Identifying the environmental factors that determine the genetic structure of populations

Landscape genetics and hierarchical genetic structure in Atlantic salmon: the interaction of gene flow and local adaptation

Disentangling evolutionary forces that may interact to determine the patterns of genetic differentiation within and among wild populations is a major challenge in evolutionary biology. The objective of this study was to assess the genetic structure and the potential influence of several ecological variables on the extent of genetic differentiation at multiple spatial scales in a widely distributed species, the Atlantic salmon, Salmo salar. A total of 2775 anadromous fish were sampled from 51 rivers along the North American Atlantic coast and were genotyped using 13 microsatellites. A Bayesian analysis clustered these populations into seven genetically and geographically distinct groups, characterized by different environmental and ecological factors, mainly temperature. These groups were also characterized by different extent of genetic differentiation among populations. Dispersal was relatively high and of the same magnitude within compared to among regional groups, which contrasted with the maintenance of a regional genetic structure. However, genetic differentiation was lower among populations exchanging similar rates of local as opposed to inter-regional migrants, over the same geographical scale. This raised the hypothesis that gene flow could be constrained by local adaptation at the regional scale. Both coastal distance and temperature regime were found to influence the observed genetic structure according to landscape genetic analyses. The influence of other factors such as latitude, river length and altitude, migration tactic, and stocking was not significant at any spatial scale. Overall, these results suggested that the interaction between gene flow and thermal regime adaptation mainly explained the hierarchical genetic structure observed among Atlantic salmon populations.

The landscape genetics of yellow perch (Perca flavescens) in a large fluvial ecosystem

Landscape genetics is being increasingly applied to elucidate the role of environmental features on the population structure of terrestrial organisms. However, the potential of this framework has been little explored in aquatic ecosystems such as large rivers. Here, we used a landscape genetics approach in order to (i) document the population structure of the yellow perch (Perca flavescens) by means of genetic variation at microsatellite markers, (ii) assess to what extent the structure was explained by landscape heterogeneity, and (iii) interpret the relevance of interactions between genetics and landscape for management and conservation. Analysis of the genetic variation among 1715 individuals from 16 localities and distributed over 310 km in the freshwater section of the Saint Lawrence River (Québec, Canada) revealed a relatively modest level of genetic structuring (F(ST) = 0.039). Application of the Monmonier's algorithm combining geographical and genetic information identified three zones of restricted gene flow defining four distinct populations. Physical barriers played a more important role on gene flow and genetic structure than waterway geographical distance. We found correlations between genetic differentiation and presence of distinct water masses in the sector of Lake Saint-Louis (r = 0.7177, P = 0.0340) and with fragmentation of spawning habitats in the sector of Lake Saint-Pierre (r = 0.8578, P = 0.0095). Our results support the treatment of four distinct biological units, which is in contrast with the current basis for yellow perch management. Finally, this study showed that landscape genetics is a powerful means to identify environmental barriers to gene flow causing genetic discontinuities in apparently highly connected aquatic landscapes.

A new Bayesian method to identify the environmental factors that influence recent migration

We present a new multilocus genotype method that makes inferences about recent immigration rates and identifies the environmental factors that are more likely to explain observed gene flow patterns. It also estimates population-specific inbreeding coefficients, allele frequencies, and local population F(ST)'s and performs individual assignments. We generate synthetic data sets to determine the region of the parameter space where our method is and is not able to provide accurate estimates. Our simulation study indicates that reliable results can be obtained when the global level of genetic differentiation (F(ST)) is >1%, the number of loci is only 10, and sample sizes are of the order of 50 individuals per population. We illustrate our method by applying it to Pakistani human data, considering altitude and geographic distance as explanatory factors. Our results suggest that altitude explains better the genetic data than geographic distance. Additionally, they show that southern low-altitude populations have higher migration rates than northern high-altitude ones.

Genetic isolation of a now extinct population of bottlenose dolphins (Tursiops truncatus)

A number of dolphin species, though highly mobile, show genetic structure among parapatric and sometimes sympatric populations. However, little is known about the temporal patterns of population structure for these species. Here, we apply Bayesian inference and data from ancient DNA to assess the structure and dynamics of bottlenose dolphin (Tursiops truncatus) populations in the coastal waters of the UK. We show that regional population structure in UK waters is consistent with earlier studies suggesting local habitat dependence for this species in the Mediterranean Sea and North Atlantic. One genetically differentiated UK population went extinct at least 100 years ago and has not been replaced. The data indicate that this was a local extinction, and not a case of historical range shift or contraction. One possible interpretation is a declining metapopulation and conservation need for this species in the UK.

