2007 John Cole, Paul VanRaden, George Wiggans, and Melvin Kuhn Animal Improvement Programs Laboratory USDA Agricultural Research Service, Beltsville, MD,

2007

John Cole, Paul VanRaden, George Wiggans, and Melvin KuhnJohn Cole, Paul VanRaden, George Wiggans, and Melvin KuhnAnimal Improvement Programs Laboratory USDA Agricultural Research Service, Beltsville, MD, USA [email protected]

2008

AIPL UpdateAIPL Update

mailto:[email protected]

NAAB Dairy Sire Evaluation Committee, October 27 2008 (2) AIPL200

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Genotyped animals Genotyped animals (October 2008)(October 2008)

Breed Bulls Cows PredictorsHolstein 12,275 2,445 7,821Jersey 1,205 369 1,428Brown Swiss 365 3 359


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Experimental DesignExperimental DesignHolstein, Jersey, and Brown Swiss breedsHolstein, Jersey, and Brown Swiss breeds

HO JE BSPredictor:Bulls born <1999 3,576 743 225Cows with data 202

Predictee:Bulls born >1999 1,759 425 118Data from 2003 used to predict independent data from 2008


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Reliability GainReliability Gain11 by Breed by BreedYield traits and NM$Yield traits and NM$

Trait HO JE BSNet merit 23 9 3Milk 23 11 0Fat 33 15 5Protein 22 4 1Fat % 43 41 10Protein % 34 29 51Gain above parent average reliability ~35%


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Reliability Gain by BreedReliability Gain by BreedHealth and type traitsHealth and type traits

Trait HO JE BSProductive life 18 12 2Somatic cell score 21 1 16Dtr pregnancy rate 16 5 -Final score 18 6 -Udder depth 35 13 3Foot angle 14 10 -Stature 26 9 3


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New Genetic TermsNew Genetic Terms

Actual vs. expected genetic similarity• Genomic relationships and inbreeding• Genomic future inbreeding (GFI) vs. EFI

Daughter merit vs. son merit Haplotyping and imputation

• Which allele is from sire vs. dam?• Which alleles are linked together?• Can missing genotypes be predicted?


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GGenomic vs. Pedigreeenomic vs. PedigreeIInbreedingnbreeding

Bull Pedigree F Genomic FO Man 4.5 15.8Ramos 2.3 11.5Shottle 5.6 11.9Planet 6.7 18.8Earnit 6.2 12.8Nifty 3.1 11.7

Correlation = .68


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GGenomic vs. enomic vs. EExpected xpected FFuture uture IInbreedingnbreeding

Bull EFI GFIBlackstar 7.9 7.9Elevation 7.6 7.4Chief 7.1 6.8Emory 7.0 6.9RC Matt 7.0 6.7Juror 7.0 6.7


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Net Merit by ChromosomeNet Merit by ChromosomePlanetPlanet - high Net Merit bull - high Net Merit bull

-20

0

20

40

60

80

100

X 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Chromosome

NM

$

NM$


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ScheduleSchedule Calculate SNP effects with each of 3

annual traditional evaluations Calculate genomic evaluations once or

more between traditional evaluations, monthly?• Recalculate SNP effects if significant

number of predictor animals added• Use existing SNP effects if only young

animals added


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Official release in 2009Official release in 2009 Information from genomic evaluations

propagated to evaluations of descendents without genotypes

NAAB to manage bull-owner notification and sharing among AI organizations

Public release of genomic evaluations• Cows soon after calculated• Bulls when enrolled with NAAB or Canadian AI

organization• Shared by agreement with owner


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Low cost genotyping researchLow cost genotyping research Develop a genetic test that is cheap

enough to enable use on most animals Provide parentage verification/discovery Provide a genetic estimate useful for first

stage screening 384 SNP proposed for first test High throughput procedures being

developed


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Reliability of evaluationsReliability of evaluations Reliability from inverse of a matrix with

order the number of genotyped animals Approximation necessary as number of

genotyped animals increases Daughter equivalents discounted by 0.6

to represent better the reliability of 2003 data in predicting bulls first evaluated in 2008


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Plans to increase accuracyPlans to increase accuracy Genotype more predictor bulls Reach 1,500 Brown Swiss, possibly

through foreign collaboration Increase genotyped Jerseys from both

domestic animals and possible foreign collaboration

Investigate across-breed analysis so Holstein data can improve accuracy for Jerseys and Brown Swiss


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HaplotypingHaplotyping Haplotyping may increase accuracy Even a SNP very close to a QTL may

have a different allele frequency Haplotype allele may have higher

correlation with the QTL May assist in imputation of

genotypes of missing SNP and perhaps whole animals


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International implicationsInternational implications All major dairy countries are

investigating genomic selection Interbull meeting in January on

integration of genomic evaluations Studs must balance competitive benefit

from treating genotypes as proprietary with benefits from sharing


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InterbullInterbull Genomics contribution to accuracy should

be reported• Avoid double counting when submitted by

multiple countries• Could be processed similar to parent

contribution Change in 10-herd requirement needed to

allow marketing bulls with only genomic information in countries without genomic evaluations


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Query exampleQuery example


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Dystocia ComplexDystocia Complex

