2007 paul vanraden, mel tooker, and melvin kuhn animal improvement programs laboratory, usda...

2007

Paul VanRaden, Mel Tooker, and Melvin KuhnPaul VanRaden, Mel Tooker, and Melvin Kuhn

Animal Improvement Programs Laboratory, USDAAgricultural Research Service, Beltsville, MD, [email protected]

2007

Research Plans for Genomics, Research Plans for Genomics, Crossbreeding, Fertility, etc. Crossbreeding, Fertility, etc.

Genex / CRI, June 2007 (2) P.M. VanRaden200

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AIPL 5-Year Plan 2007-2012AIPL 5-Year Plan 2007-2012

Objectives• Collect genotypes, new phenotypes• Document current status and effects

of management on dairy traits• Improve accuracy of predictions by

including SNP data, refining models • Estimate economic values of traits to

maximize lifetime profit


7

Genomic GoalsGenomic Goals

Predict young bulls and cows more accurately

Compare actual DNA inherited Use exact relationship matrix G

instead of expected values in A Trace chromosome segments Locate genes with large effects


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How Related are Relatives?How Related are Relatives?

Example: Full sibs • are expected to share 50% of their

DNA on average • may actually share 45% or 55% of

their DNA because each inherits a different mixture of chromosome segments from the two parents.

Combine genotype and pedigree data to determine exact fractions


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Genomic RelationshipsGenomic Relationships

Measures of genetic similarity• A = Expected % genes identical by

descent from pedigree (Wright, 1922)• G = Actual % of DNA shared (using

genotype data)• T = % genes shared that affect a

given trait (using genotype and phenotype)

Best measure depends on use


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QTL Relationship Matrix (QTL Relationship Matrix (TT))

Three bulls each +50 PTA protein. Are their QTL alleles the same?

• Possibly, but probably not.• Bull A could have 10 positive genes.• Bull B could have 10 positive genes,

not on same chromosomes as bull A.• Bull C could have 20 positive and 10

negative genes.


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Genes in Common at One LocusGenes in Common at One Locus

If Full Sib 1 inherits:

If Full Sib 2 inherits:

w,y w,z x,y x,z

w,y 2 1 1 0

w,z 1 2 0 1

x,y 1 0 2 1

x,z 0 1 1 2

w = gene from sire of sirex = gene from dam of sirey = gene from sire of damz = gene from dam of dam


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Alleles Shared by SibsAlleles Shared by Sibs

Indep-endent Loci

Percentage of alleles shared

Full sibs Half sibs

Mean SD Mean SD

1 50 35.4 25 17.7

5 50 15.8 25 7.9

10 50 11.2 25 5.6

50 50 5.0 25 2.5

100 50 3.5 25 1.8

Infinite 50 0.0 25 0.0


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Unrelated Individuals?Unrelated Individuals?

No known common ancestors Many unknown common ancestors

born before the known pedigree G = Z Z’ / number of loci Elements of Z are –p and (1 – p),

where p is allele frequency Relationships in base = 0 +/- LD


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Traditional PedigreeTraditional Pedigree

Sire of Sire

Sire

Dam of Sire

Animal

Sire of Dam

Dam

Dam of Dam


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Genomic PedigreeGenomic Pedigree


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Example of a SNP haplotypeExample of a SNP haplotype

caacgtat

caacggat

SNP

atccgaat

atccgcat

…

… …

…

SNP

tctaggat

tctcggat

SNP

…

…Chr1

Chr2

Haplotype is a set of single nucleotide polymorphisms (SNPs) associated on a single chromosome. Identification of a few alleles of a haplotype block can identify other polymorphic sites in the region.

