2004 2006 John B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD jcole@aipl.arsusda.gov Dairy Cattle Breeding.
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2004
2004
2006
John B. Cole
Animal Improvement Programs Laboratory
Agricultural Research Service, USDA, Beltsville, MD
jcole@aipl.arsusda.gov
Dairy Cattle Breeding in the United States
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U.S. dairy statistics (2004) 9.0 million cows
67,000 herds
135 cows/herd
19,000 lb (8600 kg)/cow
~93% Holsteins, ~5% Jerseys
~75% bred AI
46% milk recorded through Dairy Herd Improvement (DHI)
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U.S. dairy population and yield
0
5
10
15
20
25
30
40 50 60 70 80 90 00Year
Cow
s (m
illion
s)
0
2,000
4,000
6,000
8,000
10,000
Milk yie
ld (kg/cow
)
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DHI statistics (2004) 4.1 million cows
97% fat recorded 93% protein recorded 93% SCC recorded
25,000 herds
164 cows/herd
21,250 lb (9640 kg)/cow 3.69% fat 3.09% (true) protein
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U.S. progeny-test bulls (2000)
Major and marketing-only AI organizations plus breeder-proven
BreedsAyrshire 10 bullsBrown Swiss 53 bullsGuernsey 15 bullsHolstein 1436 bullsJersey 116 bullsMilking Shorthorn 1 bull
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National Dairy Genetic Evaluation Program
AIPL CDCB
NAAB
PDCA DHI
UniversitiesAIPL Animal Improvement Programs Lab., USDA
CDCBCouncil on Dairy Cattle BreedingDHI Dairy Herd Improvement (milk recording organizations)NAAB National Association of Animal Breeders (AI)PDCAPurebred Dairy Cattle Association (breed registries)
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AIPL mission
Conduct research to discover, test, and implement improved genetic evaluation techniques for economically important traits of dairy cattle and goats
Genetically improve efficiency of dairy animals for yield and fitness
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AIPL research objectives
Maintain a national database with animal identification, production, fitness, reproduction, and health traits to support research on dairy genetics and management
Provide data to others researchers submitting proposals compatible with industry needs
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AIPL research objectives (cont.)
Increase accuracy of genetic evaluations for traits through improved methodology and through inclusion and appropriate weighting of deviant data
Develop bioinformatic tools to automate data processing in support of quantitative trait locus detection, marker testing, and mapping methods
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AIPL research objectives (cont.)
Improve genetic rankings for overall economic merit by evaluating appropriate traits and by determining economic values of those traits in the index
Improved profit functions are derived from reviewing incomes and expenses associated with each trait available for selection
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AIPL research objectives (cont.)
Characterize dairy industry practices in milk recording, breed registry, and artificial-insemination to document status and changes in data collection and use and in observed and genetic trends in the population
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Traits evaluated Yield (milk, fat, protein volume;
component percentages)
Type/conformation
Productive life/longevity
Somatic cell score/mastitis resistance
Fertility Daughter pregnancy rate (cow) Estimated relative conception rate (bull)
Dystocia and stillbirth (service sire, daughter)
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Evaluation methods Animal model (linear)
Yield (milk, fat, protein) Type
(Ayrshire, Brown Swiss, Guernsey, Jersey) Productive life SCS Daughter pregnancy rate
Sire – maternal grandsire model (threshold) Service sire calving ease Daughter calving ease Service sire stillbirth Daughter stillbirth
Heritability25 – 40%7 – 54%
8.5%12%4%
8.6%3.6%3.0%6.5%
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Genetic trend – Milk
Phenotypic base = 11,638 kg
-3500-3000-2500-2000-1500-1000-500
