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