Haplotype 1 tca

gac Haplotype 2


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SNP PedigreeSNP Pedigreeatagatcgatcg

ctgtagcttagg

agggcgcgcagt

cgatctagatcg

cggtagatcagt

agagatcgatct

atggcgcgaacg

ctatcgctcagg

ctgtagcgatcg

agatctagatcg

agagatcgcagt

atgtcgctcacg

ctgtctagatcg

atgtcgcgcagt


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Haplotype PedigreeHaplotype Pedigreeatagatcgatcg

ctgtagcttagg

agggcgcgcagt

cgatctagatcg

cggtagatcagt

agagatcgatct

atggcgcgaacg

ctatcgctcagg

ctgtagcgatcg

agatctagatcg

agagatcgcagt

atgtcgctcacg

ctgtctagatcg

atgtcgcgcagt


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Genotype PedigreeGenotype PedigreeCount number of copies of second alleleCount number of copies of second allele

121101011112

111011120202

101121121111

122021121111

101101111100

011111012011

121120011012

0 = homozygous for first allele1 = heterozygous2 = homozygous for second allele


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Reliability from Full SibsReliability from Full SibsMarker and QTL positions identical, sib REL = 99%Marker and QTL positions identical, sib REL = 99%

Reliability Obtained From:

Full Sibs A

60 QTLs

G

30,000 QTLs

G

1 .250 .250 .250

10 .454 .494 .470

100 .495 .907 .624

Infinite .500 1.000 1.000A = traditional additive relationships, G = genomic relationships


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Bulls to GenotypeBulls to Genotype58,533 SNP Project58,533 SNP Project

Choose HO bulls with semen at BFGL

Genotype 1777 proven bulls• Born 1994-1996 with >75% REL NM• Plus 172 ancestor bulls born 1952-1993

Predict 500 bulls sampled later• Born 2001 with >75% REL NM

Include other bulls in gap years?• Born 1997-2000 (proven) or >2002 (waiting)


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Birth Years of Bulls to GenotypeBirth Years of Bulls to Genotype

0

400

800

1200

1990 1994 1998 2002 2006

Nu

mb

er

of

Bu

lls

ancestors

proven

predicted

calves

Data cutoff


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Contributors of DNAContributors of DNA500 CDDR bulls to predict, born in 2001500 CDDR bulls to predict, born in 2001

AI Organization CodeNumber of

bulls chosen

CRI / Genex 1 126

Select Sires 7 107

Alta Genetics 11 67

Accelerated 14 44

ABS Global 29 120

Semex 200 32


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Potential ResultsPotential ResultsSimulation of 50,000 SNPsSimulation of 50,000 SNPs

QTLs normally distributed, n = 100 Reliability vs parent average REL

• 58% vs 36% if QTLs are between SNPs

• 71% vs 36% if QTLs are located at SNPs (not likely)

• Higher REL if major loci and Bayesian methods used, lower if many loci (>100) affect trait


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Reliability from GenotypingReliability from Genotyping

Daughter equivalents• DETotal = DEPA + DEProg + DEY + DEG

• DEG is additional DE from genotype• REL = DEtotal / (DETotal + k)

Gains in reliability• DEG could be about 15 for Net Merit• More for traits with low heritability• Less for traits with high heritability


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Genomic Computer ProgramsGenomic Computer Programs

Simulate SNPs and QTLs• Compare SNP numbers, size of QTLs

Calculate genomic EBVs • Use selection index, G instead of A• Use iteration on data for SNP effects

Form haplotypes from genotypes• Not programmed yet


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Computing TimesComputing Times

Inversion including G matrix• Animals2 x markers to form G matrix• Animals3 to invert selection index• 10 hours for 3000 bulls, 50,000 SNPs

Iteration on genotype data• Markers x animals x iterations• 16 hours for 1000 iterations


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Distribution of Marker EffectsDistribution of Marker Effects

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

Standardized Marker Deviation

Prob

abili

ty

Error

QTL + Error


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Linear vs Non-linear ModelsLinear vs Non-linear Models

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

Standardized Marker Deviation

QTL

Est

imat

e

Bayesian

Linear


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All-Breed Model: GoalsAll-Breed Model: Goals

Evaluate crossbred animals without biasing purebred evaluations

Accurately estimate breed differences

Compare crossbreeding strategies

Compute national evaluations and examine changes

Display results without confusion


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MethodsMethods

All-breed animal model• Purebreds and crossbreds together• Relationship matrix among all• Unknown parents grouped by breed• Variance adjustments by breed• Age adjust to 36 months, not mature

