Differential Sire Contribution by Age


  The parameter file outlined below illustrates how to simulate a population where older animals are assigned a larger number of mating pairs, compared to younger animals. This type of scenario is generated by utilizing the "PARITY_MATES_DIST" parameter with the following values "2.0 1.0". These two values are utilized to generate the distribution of mating pairs. A Beta distribution, which is parameterized by two parameters, is used to generate the distribution of mating pairs. A beta distribution was utilized in order to allow for a wide range of mating scenarios. For example, to generate a scenario where younger animals are assigned a larger number of mating pairs compared to older animals, the parameters need to be changed to "1.0 2.0". The number of mating pairs by age class are generated by splitting the cumulative distribution function (CDF) into quadrants based on the number of age classes that occur within a generation. The total number of mating pairs within an age class is the proportion that falls within the CDF quadrant for a given age class.

−−−−−−−| Differential Sire Contribution by Age |−−−−−−−
−| General |−
START: sequence
SEED: 1501
−| 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
−| Selection |−
GENERATIONS: 15
INDIVIDUALS: 50 0.2 400 0.2
PROGENY: 1
PARITY_MATES_DIST: 2.0 1.0
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. The genome simulated 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. The quantitative trait simulated has a narrow sense heritability of 0.35 and only additive effects are generated (i.e. no dominance). The phenotypic variance is by default set at 1.0, and therefore the residual variance is 0.65. The founder population consisted of 50 males and 400 females. Random selecton of progeny and culling of parents was conducted for 3 generations. For each generation, a total of 50 males and 400 females are in the population. A total of 10 and 80 (0.2 replacement rate) male and female parents, respectively, are culled and re- placed by new progeny each generation. After 3 generations, animals with a high EBV were selected or culled each generation. A total of 15 gen- erations were simulated. The EBV are estimated using an animal model with a pedigree-based relationship matrix. Each mating pair produced one progeny. The mating distribution was skewed so that older animals had more mating pairs than younger animals. Parents that had pedigree-based relationships greater than 0.125 were avoided, and this was optimized based on the simulated annealing method.

  Once the program has finished, inspection of the log file will provide details on the impact of the "PARITY_MATES_DIST" option. Within the log file (lines 148-169), the mating distribution CDF is illustrated and is outlined below. When generating the number of matings for a given age class for each generation the CDF outlined below is split into quadrants based on the number of age classes that occur within a generation. Lastly, the number of matings within each age class is also outlined in the logfile for each generation.



  A potential reason for putting this into a simulation is to generate some sires with a large number of progeny, while other sires have very few progeny. The sires that generate a large number of progeny would then have a large impact on the genome of future generations which may include the spread of a lethal/sublethal mutation that the sire(s) carry.

  Utilizing the R code outlined below the following plot was generated to show the non-linear relationship of number of progeny left by a sire across different ages that a sire left the herd.

R-Code
rm(list=ls()); gc()
library(ggplot2); library(tidyverse)
## Change
setwd("/Users/jeremyhoward/Desktop/C++Code/18_GenoDiver_V3/GenoDiverFiles/")
##############################################
## Plot progeny count by age sire left herd ##
##############################################
df <- read_table2(file="Master_DataFrame",col_names = TRUE,col_type = "iiiiiiiidddddiiiiddddddddddd") %>%
  filter(.,Progeny > 0) %>% ## grab ones who have progeny count > 0
  filter(.,Sex == 0) %>% ## grab males only
  filter(.,Gen > 3) ## remove animals prior to selection

ggplot(df, aes(x=Age, y = Progeny)) + geom_point(colour = "red", size = 3) + xlab("Age Sire Left Population") +
  ylab("Number of Progeny") + ggtitle("Differential Sire Contributions by Age") + theme_bw()