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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Predicting Genetic Interactions Within and Across Breeds

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Predicting genetic interactions within and across breeds

Predicting Genetic Interactions Within and Across Breeds


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)


Purebred and crossbred data usa milk yield records

Purebred and Crossbred DataUSA milk yield records


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


All vs within breed evaluations correlations of pta milk

All- vs Within-Breed EvaluationsCorrelations of PTA Milk


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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