A Web-GIS application for the monitoring of Farm Animal Genetic Resources (FAnGR) in Switzerland

In 2007, FAO (Food and Agriculture Organization, from the United Nations) initiated the Global plan of action for Farm Animal Genetic Resources (FAnGR) to reduce further loss of genetic diversity in farm animals. One of the key issues mentioned is to identify endangered breeds to support conservation prioritization programs. In this context, the Swiss Federal office for Agriculture attributed a mandate to explore the feasibility of the implementation of a monitoring concept. The report mentioned the relevance of including the geographic location of the populations monitored. Accordingly, we used open source software (PostgreSQL, PostGIS, OpenLayers, Geoserver), to develop a WebGIS platform prototype (GenMon-CH) designed to assess pedigree information, geographical concentration, socio-economic and environmental information. GenMon-CH includes PopRep developed by the Institute of Farm Animal Genetics (FLI, Germany) to run the pedigree analysis and to provide parameters such as inbreeding coefficient, effective population size. Additionally introgression will be considered. Current developments will soon make it possible to process these indices based on genetic information as well. In parallel, the combined socioeconomic/environmental index assesses the attractiveness and the risk of potential future agricultural practice abandonment in the regions where populations are bred. Finally, a multi-criteria decision support tool aggregates criteria using the MACBETH method, which is based on a weighted average using satisfaction thresholds. The system permits to upload basic information for each animal (parents, birth date, sex, location, introgression) and to choose relevant weighting parameters and thresholds. Based on these inputs, the system completes a pedigree analysis, and computes a final ranking of breeds based on an integrated prioritization score to be visualized on a map

A WebGIS application for the monitoring of Farm Animal Genetic Resources

We developed used a WebGIS platform prototype (GenMon) based on open source software (PostgreSQL, PostGIS, OpenLayers, Geoserver), to assess pedigree information, geographical concentration, socio-economic and environmental information. GenMon includes PopRep developed by the Institute of Farm Animal Genetics (FLI, Germany) to run the pedigree analysis and to provide parameters such as inbreeding coefficient, effective population size. In parallel, the combined socio-economic/environmental index assesses the attractiveness and the risk of potential future agricultural practice abandonment in the regions where populations are bred. Finally, a multi-criteria decision support tool aggregates criteria using the MACBETH method, which is based on a weighted average using satisfaction thresholds. The system permits to upload basic information for each animal (parents, birth date, sex, location, introgression) and to choose relevant weighting parameters and thresholds

Big dairy data to unravel effects of environmental, physiological and morphological factors on milk production of mountain-pastured Braunvieh cows

The transhumance system, which consists in moving animals to high mountain pastures during summer, plays a considerable role in preserving both local biodiversity and traditions, as well as protecting against natural hazard. In cows, particularly, milk production is observed to decline as a response to food shortage and climatic stress, leading to atypical lactation curves that are barely described by current lactation models. Here, we relied on 5 million monthly milk records from over 200 000 Braunvieh and Original Braunvieh cows to devise a new model accounting for transhumance, and test the influence of environmental, physiological and morphological factors on cattle productivity. Counter to expectations, environmental conditions in the mountain showed a globally limited impact on milk production during transhumance, with cows in favourable conditions producing only 10% more compared with cows living in detrimental conditions, and with precipitation in spring and altitude revealing to be the most production-affecting variables. Conversely, physiological factors such as lactation number and pregnancy stage presented an important impact over the whole lactation cycle with 20% difference in milk production, and alter the way animals respond to transhumance. Finally, the considered morphological factors (cow height and foot angle) presented a smaller impact during the whole lactation cycle (10% difference in milk production). The present findings help to anticipate the effect of climate change and to identify problematic environmental conditions by comparing their impact with the effect of factors that are known to influence lactation

Data integration, GIS and multi-criteria decision making for the monitoring of livestock genomic resources

In 2007, FAO initiated the Global plan of action for Farm Animal Genetic Resources (FAnGR) to reduce further loss of genetic diversity in farm animals. One of the key issues mentioned is to identify endangered breeds to support conservation prioritization programs. In this context, the Swiss Federal Office for Agriculture attributed a mandate to explore the feasibility of the implementation of a monitoring concept. The report mentioned the relevance of including the geographic location of the populations monitored. Accordingly, we used open source software (PostgreSQL, PostGIS, OpenLayers, Geoserver), to develop a WebGIS platform prototype (GenMon) designed to assess pedigree information, geographical concentration, socio-economic and environmental information. GenMon includes PopRep developed by the Institute of Farm Animal Genetics (FLI, Germany) to run the pedigree analysis and to provide parameters such as inbreeding coefficient, effective population size and introgression. Current developments will soon make it possible to process these indices based on genetic information also. In parallel, the combined socio-economic/environmental index assesses the attractiveness and the risk of potential future agricultural practice abandonment in the regions where populations are bred. Finally, a multi-criteria decision support tool aggregates criteria using the MACBETH method, which is based on a weighted average using satisfaction thresholds. The system permits to upload basic information for each animal (parents, birth date, sex, location, introgression) to set weighting parameters, and tho chose relevant thresholds. Based on these inputs, the system completes a pedigree analysis, and computes a final ranking of breeds based on an integrated prioritization score to be visualized on a geographical map

Simple rules for an efficient use of geographic information systems in molecular ecology

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 that 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

A WebGIS platform for the monitoring of Farm Animal Genetic Resources (GENMON)

