitrogen-fixing bacteria recognition, 11 genes involved in flowering, and 37 genes used as control loci. We detected 1757 polymorphic sites yielding an average nucleotide diversity (pi) of 0.003 per site. Non-synonymous variation is under sizable purifying selection with 90% of amino-acid changing mutations being strongly selected against. Accessions were structured in two groups consistent with geographical origins. Each of these two groups harboured an excess of rare alleles, relative to expectations of a constant-sized population, suggesting recent population expansion. Using coalescent simulations and an approximate Bayesian computation framework we detected several instances of genes departing from selective neutrality within each group and showed that the polymorphism of two nodulation and four flowering genes has probably been shaped by recent positive selection.

Conclusion

We quantify the intensity of purifying selection in the M. truncatula genome and show that putative footprints of natural selection can be detected at different time scales in both flowering and symbiotic pathways.

Environmental and climatic determinants of molecular diversity and genetic population structure in a coenagrionid damselfly

Identifying environmental factors that structure intraspecific genetic diversity is of interest for both habitat preservation and biodiversity conservation. Recent advances in statistical and geographical genetics make it possible to investigate how environmental factors affect geographic organisation and population structure of molecular genetic diversity within species. Here we present a study on a common and wide ranging insect, the blue tailed damselfly Ischnuraelegans, which has been the target of many ecological and evolutionary studies. We addressed the following questions: (i) Is the population structure affected by longitudinal or latitudinal gradients?; (ii) Do geographic boundaries limit gene flow?; (iii) Does geographic distance affect connectivity and is there a signature of past bottlenecks?; (iv) Is there evidence of a recent range expansion and (vi) what is the effect of geography and climatic factors on population structure? We found low to moderate genetic sub-structuring between populations (mean F(ST) = 0.06, D(est) = 0.12), and an effect of longitude, but not latitude, on genetic diversity. No significant effects of geographic boundaries (e.g. water bodies) were found. F(ST)-and D(est)-values increased with geographic distance; however, there was no evidence for recent bottlenecks. Finally, we did not detect any molecular signatures of range expansions or an effect of geographic suitability, although local precipitation had a strong effect on genetic differentiation. The population structure of this small insect has probably been shaped by ecological factors that are correlated with longitudinal gradients, geographic distances, and local precipitation. The relatively weak global population structure and high degree of genetic variation within populations suggest that I. elegans has high dispersal ability, which is consistent with this species being an effective and early coloniser of new habitats.

Effect of anthropogenic landscape features on population genetic differentiation of Przewalski's gazelle: main role of human settlement

Anthropogenic landscapes influence evolutionary processes such as population genetic differentiation, however, not every type of landscape features exert the same effect on a species, hence it is necessary to estimate their relative effect for species management and conservation. Przewalski's gazelle (Procapra przewalskii), which inhabits a human-altered area on Qinghai-Tibet Plateau, is one of the most endangered antelope species in the world. Here, we report a landscape genetic study on Przewalski's gazelle. We used skin and fecal samples of 169 wild gazelles collected from nine populations and thirteen microsatellite markers to assess the genetic effect of anthropogenic landscape features on this species. For comparison, the genetic effect of geographical distance and topography were also evaluated. We found significant genetic differentiation, six genetic groups and restricted dispersal pattern in Przewalski's gazelle. Topography, human settlement and road appear to be responsible for observed genetic differentiation as they were significantly correlated with both genetic distance measures [F_ST/(1−F_ST) and F′_ST/(1−F′_ST)] in Mantel tests. IBD (isolation by distance) was also inferred as a significant factor in Mantel tests when genetic distance was measured as F_ST/(1−F_ST). However, using partial Mantel tests, AIC_c calculations, causal modeling and AMOVA analysis, we found that human settlement was the main factor shaping current genetic differentiation among those tested. Altogether, our results reveal the relative influence of geographical distance, topography and three anthropogenic landscape-type on population genetic differentiation of Przewalski's gazelle and provide useful information for conservation measures on this endangered species.

