Bio326.popgen
Marie Saitou
4/26/2021
Last updated: 2021-04-30
Checks: 7 0
Knit directory: Bio326/
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Get started
Go to: https://orion.nmbu.no/ -> JupyterHub, select 4Gb -> rstudio-4.0.2
copy the file from /net/fs-1/home01/mariesai/BIO326/popgen.simu.Rmd cp /net/fs-1/home01/mariesai/BIO326/popgen.simu.Rmd . # Data Preparation
rep=50 # number of simulations
n_gen=100 # number of generations
generation <- 1
n_AA <- 99 # number of newborn individual with AA genotype
n_AB <- 1 # number of newborn individual with AB genotype
n_BB <- 0 # number of newborn individual with BB genotype
N <- n_AA + n_AB + n_BB # total number of individuals
sAA <- 0 # advantage AA genotype
sAB <- 0 # advantage AB genotype
sBB <- 0 # advantage BB genotype
results <- data.frame(matrix(ncol = 7, nrow = 0)) # dataframe to put the results inRun simulation
for (i in 1:rep){
# initialize
next_num <- c(n_AA, n_AB, n_BB)
freqA <- (2 * next_num[1] + next_num[2]) / (2 * N)
freqB <- 1 - freqA
results <- rbind(results, c(i, 0, next_num, freqA, freqB))
for (j in 1:n_gen){
# effective frequency of allele after survival
freqA <- ((2 * next_num[1]*(1 + sAA) + next_num[2] * (1 + sAB)) /
(2 * (next_num[1] * (1 + sAA) + next_num[2] * (1 + sAB) + next_num[3] * (1 + sBB)))) # frequency of A allele in the population
freqB <- 1 - freqA # frequency of B allele in the population
# next generation
next_num <- c(rmultinom(1, N, c(freqA * freqA, 2 * freqA * freqB, freqB * freqB)))
# new frequency of allele
freqA <- (2 * next_num[1] + next_num[2]) / (2 * N) # frequency of A allele in the population
freqB <- 1 - freqA
results <- rbind(results, c(i, j, next_num, freqA, freqB))
}
}
names(results) <- c("rep", "gen", "AA", "AB", "BB", "freqA", "freqB")View results
head (results) rep gen AA AB BB freqA freqB
1 1 0 99 1 0 0.995 0.005
2 1 1 99 1 0 0.995 0.005
3 1 2 99 1 0 0.995 0.005
4 1 3 100 0 0 1.000 0.000
5 1 4 100 0 0 1.000 0.000
6 1 5 100 0 0 1.000 0.000View the allele frequency change in generations
# plot two figures
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──✓ ggplot2 3.3.3 ✓ purrr 0.3.4
✓ tibble 3.1.1 ✓ dplyr 1.0.5
✓ tidyr 1.1.3 ✓ stringr 1.4.0
✓ readr 1.4.0 ✓ forcats 0.5.1── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library(ggplot2)
# color=group, if the last freqA is 0 or 1, highlight them.
ggplot(results, aes(gen, freqB, group = rep)) + geom_line()+theme_bw()
View the final genotype frequency
# [plot2]
# results_sum <- data.frame(cbind(c("AA", "AB", "BB"),
# colMeans(results[results$gen==n_gen,c("AA", "AB", "BB")]))) %>%
# mutate(x="proportion", X2 = as.numeric(X2))
# gen is the generation we want to see
results_sum <- pivot_longer(results[results$gen == n_gen,c("rep", "AA", "AB", "BB")],
cols=c("AA", "AB", "BB"), names_to="genotype", values_to="count")
ggplot(results_sum, aes(x=rep, y=count, fill=genotype)) +
geom_col() +theme_bw()
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4
[5] readr_1.4.0 tidyr_1.1.3 tibble_3.1.1 ggplot2_3.3.3
[9] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.6 lubridate_1.7.9.2 assertthat_0.2.1 rprojroot_2.0.2
[5] digest_0.6.27 utf8_1.2.1 R6_2.5.0 cellranger_1.1.0
[9] backports_1.2.1 reprex_1.0.0 evaluate_0.14 httr_1.4.2
[13] highr_0.8 pillar_1.6.0 rlang_0.4.10 readxl_1.3.1
[17] rstudioapi_0.13 whisker_0.4 rmarkdown_2.6 labeling_0.4.2
[21] munsell_0.5.0 broom_0.7.4 compiler_4.0.2 httpuv_1.6.0
[25] modelr_0.1.8 xfun_0.21 pkgconfig_2.0.3 htmltools_0.5.1.1
[29] tidyselect_1.1.0 fansi_0.4.2 crayon_1.4.1 dbplyr_2.1.0
[33] withr_2.4.2 later_1.2.0 grid_4.0.2 jsonlite_1.7.2
[37] gtable_0.3.0 lifecycle_1.0.0 DBI_1.1.1 git2r_0.28.0
[41] magrittr_2.0.1 scales_1.1.1 cli_2.5.0 stringi_1.5.3
[45] farver_2.1.0 fs_1.5.0 promises_1.2.0.1 xml2_1.3.2
[49] ellipsis_0.3.1 generics_0.1.0 vctrs_0.3.7 tools_4.0.2
[53] glue_1.4.2 hms_1.0.0 yaml_2.2.1 colorspace_2.0-0
[57] rvest_0.3.6 knitr_1.31 haven_2.3.1