Within-breed-of-sire model examined but not used


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DataData

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 traits are still collected and evaluated in separate breed files


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Purebred and Crossbred DataPurebred and Crossbred DataUSA milk yield recordsUSA milk yield records

Breed % of total Cows born 2003

Holstein 90.5 642,354

Jersey 6.4 45,151

Brown Swiss .8 5,960

Guernsey .4 2,563

Ayrshire .3 1,926

F1 Crossbred 1.2 8,647


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Crossbred CowsCrossbred Cowswith 1with 1stst parity records parity records

Fresh year

F1 (%)

F1 cows

Back-cross

Het > 0

XX cows

2006 1.4 10153 3422 15365 6099

2005 1.2 8647 2495 12621 4465

2004 1.1 7863 1983 11191 3947

2003 .9 6248 1492 9051 3111

2002 .7 4689 1467 7338 2564


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Number of Cows with RecordsNumber of Cows with Records (with > 50% heterosis; March 2007)

Dam Sire Breed

Breed AY BS GU JE MS XX HO

AY — 29 29 221 48 43 1796

BS 20 — 50 294 42 13 2619

GU 46 96 — 288 32 16 3256

JE 181 357 155 — 116 56 3718

MS 281 52 10 71 — 5 965

XX 489 1544 308 3568 323 — 8859

HO 1843 13993 1721 35193 1858 675 —


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Number of Cows with RecordsNumber of Cows with Records (with > 50% heterosis; March 2007)

Sire Breed

Dam Breed #

Sire Breed

Dam Breed #

BS SM 25 HO DL 47

DL HO 109 HO LD 195

MO HO 73 HO MI 60

NO HO 38 HO NR 21

NR HO 23 HO RE 22

SR HO 118 HO SM 16


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Crossbred Daughters AddedCrossbred Daughters Addedfor sires in top 10 NM$ within breedfor sires in top 10 NM$ within breed

Sire

breed

Daughters

Sire Name Feb ‘07 Added

Legacy AY 157 33

Agenda BS 35 21

Excite BS 144 57

Q Impuls JE 241 20

Stetson MS 36 31


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Heterosis for Yield TraitsHeterosis for Yield Traits Percent of Parent Breed Average

Milk Fat Protein

BreedHO Sire

HO Dam

HO Sire

HO Dam

HO Sire

HO Dam

Ayrshire 2.4 -2.0 2.7 -1.8 2.9 -2.4

Brown Swiss 5.6 3.2 4.8 4.5 4.7 3.8

Guernsey 5.2 2.4 7.1 4.4 5.5 4.0

Jersey 7.5 1.6 6.6 4.5 7.2 4.1

M. Shorthrn 2.8 0.3 3.2 1.3 3.6 1.2

Heterosis 3.4 4.4 4.1


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All-Breed AnalysesAll-Breed Analyses

Crossbred animals• Now have PTAs, only 3% did before if in

breed association grading-up programs• Reliable PTAs from both parents

Purebred animals• Information from crossbred relatives• More herdmates (other breeds, crossbreds)

Routinely used in other populations• New Zealand (1994), Netherlands (1997)• USA goats (1989), calving ease (2005)


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Unknown Parent GroupsUnknown Parent Groups

Look up PTAs of known parents Estimate averages for unknowns Group unknown parents by

• Birth year • Breed • Path (dams of cows, sires of cows,

parents of bulls)• Origin (domestic vs other countries)


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All- vs Within-Breed EvaluationsAll- vs Within-Breed EvaluationsCorrelations of PTA MilkCorrelations of PTA Milk

Breed99% REL bulls

Recent bulls

Recent cows

Holstein >.999 .994 .989Jersey .997 .988 .972Brown Swiss .990 .960 .942Guernsey .991 .988 .969Ayrshire .990 .963 .943

Milking Shorthorn .997 .986 .947


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Display of PTAsDisplay of PTAs

