## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(fig.width = 6) ## ----load libraries----------------------------------------------------------- library(GenomeAdmixR) library(ggplot2) packageVersion("GenomeAdmixR") ## ----create populations------------------------------------------------------- pops <- simulate_admixture( module = ancestry_module(), migration = migration_settings(migration_rate = 0, population_size = c(100, 100), initial_frequencies = list(c(1, 1, 1, 1, 0, 0, 0, 0), c(0, 0, 0, 0, 1, 1, 1, 1))), total_runtime = 1000) pop_1 <- pops$population_1 pop_2 <- pops$population_2 ## ----calculate LD------------------------------------------------------------- mean(calculate_ld(pop = pop_1, sampled_individuals = 10, markers = 30)$ld_matrix, na.rm = TRUE) mean(calculate_ld(pop = pop_2, sampled_individuals = 10, markers = 30)$ld_matrix, na.rm = TRUE) ## ----create isofemale lines--------------------------------------------------- iso_females_pop_1 <- create_iso_female( module = ancestry_module(input_population = pop_1), n = 2, inbreeding_pop_size = 100) iso_females_pop_2 <- create_iso_female( module = ancestry_module(input_population = pop_2), n = 2, inbreeding_pop_size = 100) ## ----create from individuals-------------------------------------------------- pop_1_1 <- simulate_admixture( module = ancestry_module(input_population = iso_females_pop_1), pop_size = 1000, total_runtime = 1000) pop_1_2 <- simulate_admixture( module = ancestry_module(input_population = list(iso_females_pop_1[[1]], iso_females_pop_2[[1]])), pop_size = 1000, total_runtime = 1000) pop_2_2 <- simulate_admixture( module = ancestry_module(input_population = iso_females_pop_2), pop_size = 1000, total_runtime = 1000) ## ----FST calculation---------------------------------------------------------- f1 <- calculate_fst(pop_1_1, pop_1_2, sampled_individuals = 10, number_of_markers = 100) # this one should be highest f2 <- calculate_fst(pop_1_1, pop_2_2, sampled_individuals = 10, number_of_markers = 100) f3 <- calculate_fst(pop_1_2, pop_2_2, sampled_individuals = 10, number_of_markers = 100) f1 f2 f3