2007 paul vanraden, george wiggans, jeff o’connell, john cole, animal improvement programs...

2007

Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Animal Improvement Programs Laboratory Tad Sonstegard, and Curt Van TassellBovine Functional Genomics LaboratoryUSDA Agricultural Research Service, Beltsville, MD, USA [email protected]

2009

Dairy Cattle Breeders Have Dairy Cattle Breeders Have Adopted Genomic Selection Adopted Genomic Selection

Gordon Conference on Quantitative Genetics, Feb. 2009 (2) Paul VanRaden200

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How’s Your Genome?How’s Your Genome?


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AcknowledgmentsAcknowledgments

Genotyping and DNA extraction:• USDA Bovine Functional Genomics Lab, U.

Missouri, U. Alberta, GeneSeek, Genetics & IVF Institute, Genetic Visions, and Illumina

Computing: • AIPL staff (Mel Tooker, Leigh Walton, Jay

Megonigal) Funding:

• National Research Initiative grants– 2006-35205-16888, 2006-35205-167012006-35205-16888, 2006-35205-16701

• Agriculture Research Service• Holstein and Jersey breed associations• Contributors to Cooperative Dairy DNA Repository

(CDDR)


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CDDR ContributorsCDDR Contributors

National Association of Animal Breeders (NAAB, Columbia, MO)• ABS Global (DeForest, WI)• Accelerated Genetics (Baraboo, WI)• Alta (Balzac, AB, Canada)• Genex (Shawano, WI)• New Generation Genetics (Fort Atkinson, WI)• Select Sires (Plain City, OH)• Semex Alliance (Guelph, ON, Canada)• Taurus-Service (Mehoopany, PA)


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Genomics TimelineGenomics Timeline

Event Year

Dairy DNA repository began 1992

Cattle genome sequenced 2004

58,000 SNP selected May 2007

Illumina SNP50 chip sold Dec 2007

Prelim. genomic predictions Apr 2008

Official genomic predictions Jan 2009


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SNP Edits and CountsSNP Edits and Counts

Illumina SNP50 BeadChip 58,336

Insufficient number of beads 1,389

Unscorable SNP 4,360

Monomorphic in Holsteins 5,734

Minor allele frequency <5% 6,145

Not in H-W equilibrium 282

Highly correlated 2,010

Used for genomic prediction 38,416


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Repeatability of GenotypesRepeatability of Genotypes

2 laboratories genotyped the same 46 bulls• About 1% missing genotypes per lab• Mean of 98% SNP same (37,624 out of

38,416)– Range across animals of 20 to 2,244 SNP missingRange across animals of 20 to 2,244 SNP missing

• Mean of 99.997% SNP concordance (conflict <0.003%)

• Mean of 0.9 errors per 38,416 SNP– Range across animals of 0 to 7 SNP conflictsRange across animals of 0 to 7 SNP conflicts


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Old Genetic TermsOld Genetic Terms

Predicted transmitting ability and parent average• PTA required progeny or own records• PA included only parent data• Genomics blurs the distinction

Reliability = Corr2(predicted, true TA)• Reliability of PA could not exceed 50%

because of Mendelian sampling• Genomics can predict the other 50%• Reliability limit at birth theoretically 99%


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New Genetic TermsNew Genetic Terms

Genomic vs. pedigree relationships • Expected genes in common (A)• Actual genes in common (G)• Several formulas to compute G• Wright’s (1922) correlation matrix or

Henderson’s (1976) covariance matrix Genomic vs. pedigree inbreeding

• Correlated by 0.68

Daughter merit vs. son merit (X vs. Y)


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Differences in Differences in GG and and AA GG = genomic and = genomic and AA = pedigree relationships = pedigree relationships

Detected clones, identical twins, and duplicate samples

Detected incorrect DNA samples

Detected incorrect pedigrees

Identified correct source of DNA by genomic relationships with other animals


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Genomic Evaluation MethodsGenomic Evaluation Methods

Use Henderson’s mixed model

Replace A by G

Proposed by Nejati-Javaremi, Smith, Gibson, 1997 J. Anim Sci. 75:1738

Nonlinear regression, haplotyping or only slightly more accurate


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Worldwide Dairy GenotypingWorldwide Dairy Genotypingas of January 2009as of January 2009

Countries Animals

United States and Canada 22,344

France 8,500

Netherlands, New Zealand1 6,000

New Zealand and Ireland 4,500

Germany 3,000

Australia 2,000

Denmark, Finland, Sweden 2,0001Using a customized Illumina 50K chip (different markers)


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PhenotypesPhenotypes

26 traits plus the Net Merit index

The 6,184 bulls genotyped have >10 million phenotyped daughters (average 2,000 daughters per bull)

Most traits recorded uniformly across the world

Foreign data provided by Interbull


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Genotyped Animals (n=22,344)Genotyped Animals (n=22,344)In North America as of February 2009In North America as of February 2009

0

500

1000

1500

2000

2500

1950

1970

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

Year of Birth

Nu

mb

er

of

An

ima

ls Predictor

Predictee

Young


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Experimental Design - UpdateExperimental Design - UpdateHolstein, Jersey, and Brown Swiss breedsHolstein, Jersey, and Brown Swiss breeds

