predicting genetic interactions within and across breeds
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Predicting Genetic Interactions Within and Across Breeds. Modeling Genetic Interactions. Crossbreeding and heterosis in an all-breed animal model Estimate breed differences routinely Recommend mating strategies Inbreeding depression adjustments in genetic evaluations

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modeling genetic interactions
Modeling Genetic Interactions
  • Crossbreeding and heterosis in an all-breed animal model
    • Estimate breed differences routinely
    • Recommend mating strategies
  • Inbreeding depression adjustments in genetic evaluations
  • Within-breed interactions predicted using dominance relationships
all breed analyses
All-Breed Analyses
  • Crossbred animals
    • Will have EBVs, most did not before
    • Reliable EBVs from both parents
  • Purebred animals
    • Information from crossbred relatives
    • More contemporaries
  • Routinely used in other populations
    • New Zealand (1994), Netherlands (1997)
    • USA goats (1989)
across breed methods
Across-Breed Methods
  • All-breed animal model
    • Purebreds and crossbreds together
    • Age adjust to 36 months, not mature
    • Variance adjustments by breed
    • Unknown parents grouped by breed
    • Westell groups instead of regressing on breed fractions
  • General heterosis subtracted
unknown parent groups
Unknown Parent Groups
  • Groups formed based on
    • Birth year
    • Breed
    • Path (dams of cows, sires of cows, parents of bulls)
    • Origin (domestic vs other countries)
  • Paths have >1000 in last 15 years
  • Groups each have >500 animals
display of ptas
Display of PTAs
  • Genetic base
    • Compute on all-breed base
    • Convert back to within-breed-of-sire bases for ease of comparing to previous PTA
  • Heterosis and inbreeding
    • Both effects removed in the animal model
    • Heterosis added to crossbred animal PTA
    • Expected Future Inbreeding (EFI) and genetic merit differ with mate breed
within breed methods
Within-Breed Methods
  • Adjust for inbreeding depression
    • Remove past F, include future F
    • Expected future F (EFI) = .5 mean Aij
    • EBV0 vs EBVEFI vs unadjusted EBV
  • Optimal selection theory
    • Maximize w’ EBV0 + by.F w’ A w
    • Use of EBV0 avoids double-counting
effect of inbreeding adjustments used in usa since 2005
Effect of Inbreeding AdjustmentsUsed in USA since 2005
  • Protein genetic trend estimates
    • 3% more for EBV0 than EBV
    • 6% less for EBVEFI than EBV
  • Correlations of EBVs within breed
    • .993 corr(EBVEFI, EBV) for cows
    • .998 corr(EBVEFI, EBV) for bulls
  • Select on EBV0 for crossbreeding?
within breed interactions
Within-Breed Interactions
  • Dominance relationship matrix
    • 5.5 million Holstein cows with data
    • 1.6 million interactions among 4263 sires and maternal grandsires
    • 30 minutes for D-1, 16 hours to solve
  • Dominance variance
    • Assumed 5% of phenotypic variance
    • Estimate from Van Tassell et al, 2000
predicted sire mgs interactions 305 d milk kg heterosis 318 kg
Predicted Sire-MGS Interactions305-d milk kg (heterosis = 318 kg)

Numbers of observations below diagonal

conclusions
Conclusions
  • Can predict genetic interactions
    • Inbreeding adjustments since 2005
    • All-breed animal model expected 2007
    • Sire-MGS dominance effects within breed mostly smaller than heterosis
  • Future research on interactions
    • Specific heterosis and epistasis
    • Delivery of information to breeders
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