
The simtrait R package enables simulation of complex
traits with user-set number of causal loci and the desired heritability
of the trait (the proportion of variance due to genetic effects).
The main function requires a simulated genotype matrix, including the
true ancestral allele frequencies. These parameters are necessary to
correctly specify the desired correlation structure. See the package
bnpsd for simulating genotypes for admixed individuals
(example below).
Simulating a trait from real genotypes is possible with a good
kinship matrix estimate. See the package popkin for
accurate kinship estimation.
You can install the released version of simtrait from CRAN with:
install.packages("simtrait")Install the latest development version from GitHub:
install.packages("devtools") # if needed
library(devtools)
install_github("OchoaLab/simtrait", build_vignettes = TRUE)You can see the package vignette, which has more detailed documentation, by typing this into your R session:
vignette('simtrait')The code below has two parts: (1) simulate genotypes, and (2) simulate the trait.
The first step is to simulate genotypes from an admixed population,
to have an example where there is population structure and known
ancestral allele frequencies. We use the external package
bnpsd to achieve this.
library(bnpsd) # to simulate an admixed population
# dimensions of data/model
# number of loci
m_loci <- 10000
# number of individuals, smaller than usual for easier visualizations
n_ind <- 30
# number of intermediate subpops
k_subpops <- 3
# define population structure
# FST values for k = 3 subpopulations
inbr_subpops <- 1 : k_subpops
# bias coeff of standard Fst estimator
bias_coeff <- 0.5
# desired final Fst of admixed individuals
Fst <- 0.3
obj <- admix_prop_1d_linear(
n_ind,
k_subpops,
bias_coeff = bias_coeff,
coanc_subpops = inbr_subpops,
fst = Fst
)
admix_proportions <- obj$admix_proportions
# rescaled Fst vector for intermediate subpops
inbr_subpops <- obj$coanc_subpops
# get pop structure parameters of the admixed individuals
concestry <- coanc_admix(admix_proportions, inbr_subpops)
kinship <- coanc_to_kinship(concestry)
# draw allele freqs and genotypes
out <- draw_all_admix(admix_proportions, inbr_subpops, m_loci)
# genotypes
X <- out$X
# ancestral allele frequencies
p_anc <- out$p_ancHere we apply our package to this simulated genotype data.
library(simtrait) # load this package
# parameters of simulation
m_causal <- 100
herit <- 0.8
# create simulated trait and associated data
# version 1: known p_anc (prefered, only applicable to simulated data)
obj <- sim_trait(X = X, m_causal = m_causal, herit = herit, p_anc = p_anc)
# version 2: known kinship (more broadly applicable but fewer guarantees)
obj <- sim_trait(X = X, m_causal = m_causal, herit = herit, kinship = kinship)
# outputs in both versions:
# trait vector
obj$trait
# randomly-picked causal locus index
obj$causal_indexes
# locus effect size vector
obj$causal_coeffs
# theoretical covariance of the simulated traits
V <- cov_trait(kinship = kinship, herit = herit)