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

george.wiggans@ars.usda.gov2008AIPL Centennial (1) G.R. Wiggans

Implementing and improving genomic evaluations

G.R. Wiggans 2008AIPL Centennial (2)

Genomic evaluation procedure

Nominate animals for genotyping

Collect blood, hair, ear tissue, or semen

Extract DNA and genotype

Assign genotypes from image files

Check genotypes for call rate, parent-progeny consistency, and X homozygosity for bulls

Collect parent averages/estimated breeding values (EBV)

Calculate evaluations

Distribute evaluations to requesters

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Nominate animals

Participating artificial-insemination (AI) organizations have 5-year exclusive rights to evaluate bulls genomically

Each AI organization genotypes its first-choice flushes, which usually avoids duplicate genotypes

Web-based system being developed to collect nominations Avoid duplication Confirm validity of identification (ID) and

pedigree

Breed associations developing cow genotyping service

G.R. Wiggans 2008AIPL Centennial (4)

DNA sources

Semen from Cooperative Dairy DNA Repository primary source of historical DNA

Cow genotypes primarily from associated research projects

Blood and hair the most common sources of DNA for calves and cows

Bulls in waiting genotyped from semen

Ear tissue being evaluated but has not been used

Some DNA from samples collected for parentage testing

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DNA laboratories

Research Bovine Functional Genomics Laboratory (BFGL),

USDA (Beltsville, MD) University of Alberta (Edmonton, AB, Canada) University of Missouri (Columbia, MO) Illumina (San Diego, CA)

Commercial GeneSeek (Lincoln, NE) Genetics & IVF Institute (Fairfax, VA) Genetic Visions (Middleton, WI) DNA LandMarks (Saint-Jean-sur-Richelieu, QC,

Canada) Maxxam Analytics (Mississauga, ON, Canada) ABS (DeForest, WI, through SyGen/PIC, Franklin,

KY )

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Assign genotypes

Each laboratory sends image files to BFGL

Methods developed to achieve consistent and high call rates

Direct reporting of genotypes to AIPL planned for December 2008

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Check genotypes

90% call rate required

Each parent-progeny pair checked for conflicting homozygotes

Maternal grandsire (MGS) also checked with higher threshold of conflicts

Cases with many conflicts or no parent genotyped checked against all genotyped animals for possible parent

Heterozygous SNP on X counted(none expected for bulls)

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Collect parent averages/EBV

Combined U.S.-Canadian analysis requires comparable evaluations for all animals

Interbull evaluations used for bulls

Canadian Dairy Network provides evaluations of Canadian genotyped cows and maternal ancestors of genotyped animals

Holstein USA provides type evaluations

Previous Interbull evaluations used at traditional triannual evaluation releases

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Reliability 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

G.R. Wiggans 2008AIPL Centennial (10)

Accurate genomic evaluations

Estimates of SNP effects required

Evaluations with high reliability provide the most information

Recent animals more useful than ones from earlier generations

Reliability of genomic evaluations increases with number of predictor animals

G.R. Wiggans 2008AIPL Centennial (11)

Genotyped animals (October 2008)

Breed Bulls CowsPredicto

rs

Holstein12,27

52,445 7,821

Jersey 1,205 369 1,428Brown Swiss

365 3 359

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Schedule

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 2009

Added information from genomic evaluations propagated to evaluations of descendents without genotypes

National Association of Animal Breeders (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

G.R. Wiggans 2008AIPL Centennial (14)

Improvements

Employ bar codes on sample containers to reduce errors and improve lab efficiency Widely used

Rely on breed associations to resolve parent-progeny genotype conflicts Will happen, especially if parentage

verification is switched from microsatellites to SNP

Enroll animals that might be genotyped at birth to avoid ID issues when genotyped Needs to be required; low cost enrollment

available

G.R. Wiggans 2008AIPL Centennial (15)

Plans to increase accuracy

Genotype more predictor bulls

Reach 1,500 Brown Swiss through foreign collaboration?

Increase genotyped Jerseys from both domestic animals and possible foreign collaboration

Investigate across-breed analysis to allow data from Holsteins to improve accuracy for Jerseys and Brown Swiss

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Implications

Extraordinarily rapid implementation of genomic evaluations

Young bull acquisition and marketing as well as cow selection now based on genomic evaluations

Industry groups taking responsibility for genotyping and validation

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Financial support

National Research Initiative grants, USDA

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

Agricultural Research Service, USDA