Prediction of Breed Composition

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Prediction of Breed Composition Multibreed
Genomic Evaluations
K. M. Olson and P. M. VanRaden
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Background - Prediction of Breed

• 200 Breed specific SNP were used to verify an
  animal received the correct breed code in the
  quality control data step
• Several animals had fewer breed-specific SNPs and
  lower genomic relationships and inbreeding
• Wanted to investigate a more precise way to look
  at breed composition

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Materials Methods Prediction of Breed

• Y- Variable was breed of animal
• Used both females and males
• 3 different sizes of SNP sets were used for the
  genomic evaluation
• The Full 43,385 SNP set
• The proposed 3 K SNP set
• The 600 breed specific set
• Each breed has 200 used for the basic check
  currently not a genomic evaluation

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Materials Methods Prediction of Breed

• Training data set animal reliability set to 99
  and parent average reliability set to 50
• Proven as of July 2009
• Total of 14,039 animals across all breeds
• Validation data set reliabilities set to 0
• Unproven as of July 2009
• 15,809 animals across all breeds

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Results Prediction of Breed

• All three tests were able to determine a Holstein
  that was by pedigree 1/8 (12.5) Jersey
• 43 K test predicted her as 85.9 Holstein and
  13.3 Jersey
• 3 K predicted she was 84.4 Holstein and 15.5
  Jersey
• 600 SNP set she was 83.0 Holstein and 16.6
  Jersey

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Results Prediction of Breed
Means and standard deviations for given breed of
the validation data set
SNP set/ Breed 43 K 3 K 600
Holstein (N 14,794) 1.0000.008 1.0040.031 1.0020.019
Jersey (N 919) 0.9960.028 0.9780.063 0.9890.036
Brown Swiss (N 96) 0.9940.021 0.9890.036 0.9920.051
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Conclusions Prediction of Breed

• The 43 K chip was the most accurate at prediction
  of breed composition
• The 3 K chip could identify individuals that had
  large amounts (gt 13) of foreign DNA

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Obstacles Prediction of Breed

• There is a patent
• Located at http//www.patentstorm.us/patents/75111
  27/fulltext.html
• May not be accurate for animals from different
  populations
• foreign animals
• older animals

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Background - Multibreed

• Multibreed methods are currently used in
  traditional methods
• Only within breed methods are used for genomics
  evaluations
• Previous research has shown little improvement in
  accuracy from using all breeds with the 50K SNP
  chip however, little research has been done using
  multi-trait methodology

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Objectives Multibreed genomic evaluations

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

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Materials Methods Multibreed (Animals)

• 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 June 2009
• Holsteins 2,477
• Jerseys 410
• Brown Swiss - 182

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Material Methods Multibreed (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 multitrait method ( as explained
  by VanRaden and Sullivan, 2010)

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Results P Values for Protein Yield
Holstein Jersey Brown Swiss
Traditional
PTA lt 0.001 lt 0.001 0.061
GPTA lt 0.001 lt 0.001 0.086
R2adj 0.5045 0.4874 0.1030
Method 1
HOL GPTA lt 0.001 0.668 0.344
JER GPTA 0.873 lt 0.001 0.844
BSW GPTA 0.813 0.473 0.107
PTA lt 0.001 lt 0.001 0.054
R2adj 0.5041 0.4854 0.0978
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Results P-values for protein yield
Holstein Jersey Brown Swiss
Method 2
PTA lt 0.001 lt 0.001 0.088
GPTA lt 0.001 lt 0.001 0.316
ABGPTA 0.002 0.290 0.007
R2adj 0.5063 0.4876 0.1337
Method 3
PTA lt 0.001 lt 0.001 0.080
GPTA 0.742 0.324 0.140
MBGPTA lt 0.001 lt 0.001 0.060
R2adj 0.5060 0.4916 0.1127
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Results P-Values for protein yield
The traditional GPTA was not included in these
analyses
Holstein Jersey Brown Swiss
Method 2
PTA lt0.001 lt 0.001 0.2016
ABGPTA lt 0.001 lt 0.001 0.0023
R2adj 0.4742 0.4742 0.1336
Method 3
PTA lt 0.001 lt 0.001 0.055
MBGPTA lt 0.001 lt 0.001 0.081
R2adj 0.5060 0.4916 0.1067
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Conclusions Multibreed Genomic Evaluation

• Method 1 did not help the estimates for genomic
  evaluations
• Method 2 increased the predictive ability,
  however the traditional GPTA accounted for more
  variation than the across-breed GPTA
• Method 3 increased the predictive ability and the
  multi-breed GPTA accounted for more variation
  than the traditional GPTA

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Implications

• The multibreed genomic evaluations do slightly
  increase the accuracy of the evaluations, but may
  not warrant the increased computational demands
• A higher density SNP chip would most likely
  increase the gains in accuracy for multibreed
  genomic evaluations
• Multibreed would be needed for genomic selection
  in crossbred herds
• Not much demand for that yet