Workshop Animal genomics and breeding for sustainable production

Workshop “Animal genomics and breeding for sustainable production” 10 -11 October 2016 – Brussels, Belgium Joëlle Dupont, INRA

PROLIFIC ( www. euprolific. eu) “Pluridisciplinary study for a RObust and sustainab. Le Improvement of Fertility In Cows” Grant agreement nº 311776 Call: FP 7 -KBBE-2012 -6 Type of funding scheme: Collaborative project (small or medium-scale focused research project targeted to SMEs) Consortium: 13 partners from 8 countries: 7 Universities or Research Institutes, 4 SMEs, 1 industry and 1 company taking care of the project management Budget: € 3 million Duration: 4 years: Date of Start: February 1 st 2013 End : January 31 st 2017

Objectives (1) To develop innovative solutions for a robust and sustainable improvement of fertility in dairy cows To develop models to support on farm decision at different levels: animal fertility, herd management, and socio-economic impact for the farm and the farmer (WP 1: Multilevel integration and modelling of reproductive performances at different scales) To identify genes and pathways involved in the adaptation of the reproductive function to different environmental conditions, especially low input feeding systems (WP 2: Molecular approach for fertility markers) To identify the functional quantitative trait nucleotides for days till first luteal activity (based on progesterone measures) and estimate genomic breeding values using whole sequence information on individuals (WP 3: From genomics to selection)

Objectives (2) To study the adaptative response of animals to different feeding systems and management strategies (WP 4: Innovation in farm nutritional management to optimize cow fertility) To develop decision support tools to optimise the timing of reproductive management decisions, improve the rate of successful inseminations, and provide reproductive performance benchmarking. (WP 5: Multi-site demonstration of reproductive management tools) Herd Navigator: Detection of heat, ketosis, and mastitis and why not more things….

Achievements (1) Development of a Reproductive Model (RPM) that simulates the hormonal dynamics involved in the regulation of the reproductive success of cow (Simulation of irregular cyclicity, fertilization failure and embryonic mortality) (WP 1) Development of a statistical model to predict chance of insemination success from the analysis of P 4 dynamics (WP 1) => Deployment of one Insemination prognostic tool (Insemination Worth Predictor: a decision support tool that describes the chance of successful insemination at one given oestrus, WP 5) Development and Deployment of a reproductive management timing optimizer : a tool to simulate the effects of changes in reproductive management on the herd performances (WP 1 and WP 5) Development of a methane emission model (WP 1)

Achievements (2) List of genes and network of genes differentially Expressed/deregulated under metabolic imbalance or exposure to pathogens => Functional information to orientate future genetic studies, information to design new diagnosis and prognosis tools, basic information for future research on health and resilience (WP 2, WP 4) Identification of a new trait: commencement of luteal activity (CLA) (higher heritability and repeatability than traditional fertility traits) => CLA could be a good trait to select on without impairing milk production => Identifying functional mutations for CLA (WP 3) Identification of genetic variants associated with endocrine fertility traits through genome-wide association studies (detected variants can be used in breeding programs to enhance genomic selection) (WP 3)

What was not achieved Connection WP 2 to WP 1 (Insertion of new markers in the reproductive models) Integration of all the Omics data in different tissues Genomic studies on the embryo (WP 2)

Lessons learnt/Challenges: To use the models to test research hypotheses hardly affordable from a systemic point of view by the experimental method (for ex. to quantify the combined effects of milk production and nutritional status on all components of the reproductive responses and to estimate the effects on variability intra herd) To make operational all the modelling data in the farms Design new diagnosis and prognosis tools from omics data

Thank you!