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TRANSCRIPT
IRDBF 2016
GENO’S USE OF GS
Trygve R. Solberg
Sustainability, breeding goal
GS in a nutshell and why
R&D plattforms in Geno
Summary
Geno’s use of GS
Brief description of NR population/breed
• Population size 203,000
• 94% of cows in Norway are NR
• 1.6 doses per pregnancy (average)
• Average NR in Norway: 7,600 litres of milk, 4.2% fat and
3.5% protein
• Average herd size 25
• >40% of milk in Norway comes from cows milked in robot
systems
• Calving interval: 12.2 months
• 50% of calves born are polled
Sustainability, breeding goal
GS in a nutshell and why
R&D plattforms in Geno
Summary
Geno’s use of GS
A SUSTAINABLE BREEDING PROGRAM OVER TIME HAS ENSURED A
BREED WITH EXCELLENT FERTILITY, HEALTH AND PRODUCTION
TRAITS…
Summary – Page 399
NORWEGIAN RED – selection for
functional traits
• NRF is a high-producing dairy breed in which
fertility and health have been included in a
selection index since the 1970s.
• NRF demonstrates that production and functional
traits can be successfully balanced in a sustainable
breeding program.
• This achievement has been based on an effective
recording system and a willingness to place
sufficient weight on the functional traits.
• The program is run by GENO, a cooperative
owned and managed by Norwegian dairy farmers.
THE BREEDING PROGRAM IS BASED ON SCIENCE AND A WILLINGNESS
TO IMPLEMENT NEW TRAITS, TECHNOLOGIES AND NEW KNOWLEGDE
6
For farmers, by farmers: Geno is a farmer cooperative and breeding organization owned by 9,400 dairy farmers
in Norway
Balanced breeding goal and long term selection: Geno has overcome the negative correlation between
production and fertility/health traits by keeping a balanced breeding goal in a large and robust selection program
over the last 30 years
Unmatched fertility, health and production traits: The result is excellent production performance in combination
with the world’s best performance on fertility and health traits
1980 1985 1990 1995 2000 2005 2010 2015
Health
Production
Fertility
Development of key traitsBreeding goal
Genetic value per trait indexed to 1980 level
The road to success for Norwegian Red
Type traits
Fertility
1967
Meat
2009200319891975 1997
Health
Milk
1981
Weight in breeding goal over time
Sustainability, breeding goal
GS in a nutshell and why
R&D plattforms in Geno
Summary
Geno’s use of GS
GENOMIC SELECTION IN A NUTSHELL
Reference
PopulationSelection
Candidates
c
DNA
c
DNAc
DNAWeight
c
DNAWeight
Prediction
Equation
Predicted Weight based on genomic data
alone
WHY THIS FOCUS ON GENOMIC SELECTION?
• ΔG=𝑟∗𝐼∗σ2
𝐿
– GS potetially double genetic gain by halving thegeneration interval, given the same accuracy as progeny testing
– GS increase the accuracy in selecting the best animals(calves)
– GS gives the oportunity to select for specific genes or gene variants (e.g. polled gene, FMO3 m.m.)
ΔG = genetic change
r = accuracy on the EBV
I = selection intensity
σ2 = genetic variation
L = generation interval
DAIRY CATTLE IS THE SHOW-CASE FOR GS
• Selection of young bulls/no progeny test (Schaeffer, 2006):
– Saves costs of progeny test: ~40,000 $/bull
– Reduce generation interval by factor 2
– Also use GS on bull dams
– double DG
– Saves $23 million/yr in Canada
– In Holsteins: promise is realised (Tom Lawlor, Holstein Assoc., EAAP 2015)
• Breeding companies around the world have adopted GS
– Holsteins: GS accuracies ~0.9
– Are there exceptions? => endangered breeds
SMALL BREEDS HAVE DIFFICULTIES?
• Limited sized reference populations (not enoughproven bulls)
• Solutions:
– Include females in the reference population
– Across breed GS
• For small breeds:
– Target GS accuracy ~0.9
– Perhaps not for traits with h2<0.01• Are these low h2 significant ? (in statistical or practical terms)
• R&D on these traits needed (e.g. better model, better recording; indicator trait with high h2; multitrait models)
Sustainability, breeding goal
GS in a nutshell and why
R&D plattforms in Geno
Summary
Geno’s use of GS
GENO’S 5 RESEARCH PLATFORMS
Genomics Reproduction
technology
Methodology/
Tools
• Increased genetic gain
• Increased value for the customer (national and international)
• Focus on implementation
Animal health
and welfare
Phenomics
• Genotyping
• Imputation
• Bioinformatics
• GBLUP
• ssGBLUP
• Breeding
planning
• ROH
• --
• Novel traits
• High precision
fenotypes
• AMS data
• Heatime
• FTIR
• NIR
• Feed efficiency
• Fertility
• Male
• Female
• IVF/MOET
• Sperm
technology
• GBLUP
• ssGBLUP
• GLM
• Optimal
contribution
• Multivariate
statistics
• New health data
• Anim. welfare
indicators
• Sustainable
breeding program
• Genotyping/Imputation/Bioinformatics
• Developed customized SNP-chip for Norwegian red
• Imputed our reference population to HD genotypes
• Streamlined workflow for QA, QR on new genotyped animals
• GS for feed efficiency («Foods of Norway»)
• Develop GEBV’s for feed efficiency based on HD phenotype data, i.e.
