WP1 has the following objectives. The coordinator will design and implement a consortium agreement (CA), setting out ClimGen’s procedures and working methods with respect to research, results, IPR, reporting, dissemination and exploitation activities. The governance, operation and structure of the project will be established and made operational, receiving full administrative support. If necessary, tasks will be specified and clarified with partners and the consortium. All management, administrative and financial procedures will be communicated to partners. Project progress and outputs will be planned, monitored and controlled, taking corrective actions where necessary. Project funds will be administered in the interest of the success of the project, by agreement with the consortium, and according to individual partner performance. Partners will be supported in IPR management. The Commission will be provided with clear and correct updates of project progress.

This WP is dedicated to collecting data from case studies already available to the project, their analysis and synthesis. Case studies of success and failures to conserve livestock breeds, and/or their genetic resource, will be collected by the partners with help of stakeholders. This will include both rare breeds and industrial breeds for contrast. For each case study an assessment will be carried out to determine the availability of genetic and ‘omic data and its implication on the outcome of the case study (e.g. successful conservation). When possible, additional strategic sampling and environmental data collection will be planned and accomplished to complete datasets. These case studies will be used to identify variables influencing breed choice (e.g. socio-economic factors) and variables influencing the sustainability of the genetic resource. The synthesis of this WP will shed light into policy making trends and on the necessities of conservation strategies regarding ‘omic data.

This WP will assess the effect of heat stress beyond the standard signature of selection searched for in genomic data. For this purpose a natural case study and two experimental approaches will be analysed to identify both expression and epigenetic changes influencing the capacity of animals to deal with heat and associated stressors. The natural case study will characterize methylation patterns in Moroccan sheep and goats inhabiting an environmental contrast north and south of the Atlas Mountains. Contrastingly, the experimental approach will seek to i) compare methylation patterns in cohorts of pigs under heat stress versus controls, and ii) characterise expression profiles of the immune system in red-legged partridges exposed to heat stress. For these studies ClimGen will produce ‘omic data using the latest next generation sequencing technologies (e.g. MeDIP-seq, RNA-seq and sequencing after bisulphite treatment).

This WP will be dedicated to the development of databases for the data gathered during this project, the set up of analysis pipelines for integrated and comparative data analysis. For this purpose a database will be created with MySQL to store data for each livestock species. The database will hold information on individuals’ SNP and CNV polymorphisms, as well as expression profiles and epigenetic marks (when available). All this information will be updated with the latest genome reference available to harmonise the genomic coordinates of the various marker types. The database will have an integrated WebGIS interface enabling users to carry out queries based on markers of interest, and/or geographic information in order to identify the relationship between environmental variables and genomic data. Additionally, the database will allow carrying out association studies between genomic data and environmental variables using integrated landscape genomics tools.

Upon completion of the database in WP4, the raw data needed to carry out association studies searching for signatures of local adaptation will be possible. The initial step will require grouping data for each livestock species on the basis of ‘omic information available in the database and geographical coordinates for the sampling sites. For this purpose various methodologies will be applied depending on the nature of the data available. All datasets will first require testing for the presence of underlying population structure. Datasets comprising populations will be analysed on the one hand with methodologies that allow accounting for structure (e.g. BayeScan), and on the other hand, the signature of selection will be tested against geographic distribution models to assess their correlation (e.g. LFMM). For unstructured data, the correlation between geography and ‘omic data will be directly carried out (e.g. SamBada).