10 Breeding Program Simulation

10.1 Complete Breeding Cycle

# Typical cycle
pop <- breeding.diploid(
  pop,
  # 1. Phenotype
  phenotyping = "all",
  phenotyping.gen = 5,
  heritability = 0.5,

  # 2. Genotype (if needed)
  genotyping.gen = 5,
  share.genotyped = 0.8,

  # 3. Predict breeding values
  bve = TRUE,
  bve.gen = 1:5,

  # 4. Select parents
  selection.criteria = "bve",
  selection.size = c(10, 20),
  selection.gen = 5,

  # 5. Mate and create offspring
  breeding.size = c(50, 50),
  name.cohort = "Gen6"
)

10.2 Multi-Stage Selection

# Stage 1: Phenotypic pre-selection
pop <- breeding.diploid(
  pop,
  phenotyping = "all",
  phenotyping.gen = 3,
  heritability = 0.3
)

pop <- set.class(pop, gen = 3, class = 1)  # Mark all as candidates

# Select top 50% phenotypically
pheno <- get.pheno(pop, gen = 3)
cutoff <- quantile(pheno, 0.5)
# ... mark individuals above cutoff as class 2 ...

# Stage 2: Genotype and genomic selection on survivors
pop <- breeding.diploid(
  pop,
  genotyping.gen = 3,
  genotyping.class = 2,     # Only genotype class 2
  bve = TRUE,
  selection.criteria = "bve",
  selection.class = 2,       # Select from class 2
  selection.size = c(10, 10),
  breeding.size = c(100, 100)
)

10.3 Progeny Testing

# Year 1: Create test progeny
pop <- breeding.diploid(
  pop,
  selection.m.gen = 5,
  selection.f.gen = 5,
  selection.size = c(50, 50),
  breeding.size = c(500, 500),    # Many test progeny
  name.cohort = "TestProgeny"
)

# Year 2: Phenotype progeny
pop <- breeding.diploid(
  pop,
  phenotyping.cohorts = "TestProgeny",
  phenotyping = "all",
  heritability = 0.5
)

# Calculate parent averages from progeny
pop <- breeding.diploid(
  pop,
  bve = TRUE,
  bve.gen = 6  # Use progeny phenotypes
)

# Year 3: Select bulls based on daughter performance
pop <- breeding.diploid(
  pop,
  selection.criteria = "bve",
  selection.m.gen = 5,      # Original candidate bulls
  selection.size = c(5, 20),
  breeding.size = c(100, 100)
)

10.4 Nucleus Breeding Program

# Create nucleus
pop <- creating.diploid(
  nsnp = 10000, nindi = 100,
  n.additive = 100,
  name.cohort = "Nucleus"
)

# Nucleus selection (intensive)
pop <- breeding.diploid(
  pop,
  phenotyping.cohorts = "Nucleus",
  heritability = 0.5,
  bve = TRUE,
  selection.criteria = "bve",
  selection.size = c(5, 10),
  breeding.size = c(50, 50),
  name.cohort = "Nucleus_Gen2"
)

# Commercial multiplier (uses nucleus genetics)
pop <- breeding.diploid(
  pop,
  selection.m.cohorts = "Nucleus_Gen2",
  selection.f.cohorts = "Nucleus",    # Or commercial dams
  selection.size = c(10, 100),
  breeding.size = c(500, 500),
  name.cohort = "Commercial"
)

10.5 Summary

Complete breeding cycles
Multi-stage selection
Progeny testing schemes
Nucleus breeding programs

Continue to Chapter 11: Plant vs Animal Breeding!

# Breeding Program Simulation {#sec-breeding-simulation} ## Complete Breeding Cycle ```{r} #| eval: false # Typical cycle pop <- breeding.diploid( pop, # 1. Phenotype phenotyping = "all", phenotyping.gen = 5, heritability = 0.5, # 2. Genotype (if needed) genotyping.gen = 5, share.genotyped = 0.8, # 3. Predict breeding values bve = TRUE, bve.gen = 1:5, # 4. Select parents selection.criteria = "bve", selection.size = c(10, 20), selection.gen = 5, # 5. Mate and create offspring breeding.size = c(50, 50), name.cohort = "Gen6" ) ``` ## Multi-Stage Selection ```{r} #| eval: false # Stage 1: Phenotypic pre-selection pop <- breeding.diploid( pop, phenotyping = "all", phenotyping.gen = 3, heritability = 0.3 ) pop <- set.class(pop, gen = 3, class = 1) # Mark all as candidates # Select top 50% phenotypically pheno <- get.pheno(pop, gen = 3) cutoff <- quantile(pheno, 0.5) # ... mark individuals above cutoff as class 2 ... # Stage 2: Genotype and genomic selection on survivors pop <- breeding.diploid( pop, genotyping.gen = 3, genotyping.class = 2, # Only genotype class 2 bve = TRUE, selection.criteria = "bve", selection.class = 2, # Select from class 2 selection.size = c(10, 10), breeding.size = c(100, 100) ) ``` ## Progeny Testing ```{r} #| eval: false # Year 1: Create test progeny pop <- breeding.diploid( pop, selection.m.gen = 5, selection.f.gen = 5, selection.size = c(50, 50), breeding.size = c(500, 500), # Many test progeny name.cohort = "TestProgeny" ) # Year 2: Phenotype progeny pop <- breeding.diploid( pop, phenotyping.cohorts = "TestProgeny", phenotyping = "all", heritability = 0.5 ) # Calculate parent averages from progeny pop <- breeding.diploid( pop, bve = TRUE, bve.gen = 6 # Use progeny phenotypes ) # Year 3: Select bulls based on daughter performance pop <- breeding.diploid( pop, selection.criteria = "bve", selection.m.gen = 5, # Original candidate bulls selection.size = c(5, 20), breeding.size = c(100, 100) ) ``` ## Nucleus Breeding Program ```{r} #| eval: false # Create nucleus pop <- creating.diploid( nsnp = 10000, nindi = 100, n.additive = 100, name.cohort = "Nucleus" ) # Nucleus selection (intensive) pop <- breeding.diploid( pop, phenotyping.cohorts = "Nucleus", heritability = 0.5, bve = TRUE, selection.criteria = "bve", selection.size = c(5, 10), breeding.size = c(50, 50), name.cohort = "Nucleus_Gen2" ) # Commercial multiplier (uses nucleus genetics) pop <- breeding.diploid( pop, selection.m.cohorts = "Nucleus_Gen2", selection.f.cohorts = "Nucleus", # Or commercial dams selection.size = c(10, 100), breeding.size = c(500, 500), name.cohort = "Commercial" ) ``` ## Summary - Complete breeding cycles - Multi-stage selection - Progeny testing schemes - Nucleus breeding programs Continue to [Chapter 11: Plant vs Animal Breeding](11-plant-vs-animal.qmd)!