Markers on BTA 18 had the largest effects for several traits:• Dystocia: Sire and daughter calving

ease• Conformation: rump width, stature,

strength, and body depth• Efficiency: longevity and net merit

Large calves contribute to shorter PL and decreased NM$


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SNP Position (bp) Trait Effect (SD) Functional class Gene name rs41628662 41,453,097 NM 0.07 unknown unknown ss86324977 57,125,868 BDep 0.32 intron DCE 0.03 DSB 0.01

Sialic acid binding Ig-like lectin 9 (SIGLEC)

RWid 0.32 SCE 0.05 SSB 0.01 Stat 0.25 Str 0.32 ss105250374 57,639,664 PL 0.14 intron Zinc finger

protein 577

Markers on BTA18 with large effectsMarkers on BTA18 with large effects


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SIGLEC proteinsSIGLEC proteins

Human Siglec-9 highly expressed in the placenta (Foussias, 2000).

Human Siglec-6 may be involved in initiation of parturition (Brinkman-Van der Linden et al., 2007).

Siglec-6 binds and sequesters leptin (Brinkman-Van der Linden et al., 2007).


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Proposed mode of actionProposed mode of action

Leptin-deficient mice delay parturition (Mounzih et al., 1998).

Homozygotes may express high levels of Siglec-6, resulting in leptin deficiency and delayed parturition.

ss86324977 may result in increased calf size associated with longer gestation lengths.


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Interbull fertility traitsInterbull fertility traits

Heifer conception as a rate (heifer conception rate).

Ability to recycle after calving (days to first breeding).

Cow conception as a rate (cow conception rate) and interval (first breeding to conception).

Calving to conception (days open).


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Status of fertility traitsStatus of fertility traits Traits 1 and 3 (heifer and cow

conception rates) submitted to Sept. 2008 test run.

Work on Trait 2 (days to first breeding) is underway.

Trait 4 (ability to conceive as an interval) is now required.

We already report Trait 5 (DPR).


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Heifer and cow conception rateHeifer and cow conception rate

Heifer conception rate (HCR) is the percentage of inseminated heifers that become pregnant at each service.

Cow conception rate (CCR) is defined as the percentage of inseminated cows that become pregnant at each service.


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Conception rate variance componentsConception rate variance components

Trait repHeifer CR 1.0% 2.5% 12%Cow CR 1.6% 1.2% 7%DPR 4.0% 12.0% 20%

h2 σ2pe


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Correlations among fertility PTACorrelations among fertility PTA

Trait PairHCR, CCR 0.41HCR, DPR 0.34CCR, DPR 0.85

rPTA

Correlations among PTA for bulls with at least100 CR daughters.


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Distribution of Heifer CR PTADistribution of Heifer CR PTA

1 2 3 4 5 6 7 8 9 10 11 12 130

1000

2000

3000

4000

5000

6000

7000

8000

Sire PTA for Cow CR

N


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Distribution of Cow CR PTADistribution of Cow CR PTA

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 170

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Sire PTA for Cow CR

N


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Top bulls – Heifer CRTop bulls – Heifer CR

Bull Daus PTA HCR PTA DPRGarter 7,392 +5.3 -2.6Blitz 11,879 +4.5 -2.8Outside 4,866 +4.2 +1.4Freedom 3,465 +4.0 +2.4Marathon 2,045 +3.8 +0.1Adam 4,269 +3.8 +0.4Die-Hard 8,440 +3.7 +2.9


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Bottom bulls – Heifer CRBottom bulls – Heifer CR

Bull Daus PTA HCR PTA DPRBoliver 3,710 -5.4 -0.4Cadence 2,017 -4.7 +1.1Aaron 3,665 -3.6 -2.3Dawson 5,008 -3.5 -0.9Theo 4,189 -3.3 -2.4Pippen 6,498 -3.2 -0.5Demand 2,281 -3.1 -2.8


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Top bulls – Cow CRTop bulls – Cow CR

Bull Daus PTA HCR PTA DPRExtra Special 2,314 +7.9 +4.7Bret 5,214 +7.1 +3.2Wizard 10,122 +6.6 +3.6Die Hard 21,552 +5.9 +2.9Lynch 22,803 +5.3 +1.8Consultant 2,373 +5.1 +1.7Boss Iron 2,615 +5.0 +3.2


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Bottom bulls – Cow CRBottom bulls – Cow CR

Bull Daus PTA HCR PTA DPRModesto 2,530 -7.5 -4.7Glenn 2,250 -6.0 -3.5Matter 2,777 -6.0 -3.9Hi Metro 9,380 -5.8 -3.2Vital Sign 4,110 -5.5 -4.1Hillcrest 6,378 -5.0 -3.4Manat 3,700 -5.0 -3.0


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AcknowledgmentsAcknowledgments

Genomics work supported by NRI Grants 2006-35205-16888 and 2006-35205-16701 and by NAAB.

Much of the work on heifer and cow CR was carried out by Dr. Melvin Kuhn while he was at AIPL.

Documents

2007 John Cole, Paul VanRaden, George Wiggans, and Melvin Kuhn Animal Improvement Programs Laboratory USDA Agricultural Research Service, Beltsville, MD,