WP3: Genetics of trade-offs and synergies between resilience and efficiency related traits

The aim of WP3 is to identify trade-offs and synergies between resilience and efficiency and other production related traits under genetic control. We also aim to identify the underlying biological mechanisms for resilience and efficiency and develop prediction models to manage such trade-offs and optimise resilience and efficiency in challenging conditions.

During the first year, we collated over 1100 genetic parameter estimates (heritabilities and correlation between efficiency and resilience traits). Data came from 13 partners and 14 datasets in 23 populations of meat sheep (Scottish Blackface, Lleyn, Dorset, Texel, Romane, Blanc du Massif Central, Corriedale, Merino), dairy sheep (Sarda, Manech, Churra, Lacaune) and goat (Alpine, Saanen, Yorkshire composite). By analysing these data, we expect to have a good overview of trade-offs and synergies under genetic control.

During the first period, INRA and UELEON also started to produce four experimental data to better understand the biological mechanisms underlying those trade-offs and synergies and how they affect resilience and efficiency. UELEON creates lines of Assaf sheep selected for high and low milk production. INRA creates lines of Romane sheep selected for high and low resistance to gastro-intestinal parasite. In the same breed they create lines based on high and low feed efficiency. Regarding goats, INRA creates lines divergently selected for functional longevity in the Alpine breed. These animals will undergo nutritional and infection challenges.

Using the experimental data generated, UEDIN and INRA developed a data-driven mechanistic host-pathogen interaction model for gastro-intestinal parasite infections in sheep. The first application of the model is to determine energy costs associated with immune – production trade-offs. The model will then be extended at a population level to integrate genetic variation in its key underlying parameter. Finally, work has started on adapting and extending a resource allocation model to allow it to simulate the impact of nutritional challenges on performance trajectories and trade-offs between life functions. This has included two main developments: relative contribution of acquisition and allocation of resource to the response to nutritional challenge and, second, the quantification of individual variation in response-recovery profiles in dairy goats.

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