Towards improvement of ruminant breeding through genomic and epigenomic approaches
More sustainable breeding programs and linked technologies need to be in line with social demand and to take into account genetic diversity as well as required adaption to climate change. Towards these aims, RUMIGEN will ensure engagement of a variety of stakeholders to assess social perception of breeding objectives and related technologies in ruminants. It will define “rooms of acceptance” which will be implemented in sustainable breeding scenarios that will then be assessed through multi-actor panels. To define new breeding programs, RUMIGEN, which brings together partners representing pan-European leaders on ruminant breeding, will question trade-offs and pleiotropic effects through adaptation to environmental stressors such as heat stress. Cosmopolitan and local dairy cattle breeds will be analysed evaluating mid to long term effects as well as impact on next generation (fetal programming). This work will rely on large-scale data from commercial farms combined with climatic data, taking advantage of the large panel of situations across Europe and of a unique design involving half-sister cows raised in contrasted conditions, Denmark and India. Diversity will be also studied with new breed genome assemblies. Methods will be developed to account for rare alleles and to maintain diversity. An epigenotyping platform will be designed to (i) explore sperm epigenome influences on bull fertility and progeny, (ii) decipher underlying mechanisms of epigenetic inheritance and (iii) evaluate how much epigenetic biomarkers improve phenotype prediction. RUMIGEN will answer questions on genome editing specificity and safety issues, assessing its potential to preserve genetic diversity and increase genetic gains. Overall, RUMIGEN will develop models combining genomics, epigenomics and biotechnologies to improve genomic selection, preserve genetic biodiversity and avoid genetic load. Improved phenotype prediction will pave the way to new management indexes for precision farming.