8 Selection Strategies

8.1 Overview

Selection is controlled through breeding.diploid() parameters. Key options:

selection.criteria - What to select on
selection.size - How many to select
selection.m.gen / selection.f.gen - Which generations
selection.index - Multi-trait selection

8.2 Random Selection

pop <- breeding.diploid(
  pop,
  selection.criteria = "random",
  selection.size = c(20, 20),
  breeding.size = c(100, 100)
)

8.3 Phenotypic Selection

# Phenotype first
pop <- breeding.diploid(pop, phenotyping = "all", heritability = 0.5)

# Select on phenotypes
pop <- breeding.diploid(
  pop,
  selection.criteria = "pheno",
  selection.size = c(10, 10),
  breeding.size = c(50, 50)
)

8.4 Genomic Selection (BVE)

# Calculate BVEs
pop <- breeding.diploid(pop, bve = TRUE, bve.gen = 1:3)

# Select on estimated breeding values
pop <- breeding.diploid(
  pop,
  selection.criteria = "bve",
  selection.size = c(10, 10),
  breeding.size = c(50, 50)
)

8.5 Selection on True Breeding Values

# Select on known BVs (simulation only!)
pop <- breeding.diploid(
  pop,
  selection.criteria = "bv",
  selection.size = c(5, 5),
  breeding.size = c(100, 100)
)

8.6 Multi-Trait Selection Index

# Define economic weights
pop <- breeding.diploid(
  pop,
  selection.criteria = "bve",
  selection.index = c(1, 10, 5),  # Weights for traits 1,2,3
  selection.size = c(10, 10),
  breeding.size = c(50, 50)
)

8.7 Truncation Selection with Variable Intensity

# Select top males, more females
pop <- breeding.diploid(
  pop,
  selection.criteria = "bve",
  selection.size = c(5, 20),     # 5% males, 20% females
  selection.m.gen = 4,
  selection.f.gen = 4,
  breeding.size = c(50, 50)
)

8.8 Threshold Selection

# Only individuals above threshold
pop <- breeding.diploid(
  pop,
  selection.criteria = "bve",
  selection.m.threshold = 105,   # BVE > 105
  selection.f.threshold = 100,   # BVE > 100
  breeding.size = c(50, 50)
)

8.9 Optimal Genetic Contribution (OGC)

Balance genetic gain with diversity:

# Requires optiSel package
library(optiSel)

pop <- breeding.diploid(
  pop,
  selection.algorithm = "ogc",
  ogc.target.inbreeding = 0.01,  # Max 1% inbreeding increase
  selection.size = c(20, 20),
  breeding.size = c(100, 100)
)

8.10 Custom Selection Function

# Define custom selection
my_selector <- function(population, gen, sex) {
  bv <- get.bv(population, gen = gen, sex = sex)
  pheno <- get.pheno(population, gen = gen, sex = sex)

  # Custom criterion: BV + 0.5*pheno
  score <- bv[1,] + 0.5 * pheno[1,]

  # Return indices of selected individuals
  return(order(score, decreasing = TRUE)[1:10])
}

# Use in breeding
pop <- breeding.diploid(
  pop,
  selection.m.function = my_selector,
  selection.f.function = my_selector,
  breeding.size = c(50, 50)
)

8.11 Summary

Random, phenotypic, genomic, and true BV selection
Multi-trait selection indices
Threshold and OGC methods
Custom selection functions

Continue to Chapter 9: Mating Designs!

# Selection Strategies {#sec-selection-strategies} ## Overview Selection is controlled through `breeding.diploid()` parameters. Key options: - `selection.criteria` - What to select on - `selection.size` - How many to select - `selection.m.gen` / `selection.f.gen` - Which generations - `selection.index` - Multi-trait selection ## Random Selection ```{r} #| eval: false pop <- breeding.diploid( pop, selection.criteria = "random", selection.size = c(20, 20), breeding.size = c(100, 100) ) ``` ## Phenotypic Selection ```{r} #| eval: false # Phenotype first pop <- breeding.diploid(pop, phenotyping = "all", heritability = 0.5) # Select on phenotypes pop <- breeding.diploid( pop, selection.criteria = "pheno", selection.size = c(10, 10), breeding.size = c(50, 50) ) ``` ## Genomic Selection (BVE) ```{r} #| eval: false # Calculate BVEs pop <- breeding.diploid(pop, bve = TRUE, bve.gen = 1:3) # Select on estimated breeding values pop <- breeding.diploid( pop, selection.criteria = "bve", selection.size = c(10, 10), breeding.size = c(50, 50) ) ``` ## Selection on True Breeding Values ```{r} #| eval: false # Select on known BVs (simulation only!) pop <- breeding.diploid( pop, selection.criteria = "bv", selection.size = c(5, 5), breeding.size = c(100, 100) ) ``` ## Multi-Trait Selection Index ```{r} #| eval: false # Define economic weights pop <- breeding.diploid( pop, selection.criteria = "bve", selection.index = c(1, 10, 5), # Weights for traits 1,2,3 selection.size = c(10, 10), breeding.size = c(50, 50) ) ``` ## Truncation Selection with Variable Intensity ```{r} #| eval: false # Select top males, more females pop <- breeding.diploid( pop, selection.criteria = "bve", selection.size = c(5, 20), # 5% males, 20% females selection.m.gen = 4, selection.f.gen = 4, breeding.size = c(50, 50) ) ``` ## Threshold Selection ```{r} #| eval: false # Only individuals above threshold pop <- breeding.diploid( pop, selection.criteria = "bve", selection.m.threshold = 105, # BVE > 105 selection.f.threshold = 100, # BVE > 100 breeding.size = c(50, 50) ) ``` ## Optimal Genetic Contribution (OGC) Balance genetic gain with diversity: ```{r} #| eval: false # Requires optiSel package library(optiSel) pop <- breeding.diploid( pop, selection.algorithm = "ogc", ogc.target.inbreeding = 0.01, # Max 1% inbreeding increase selection.size = c(20, 20), breeding.size = c(100, 100) ) ``` ## Custom Selection Function ```{r} #| eval: false # Define custom selection my_selector <- function(population, gen, sex) { bv <- get.bv(population, gen = gen, sex = sex) pheno <- get.pheno(population, gen = gen, sex = sex) # Custom criterion: BV + 0.5*pheno score <- bv[1,] + 0.5 * pheno[1,] # Return indices of selected individuals return(order(score, decreasing = TRUE)[1:10]) } # Use in breeding pop <- breeding.diploid( pop, selection.m.function = my_selector, selection.f.function = my_selector, breeding.size = c(50, 50) ) ``` ## Summary - Random, phenotypic, genomic, and true BV selection - Multi-trait selection indices - Threshold and OGC methods - Custom selection functions Continue to [Chapter 9: Mating Designs](09-mating-designs.qmd)!