George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD [email protected] 2008 Genetic trends

George R. WiggansAnimal Improvement Programs LaboratoryAgricultural Research Service, USDA, Beltsville, MD

[email protected] 2008

Genetic trends in dairy cattleover the next 25 years …

where are weheaded and howwill we get there

G.R. Wiggans 2008National Breeders Roundtable (2)

National Dairy Genetic Evaluation Program

AIPL CDCB

NAAB

PDCA DHI

UniversitiesAIPL Animal Improvement Programs Lab., USDA

CDCB Council on Dairy Cattle BreedingDHI Dairy Herd Improvement (milk recording organizations)NAAB National Association of Animal Breeders (AI)PDCA Purebred Dairy Cattle Association (breed registries)


DHI statistics (2007)

4.4 million cows 98% fat recorded 95% protein recorded 94% somatic cell count recorded

23,500 herds

184 cows per herd

23,560 pounds milk per cow 3.69% fat 3.09% (true) protein


Traits evaluated

Yield (milk, fat, protein volume; component percentages)

Type/conformation

Productive life/longevity

Somatic cell score (SCS)/mastitis resistance

Fertility Daughter pregnancy rate (DPR; cow) Estimated relative conception rate (bull)

Calving ease/dystocia (service sire, daughter)


Evaluation methods

Animal model (linear) Heritability Yield (milk, fat, protein) 25–40% Type (Ayrshire, Brown Swiss, 7–54%Guernsey, Jersey)

Productive life 8.5% SCS 12% DPR 4%

Sire-maternal grandsire model (threshold) Service sire calving ease 8.6% Daughter calving ease 3.6%


Dairy cattle breeding

Long generation interval – 5 years

High value of individuals –$2,000 per cow

Intensive management –milking 2–3 times per day

Bull semen suitable for dilution –500 doses per collection day)


U.S. progeny-test bulls (2006) Major and marketing-only AI

organizations plus breeder proven

Breeds Ayrshire – 13 Brown Swiss – 30 Guernsey – 12 Holstein – 1,493 Jersey – 151 Milking Shorthorn – 8

260 new bulls returned to service per year


Genetic-economic indexes

Trait

Relative value (%)Chees

e merit

Netmerit

Fluid meri

tProtein (lb) 36 33 9Fat (lb) 18 22 22Milk (lb) –10 0 24Productive life (mo) 9 11 11SCS (log base 2) –7 –9 –9Udder composite 6 7 7Feet/legs composite 3 4 4Body size composite –2 –3 –3DPR (%) 5 7 7Service sire calving difficulty (%)

–2 –2 –2

Daughter calving difficulty (%)

–2 –2 –2


Index changes

PTA traits included

Relative emphasis on traits in index (%)

PD$1971

MFP$1976

CY1984

NM1994

NM2000

NM2003

Milk (lb) 52 27 –2 6 5 0Fat (lb) 48 46 45 25 21 22Protein (lb) … 27 53 43 36 33Productive life … … … 20 14 11SCS … … … –6 –9 –9Udder composite … … … … 7 7Feet/legs composite … … … … 4 4Body size composite … … … … –4 –3DPR … … … … … 7Service sire calving difficulty

… … … … … –2

Daughter calving difficulty

… … … … … –2


International reach

Semen and embryos marketed internationally

Interbull Evaluation Centre (Sweden) ranks all bulls for each participating country

Correlations between countries of <1 accommodated

Some foreign bulls used as sires of sons

U.S. and Canadian semen used widely in South America

Red breeds more popular in Europe than in North America


PTA milk prediction

-3,000

-2,000

-1,000

0

1,000

2,000

3,000

4,000

5,000

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

Year

PTA m

ilk

(lb)

Mean

Predicted mean from 10 years' data

Predicted mean from all data


-600

-400

-200

0

200

400

600

800

1,000

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

Year

Net

mer

it ($)

Mean



Net merit prediction


PTA DPR prediction (curvilinear)

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

Year

PTA D

PR (%

)

Mean




PTA DPR prediction (linear)

-4

-3

-2

-1

0

1

2

3

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

Year

PTA D

PR (%

)%

Mean




Holstein milk yield

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

1970 1975 1980 1985 1990 1995 2000 2005

Year

Milk

(lb

)

