Multibreed genomic evaluations in purebred dairy cattle
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Multibreed Genomic Evaluations in Purebred Dairy Cattle. K. M. Olson 1 and P. M. VanRaden 2. 1 National Association of Animal Breeders 2 AIPL, ARS, USDA Beltsville, MD [email protected] Background. Multibreed methods are currently used in traditional evaluations

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Multibreed Genomic Evaluations in Purebred Dairy Cattle

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Multibreed genomic evaluations in purebred dairy cattle

Multibreed Genomic Evaluations in Purebred Dairy Cattle

K. M. Olson1 and P. M. VanRaden2

1National Association of Animal Breeders

2AIPL, ARS, USDA

Beltsville, MD

[email protected]


Background

Background

  • Multibreed methods are currently used in traditional evaluations

  • Only within breed methods are used for genomic evaluations

  • Previous research has shown little improvement in accuracy from combining breeds for genomic evaluations however, little research has been done using multi-trait methodology


Background1

Background

  • Smaller breeds are interested in genomic evaluations

  • Genomic evaluations on crossbreds

    • 1999 2,236 1st lactation crossbreds, 2009 there were 23,209

    • With the 3k might be more demand

      • Currently, system not set up to handle crossbred data


Objectives

Objectives

  • To investigate different methods of multibreed genomic evaluations using purebred Holsteins, Jerseys, and Brown Swiss genotypes


Materials methods animals

Materials & Methods – Animals

  • Animals genotype Illumina BovineSNP50

    • 43,385 SNP

  • The training data set - animals were proven by Nov. 2004

    • Holsteins – 5,331

    • Jerseys – 1,361

    • Brown Swiss – 506

  • The validation data set - animals were unproven as of Nov. 2004 and proven by Aug. 2009

    • Holsteins – 2,507

    • Jerseys – 413

    • Brown Swiss - 185


Overview methods

Overview - Methods

  • Method 1 estimated SNP effects within breed then applied those effects to the other breeds

  • Method 2 (across-breed) used a common set of SNP effects from the combined breed genotypes and phenotypes

  • Method 3 (multi-breed) used a correlated SNP effects using a multi-trait method


Method 1 breed snp effects

Method 1 (breed SNP effects)

  • Estimated SNP effects within breed

  • Applied those SNP effects to the other breeds

  • Multiple regressions were used to test the GPTA using other breeds SNP effects along with PA


Method 2 across breed

Method 2 - (across-breed)

  • All breeds were treated as one population

    • Base allele frequency assumed to be 0.33 for each breed

  • Breed PTAs were converted to the Holstein 2004 Base

  • Multiple regressions were used to test across breed GPTA along with PA


Method 3 multi breed

Method 3 – (multi-breed)

  • Used a multi-trait genomic method as explained by VanRaden and Sullivan, 2010

    • Breeds instead of countries

    • Animals were purebreds

      • Their information only used for their respective breed

      • Assumption of independent residuals

  • Three levels of correlation were tested

    • 0.20, 0.30, and 0.55 for Protein yield


Results prediction of protein yield p values

Results – prediction of protein yield P-Values


R 2 adjusted for method 1

0.8

0.7

0.6

HO SNP

0.5

JE SNP

2

0.4

R

BS SNP

0.3

PA Only

0.2

0.1

0

Holstein

Jersey

Brown Swiss

R2 adjusted for Method 1


Correlation gptas and other breeds gptas

Correlation GPTAs and other Breeds’ GPTAs


Results prediction of protein yield p values1

Results – prediction of protein yield P-Values


Results r 2 for protein yield

Results – R2 for protein yield


Correlation with traditional gpta

Correlation with traditional GPTA


R 2 of different correlation levels for multi breed

R2 of different correlation levels for multi-breed

The correlation yielding best results was 0.30 - results in

0.09 sharing between breeds

Denser SNP panels would likely result in a higher correlation,

therefore greater gains across breeds


Conclusions

Conclusions

  • Using another breeds SNP estimates did not help

  • Across-breed method increased the predictive ability, however the traditional GPTA accounted for more variation than the across-breed GPTA

  • Multi-breed increased the predictive ability and the multi-breed GPTA accounted for more variation than the traditional GPTA


Implications

Implications

  • The multi-breed does slightly increase the accuracy, but may not warrant the increased computational demands

  • Higher density SNP chips would most likely increase the gains in accuracy for multi-breed genomic evaluations

  • Across-breed or multi-breed would be needed for genomic selection in crossbred herds

    • Not much demand for that yet


Questions

Questions


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