0500
1000
1960 1970 1980 1990 2000
Holstein birth year
Bre
ed
ing
valu
e (
kg
)
sires cows
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Genetic trend – Fat
-125
-100
-75
-50
-25
0
25
1960 1970 1980 1990 2000
Holstein birth year
Bre
edin
g va
lue (kg
) Phenotypic base = 424 kg
sires cows
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Genetic trend – Protein
-125
-100
-75
-50
-25
0
25
1975 1980 1985 1990 1995 2000
Holstein birth year
Bre
edin
g va
lue (kg
) Phenotypic base = 350 kg
sirescows
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Genetic trend – Productive life (mo)
-7
-6
-5
-4
-3
-2
-1
0
1
1960 1970 1980 1990 2000
Holstein birth year
Bre
edin
g va
lue (m
onth
s) Phenotypic base = 24.6 months
sirescows
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Genetic trend – Somatic cell score
-.15
-.10
-.05
.00
.05
.10
1985 1990 1995 2000
Holstein birth year
Bre
edin
g va
lue (lo
g bas
e 2)
Phenotypic base = 3.08 (log base 2)
sires
cows
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Genetic trend – Daughter pregnancy rate (%)
-2
-1
0
1
2
3
4
5
1960 1970 1980 1990 2000
Holstein birth year
Bre
edin
g va
lue (%)
Phenotypic base = 21.53%
sirescows
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Genetic trend – calving ease
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
1990 1992 1994 1996 1998 2000 2002
Holstein birth year
PTA %
DBH
(diffi
cult
bir
ths
in h
eifers
)
Phenotypic base = 8.47% DBHPhenotypic base = 7.99% DBH
servicesire
daughter
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Genetic trend – stillbirth
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
1980 1985 1990 1995 2000Holstein birth year
PTA %
Sti
llbir
ths
Phenotypic base = 8% SBHservice
sire
daughter
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Genetic-economic indices
Trait
Relative value (%)Net merit
Cheesemerit
Fluid merit
Milk (lb) 0 -12 24Fat (lb) 23 18 23Protein (lb) 23 28 0Productive life (mo) (PL) 17 13 17Somatic cell score (log2) (SCS)
–9 –7 -9
Udder composite (UDC) 6 5 6Feet/legs composite (FLC) 3 3 3Body size composite (BSC) –4 –3 -4Daughter pregnancy rate (%) (DPR)
9 7 8
Calving ability ($) (CA$) 6 4 6
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Index changes
Trait
Relative emphasis on traits in index (%)PD$
(1971)MFP$(1976)
CY$(1984)
NM$(1994)
NM$(2000)
NM$(2003)
NM$(2006
)Milk 52 27 –2 6 5 0 0Fat 48 46 45 25 21 22 23Protein
… 27 53 43 36 33 23
PL … … … 20 14 11 17SCS … … … –6 –9 –9 –9UDC … … … … 7 7 6FLC … … … … 4 4 3BDC … … … … –4 –3 –4DPR … … … … … 7 9SCE … … … … … –2 …DCE … … … … … –2 …CA$ … … … … … … 6
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Introduction
At the same level of production cows with high persistency milk more at the end than the beginning of lactation
Best prediction of persistency is calculated as a function of trait-specific standard lactation curves and the linear regression of a cow’s test day deviations on days in milk
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Best Prediction
Selection IndexSelection Index Predict missing yields from measured Predict missing yields from measured
yieldsyields Condense daily into lactation yield and Condense daily into lactation yield and
persistencypersistency Only phenotypic covariances are neededOnly phenotypic covariances are needed Mean and variance of herd assumed knownMean and variance of herd assumed known
Reverse predictionReverse prediction Daily yield predicted from lactation yield Daily yield predicted from lactation yield
and persistencyand persistency
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PersistencyCole and VanRaden 2006 JDS 89:2722-2728
DefinitionDefinition305 daily yield deviations (DIM 305 daily yield deviations (DIM - DIM- DIMoo))
Uncorrelated with yield by Uncorrelated with yield by choosing DIMchoosing DIMoo
DIMDIMo o were were 161161, , 159159, , 166166, and , and 155155 for M, F, P, and SCS for M, F, P, and SCS
• DIMDIM00 have increased over time have increased over timeStandardized estimateStandardized estimate
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Cow with Average Persistency
0
5
10
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20