Genetic panmixia and demographic dependence across the North Atlantic in the deep-sea fish, blue hake (Antimora rostrata)

The efficient investment of resources and effort into conservation strategies depends on the accurate identification of management units. At the same time, understanding the processes by which population structure evolves requires an understanding of the conditions under which panmixia may exist. Here, we study a species with an unusual, apparently sex-biased pattern of distribution, and test the hypothesis that distribution processes associated with this pattern (for example, congregating at a single dominant spawning site or periodic mixing during reproduction) could lead to panmixia over a large geographic range. Using 13 microsatellite markers, we compared 393 blue hake (Antimora rostrata) from 11 sample sites across a geographic range of over 3000 km, and found no evidence of population structure. We estimated current effective population size and found it to be large (~15,000) across the sampled area. In addition, we used simulation models to test expectations about demographic correlation among populations and our ability to detect relevant levels of gene flow. All data were consistent with the interpretation of long-range panmixia.

Predictions of native American population structure using linguistic covariates in a hidden regression framework

BACKGROUND

The mainland of the Americas is home to a remarkable diversity of languages, and the relationships between genes and languages have attracted considerable attention in the past. Here we investigate to which extent geography and languages can predict the genetic structure of Native American populations.

METHODOLOGY/PRINCIPAL FINDINGS

Our approach is based on a Bayesian latent cluster regression model in which cluster membership is explained by geographic and linguistic covariates. After correcting for geographic effects, we find that the inclusion of linguistic information improves the prediction of individual membership to genetic clusters. We further compare the predictive power of Greenberg's and The Ethnologue classifications of Amerindian languages. We report that The Ethnologue classification provides a better genetic proxy than Greenberg's classification at the stock and at the group levels. Although high predictive values can be achieved from The Ethnologue classification, we nevertheless emphasize that Choco, Chibchan and Tupi linguistic families do not exhibit a univocal correspondence with genetic clusters.

CONCLUSIONS/SIGNIFICANCE

The Bayesian latent class regression model described here is efficient at predicting population genetic structure using geographic and linguistic information in Native American populations.

SPLATCHE2: a spatially explicit simulation framework for complex demography, genetic admixture and recombination

SUMMARY

SPLATCHE2 is a program to simulate the demography of populations and the resulting molecular diversity for a wide range of evolutionary scenarios. The spatially explicit simulation framework can account for environmental heterogeneity and fluctuations, and it can manage multiple population sources. A coalescent-based approach is used to generate genetic markers mostly used in population genetics studies (DNA sequences, SNPs, STRs or RFLPs). Various combinations of independent, fully or partially linked genetic markers can be produced under a recombination model based on the ancestral recombination graph. Competition between two populations (or species) can also be simulated with user-defined levels of admixture between the two populations. SPLATCHE2 may be used to generate the expected genetic diversity under complex demographic scenarios and can thus serve to test null hypotheses. For model parameter estimation, SPLATCHE2 can easily be integrated into an Approximate Bayesian Computation (ABC) framework.

AVAILABILITY AND IMPLEMENTATION

SPLATCHE2 is a C++ program compiled for Windows and Linux platforms. It is freely available at www.splatche.com, together with its related documentation and example data.

CONTACT

mathias.currat@unige.ch

Taking the chaos out of genetic patchiness: seascape genetics reveals ecological and oceanographic drivers of genetic patterns in three temperate reef species