Background In 2007, the Food and Agriculture Organization of the United Nations (FAO) initiated the Global plan of action for Farm Animal Genetic Resources (FAnGR). The main goal of this plan is to reduce further loss of genetic diversity in farm animals, so as to protect and promote the diversity of farm animal resources. An important step to reach this goal is to monitor and prioritize endangered breeds in the context of conservation programs. Methodology/Web portal implementation The GENMON WebGIS platform is able to monitor FAnGR and to evaluate the degree of endangerment of livestock breeds. The system takes into account pedigree and introgression information, the geographical concentration of animals, the cryo-conservation plan and the sustainability of breeding activities based on socio-economic data as well as present and future land use conditions. A multi-criteria decision tool supports the aggregation of the multi-thematic indices mentioned above using the MACBETH method, which is based on a weighted average using satisfaction thresholds. GENMON is a monitoring tool to reach subjective decisions made by a government agency. It relies on open source software and is available at http://lasigsrv2.epfl.ch/genmon-ch. Results/Significance GENMON allows users to upload pedigree-information (animal ID, parents, birthdate, sex, location and introgression) from a specific livestock breed and to define species and/or region-specific weighting parameters and thresholds. The program then completes a pedigree analysis and derives several indices that are used to calculate an integrated score of conservation prioritization for the breeds under investigation. The score can be visualized on a geographic map and allows a fast, intuitive and regional identification of breeds in danger. Appropriate conservation actions and breeding programs can thus be undertaken in order to promote the recovery of the genetic diversity in livestock breeds in need. The use of the platform is illustrated by means of an example based on three local livestock breeds from different species in Switzerland

Relationship between land cover type and Body Mass Index in Geneva

Past studies conducted in urban areas analyzed the impact of the presence of green spaces on public health, and highlighted in particular the psychological benefits of interacting with nature. To investigate a supposed relationship between overweight and dense built environment, we focused on the State of Geneva, Switzerland, and calculated the correlation between Body Mass Index (BMI) in a representative sample of 6663 adults and the percentage of natural areas at the locations where these individuals were living. To this end, we used populationbased health data from the “Bus Sante” study (Geneva University Hospitals) and multi-scale land cover maps obtained by means of satellite images and LiDAR data classification. We found little correlation between BMI (as a proxy for health) and land cover data and were not able to verify the working hypothesis at local and regional scales. However, an important phenomenon highlighted here is the difference in the results obtained between the city center and the whole State

Persistent spatial clusters of high body mass index in a Swiss urban population as revealed by the 5-year GeoCoLaus longitudinal study

OBJECTIVE Body mass index (BMI) may cluster in space among adults and be spatially dependent. Whether and how BMI clusters evolve over time in a population is currently unknown. We aimed to determine the spatial dependence of BMI and its 5-year evolution in a Swiss general adult urban population, taking into account the neighborhood- and individual-level characteristics. DESIGN Cohort study SETTING Swiss general urban population PARTICIPANTS 6,481 geo-referenced individuals from the CoLaus cohort at baseline (age range 35–74 years, period=2003-2006) and 4,460 at follow-up (period=2009-2012). OUTCOME MEASURES Body weight and height were measured by trained health care professionals with participants standing without shoes in light indoor clothing. BMI was calculated as weight (kg) divided by height squared (m2). Participants were geocoded using their postal address (geographic coordinates of the place of residence). Getis-Ord Gi statistic was used to measure the spatial dependence of BMI values at baseline and its evolution at follow-up. RESULTS BMI was not randomly distributed across the city. At baseline and at follow-up, significant clusters of high versus low BMIs were identified and remained stable during the two periods. These clusters were meaningfully attenuated after adjustment for neighborhood-level income but not individual-level characteristics. Similar results were observed among participants who developed obesity. CONCLUSIONS To our knowledge, this is the first study to report longitudinal changes in BMI clusters in adults from a general population. Spatial clusters of high BMI persisted over a 5-year period and were mainly influenced by neighborhood-level income. ARTICLE SUMMARY STRENGTHS AND LIMITATIONS OF THE STUDY • As far as we know, this is the first study to report the persistence of spatial clusters of high BMI values over a 5-year period in adults from a general population • The observed east to west pattern of BMI clustering fits known socio-economic and ethno-cultural differences distinguishing these opposite regions of the city of Lausanne, Switzerland • A consequence of the social policy applied by the city is likely to fix populations with modest income in subsidized housing located in specific areas • While recruitment methods of the CoLaus study aimed at collecting information on a representative sample of the general population, adult participants and non-participants to the CoLaus study may differ and participation bias cannot be excluded • We considered several individual-level covariates but data on individual income was missing. We used instead the median income of the including city statistical sector

High performance computation of landscape genomic models including local indicators of spatial association

With the increasing availability of both molecular and topo-climatic data, the main challenges facing landscape genomics — i.e. the combination of landscape ecology with population genomics — include processing large numbers of models and distinguishing between selection and demographic processes (e.g. population structure). Several methods address the latter, either by estimating a null model of population history or by simultaneously inferring environmental and demographic effects. Here we present Samβada, an approach designed to study signatures of local adaptation, with special emphasis on high performance computing of large-scale genetic and environmental datasets. Samβada identifies candidate loci using genotype-environment associations while also incorporating multivariate analyses to assess the effect of many environmental predictor variables. This enables the inclusion of explanatory variables representing population structure into the models in order to lower the occurrences of spurious genotype-environment associations. In addition, Samβada calculates Local Indicators of Spatial Association (LISA) for candidate loci to provide information on whether similar genotypes tend to cluster in space, which constitutes a useful indication of the possible kinship between individuals. To test the usefulness of this approach, we carried out a simulation study and analysed a dataset from Ugandan cattle to detect signatures of local adaptation with Samβada, BayEnv, LFMM and an FST outlier method (FDIST approach in Arlequin) and compare their results. Samβada — an open source software for Windows, Linux and Mac OS X available at http://lasig.epfl.ch/sambada — outperforms other approaches and better suits whole genome sequence data processing

AdaptMap: exploring goat diversity and adaptation

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