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0 30 60 90 120 150 180 210 240 270 300Days in Milk
Kilo
gram
s
Best PredictionStandard CurveTest Days
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Highest Cow Persistency
0
10
20
30
40
50
60
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90
100
0 30 60 90 120 150 180 210 240 270 300Days in Milk
Kilo
gra
ms
Best Prediction
Standard Curve
Test Days
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Lowest Cow Persistency
0
10
20
30
40
50
60
70
80
90
100
0 30 60 90 120 150 180 210 240 270 300
Days in Milk
Kilo
gram
s
Best Prediction
Standard Curve
Test Days
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ModelRepeatability animal model:
yijkl = hysi + lacj + ak + pek + β(dojk) + eijkl
yijkl = persistency of milk, fat, protein, or SCS
hysi = fixed effect of herd-year-season of calving I
lacj = fixed effect of lactation j
ak = random additive genetic effect of animal k
pek = random permanent environmental effect of animal k
dojk = days open for lactation j of animal k
eijkl = random residual error
ijkljkkkjiijkl e)β(dopealachysy ijkljkkkjiijkl e)β(dopealachysy
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(Co)variance Components
σa2 σpe
2 σe2 h2 rept
PM 0.10 0.09 0.85 0.10 0.18
PF 0.07 0.08 0.79 0.07 0.15
PP 0.08 0.07 0.70 0.09 0.17
PSCS 0.02 0.03 0.64 0.03 0.07
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Correlations Among Persistency Traits
PM PF PP PSCS
PM 0.83 0.87 -0.48
PF 0.72 0.82 -0.41
PP 0.91 0.72 -0.58
PSCS -0.19 -0.11 -0.14
1Genetic correlations above diagonal, residual correlations below diagonal.
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Genetic Correlations Among Persistency and Yield
M F P SCS
PM 0.05 0.10 0.03 -0.04
PF 0.12 0.12 0.00 0.00
PP -0.02 0.08 -0.09 -0.11
PSCS -0.23 -0.28 -0.20 0.41
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Factors Affecting Persistency
Parity: 1st lactation cows tend to have flatter lactation curves than later lactation cows
Nutrition: underfeeding energy will reduce peak yield, leading to higher persistency
Stress: low persistency in cows under handling or heat stress
Diseases?
Breed differences?
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Summary
Heritabilities and repeatabilities are low to moderate
Routine genetic evaluations for persistency are feasible
The shape of the lactation curve may be altered without affecting production
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Diseases and PersistencyAppuhamy, Cassell, and Cole 2006
Other measures may improve disease resistance through indirect selection, e.g. productive life (PL), body condition scores, and persistency
Studies of the effect of diseases on milk yield is abundant in literature
Investigations of relationships between diseases and other traits are lacking (Muir et al., 2004)
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Objectives
Investigate the effect of common health disorders on persistency
Estimate phenotypic correlations among diseases and persistency
Measure breed effects (Holstein and Jersey) on these relationships
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Materials and Methods
Daily milk yield records of Holstein and Jersey cows at the Virginia Tech Dairy Complex from 07/18/2004 to 06/07/2006 Holstein Jersey
First lactation (L1) 41 10
Second lactation (L2)
34 08
Third and later (L3+)
40 15
Total Lactations 115 33
Total cows 93 33
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Definition of Disease Variables
Mastitis (MAST) : All causes of udder infections
MAST1 : in first 100 days (stage1)
MAST2 : after 100th DIM (stage2)
Post Partum Metabolic Diseases (METAB): Milk fever and/or ketosis
Other diseases: LAME, DA, MET, PNEU, DIARR
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Statistical Analysis
where:
Yijklm = Lactation persistency of cow m
Li = Effect of ith lactation (i = 1, 2, & 3)
YSj = Effect of jth calving year-season ( j=1, 2, 3, 4, 5 & 6)
Dk = Effect of kth status of the disease ( k =1 or 0)
Ol = Effect of lth status of other diseases (l=1 or 0)
eijklm = residual effect
(Other diseases includes all diseases beside the disease of interest.)