Marine species frequently show weak and/or complex genetic structuring that is commonly dismissed as 'chaotic' genetic patchiness and ecologically uninformative. Here, using three datasets that individually feature weak chaotic patchiness, we demonstrate that combining inferences across species and incorporating environmental data can greatly improve the predictive value of marine population genetics studies on small spatial scales. Significant correlations in genetic patterns of microsatellite markers among three species, kelp bass Paralabrax clathratus, Kellet's whelk Kelletia kelletii and California spiny lobster Panulirus interruptus, in the Southern California Bight suggest that slight differences in diversity and pairwise differentiation across sampling sites are not simply noise or chaotic patchiness, but are ecologically meaningful. To test whether interspecies correlations potentially result from shared environmental drivers of genetic patterns, we assembled data on kelp bed size, sea surface temperature and estimates of site-to-site migration probability derived from a high resolution multi-year ocean circulation model. These data served as predictor variables in linear models of genetic diversity and linear mixed models of genetic differentiation that were assessed with information-theoretic model selection. Kelp was the most informative predictor of genetics for all three species, but ocean circulation also played a minor role for kelp bass. The shared patterns suggest a single spatial marine management strategy may effectively protect genetic diversity of multiple species. This study demonstrates the power of environmental and ecological data to shed light on weak genetic patterns and highlights the need for future focus on a mechanistic understanding of the links between oceanography, ecology and genetic structure.

Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata Née) in the face of climate change

Rapid climate change jeopardizes tree populations by shifting current climate zones. To avoid extinction, tree populations must tolerate, adapt, or migrate. Here we investigate geographic patterns of genetic variation in valley oak, Quercus lobata Née, to assess how underlying genetic structure of populations might influence this species' ability to survive climate change. First, to understand how genetic lineages shape spatial genetic patterns, we examine historical patterns of colonization. Second, we examine the correlation between multivariate nuclear genetic variation and climatic variation. Third, to illustrate how geographic genetic variation could interact with regional patterns of 21st Century climate change, we produce region-specific bioclimatic distributions of valley oak using Maximum Entropy (MAXENT) models based on downscaled historical (1971-2000) and future (2070-2100) climate grids. Future climatologies are based on a moderate-high (A2) carbon emission scenario and two different global climate models. Chloroplast markers indicate historical range-wide connectivity via colonization, especially in the north. Multivariate nuclear genotypes show a strong association with climate variation that provides opportunity for local adaptation to the conditions within their climatic envelope. Comparison of regional current and projected patterns of climate suitability indicates that valley oaks grow in distinctly different climate conditions in different parts of their range. Our models predict widely different regional outcomes from local displacement of a few kilometres to hundreds of kilometres. We conclude that the relative importance of migration, adaptation, and tolerance are likely to vary widely for populations among regions, and that late 21st Century conditions could lead to regional extinctions.

Landscape genetics: where are we now?

Landscape genetics has seen rapid growth in number of publications since the term was coined in 2003. An extensive literature search from 1998 to 2008 using keywords associated with landscape genetics yielded 655 articles encompassing a vast array of study organisms, study designs and methodology. These publications were screened to identify 174 studies that explicitly incorporated at least one landscape variable with genetic data. We systematically reviewed this set of papers to assess taxonomic and temporal trends in: (i) geographic regions studied; (ii) types of questions addressed; (iii) molecular markers used; (iv) statistical analyses used; and (v) types and nature of spatial data used. Overall, studies have occurred in geographic regions proximal to developed countries and more commonly in terrestrial vs. aquatic habitats. Questions most often focused on effects of barriers and/or landscape variables on gene flow. The most commonly used molecular markers were microsatellites and amplified fragment length polymorphism (AFLPs), with AFLPs used more frequently in plants than animals. Analysis methods were dominated by Mantel and assignment tests. We also assessed differences among journals to evaluate the uniformity of reporting and publication standards. Few studies presented an explicit study design or explicit descriptions of spatial extent. While some landscape variables such as topographic relief affected most species studied, effects were not universal, and some species appeared unaffected by the landscape. Effects of habitat fragmentation were mixed, with some species altering movement paths and others unaffected. Taken together, although some generalities emerged regarding effects of specific landscape variables, results varied, thereby reinforcing the need for species-specific work. We conclude by: highlighting gaps in knowledge and methodology, providing guidelines to authors and reviewers of landscape genetics studies, and suggesting promising future directions of inquiry.

