Correlated Response to Selection


  The parameter file outlined below illustrates how to simulate a population undergoing direct selection for a trait along with indirect selection for a genetically correlated trait. In this scenario two traits are simulated with a correlation of 0.40 and 0.15 for the additive genetic and residual environmental effects between trait 1 and trait 2. If two traits are being simulated and selection is based on 'ebv', by default, the first trait is the one being directly selected and the second trait is undergoing indirect selection.

−−−−−−−| Running the Program Example |−−−−−−−
−| General |−
START: founder
SEED: 1500
−| Genome & Marker |−
CHR: 3
CHR_LENGTH: 150 150 150
NUM_MARK: 4000 4000 4000
QTL: 150 150 150
−| Population |−
FOUNDER_Effective_Size: Ne70
MALE_FEMALE_FOUNDER: 50 400 random 3
VARIANCE_A: 0.35 0.40 0.35
VARIANCE_R: 0.65 0.15 0.65
−| Selection |−
GENERATIONS: 15
INDIVIDUALS: 50 0.2 400 0.2
PROGENY: 1
SELECTION: ebv high
EBV_METHOD: pblup
CULLING: ebv 5
-| Mating |-
MATING: random125 simu_anneal

Parameter File Summary
  Sequence information is generated for three chromosomes with a length of 150 Megabases (Mb). The simulated genome has a high degree of short-range LD (Ne70). The SNP panel contains 12,000 markers (i.e. 4,000 markers per chromosome). For each chromosome, 150 randomly placed QTL and zero FTL mutations were generated. Across both quantitative traits simulated, a narrow sense heritability of 0.35 and only additive effects was generated (i.e. no dominance). The residual variance across both traits is 0.65. The two traits are simulated with a correlation of 0.40 and 0.15 for the additive genetic and residual environmental effects between trait 1 and trait 2. The founder population consisted of 50 males and 400 females. For each generation, a total of 50 males and 400 females are in the population. Random selecton of progeny and culling of parents was conducted for 3 generations. A total of 10 and 80 (0.2 replacement rate) male and female parents, respectively, are culled and replaced by new progeny each generation. After 3 generations, animals with a high estimated breeding value for trait 1 were selected or culled each generation. The EBV are estimated using an animal model with a pedigree-based relationship matrix. Fifteen generations were simulated. Each mating pair produced one progeny. Parents that had pedigree-based relationships greater than 0.125 were avoided, and this was optimized based on the simulated annealing method.

  Utilizing the R code outlined below the following plots were generated from the output files to see the correlated response in the second trait due to direct selection on trait one.

R-Code
rm(list = ls()); gc()
library(ggplot2); library(tidyverse)
## Change
setwd("/Users/jeremyhoward/Desktop/C++Code/18_GenoDiver_V3/GenoDiverFiles/")
#############################
## Plot True Genetic Value ##
#############################
df <- read_table2(file="Summary_Statistics_DataFrame_Performance",col_names = TRUE,col_type = "dcccccccccccc") %>%
mutate(tbv1 = as.numeric(matrix(unlist(strsplit(tbv1, "[()]")), ncol = 2, byrow = TRUE)[, 1]),
               tbv2 = as.numeric(matrix(unlist(strsplit(tbv2, "[()]")), ncol = 2, byrow = TRUE)[, 1])) %>%
select(Generation,tbv1,tbv2)

ggplot(df, aes(x = Generation, y = tbv1)) + geom_line(size = 1) + ggtitle("Genetic Trend Trait 1") + theme_bw() +
theme(plot.title = element_text(hjust = 0.5)) + ylab("Mean True Breeding Value ")

ggplot(df, aes(x = Generation, y = tbv2)) + geom_line(size = 1) + ggtitle("Genetic Trend Trait 2") + theme_bw() +
theme(plot.title = element_text(hjust = 0.5)) + ylab("Mean True Breeding Value ")