bnpsd (version 1.3.13)

bnpsd: A package for modeling and simulating an admixed population

Description

The underlying model is called the BN-PSD admixture model, which combines the Balding-Nichols (BN) allele frequency model for the intermediate subpopulations with the Pritchard-Stephens-Donnelly (PSD) model of individual-specific admixture proportions. The BN-PSD model enables the simulation of complex population structures, ideal for illustrating challenges in kinship coefficient and FST estimation. Simulated loci are drawn independently (in linkage equilibrium).

See Also

Useful links:

• https://github.com/StoreyLab/bnpsd/

• Report bugs at https://github.com/StoreyLab/bnpsd/issues

Examples

# NOT RUN {
# dimensions of data/model
# number of loci
m_loci <- 10
# number of individuals
n_ind <- 5
# number of intermediate subpops
k_subpops <- 2

# define population structure
# FST values for k = 2 subpopulations
inbr_subpops <- c( 0.1, 0.3 )
# admixture proportions from 1D geography
admix_proportions <- admix_prop_1d_linear( n_ind, k_subpops, sigma = 1 )
# also available:
# - admix_prop_1d_circular
# - admix_prop_indep_subpops

# get pop structure parameters of the admixed individuals
# the coancestry matrix
coancestry <- coanc_admix( admix_proportions, inbr_subpops )
# FST of admixed individuals
Fst <- fst_admix( admix_proportions, inbr_subpops )

# draw all random allele freqs and genotypes
out <- draw_all_admix( admix_proportions, inbr_subpops, m_loci )
# genotypes
X <- out$X
# ancestral allele frequencies (AFs)
p_anc <- out$p_anc

# OR... draw each vector or matrix separately
# provided for additional flexibility
# ancestral AFs
p_anc <- draw_p_anc( m_loci )
# independent subpops (intermediate) AFs
p_subpops <- draw_p_subpops( p_anc, inbr_subpops )
# individual-specific AFs
p_ind <- make_p_ind_admix( p_subpops, admix_proportions )
# genotypes
X <- draw_genotypes_admix( p_ind )

### Examples with a tree for intermediate subpopulations

# tree allows for correlated subpopulations
# (prev examples had independent subpopulations)

# best to start by specifying tree in Newick string format
tree_str <- '(S1:0.1,(S2:0.1,S3:0.1)N1:0.1)T;'
# and turn it into `phylo` object using the `ape` package
library(ape)
tree_subpops <- read.tree( text = tree_str )
# true coancestry matrix corresponding to this tree
coanc_subpops <- coanc_tree( tree_subpops )

# admixture proportions from 1D geography
# (constructed again but for k=3 tree)
k_subpops <- nrow( coanc_subpops )
admix_proportions <- admix_prop_1d_linear( n_ind, k_subpops, sigma = 0.5 )

# get pop structure parameters of the admixed individuals
# the coancestry matrix
coancestry <- coanc_admix( admix_proportions, coanc_subpops )
# FST of admixed individuals
Fst <- fst_admix( admix_proportions, coanc_subpops )

# draw all random allele freqs and genotypes, tree version
out <- draw_all_admix( admix_proportions, tree_subpops = tree_subpops, m_loci = m_loci )
# genotypes
X <- out$X
# ancestral allele frequencies (AFs)
p_anc <- out$p_anc

# OR... draw tree subpops (intermediate) AFs separately
p_subpops_tree <- draw_p_subpops_tree( p_anc, tree_subpops )

# }