Speciation genetics: current status and evolving approaches

The view of species as entities subjected to natural selection and amenable to change put forth by Charles Darwin and Alfred Wallace laid the conceptual foundation for understanding speciation. Initially marred by a rudimental understanding of hereditary principles, evolutionists gained appreciation of the mechanistic underpinnings of speciation following the merger of Mendelian genetic principles with Darwinian evolution. Only recently have we entered an era where deciphering the molecular basis of speciation is within reach. Much focus has been devoted to the genetic basis of intrinsic postzygotic isolation in model organisms and several hybrid incompatibility genes have been successfully identified. However, concomitant with the recent technological advancements in genome analysis and a newfound interest in the role of ecology in the differentiation process, speciation genetic research is becoming increasingly open to non-model organisms. This development will expand speciation research beyond the traditional boundaries and unveil the genetic basis of speciation from manifold perspectives and at various stages of the splitting process. This review aims at providing an extensive overview of speciation genetics. Starting from key historical developments and core concepts of speciation genetics, we focus much of our attention on evolving approaches and introduce promising methodological approaches for future research venues.

Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data

The Mantel test is widely used to test the linear or monotonic independence of the elements in two distance matrices. It is one of the few appropriate tests when the hypothesis under study can only be formulated in terms of distances; this is often the case with genetic data. In particular, the Mantel test has been widely used to test for spatial relationship between genetic data and spatial layout of the sampling locations. We describe the domain of application of the Mantel test and derived forms. Formula development demonstrates that the sum-of-squares (SS) partitioned in Mantel tests and regression on distance matrices differs from the SS partitioned in linear correlation, regression and canonical analysis. Numerical simulations show that in tests of significance of the relationship between simple variables and multivariate data tables, the power of linear correlation, regression and canonical analysis is far greater than that of the Mantel test and derived forms, meaning that the former methods are much more likely than the latter to detect a relationship when one is present in the data. Examples of difference in power are given for the detection of spatial gradients. Furthermore, the Mantel test does not correctly estimate the proportion of the original data variation explained by spatial structures. The Mantel test should not be used as a general method for the investigation of linear relationships or spatial structures in univariate or multivariate data. Its use should be restricted to tests of hypotheses that can only be formulated in terms of distances.

Spatially explicit Bayesian clustering models in population genetics

This article reviews recent developments in Bayesian algorithms that explicitly include geographical information in the inference of population structure. Current models substantially differ in their prior distributions and background assumptions, falling into two broad categories: models with or without admixture. To aid users of this new generation of spatially explicit programs, we clarify the assumptions underlying the models, and we test these models in situations where their assumptions are not met. We show that models without admixture are not robust to the inclusion of admixed individuals in the sample, thus providing an incorrect assessment of population genetic structure in many cases. In contrast, admixture models are robust to an absence of admixture in the sample. We also give statistical and conceptual reasons why data should be explored using spatially explicit models that include admixture.

Genetic structure of a recent climate change-driven range extension

The life-history strategies of some species make them strong candidates for rapid exploitation of novel habitat under new climate regimes. Some early-responding species may be considered invasive, and negatively impact on 'naïve' ecosystems. The barrens-forming sea urchin Centrostephanus rodgersii is one such species, having a high dispersal capability and a high-latitude range margin limited only by a developmental temperature threshold. Within this species' range in eastern Australian waters, sea temperatures have increased at greater than double the global average rate. The coinciding poleward range extension of C. rodgersii has caused major ecological changes, threatening reef biodiversity and fisheries productivity. We investigated microsatellite diversity and population structure associated with range expansion by this species. Generalized linear model analyses revealed no reduction in genetic diversity in the newly colonized region. A 'seascape genetics' analysis of genetic distances found no spatial genetic structure associated with the range extension. The distinctive genetic characteristic of the extension zone populations was reduced population-specific F(ST), consistent with very rapid population expansion. Demographic and genetic simulations support our inference of high connectivity between pre- and post-extension zones. Thus, the range shift appears to be a poleward extension of the highly-connected rangewide population of C. rodgersii. This is consistent with advection of larvae by the intensified warm water East Australian current, which has also increased Tasmanian Sea temperatures above the species' lower developmental threshold. Thus, ocean circulation changes have improved the climatic suitability of novel habitat for C. rodgersii and provided the supply of recruits necessary for colonization.