Genetic base• Convert all-breed base to within-breed

bases (or vice versa)• PTAbrd = (PTAall – meanbrd) SDbrd/SDHO

• PTAall = PTAbrd (SDHO/SDbrd) + meanbrd

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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All-Breed PTAs – March Test RunAll-Breed PTAs – March Test Run

Genetic correlations mostly same• JE increase .02 for PL and .01 for SCS• BS decrease .01 for fat and SCS• AY increase .01 for PL

USA bulls in top 100 differ little• Numbers are averages across all scales• JE improve for SCS, fat (26 vs 25)• JE decline for milk, protein (59 vs 62)• BS decline for yield (10 vs 15)• HO improve for yield (17 vs 16)


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Jersey and Swiss PTAsJersey and Swiss PTAs

Base cow means changed little

Base cow SD changed little

Top bulls for protein dropped by ~9 lbs, bottom bulls dropped by ~4 lbs in both breeds

Unknown parent grouping, heterosis may be responsible


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All-breed Trend ValidationAll-breed Trend Validation

85 tests, 6 were significant (.05)• None significant for milk or SCS• 1 of 15 for fat and for protein• 2 of 15 for PL and for DPR

Increase in DPR repeatability made trend more negative, helped tests


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Daughter Pregnancy RateDaughter Pregnancy RateGenetic trend on all-breed baseGenetic trend on all-breed base

-1012345678

1960 1970 1980 1990 2000

Birth Year

Bre

ed

ing

Va

lue Ayrshire

Brwn Sws

Guernsey

Holstein

Jersey

Mlk Shrtn


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Assumed Effects – Other TraitsAssumed Effects – Other TraitsTransmitting ability differences from HolsteinTransmitting ability differences from Holstein

Size Udder F&LCalving Difficulty

Still-birth

Jersey −10.4 −1.4 −2.1 −7.1 −1.5

B. Swiss 0.0 0.7 0.3 −3.2 −0.7

Guernsey −7.3 −0.5 −1.3 −4.6 1.1

Ayrshire −5.8 −1.6 −0.9 −3.5 −1.2

M. Short. −4.2 0.1 −0.6 −0.1 −2.4

Heterosis 0.9 0.0 0.0 0.0 0.0


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Merit of FMerit of F11 Holstein Crossbreds Holstein Crossbreds2006 Merit Indexes2006 Merit Indexes

Second Breed NM$ CM$ FM$

Ayrshire −304 −261 −364

Brown Swiss 55 139 −78

Guernsey −408 −405 −503

Jersey 31 153 −158

M. Shorthorn −498 −461 −547

Compared to 2005 genetic base for Holstein


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Later Generation CrossesLater Generation Crosses

Holstein backcross or

multi-breed NM$ CM$ FM$

HO x (BS x HO) +28 +70 −39

HO x (JE x HO) +16 +77 −79

BS x (JE x HO) −32 +109 −251

JE x (BS x HO) −44 +116 −292

HO x (BS x JE) +44 +147 −118

Compared to 2005 genetic base for Holstein


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Butterfat yield of three breed crosses was greater than from their F1 crossbred dams.

Three breed crosses averaged 14,927 pounds of milk and 641 pounds of butterfat as 2-year-olds in 1947.

USDA Yearbook of Agriculture 1947Three-Breed CrossesThree-Breed Crosses


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Crossbreeding ConclusionsCrossbreeding Conclusions

All-breed model accounts for:• Breed effects and general heterosis• Unequal variances within breed

Implemented in May 2007• PTA converted back to within-breed

bases, crossbreds to breed of sire• PTA changes larger in breeds with

fewer animals


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Cow Fertility ResearchCow Fertility Research

Daughter Pregnancy Rate works well, except that• Other traits are evaluated by Interbull• Other countries don’t use DPR in

their indexes, and their calving interval data comes too late

Synchronization changes traits


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Emphasis on Fertility, LongevityEmphasis on Fertility, Longevity(% of total merit)(% of total merit)

