Biogeoinformatics for the management of Farm Animal Genetic Resources (FAnGR)

In the context of both severe selection in farm animals and potential effects of climate change, it is crucial to implement a sustainable management of the breeding practice, supported by a judicious use of geographic information technologies. Based on this observation, this thesis advocates the use of biogeoinformatics (the combined use of biology, geographic information and informatics) to cope with the challenges encountered by the livestock sector. Indeed, although biogeoinformatics can provide key insights for FAnGR (Farm Animal Genetic Resources) management, the variety and complexity of tasks involved hinders a wider usage of this type of analyses. The thesis shows how novel dedicated tools are likely to facilitate the adoption of biogeoinformatics by animals scientists and by stakeholders in the livestock sector, while investigating three main challenges related to FAnGR management, namely i) erosion of genetic diversity, ii) effects of climate change on the breeding activity, and iii) pressure on typical cultural breeding practices such as high alpine grazing. On this basis, the thesis is organised around three axes:

Preserving locally adapted breeds In order to prevent the erosion of genetic diversity, locally adapted breeds should be monitored to prevent their extinction. To this end, we developed the open source GENMON WebGIS platform, able to monitor FAnGR and to evaluate the degree of endangerment of livestock breeds. The system integrates various sources of information that are linked with the help of geographic information: pedigree and introgression, geographical concentration of animals, cryo-conservation and sustainability of breeding activities. The score can be visualised on a map and allows a fast and regional identification of breeds in danger.
Preserving locally adapted genetic variations Considering the pace at which genetic diversity is being eroded, it has become urgent to identify and then preserve important genetic variations linked to locally adapted phenotypes. In this context, the recent SamÎ²ada software was designed to search for signatures of local adaptation through the study of genomeâenvironment association. However, preâ and postprocessing of data for this analysis can be labourâintensive, and, we therefore developed the R.SamBada R package providing a pipeline for landscape genomic analyses. Based on SamÎ²ada, it spans from the retrieval of environmental conditions at sampling locations to gene annotation using the Ensembl genome browser. As a result, it grants access to biogeoinformatic analyses to researchers with no skills in geography.
Preserving a traditional farming technique suited for local breeds The preservation of locally adapted breeds is also strongly linked with the conservation of traditional farming techniques, which in Switzerland, include the grazing of high alpine pastures during summer. One major effect of this transhumance on cows is that milk production declines due to food shortage and climatic stress. Here, we developed a new mathematical model to fit a lactation curve for mountain-pastured cows, and tested the influence of environmental, physiological, and morphological factors on the production using five million monthly milk records from Braunvieh cows. When compared to physiological factors, environmental variables show a limited impact on milk production at alpine pastures, precipitation in spring being the most important.

Species conservation in the face of global environmental changes: surface-based modelling of geo-environmental data to identify vulnerable populations

