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Multi-Breed Genetic Evaluations Lessons from UK Dairy evaluations. Marco Winters. DairyCo Breeding+. Responsible for Genetic Evaluation in UK Independent and Paid for by dairy farmers All breeds and crosses : Production traits SCC Lifespan Fertility Index Type (excl. B&W) Calving Ease.

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Presentation Transcript
dairyco breeding
DairyCo Breeding+

Responsible for Genetic Evaluation in UK

Independent and Paid for by dairy farmers

All breeds and crosses :

Production traits

SCC

Lifespan

Fertility Index

Type (excl. B&W)

Calving Ease

who do we work with
Who do we work with?

Breed Societies Milk Recording Service partner

Critical success factors;

Recording (ICAR accredited)

Collaboration – (inter)nationally

the breeders toolbox
The Breeders ‘toolbox’
  • Dairy breeding has never been so easy !
    • Many bulls on offer from many breeds
    • Many genetic indexes available to use
  • However, they only add value if they are used !
    • Regardless of heritability
impact of genetics lower h 2
Impact of Genetics – lower h2

Daughter average – Lactation SCC

future challenges competitiveness
Future Challenges - Competitiveness
  • What are the future genetic needs ?
    • Consider future economic conditions
    • Consider different ‘non-economic’ demands
      • E.g. environment, welfare, consumer
    • Consider ever-widening range of production systems
  • What are implications for Genetic evaluations ?
    • Are we making best use of available data?
genetic evaluations
Genetic Evaluations

Performance = Genetics + Environment

Genetic evaluations based on:

Pedigree information

(Genomic information)

Performance recording (e.g. Milk, SCC)

Correcting for environmental effects

Progeny performance

Proper adjustment for genetic merit of mate

Genetic gain improves with higher accuracy

(but there is a trade-off with Generation Interval)

Time & Accuracy

uk situation pre 2010
UK situation – Pre 2010

Aim: How can we maximise the accuracy of evaluations?

Using all existing data

Without bias to existing evaluations

Not all recorded data was being used

Some breeds excluded altogether

Crossbreds largely excluded

Not all breeds had full set of traits evaluated

Not all data was being used optimally

Split proofs for the same bulls across breeds

Suboptimal use of pedigree contributions

Herdmate contemporaries not always included

Growing interest in crossbreeding

breed proportions 2013 live cows
Breed proportions 2013 – Live cows

20% of cows not pure (>87.5% purity)

Most are result of breed replacement

89% are >75% ‘pure’

5.3% are 1st generation crosses

Up 1.5% during last five years

dealing with mixed breed data
Dealing with mixed breed data
  • Correction for difference in variance
  • Fitting full pedigree
    • Separate groups for unknown parents by breed
    • Widespread use of AI has established many links
  • Correction for Heterosis / Recombination
    • Crosses between four main breed groups considered
      • Holstein
      • British Friesian
      • Reds (Ayrshire, Shorthorn, Brown Swiss, Montbeliarde)
      • Other (Jersey, Guernsey, rest)
example animal 11779014
Example animal - 11779014

Animal Breed Code %Breed Origin

11779014 68 50.00 NZ Jersey

11779014 76 12.50 N. American Jersey

11779014 04 6.25 UK Jersey

11779014 66 6.25 Danish Jersey

11779014 78 25.00 NZ Ayrshire

heterosis of 5
Heterosis of 5%

Offspring better than average of its parents

useful heterosis
Useful Heterosis

Offspring are better than either of their parents

all breed evaluations background
All breed evaluations- background

Already routinely used in other countries:

E.g. Ireland, The Netherlands, New Zealand, and USA

DairyCo commissioned feasibility study (‘07/08)

Results of feasibility were promising

EGENES undertook further development work (08/09)

International validation run in August 2009 (interbull)

Implementation in January 2010

impact
Impact

Largest changes for:

Bulls used heavily in crossbreeding

Bulls with limited information

Few daughters

Few herds

Therefore;

Smaller breed populations relatively more change

But also have largest gains in reliability

all breed evaluations
All-breed evaluations

Best use of all data; Two examples

Morwick Sand Ranger (Red Holstein)

Pure-bred analysis;

399 daughters in Holstein proof

392 daughters in Ayrshire proof

12 daughters in Shorthorn proof

All-breed  837dtrs in combined proof

B Jurist (Swedish Red)

Pure-bred analysis;

0 dtrs in Holstein proof (not allowed)

127 dtrs in Ayrshire proof

21 dtrs in Shorthorn proof

All-breed  766dtrs in combined proof

presentation of proofs
Presentation of proofs

Each animal receives only one proof

Post evaluation

Animals get assigned to breed groups

Each breed group has own genetic base

Reset in January 2010 to average of cows born in 2005

Example:

£PLI index applied to all breeds (Guernsey has own Merit Index)

on going requirements
On-going requirements

Accurate data needed (lots of it !)

Currently >100M records used

Accurate animal identification

Harmonised trait definitions (ICAR)

Sharing (pooling) of Data

Internationally

conclusion and future
Conclusion and Future

All-breed evaluations implemented in 2010

Improved Accuracy of evaluations – within and across breeds

New breed and trait evaluations added

Industry response has been positive

Separate breed lists helped this situation

However, one single list would help those x-breeding

Future possibly All-breed genomic evaluations

Within breed genomics for Holstein - 2012

More R&D needed to ‘translate’ DNA info to other breeds