Mean milk yield Maximum milk yield

Trend for mean milk yield Trend for maximum milk yield


Goals beyond increased yield Improve fertility

Increase herdlife

Improve disease resistance

Reduce calving difficulty

Improve efficiency


Options for increasing progress Crossbreeding

Increased selection intensity

Adoption of new technologies


Crossbreds

Increasing interest

Way to increase fertility

Scandinavian Red breeds proposed

Hybrid vigor observed


All-breed animal model

Purebreds and crossbreds together

Unknown parents grouped by breed

Variance adjustments by breed

Age adjusted to 36 months, not maturity


Genomics

Genotype calves

Calculate genomic evaluation

Select intensively

Reduce cost of finding top bulls

Increase rate of genetic progress


Getting started

Select animals to genotype

Assign identification to animals

Collect tissue samples

Extract DNA

Check DNA quality and standardize concentration

Begin 3-day genotyping process


Genomic evaluation workflow Check genotypes for inheritance

errors

Calculate genomic relationships

Infer missing genotypes

Estimate single-nucleotide polymorphism (SNP) effects


Evaluation workflow – cont.

Combine genomic information with parent average

Based on gain from genomics over parent average for animals with genotypes

Apply to all traits

Distribute results


First genomic evaluation

750 animals nominated for genotyping

Over 5,285 predictor bulls from United States and Canada

Embryo flushes

AI organization that arranged for genotyping have first choice

More information at http://aipl.arsusda.gov/reference/changes/eval0804.html


Reliabilities and squared correlations

Squared correlation

× 100

Reliability (%)Tradi

-tiona

l Genomic

TraitPA

Genomic PA

Realized

Gain

Net merit 11 28 30 53 23Milk (lb) 28 49 35 58 23Fat (lb) 15 44 35 68 33Protein (lb) 27 47 35 57 22Fat (%) 25 63 35 78 43Protein (%) 28 58 35 69 34Productive life 17 27 27 45 18SCS 23 38 30 51 21DPR 20 29 25 41 16Service sire calving ease

27 29 28 31 3

Daughter calving ease

14 22 25 40 15

Final score 23 36 24 42 18


Marker effects for net merit


SNP density comparison

PA reliability (%)

Genomic reliability (%)

Trait 10K

20K 40K

Net merit 30 48 50 53Milk (lb) 35 53 56 58Fat (lb) 35 64 66 68Protein (lb) 35 54 56 57Productive life

27 38 41 45

SCS 30 45 47 51DPR 25 37 39 41


Conclusions

Genomic predictions significantly better than parent average (P < .0001) for all 26 traits tested

Gains in reliability equivalent on average to 11 daughters with records

Analysis used 3,576 historical bulls Current data includes 5,285 proven bulls

Larger populations require more SNPs


Current status

Field test results distributed for 750 nominated animals

Extension to Jersey and Brown Swiss in progress

Transition to commercial genotyping labs

Extension to cows planned for June


SNP project outcomes

Genome-wide selection

Parentage verification and traceability panels

Enhanced mapping for quantitative trait loci and gene discovery


Future plans

Evaluations of animals not genotyped updated using genomic information (3 times per year)

Genomic evaluations calculated and released more frequently (monthly? weekly?)

Bull evaluations made public when bull enrolled with NAAB

Cow evaluations made public immediately at USDA web site

January 2009 target for public release


Genomic selection (New Zealand) Identify top 30,000 bull calves

annually based on parent average

Genotype by 6 days old with 768 SNP

Genotype top 500 bull calves with 50K SNP chip

Keep top 100 bull calves


Genomic selection (NZ) – cont. At 1 year, limited progeny test to

check for undesirable recessives

At 2 years, market as part of DNA team

When progeny tested, graduate best to progeny-proven team


Research topics

Differential inclusion of X-chromosome effects to predict bulls versus cows

Contribution of cows to accuracy of genomic prediction

Benefit of genotyping more predictor bulls

Optimum methods for combining genomic and current evaluation


Research topics – cont.

Practicality of screening and parentage verification with low-cost, low-SNP number assay

Potential of freely sharing enough SNP for accurate parentage discovery

Computational methods to improve accuracy, such as haplotyping


Summary

Genomic prediction has great promise

Extensive changes in bull acquisition and marketing and in cow selection expected

Routine genotyping and validation will become industry rather than research responsibilities


Where do we go from here

Economic indexes adjusted as conditions change

Traits added as their collection becomes feasible and value demonstrated

Dairies increase in size and technological sophistication

Selection adapts the cow to meet human needs


Senior research staff

Documents

George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD [email protected] 2008 Genetic trends