Pijklm = Li + Yj +Dk + Ol + eijklm
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Disease incidence rates in Holstein (H) & Jersey (J) cows
0
10
20
30
MAST1 MAST2 METAB OTHER
Disease
Inci
denc
e ra
te (
%)
H
J
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Diseases and Breed on Persistency
** Significant (p<0.05)
Factor Levels LS Mean Correlation
MAST1**
0 -0.18
-0.241 -0.76
MAST2
0 -0.3
-0.091 -0.55
METAB
0 -0.35
-0.081 0.37
BREED**
H -0.11
J -0.74
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Conclusions
Mastitis in early lactation has a significant, negative effect on persistency
Mastitis in late lactation and post partum metabolic diseases have non-significant, but negative, effects on persistency
Persistency differs significantly between Holstein and Jersey cows
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Goals
Evaluate crossbred animals without biasing purebred evaluations
Accurately estimate breed differences
Compute national evaluations and examine changes
PTA of purebreds and crossbreds Changes in reliability
Display results without confusion
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Methods
All-breed animal modelPurebreds and crossbreds togetherUnknown parents grouped by breedVariance adjustments by breedAge adjust to 36 months, not mature1988 software, good convergence
Within-breed-of-sire model examined but not used
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Unknown Parent Groups
Groups formed based onBirth year (flexible) Breed (must have >10,000 cows)Path (dams of cows, sires of cows, parents of bulls)
Origin (domestic vs other countries)
Paths have >1000 in last 15 years
Groups each have >500 animals
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Data
Numbers of cows of all breeds 22.6 million for milk and fat 16.1 million for protein 22.5 million for productive life 19.9 million for daughter pregnancy rate 10.5 million for somatic cell score
Type evaluated in separate breed files
Calving ease joint HO, BS, and HO x BS
Goats in all-breed model since 1988
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Crossbred Cowswith 1st parity records
YearF1 (%)
F1 cows
Back-
crossHet
> 0XX
cows
2005 1.3 8647 2495 12621
4465
2004 1.2 7863 1983 11191
3947
2003 .9 6248 1492 9051 3111
2002 .7 4689 1467 7338 2564
2001 .5 3491 1330 5878 2081
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Reliability
Crossbred cowsWill have PTA, most did not before
Accurate PTA from both parents
Purebred animalsInformation from crossbred relatives
More contemporaries
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All- vs Within-Breed Evaluations
Correlations of PTA Milk
Breed
99% REL bulls
Recent bulls
Recent cows
Holstein >.999 .994 .989Jersey .997 .988 .972Brown Swiss .990 .960 .942Guernsey .991 .988 .969Ayrshire .990 .963 .943Milking Shorthorn
.997 .986 .947
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Display of PTAs
Genetic base Convert all-breed base back to within-
breed-of-sire bases Each animal gets just one PTA PTAbrd = (PTAall – meanbrd) SDbrd/SDall
Heterosis and inbreeding Both effects removed in the animal model Heterosis added to crossbred animal PTA Expected Future Inbreeding (EFI) and
merit differ with mate breed
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Schedule
Interbull test run Feb. 1, 2006Trend validationConvert all-breed PTA back to within-breed bases
Scientific publication (JDS)
ImplementationExpected May 2007
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Conclusions
All breed model accounts for:General heterosisUnknown parent groups by breedHeterogeneous variance by breed
PTA converted back to within breed bases, crossbreds to breed of sire
PTA changes more in breeds with fewer animals
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SNP Project Outcomes
Genome-wide selection
Parentage verification & traceability panels
Enhanced QTL mapping & gene discovery
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Linkage disequilibrium (LD)
Non-random association of alleles at two or more loci, not necessarily on the same chromosome
Not the same as linkage, which describes the association of two or more loci on a chromosome with limited recombination between them
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The population isyoung enough that large segments of the genome are not disrupted by recombination (LD)
Concept of a HapMap
ManyGenerations
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Genome Selection
Animals are genotyped at birth
Genomic EBV calculated for many traits
Even those not typically recorded (e.g. semen quality)
Accuracy is predicted to be similar to progeny test evaluation
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Advantages of Genome Selection
Generation intervals can be reduced
Costs of progeny testing can be decreased
More accurate selection among full sibs
Decreased risk in selection program
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Low-cost parentage verification
SNP tests may make parentage validation cheap enough for widespread adoption
Develop a database and software to check parentage and suggest alternatives for invalid IDs
Determine rate of parentage errors in a sample of herds
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Ongoing Work
New traits Stillbirth (HOL) Milking speed (BSW) Rear legs/rear view (BSW, GUE) Bull fertility (transferred from
DRMS)
Improved online tools Fully buzzword-compliant Web services for data delivery
Choice of scales
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