Ctry Fert Long Ctry Fert Long

USA 9 17 DNK 8 6

DEU 1 25 AUS 9 8

NLD 8 16 NZL 7 6

FRA 13 13 GBR 7 15

CAN 5 7 SWE 15 5

ITA 8 IRL 22 18


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Days Open Genetic Correlations Days Open Genetic Correlations Jorjani, 2005 Interbull BulletinJorjani, 2005 Interbull Bulletin

DFS ESP GBR IRL NLD NZL USA

DFS .91 .91 .75 .93 .76 .91

ESP .91 .89 .82 .90 .83 .93

GBR .91 .89 .84 .96 .84 .85

IRL .75 .82 .84 .80 .70 .80

NLD .93 .90 .96 .80 .83 .88

NZL .76 .83 .84 .85 .83 .73

USA .91 .93 .85 .80 .88 .73

DFS = Denmark-Finland-Sweden


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DPR Results – March Test Run DPR Results – March Test Run Holstein genetic correlationsHolstein genetic correlations

Eval Model BEL DFS ESP GBR IRL ITA NLD NZL

MarAll breed 86 89 93 83 76 90 86 62

FebWithin breed 85 … 93 83 72 86 85 60

Diff +1 … 0 0 +4 +4 +1 +2

March model also included an increase in repeatability


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Daughter Conception Rate Daughter Conception Rate Genetic CorrelationsGenetic Correlations

Jorjani, 2005 Interbull BulletinJorjani, 2005 Interbull Bulletin

CAN DEU DFS FRA ISR NLD

CAN .90 .80 .78 .72 .70

DEU .90 .72 .92 .65 .47

DFS .80 .72 .75 .96 .62

FRA .78 .92 .75 .70 .42

ISR .72 .65 .96 .70 .64

NLD .70 .47 .62 .42 .64DFS = Denmark-Finland-Sweden


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Days to 1Days to 1stst Insemination Insemination Genetic CorrelationsGenetic Correlations

Interbull, May 2007Interbull, May 2007

CHE DFS ITA NLD NZL

CHE .95 .87 .90 .61

DFS .95 .90 .91 .57

ITA .87 .90 .86 .70

NLD .90 .91 .86 .55

NZL .61 .57 .70 .55

DFS = Denmark-Finland-Sweden


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Fertility Trait IndexesFertility Trait Indexes% relative emphasis% relative emphasis

Trait USA NLD ITA CAN DNK1 DEU FRA

Days 1st Insem.

69 33 19 25 15

Non− Return

31 41 20 65 70 100 100

Days Open

100 26 61

Heifer fertility

10 15

1Time from first to last insemination replaces non−return rate


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Predict Longevity from FertilityPredict Longevity from Fertility

Which cow fertility trait contributes most to longevity?• Days to first insemination (DFI), or• Non−return rate (NR)

Combined longevity includes• 23% DFI and 12% NR in CAN• Only DFI in NLD• Correlations = .33 DFI, .11 NR in USA


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DPR - Top 100 bullsDPR - Top 100 bullsBorn in last 12 years, March 2007 test runBorn in last 12 years, March 2007 test run

Country with most daughters

Scale DFS ESP GBR IRL ITA NLD NZL USA

BEL 14 4 10 6 1 6 54 4

DFS 29 3 11 6 3 17 27 3

ESP 15 5 10 4 1 5 52 8

GBR 18 7 13 7 4 11 30 7

IRL 12 3 10 10 1 5 58 0

ITA 18 3 8 3 3 8 55 2

NLD 18 4 10 4 1 22 38 3

NZL 1 0 0 1 0 0 97 0

USA 17 4 8 3 2 6 53 7

Total 142 33 80 44 16 80 464 34


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Calving Interval CorrelationsCalving Interval Correlationswith other traits in the same countrywith other traits in the same country

Ctry Birth Milk Fat Prot Long SCS

DNK −.40 −.31 −.28 −.32 .34 −.18

ESP −.08 −.38 −.29 −.35 .38 −.16

GBR −.27 −.36 −.36 −.42 .30 −.13

IRL −.20 −.40 −.35 −.37 .49

NLD −.41 −.52 −.43 −.50 .06 −.13

NZL −.11 −.32 −.05 −.21 .59 −.10

USA −.04 −.21 −.21 −.17 .48 −.12


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Conception RateConception Rate(Trait 4 correlations with other traits)(Trait 4 correlations with other traits)