Dispersal and population structure at different spatial scales in the subterranean rodent Ctenomys australis

Background

The population genetic structure of subterranean rodent species is strongly affected by demographic (e.g. rates of dispersal and social structure) and stochastic factors (e.g. random genetic drift among subpopulations and habitat fragmentation). In particular, gene flow estimates at different spatial scales are essential to understand genetic differentiation among populations of a species living in a highly fragmented landscape. Ctenomys australis (the sand dune tuco-tuco) is a territorial subterranean rodent that inhabits a relatively secure, permanently sealed burrow system, occurring in sand dune habitats on the coastal landscape in the south-east of Buenos Aires province, Argentina. Currently, this habitat is threatened by urban development and forestry and, therefore, the survival of this endemic species is at risk. Here, we assess population genetic structure and patterns of dispersal among individuals of this species at different spatial scales using 8 polymorphic microsatellite loci. Furthermore, we evaluate the relative importance of sex and habitat configuration in modulating the dispersal patterns at these geographical scales.

Results

Our results show that dispersal in C. australis is not restricted at regional spatial scales (~ 4 km). Assignment tests revealed significant population substructure within the study area, providing support for the presence of two subpopulations from three original sampling sites. Finally, male-biased dispersal was found in the Western side of our study area, but in the Eastern side no apparent philopatric pattern was found, suggesting that in a more continuous habitat males might move longer distances than females.

Conclusions

Overall, the assignment-based approaches were able to detect population substructure at fine geographical scales. Additionally, the maintenance of a significant genetic structure at regional (~ 4 km) and small (less than 1 km) spatial scales despite apparently moderate to high levels of gene flow between local sampling sites could not be explained simply by the linear distance among them. On the whole, our results support the hypothesis that males disperse more frequently than females; however they do not provide support for strict philopatry within females.

Spatial genetic structure of Aedes aegypti mosquitoes in mainland Southeast Asia

Aedes aegypti mosquitoes originated in Africa and are thought to have spread recently to Southeast Asia, where they are the major vector of dengue. Thirteen microsatellite loci were used to determine the genetic population structure of A. aegypti at a hierarchy of spatial scales encompassing 36 sites in Myanmar, Cambodia and Thailand, and two sites in Sri Lanka and Nigeria. Low, but significant, genetic structuring was found at all spatial scales (from 5 to >2000 km) and significant F IS values indicated genetic structuring even within 500 m. Spatially dependent genetic-clustering methods revealed that although spatial distance plays a role in shaping larger-scale population structure, it is not the only factor. Genetic heterogeneity in major port cities and genetic similarity of distant locations connected by major roads, suggest that human transportation routes have resulted in passive long-distance migration of A. aegypti. The restricted dispersal on a small spatial scale will make localized control efforts and sterile insect technology effective for dengue control. Conversely, preventing the establishment of insecticide resistance genes or spreading refractory genes in a genetic modification strategy would be challenging. These effects on vector control will depend on the relative strength of the opposing effects of passive dispersal.

Local selection and population structure in a deep-sea fish, the roundnose grenadier (Coryphaenoides rupestris)

Local populations within a species can become isolated by stochastic or adaptive processes, though it is most commonly the former that we quantify. Using presumably neutral markers we can assess the time-dependent process of genetic drift, and thereby quantify patterns of differentiation in support of the effective management of diversity. However, adaptive differences can be overlooked in these studies, and these are the very characteristics that we hope to conserve by managing neutral diversity. In this study, we used 16 hypothetically neutral microsatellite markers to investigate the genetic structure of the roundnose grenadier in the North Atlantic. We found that one locus was a clear outlier under directional selection, with F(ST) values much greater than at the remaining loci. Differentiation between populations at this locus was related to depth, suggesting directional selection, presumably acting on a linked locus. Considering only the loci identified as neutral, there remained significant population structure over the region of the North Atlantic studied. In addition to a weak pattern of isolation by distance, we identified a putative barrier to gene flow between sample sites either side of the Charlie-Gibbs Fracture Zone, which marks the location where the sub-polar front crosses the Mid-Atlantic Ridge. This may reflect a boundary across which larvae are differentially distributed in separate current systems to some extent, promoting differentiation by drift. Structure due to both drift and apparent selection should be considered in management policy.

