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
sustainabLe Improvement of Fertility In Cows”
Grant agreement nº 311776
Call: FP7-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 1st 2013
End : January 31st 2017
Objectives (1)
To develop innovative solutions
for a robust and sustainable improvement of fertility in dairy cows
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To develop models to support on farm decision at different levels: animal fertility, herd  11 r 
management, and socio-economic impact for the farm and the farmer
t 1
(WP1: 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
(WP2: 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
(WP3: From genomics to selection)
t 1
1t  1 T VS1


1   1 r 1
Objectives (2)
To study the adaptative response of animals to different feeding systems and management strategies
(WP4: 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.
(WP5: 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) (WP1)
Development of a statistical model to predict chance of insemination success from the analysis of
P4 dynamics (WP1) => Deployment of one Insemination prognostic tool (Insemination Worth
Predictor: a decision support tool that describes the chance of successful insemination at one
given oestrus, WP5)
Development and Deployment of a reproductive management timing optimizer : a tool to simulate
the effects of changes in reproductive management on the herd performances (WP1 and WP5)
Development of a methane emission model (WP1)
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 (WP2, WP4)
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 (WP3)
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) (WP3)
What was not achieved
Connection WP2 to WP1 (Insertion of new markers in the reproductive models)
Integration of all the Omics data in different tissues
Genomic studies on the embryo (WP2)
Lessons learnt/Challenges
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!