CAN −.06 −.07 −.09 −.09 .09 −.04

DEU .06 −.01 −.03 −.02 .17 −.04

DNK −.41 −.43 −.37 −.47 .23 −.17

FRA .09 .00 −.02 .00 .28 −.08

ISR −.06 −.07 −.16 −.27 .38 −.15

NLD −.41 −.39 −.37 −.48 .08 −.04


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Calving to First InseminationCalving to First Insemination(Trait 2 correlations with other traits)(Trait 2 correlations with other traits)


CAN −.01 −.26 −.16 −.21 .32 −.12

DNK −.34 −.42 −.36 −.40 .25 −.22

NLD −.33 −.49 −.46 −.50 .04 −.15

NZL −.08 −.29 −.05 −.19 .49 −.10


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Heifer FertilityHeifer Fertility(Trait 1 correlations with other traits)(Trait 1 correlations with other traits)


CAN −.09 −.11 −.09 −.14 .14 −.10

DNK −.27 −.23 −.19 −.27 .03 −.05

GBR −.43 −.48 −.22 −.44 .13 −.12


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Cow Fertility ConclusionsCow Fertility Conclusions

Fertility and longevity receive a total of 8% to 40% of selection

Fertility definitions not uniform

Days to 1st insemination is more important than conception rate?

Selection for fertility reduces costs and increases longevity


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Bull Fertility ResearchBull Fertility ResearchDr. Melvin KuhnDr. Melvin Kuhn

I. Multiple services and an expanded service sire (SSR) term

II. “Type” of model: Linear, Threshold

III. Unconfirmed breedings: outcome not known with certainty

IV. Edits and Modeling of nuisance variables


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Service Sire EffectsService Sire Effects

SSR inbreeding

Inbreeding of the Mating

SSR age at mating

Stud and Stud*year

Additive genetic effect (very low heritability)


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Results: CorrelationsResults: Correlations Services

Method Predictor All 1st onlySimulation Expanded 87.2 81.0

SSR only 83.0 71.4

Split-herd Expanded 56.1 45.5SSR only 44.0 37.5

Future yr Expanded 32.1 29.4SSR only 29.3 23.6

Expd Stud*yr: 38.1 Expd, no Stud*yr: 31.6


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Linear/Threshold ModelLinear/Threshold ModelConclusions to date:Conclusions to date:

Little, if any, difference in predictions between the 2 models

Use of a good estimate of std. dev. of the predictor in thr model probability calculations may improve thr model evaluations

Threshold/Linear model is, at most and if anything at all, only a minor issue

Linear model will likely be implemented because it is computationally faster, more reliable, and simpler


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Sexed Semen (S) MatingsSexed Semen (S) Matings 22,843 S-matings reported as of April 2007 92% are Holstein, most of remainder are

Jersey 61% are on heifers (not eligible for ERCR) 69% are 1st services 4,040 ERCR-eligible Holstein S-matings 398 bulls Only 2 bulls with at least 300 ERCR-eligible

S-matings


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Bull Fertility SummaryBull Fertility Summary Research on use of multiple services and an

expanded service sire term is complete

Linear/Thr model is, at most, of minor importance only for this trait; will likely implement linear model

Expect to delete unconfirmed matings and treat those with positive preg ck as successes but impact will be evaluated

Implementation expected January 2008


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Test Day Model - Potential BenefitsTest Day Model - Potential Benefits

Increased accuracy of evaluations • Account for lactation curve differences• Account for genetic differences by parity• Evaluate persistency, rate of maturity• Include milk-only records if multi-trait• Possible earlier selection of bull dams• Promote as state-of-the-art system

Management effects more accurate• Could provide to DRPCs and herd owners

2007 paul vanraden, mel tooker, and melvin kuhn animal improvement programs laboratory, usda...

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