Genetics in geographically structured populations: defining, estimating and interpreting F(ST)

Wright's F-statistics, and especially F(ST), provide important insights into the evolutionary processes that influence the structure of genetic variation within and among populations, and they are among the most widely used descriptive statistics in population and evolutionary genetics. Estimates of F(ST) can identify regions of the genome that have been the target of selection, and comparisons of F(ST) from different parts of the genome can provide insights into the demographic history of populations. For these reasons and others, F(ST) has a central role in population and evolutionary genetics and has wide applications in fields that range from disease association mapping to forensic science. This Review clarifies how F(ST) is defined, how it should be estimated, how it is related to similar statistics and how estimates of F(ST) should be interpreted.

A landscape genetics approach for quantifying the relative influence of historic and contemporary habitat heterogeneity on the genetic connectivity of a rainforest bird

Landscape genetics is an important framework for investigating the influence of spatial pattern on ecological process. Nevertheless, the standard analytic frameworks in landscape genetics have difficulty evaluating hypotheses about spatial processes in dynamic landscapes. We use a predictive hypothesis-driven approach to quantify the relative contribution of historic and contemporary processes to genetic connectivity. By confronting genetic data with models of historic and contemporary landscapes, we identify dispersal processes operating in naturally heterogeneous and human-altered systems. We demonstrate the approach using a case study of microsatellite polymorphism and indirect estimates of gene flow for a rainforest bird, the logrunner (Orthonyx temminckii). Of particular interest was how much information in the genetic data was attributable to processes occurring in the reconstructed historic landscape and contemporary human-modified landscape. A linear mixed model was used to estimate appropriate sampling variance from nonindependent data and information-theoretic model selection provided strength of evidence for alternative hypotheses. The contemporary landscape explained slightly more information in the genetic differentiation data than the historic landscape, and there was considerable evidence for a temporal shift in dispersal pattern. In contrast, migration rates estimated from genealogical information were primarily influenced by contemporary landscape change. We discovered that landscape heterogeneity facilitated gene flow before European settlement, but contemporary deforestation is rapidly becoming the most important barrier to logrunner dispersal.

Spatial inference of admixture proportions and secondary contact zones

Genetic admixture of distinct gene pools is the consequence of complex spatiotemporal processes that could have involved massive migration and local mating during the history of a species. However, current methods for estimating individual admixture proportions lack the incorporation of such a piece of information. Here, we extend Bayesian clustering algorithms by including global trend surfaces and spatial autocorrelation in the prior distribution on individual admixture coefficients. We test our algorithm by using spatially explicit and realistic coalescent simulations of colonization followed by secondary contact. By coupling our multiscale spatial analyses with a Bayesian evaluation of model complexity and fit, we show that the algorithm provides a correct description of smooth clinal variation, while still detecting zones of sharp variation when they are present in the data. We also apply our approach to understand the population structure of the killifish, Fundulus heteroclitus, for which the algorithm uncovers a presumed contact zone in the Atlantic coast of North America.

Using genetics to understand the dynamics of wild primate populations

While much can be learned about primates by means of observation, the slow life history of many primates means that even decades of dedicated effort cannot illuminate long-term evolutionary processes. For example, while the size of a contemporary population can be estimated from field censuses, it is often desirable to know whether a population has been constant or changing in size over a time frame of hundreds or thousands of years. Even the nature of "a population" is open to question, and the extent to which individuals successfully disperse among defined populations is also difficult to estimate by using observational methods alone. Researchers have thus turned to genetic methods to examine the size, structure, and evolutionary histories of primate populations. Many results have been gained by study of sequence variation of maternally inherited mitochondrial DNA, but in recent years researchers have been increasingly focusing on analysis of short, highly variable microsatellite segments in the autosomal genome for a high-resolution view of evolutionary processes involving both sexes. In this review we describe some of the insights thus gained, and discuss the likely impact on this field of new technologies such as high-throughput DNA sequencing.