HOL JER BSW

Predictor:

Bulls born <2000 4,422 1,149 225

Cows with data 947 212

Total 5,369 1,361 225

Predicted:

Bulls born >2000 2,035 388 118

Data from 2004 used to predict independent data from 2009


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Reliability GainReliability Gain11 by Breed by BreedYield traits and NM$ of young bullsYield traits and NM$ of young bulls

Trait HO JE BS

Net merit 24 8 3

Milk 26 6 0

Fat 32 11 5

Protein 24 2 1

Fat % 50 36 10

Protein % 38 29 5

1Gain above parent average reliability ~35%


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Reliability Gain by BreedReliability Gain by BreedHealth and type traits of young bullsHealth and type traits of young bulls

Trait HO JE BS

Productive life 32 7 2

Somatic cell score 23 3 16

Dtr pregnancy rate 28 7 -

Final score 20 2 -

Udder depth 37 20 3

Foot angle 25 11 -

Trait average 29 13 N/A


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Value of Genotyping More AnimalsValue of Genotyping More AnimalsActual andActual and predicted gains predicted gains for 27 traits and for Net Meritfor 27 traits and for Net Merit

Bulls Reliability Gain

Predictor Predicted NM$ 27 trait avg

2130 261 13 17

3576 1759 23 23

4422 2035 24 29

6184 7330 31 30

Cows:

947

1916


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SimulationSimulation Results ResultsWorld Holstein PopulationWorld Holstein Population

40,360 older bulls to predict 9,850 younger bulls in Interbull file

50,000 or 100,000 SNP; 5,000 QTL

Reliability vs. parent average REL• Genomic REL = corr2 (EBV, true BV) • 81% vs 30% observed using 50K• 83% vs 30% observed using 100K


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Marker Effects for Net Merit Marker Effects for Net Merit


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Significance Tests are StupidSignificance Tests are Stupid


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Insignificant SNP EffectsInsignificant SNP Effects

Traditional selection on PA• 50 : 50 chance of better chromosome

1 SNP with tiny effect• 50.01 : 49.99 chance

38,416 SNPs with tiny effects• 70 : 30 chance

Only test overall sum of effects!


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X, X, YY, , Pseudo-autosomalPseudo-autosomal SNPs SNPs

487 SNPs

35 SNPs

0 SNPs

35 SNPs


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Net Merit by Chromosome for O-ManNet Merit by Chromosome for O-ManTop bull, +$778 Lifetime Net Merit Top bull, +$778 Lifetime Net Merit

-40

-20

0

20

40

60

80

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

Chromosome

NM

$

NM$


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Progeny Tested BullProgeny Tested BullO-ManO-Man

Semen sales ~200,000 units / year Semen price $40 / unit Income > $5 million / year 40,144 daughters already milking

• 29,811 in United States• 1,963 in France, 1,895 in Denmark,

1,716 in Italy, 839 in Holland, etc.


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O-Man Daughters O-Man Daughters vs. Average Cowsvs. Average Cows

TraitO-Man

daughterAverage Holstein

Milk (gallons/day) 10.4 10.0

Protein (lbs/day) 2.78 2.58

Cell count (1000/ml) 205 262

Productive life (mo) 33.8 27.7

Pregnancy rate (%) 25.7 23.1

Calving difficulty (%) 3% 8%


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Genomic Tested BullsGenomic Tested BullsAvailable Jan 2009Available Jan 2009

Age (yrs) Reliability Net Merit

Freddie 4 69 918

Al 1 67 914

Russell 1 65 854

Alan 1 68 841

O-Man 10 99 778


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Adoption of Genomic TestingAdoption of Genomic TestingUS young bulls purchased by AI companiesUS young bulls purchased by AI companies

Birth Year

Bulls Sampled

Bulls Tested

Genomic Tested %

2008* 193 166 86

2007* 1455 896 62

2006 1657 717 43

2005 1642 818 50

2004 1638 742 45

* 2007-2008 counts are incomplete


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Genetic ProgressGenetic Progress

Assume 60% REL for net merit• Sires mostly 1-3 instead of 6 years old• Dams of sons mostly heifers with 60% REL

instead of cows with phenotype and genotype (66% REL)

Progress could increase by >50%• 0.37 vs. 0.23 genetic SD per year• Reduce generation interval more than

accuracy


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Low Density SNP ChipLow Density SNP Chip

Choose 384 marker subset• SNP that best predict net merit• Parentage markers to be shared

Use for initial screening of cows• 40% benefit of full set for 10% cost• Could get larger benefits using

haplotyping (Habier et al., 2008)


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ConclusionsConclusions

High accuracy requires very many genotypes and phenotypes

Most traits are very quantitative (few major genes)

Genomic reliability > traditional • 30-40% with traditional parent average• 60-70% using 8,100 genotyped Holsteins • 81-83% from 40,000 simulated bulls

2007 paul vanraden, george wiggans, jeff o’connell, john cole, animal improvement programs...

Documents

quantitative genetics

paul vanraden

gordon conference

pa slide

interbull slide

genomic selection slide

genetics ivf institute

reliability gain