Estelle Rochat

Human activities are resulting in many land-use changes, particularly due to urbanisation and intensification of agricultural practices. Because of these changes, in addition to climate change, many species are facing habitat degradation. In order to avoid extinction under these conditions, they can either move to more favourable areas or adapt to their new environment. To develop appropriate conservation measures, it is essential to identify vulnerable populations that may not be able to disperse or adapt. In this context, modelling tools can be used to predict the potential impact of environmental changes on species and populations. However, to date, few approaches take into account the degree of exposure, the possibility of dispersal and the potential for adaptation. In this thesis, we present modelling approaches based on geo-environmental data to integrate these three elements. First, we use ecological niche models to estimate the distribution of suitable habitats for a given species as a function of environmental conditions. We propose an improvement of commonly used models by developing an approach to integrate spatio-temporal variability of environmental predictors. In addition, we develop a nested model to predict the distribution of vector-borne pathogens. This model can be used to identify populations that may be threatened by an increasing presence of pathogens in their habitat. We use it to model the evolution of the distribution of Ixodes ricinus ticks and their Chlamydiales bacterial pathogen. We then use landscape graphs to analyse the connectivity between habitats and estimate the possibilities for threatened species to move to more favourable areas. Connectivity is also essential for maintaining gene flow and genetic diversity, which is necessary for greater adaptive capacity. We propose here an approach combining landscape graphs, simulations and empirical genetic data to identify the impact of reduced connectivity on population persistence and genetic diversity. We use it to identify butterfly populations that are threatened by increasing fragmentation in an urban landscape. Finally, we develop the concept of "Spatial Areas of Genotypes Probability" (SPAG) to better analyse the adaptive potential of populations. SPAGs make it possible to model the probability of finding locally adapted genetic variants in a given territory, as well as to identify vulnerable populations lacking in genetic variants that would favour adaptation to future climate conditions. We use it to highlight populations of Moroccan and European goats that are poorly adapted to the climatic conditions predicted under a climate change scenario for 2070 (strong variations in precipitation or increased drought). To conclude, we show how the three modelling approaches presented can be combined and integrated into a more general conservation framework to identify vulnerable populations facing high exposure to environmental changes, low dispersal possibilities and reduced adaptive capacity.

EPFL2020

GEOME : A web-based landscape genomics geocomputation platform

Stéphane Joost, Michaël Kalbermatten

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

International Society for Photogrammetry and Remote Sensing2010

Advances in farm animal genomic resources

Stéphane Joost

The history of livestock started with the domestication of their wild ancestors: a restricted number of species allowed to be tamed and entered a symbiotic relationship with humans. In exchange for food, shelter and protection, they provided us with meat, eggs, hides, wool and draught power, thus contributing considerably to our economic and cultural development. Depending on the species, domestication took place in different areas and periods. After domestication, livestock spread over all inhabited regions of the earth, accompanying human migrations and becoming also trade objects. This required an adaptation to different climates and varying styles of husbandry and resulted in an enormous phenotypic diversity. Approximately 200 years ago, the situation started to change with the rise of the concept of breed. Animals were selected for the same visible characteristics, and crossing with different phenotypes was reduced. This resulted in the formation of different breeds, mostly genetically isolated from other populations. A few decades ago, selection pressure was increased again with intensive production focusing on a limited range of types and a subsequent loss of genetic diversity. For short-term economic reasons, farmers have abandoned traditional breeds. As a consequence, during the 20th century, at least 28% of farm animal breeds became extinct, rare or endangered. The situation is alarming in developing countries, where native breeds adapted to local environments and diseases are being replaced by industrial breeds. In the most marginal areas, farm animals are considered to be essential for viable land use and, in the developing world, a major pathway out of poverty. Historic documentation from the period before the breed formation is scarce. Thus, reconstruction of the history of livestock populations depends on archaeological, archeo-zoological and DNA analysis of extant populations. Scientific research into genetic diversity takes advantage of the rapid advances in molecular genetics. Studies of mitochondrial DNA, microsatellite DNA profiling and Y-chromosomes have revealed details on the process of domestication, on the diversity retained by breeds and on relationships between breeds. However, we only see a small part of the genetic information and the advent of new technologies is most timely in order to answer many essential questions. High-throughput single-nucleotide polymorphism genotyping is about to be available for all major farm animal species. The recent development of sequencing techniques calls for new methods of data management and analysis and for new ideas for the extraction of information. To make sense of this information in practical conditions, integration of geo-environmental and socio-economic data are key elements. The study and management of farm animal genomic resources (FAnGR) is indeed a major multidisciplinary issue. The goal of the present Research Topic was to collect contributions of high scientific quality relevant to biodiversity management, and applying new methods to either new genomic and bioinformatics approaches for characterization of FAnGR, to the development of FAnGR conservation methods applied ex-situ and in-situ, to socio-economic aspects of FAnGR conservation, to transfer of lessons between wildlife and livestock biodiversity conservation, and to the contribution of FAnGR to a transition in agriculture (FAnGR and agro-ecology).

Frontiers2016