Selection and sticklebacks

Over the last decade, there has been increasing circumstantial evidence for the action of natural selection in the genome, arising largely from molecular genetic surveys of large numbers of markers. In nonmodel organisms without densely mapped markers, a frequently used method is to identify loci that have unusually high or low levels of genetic differentiation, or low genetic diversity relative to other populations. The paper by Mäkinen et al. (2008a) in this issue of Molecular Ecology reports the results of a survey of microsatellite allele frequencies at more than 100 loci in seven populations of the three-spined stickleback (Gasterosteus aculeatus). They show that a microsatellite locus and two indel markers located within the intron of the Eda gene, known to control the number of lateral plates in the stickleback (Fig. 1), tend to be much more highly genetically differentiated than other loci, a finding that is consistent with the action of local selection. They identify a further two independent candidates for local selection, and, most intriguingly, they further suggest that up to 15% of their loci may provide evidence of balancing selection.

Correcting for ascertainment bias in the inference of population structure

BACKGROUND

The ascertainment process of molecular markers amounts to disregard loci carrying alleles with low frequencies. This can result in strong biases in inferences under population genetics models if not properly taken into account by the inference algorithm. Attempting to model this censoring process in view of making inference of population structure (i.e.identifying clusters of individuals) brings up challenging numerical difficulties.

METHOD

These difficulties are related to the presence of intractable normalizing constants in Metropolis-Hastings acceptance ratios. This can be solved via an Markov chain Monte Carlo (MCMC) algorithm known as single variable exchange algorithm (SVEA).

RESULT

We show how this general solution can be implemented for a class of clustering models of broad interest in population genetics that includes the models underlying the computer programs STRUCTURE, GENELAND and GESTE. We also implement the method proposed for a simple example and show that it allows us to reduce the bias substantially.

AVAILABILITY

Further details and a computer program implementing the method are available from http://folk.uio.no/gillesg/AscB/.

Interactions between environmental factors can hide isolation by distance patterns: a case study of Ctenomys rionegrensis in Uruguay

Identifying the factors responsible for the structuring of genetic diversity is of fundamental importance for biodiversity conservation. However, arriving at such understanding is difficult owing to the many factors involved and the potential interactions between them. Here, we present an example of how such interactions can preclude us from arriving at a complete characterization of the demographic history and genetic structure of a species. Ctenomys rionegrensis is a species with restricted dispersal abilities and, as such, should exhibit an isolation by distance (IBD) pattern, which previous studies were unable to uncover. It was therefore concluded that this species underwent a recent population expansion. Using a novel hierarchical Bayesian method, we show that the inability to detect the IBD pattern is due to the interaction between elevation and geographical distance. We posit that populations in low areas suffer periodic floods that may reduce local population sizes, increasing genetic drift, a process that masks the effect of distance on genetic differentiation. Our results do not refute the possibility that the populations of C. rionegrensis underwent a recent population expansion but they indicate that an alternative scenario described by a metapopulation model at or near migration-drift equilibrium cannot be excluded either.

A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective

Identifying loci under natural selection from genomic surveys is of great interest in different research areas. Commonly used methods to separate neutral effects from adaptive effects are based on locus-specific population differentiation coefficients to identify outliers. Here we extend such an approach to estimate directly the probability that each locus is subject to selection using a Bayesian method. We also extend it to allow the use of dominant markers like AFLPs. It has been shown that this model is robust to complex demographic scenarios for neutral genetic differentiation. Here we show that the inclusion of isolated populations that underwent a strong bottleneck can lead to a high rate of false positives. Nevertheless, we demonstrate that it is possible to avoid them by carefully choosing the populations that should be included in the analysis. We analyze two previously published data sets: a human data set of codominant markers and a Littorina saxatilis data set of dominant markers. We also perform a detailed sensitivity study to compare the power of the method using amplified fragment length polymorphism (AFLP), SNP, and microsatellite markers. The method has been implemented in a new software available at our website (http://www-leca.ujf-grenoble.fr/logiciels.htm).

