GEOME : A web-based landscape genomics geocomputation platform

Landscape ecologists and resource conservation managers increasingly need geo-referenced data but are not trained to efficiently use Geographical Information Systems together with appropriate geo-environmental information and spatial analysis approaches. In the present context of rapid global climate change, they show a renewed interest to study adaptation in species of interest (wildlife, livestock, plants) with the help of landscape genomics. This emerging research field is at the interface of genome sciences, environmental resources analysis, bioinformatics, geocomputation and spatial statistics. Their combination permits to assess the level of association between specific genomic regions of living organisms and environmental factors, to better understand the genetic basis of local adaptation. Landscape genomics is thus able to provide objective criteria to prioritize species which are the most worthwhile preserving. In livestock science for instance, husbandry based on adapted breeds is of key importance to emerging countries. Consequently, to facilitate the use of landscape genomics, the GEOME project proposes a WebGIS-based platform for the integrated analysis of environmental, ecological and molecular data through the implementation of an original set of combined geocomputation, databases, spatial analysis and population genetics tools. The platform will gather two existing software to identify genomic regions under selection (BayeScan and MatSAM) using a WebGIS solution named MapIntera. The latter is a multiple views exploratory visualization interface allowing users to interactively browse and query multiple dynamic layouts (e.g. graphs, maps). It is based on a SVG graphical interface coupled to Javascript interactivity and AJAX technology for the retrieval of information from a spatial database. The platform will be connected to a High Performance Computing infrastructure, able to handle and process very large genome and environmental data sets

Simple rules for an efficient use of Geographic Information Systems in molecular ecology

Stéphane Joost, Kevin Leempoel, Estelle Rochat, Ivo Widmer

Geographic Information Systems (GIS) are becoming increasingly popular in the context of molecular ecology and conservation biology thanks to their display options efficiency, flexibility and management of geodata. Indeed, spatial data for wildlife and livestock species is becoming a trend with many researchers publishing genomic data that is specifically suitable for landscape studies. GIS uniquely reveal the possibility to overlay genetic information with environmental data and, as such, allow us to locate and analyze genetic boundaries of various plant and animal species or to study gene-environment associations (GEA). This means that, using GIS, we can potentially identify the genetic bases of species adaptation to particular geographic conditions or to climate change. However, many biologists are not familiar with the use of GIS and underlying concepts and thus experience difficulties in finding relevant information and instructions on how to use them. In this paper, we illustrate the power of free and open source GIS approaches and provide essential information for their successful application in molecular ecology. First, we introduce key concepts related to GIS than are too often overlooked in the literature, for example coordinate systems, GPS accuracy and scale. We then provide an overview of the most employed open-source GIS-related software, file formats and refer to major environmental databases. We also reconsider sampling strategies as high costs of Next Generation Sequencing (NGS) data currently diminish the number of samples that can be sequenced per location. Thereafter, we detail methods of data exploration and spatial statistics suited for the analysis of large genetic datasets. Finally, we provide suggestions to properly edit maps and to make them as comprehensive as possible, either manually or trough programming languages.

2017

GEOME: Towards an integrated web-based landscape genomics platform

Stéphane Joost, Michaël Kalbermatten

The aim of the GEOME project is to develop a WebGIS-based platform for the integrated analysis of environmental, ecological and molecular data through the implementation of an original set of combined databases, spatial analysis and population genetics tools, in a High Performance Computing context. GEOME will support tasks of landscape ecologists and resource conservation managers who increasingly have to use geo-referenced data but are not trained to efficiently use Geographical Information Systems together with appropriate geoenvironmental information and spatial analysis approaches. Preliminary developments (three first applications) are already available here: http://lasigpc8.epfl.ch/geome/

Instituto Politécnico de Bragança2010

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.