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Predicting Genetic Interactions Within and Across Breeds

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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Predicting Genetic Interactions Within and Across Breeds

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  1. Predicting Genetic Interactions Within and Across Breeds

  2. 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

  3. 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)

  4. Purebred and Crossbred DataUSA milk yield records

  5. 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

  6. 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

  7. All- vs Within-Breed EvaluationsCorrelations of PTA Milk

  8. 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

  9. 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

  10. 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?

  11. 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

  12. Predicted Sire-MGS Interactions305-d milk kg (heterosis = 318 kg) Numbers of observations below diagonal

  13. 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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