Inference of structure in subdivided populations at low levels of genetic differentiation--the correlated allele frequencies model revisited

MOTIVATION

This article considers the problem of estimating population genetic subdivision from multilocus genotype data. A model is considered to make use of genotypes and possibly of spatial coordinates of sampled individuals. A particular attention is paid to the case of low genetic differentiation with the help of a previously described Bayesian clustering model where allele frequencies are assumed to be a priori correlated. Under this model, various problems of inference are considered, in particular the common and difficult, but still unaddressed, situation where the number of populations is unknown.

RESULTS

A Markov chain Monte Carlo algorithm and a new post-processing scheme are proposed. It is shown that they significantly improve the accuracy of previously existing algorithms in terms of estimated number of populations and estimated population membership. This is illustrated numerically with data simulated from the prior-likelihood model used in inference and also with data simulated from a Wright-Fisher model. Improvements are also illustrated on a real dataset of eighty-eight wolverines (Gulo gulo) genotyped at 10 microsatellites loci. The interest of the solutions presented here are not specific to any clustering model and are hence relevant to many settings in populations genetics where weakly differentiated populations are assumed or sought.

AVAILABILITY

The improvements implemented will be made available in version 3.0.0 of the R package Geneland. Informations on how to get and use the software are available from http://folk.uio.no/gillesg/Geneland.html.

SUPPLEMENTARY INFORMATION

http://folk.uio.no/gillesg/CFM/SuppMat.pdf.

The use of neutral and non-neutral SSRs to analyse the genetic structure of a Tunisian collection of Medicago truncatula lines and to reveal associations with eco-environmental variables

In this study, we investigated the genetic diversity of a collection of 136 Medicago truncatula lines from 10 Tunisian natural populations collected in well-defined locations and in various ecological conditions of soil, salinity and water availability. The genetic diversity was evaluated using a set of 18 microsatellites (SSRs), representing the 8 chromosomes of M. truncatula. A neutrality test showed that 7 SSRs were non-neutral with evidence of balancing selection. The 11 neutral SSRs revealed a geographical pooling with the Tunisian Dorsale axis restricting migration of alleles. The 7 non-neutral alleles demonstrate a correlation with rainfall, altitude and salinity environmental variables suggesting that these SSRs are linked to genes involved in water use efficiency, resistance to salinity or adaptation to altitude, and that there is local adaptation of M. truncatula to these variables. This demonstrates that the choice of so-called neutral markers should be carefully evaluated in population genetic studies. This study illustrates the genetic diversity occurring in natural Tunisian populations of M. truncatula and describes the first collection of this species dedicated to natural variation involved in adaptation to the environment.

An approximate Bayesian computation approach to overcome biases that arise when using amplified fragment length polymorphism markers to study population structure

There is great interest in using amplified fragment length polymorphism (AFLP) markers because they are inexpensive and easy to produce. It is, therefore, possible to generate a large number of markers that have a wide coverage of species genomes. Several statistical methods have been proposed to study the genetic structure using AFLPs but they assume Hardy-Weinberg equilibrium and do not estimate the inbreeding coefficient, F(IS). A Bayesian method has been proposed by Holsinger and colleagues that relaxes these simplifying assumptions but we have identified two sources of bias that can influence estimates based on these markers: (i) the use of a uniform prior on ancestral allele frequencies and (ii) the ascertainment bias of AFLP markers. We present a new Bayesian method that avoids these biases by using an implementation based on the approximate Bayesian computation (ABC) algorithm. This new method estimates population-specific F(IS) and F(ST) values and offers users the possibility of taking into account the criteria for selecting the markers that are used in the analyses. The software is available at our web site (http://www-leca.ujf-grenoble.fr/logiciels.htm). Finally, we provide advice on how to avoid the effects of ascertainment bias.