FTIR data and recordings from selected experimental herds
• Effect of GS on low heritable traits and heterosis effects on F1
• Prediction of crossbreeding animals (e.g. NRFxHOL)
• Estimate heterosis effects on crossbred anim.
Genomics PhenomicsReproduction
technologyMethodology/
ToolsAnimal healthand welfare
ACTION POINTS TO INCREASE RELIABILITY OF
GENOMIC PROOFS
• Customized SNP-chip
• Re-genotyping individuals in our reference base
• Better methods for imputation
• Better methods (GS «one-step»)
• More knowledge!
Geno’s aim is to maximise genetic gain and create value for our
customers
Scientific improvements
• Improved method (ssGBLUP)
• Improved accuracy by
genotyping females
• Improved accuracy using
ssGBLUP and including
females in ref. pop.
• Improved accuracy by
customized SNP-chip
• Increasing genetic gain
Change in the market• Genomic bulls more accepted
• Innovation, technology driven
• We are selling genetics and
increased value
Economy• Reduced costs due to reduced
number of waiting bulls
• Utilization of data from milking robots
• High precision objective data, e.g. milking speed, box time, behaviour
• Data from Heatime
• Measure of heat, used to improve fertility index
• FTIR spectra in milk
• High precision phenotypes
• Used to predict e.g. feed efficiency
Genomics PhenomicsReproduction
technologyMethodology/
ToolsAnimal healthand welfare
Sustainability, breeding goal
GS in a nutshell and why
R&D plattforms in Geno
Summary
Geno’s use of GS
GENO’S ROBUST SELECTION PROCESS ENSURES HIGH RELIABILITY
AND A VERY LOW INBREEDING COEFFICIENT
19
~30%
70%
NR population
~2,500 2,500 bull calves nominated for genotyping
annually based on pedigree informationGenotyped bull calves
150Bull calves purchased
to test station
150 bull calves purchased each year based
on genomic values and phenotypic
assessments
40-50Genomic bulls per year 40-50 genomic selected bulls
14-16Progeny tested bulls An average of 14-16 proven bulls per year
Overview of selection process1) Comments
~100,000 100,000 new bull calves in the NR populationNew bull calves
Qualified bull calves 4,0004,000 bull calves qualified based on PA and
father TMI
1) Until 2011, Geno purchased 300, and progeny tested 125 bulls per year. The numbers were reduced when pre-selection based on genomic breeding values was introduced in 2012.
2) Most bulls get close to 250 daughters when all the first crop are included in the EBV
NORWEGIAN FARMERS HAS BEEN REGISTERING DATA IN THE MILK
RECORDING SYSTEM OVER THE LAST 50 YEARS
The Norwegian Dairy Herd Recording System in operation since the 1960s
SLAUGHTER HOUSESDAIRY FARMERS VETERINARIANS ADVISORS AI TECHNICIANS
Production
per cow (milk)(Milk yield, fat
content, protein
content, SCC,
etc.)
Health card
per cow(Diagnostics, use
of antibiotics, vet
treatments, etc.)
Fertility data
per cow(Calving interval,
NRR, CR, etc.)
Conformation
data per cow(Hoof quality,
udder balance,
stature, etc.)
Carcasses(fat, carcass
grading, etc.)
Pedigree data(Breeding data,
heritage trees,
semen & blood
samples)
Management
data per cow(Temperament,
stillbirths, calving
ease, leakage,
culling etc.)
Sustainability, breeding goal
GS in a nutshell and why
R&D plattforms in Geno
Summary
Geno’s use of GS
SUMMARY
• GS really works in dairy cattle
– Implemented all around the world (except endangered breeds)
– Proven in practice in many countries
– Geno’s breeding program is now 100% genomic selection
– Can implement as pre-selection or as full genomic selection
– Pre-selection : • Less gain
• No cost reduction (except in case of frozen bull program)
• Functional vs. Production trait
– Bull selection: approx. same accuracy ratio as ProgTest• future expected to be in favor of GS
– Bull dam selection: clearly more functional trait gain in GS scheme