Characterizing the Influence of Effective Population Size on the Rate of Adaptation: Gillespie's Darwin Domain

Characterizing the role of effective population size in dictating the rate of adaptive evolution remains a major challenge in evolutionary biology. Depending on the underlying distribution of fitness effects of new mutations, populations of different sizes may differ vastly in their rate of adaptation. Here, we collect polymorphism data at over 100 loci for two closely related Drosophila species with different current effective population sizes (N-e), Drosophila miranda and D. pseudoobscura, to evaluate the prevalence of adaptive evolution versus genetic drift in molecular evolution. Utilizing these large and consistently sampled data sets, we obtain greatly improved estimates of the demographic histories of both species. Specifically, although current N-e differs between these species, their ancestral sizes were much more similar. We find that statistical approaches capturing recent adaptive evolution (using patterns of polymorphisms) detect higher rates of adaptive evolution in the larger D. pseudoobscura population. In contrast, methods aimed at detecting selection over longer time periods (i.e., those relying on divergence data) estimate more similar rates of adaptation between the two species. Thus, our results suggest an important role of effective population size in dictating rates of adaptation and highlight how complicated population histories-as is probably the case for most species-can effect rates of adaptation. Additionally, we also show how different methodologies to detect positive selection can reveal information about different timescales of adaptive evolution.

Multi-scale landscape genomic models

Kevin Leempoel

Environmental heterogeneity is one of the main actors of biodiversity and species adaptation as it exerts a selective pressure on observable characteristics of living organisms. Consequently, local adaptation favours certain genetic variants and, by doing so, leaves a footprint in the genetic heritage across populations. The identification of these adaptive genetic variations is the main objective of landscape genomics and allows, among other things, to study the role of specific regions of the genome in evolutionary processes. Landscape genomics studies also provide essential information for species conservation and for the prediction of migrations due to environmental changes. To identify these adaptations to the environment, it is necessary to define a study area where the populations are sampled. However, defining the scale of the study area is not a trivial task. In fact, whether the work is carried out at a local or at a broad scale determines the relevance of environmental factors and the type of signature of selection that will be observed. In addition, the concept of scale in ecology takes into account not only the extent of the study area but also the pattern and density of the geographic distribution of observations, and the spatial resolution of predictors (environmental variables), which is intrinsically linked to the extent. However, a priori indications about the relevance of any resolution over another are rare in the literature and it is therefore essential to question this issue. In this thesis, we propose a multi-scale landscape genomic framework to identify signatures of adaptation to the environment. This multidisciplinary framework lies at the interface between geographic information systems, spatial analysis, environmental modelling, population genetics and computer science. Specifically, we focus on the relevance of variables derived from Digital Elevation Models (DEMs) and on the application of multi-scale analysis aiming to detect signatures of selection. We applied this analytical framework to three case studies, comprising four species: Biscutella laevigata sampled at a local scale, Plantago major at a regional scale, sheep and goats at a large scale. In particular, the case of B. laevigata allowed us to evaluate the role of topographic features based on Very High Resolution DEMs and to include DEM-derived variables as predictors in association models to study the adaptation of species to their local environment. On the other hand, the case of Moroccan sheep and goats permitted to include for the first time whole genome sequence data within landscape genomic models. The results revealed several important findings. We showed that micro-climate variability is highly dependent on topographic factors at a local scale and that therefore, DEMs are relevant for understanding species adaptation to a mountainous environment. We also demonstrated that it is essential to consider the scale of spatial representativeness by assessing DEM-derived variables at various spatial resolutions. Indeed, two out of three case studies showed that the models involving topographic variables were sensitive to changes in resolution. In summary, we used several landscape genetics approaches to understand the role of environmental factors in the local adaptation of various species. Our findings mainly provide an important contribution to the understanding and use of scale in landscape genomics, also useful in landscape ecology.

EPFL2015

The population genomics of rapid adaptation: disentangling signatures of selection and demography in white sands lizards

Jeffrey David Jensen, Stefan Jean Laurent, Nicky Louise Ormond, Susanne Petra Pfeifer

Understanding the process of adaptation during rapid environmental change remains one of the central focal points of evolutionary biology. The recently formed White Sands system of southern New Mexico offers an outstanding example of rapid adaptation, with a variety of species having rapidly evolved blanched forms on the dunes that contrast with their close relatives in the surrounding dark soil habitat. In this study, we focus on two of the White Sands lizard species, Sceloporus cowlesi and Aspidoscelis inornata, for which previous research has linked mutations in the melanocortin-1 receptor gene (Mc1r) to blanched coloration. We sampled populations both on and off the dunes and used a custom sequence capture assay based on probed fosmid libraries to obtain >50kb of sequence around Mc1r and hundreds of other random genomic locations. We then used model-based statistical inference methods to describe the demographic and adaptive history characterizing the colonization of White Sands. We identified a number of similarities between the two focal species, including strong evidence of selection in the blanched populations in the Mc1r region. We also found important differences between the species, suggesting different colonization times, different genetic architecture underlying the blanched phenotype and different ages of the beneficial alleles. Finally, the beneficial allele is dominant in S.cowlesi and recessive in A.inornata, allowing for a rare empirical test of theoretically expected patterns of selective sweeps under these differing models.

Wiley-Blackwell2016

Multi-scale landscape genomic models

Kevin Leempoel

EPFL2015