Landscape genetics goes to sea

A recent study revealing geographical and environmental barriers to gene flow in the harbour porpoise shows the great potential of 'landscape genetics' when applied to marine organisms.

Genetic variation and population structure in native Americans

We examined genetic diversity and population structure in the American landmass using 678 autosomal microsatellite markers genotyped in 422 individuals representing 24 Native American populations sampled from North, Central, and South America. These data were analyzed jointly with similar data available in 54 other indigenous populations worldwide, including an additional five Native American groups. The Native American populations have lower genetic diversity and greater differentiation than populations from other continental regions. We observe gradients both of decreasing genetic diversity as a function of geographic distance from the Bering Strait and of decreasing genetic similarity to Siberians--signals of the southward dispersal of human populations from the northwestern tip of the Americas. We also observe evidence of: (1) a higher level of diversity and lower level of population structure in western South America compared to eastern South America, (2) a relative lack of differentiation between Mesoamerican and Andean populations, (3) a scenario in which coastal routes were easier for migrating peoples to traverse in comparison with inland routes, and (4) a partial agreement on a local scale between genetic similarity and the linguistic classification of populations. These findings offer new insights into the process of population dispersal and differentiation during the peopling of the Americas.

Going the distance: human population genetics in a clinal world

Global human genetic variation is greatly influenced by geography, with genetic differentiation between populations increasing with geographic distance and within-population diversity decreasing with distance from Africa. In fact, these 'clines' can explain most of the variation in human populations. Despite this, population genetics inferences often rely on models that do not take geography into account, which could result in misleading conclusions when working at global geographic scales. Geographically explicit approaches have great potential for the study of human population genetics. Here, we discuss the most promising avenues of research in the context of human settlement history and the detection of genomic elements under natural selection. We also review recent technical advances and address the challenges of integrating geography and genetics.

Generalized analysis of molecular variance

Many studies in the fields of genetic epidemiology and applied population genetics are predicated on, or require, an assessment of the genetic background diversity of the individuals chosen for study. A number of strategies have been developed for assessing genetic background diversity. These strategies typically focus on genotype data collected on the individuals in the study, based on a panel of DNA markers. However, many of these strategies are either rooted in cluster analysis techniques, and hence suffer from problems inherent to the assignment of the biological and statistical meaning to resulting clusters, or have formulations that do not permit easy and intuitive extensions. We describe a very general approach to the problem of assessing genetic background diversity that extends the analysis of molecular variance (AMOVA) strategy introduced by Excoffier and colleagues some time ago. As in the original AMOVA strategy, the proposed approach, termed generalized AMOVA (GAMOVA), requires a genetic similarity matrix constructed from the allelic profiles of individuals under study and/or allele frequency summaries of the populations from which the individuals have been sampled. The proposed strategy can be used to either estimate the fraction of genetic variation explained by grouping factors such as country of origin, race, or ethnicity, or to quantify the strength of the relationship of the observed genetic background variation to quantitative measures collected on the subjects, such as blood pressure levels or anthropometric measures. Since the formulation of our test statistic is rooted in multivariate linear models, sets of variables can be related to genetic background in multiple regression-like contexts. GAMOVA can also be used to complement graphical representations of genetic diversity such as tree diagrams (dendrograms) or heatmaps. We examine features, advantages, and power of the proposed procedure and showcase its flexibility by using it to analyze a wide variety of published data sets, including data from the Human Genome Diversity Project, classical anthropometry data collected by Howells, and the International HapMap Project.

Humans exhibit great genetic diversity. Understanding the factors that contribute to and sustain this diversity is an important research area. Not only can such understanding shed light on human origins, but it can also assist in the discovery of genes and genetic factors that contribute to debilitating diseases. Statistical analysis methods that can facilitate the identification of factors contributing to or associated with human genetic diversity are growing in number as new high-throughput molecular genetic assays and technologies are developed. We consider the use of an analysis method termed generalized analysis of molecular variance (GAMOVA), which builds off of previously proposed analysis methods for testing hypotheses about the factors associated with genetic background diversity. We apply the method in a wide variety of settings and show that it is both flexible and powerful. GAMOVA has great potential to assist in population-based human genetic studies, as it can be used to address questions such as: Is a sample of affected cases and unaffected controls from a homogeneous population, or is there evidence of heterogeneity that could affect the results of an association study? Is there reason to believe that the ancestry of a set of individuals influences the traits that they have?