simpheno: Quantitative trait simulation using real genotype data

In GHap: Genome-Wide Haplotyping

Quantitative trait simulation using real genotype data

Description

Simulates phenotypes from a quantitative trait with arbitrary variant-specific heritabilities.

Usage

ghap.simpheno(object, h2, r2 = 0, nrep = 1,
              balanced = TRUE, seed = NULL, 
              only.active.samples = TRUE,
              ncores = 1, verbose = TRUE)

Arguments

object	A valid GHap object (phase or plink).
h2	A named numeric value specifying the heritability per variant. The sum of variant-specific heritabilities will be set as the narrow-sense heritability, and must not exceed 1.
r2	A numeric value specifying the repeatability (default = 0). Only relevant if nrep > 1.
nrep	A numeric value specifying the number of repeated measures per subject.
balanced	A logical value specifying whether the output data should be balanced (default = TRUE). If balanced = FALSE, the number of repeated measures per subject will be heterogeneous, following a uniform distribution with minimum zero and maximum nrep. Only relevant if nrep > 1.
seed	A numeric value used to set the random number generation state (default = NULL). This is useful for reproducibility of the results.
only.active.samples	A logical value specifying whether only active samples should be used for calculations (default = TRUE).
ncores	A numeric value specifying the number of cores to be used in parallel computations (default = 1).
verbose	A logical value specfying whether log messages should be printed (default = TRUE).

Details

The simulation considers the model:

\mathbf{y} = \mathbf{Zu} + \mathbf{Zp} + \mathbf{e}

where \mathbf{u} is a vector of breeding values, \mathbf{p} is a vector of permanent environmental effects, \mathbf{Z} is an incidence matrix mapping \mathbf{y} to \mathbf{u} and \mathbf{p}, and \mathbf{e} is the vector of residuals. True breeding values are computed from the sum of causal variant effects specified in the 'h2' argument. Both the residual and permanent environmental effects are sampled from normal distributions.

Value

The function returns a data frame with items:

POP	Original population label.
ID	Individual name.
PHENO	Phenotypic observation.
TBV	True breeding value.
REP	Permanent environmental effect (only present if nrep > 1).
RESIDUAL	Residual value.

Author(s)

Yuri Tani Utsunomiya <[e-mail]>

Examples

# #### DO NOT RUN IF NOT NECESSARY ###
# 
# # Copy the example data in the current working directory
# exfiles <- ghap.makefile(dataset = "example",
#                          format = "phase",
#                          verbose = TRUE)
# file.copy(from = exfiles, to = "./")
# 
# # Load phase data
# phase <- ghap.loadphase("example")
# 
# ### RUN ###
# 
# # Subset Pure1 population
# pure1 <- unique(phase$id[which(phase$pop == "Pure1")])
# phase <- ghap.subset(object = phase, ids = pure1,
#                      variants = phase$marker)
# freq <- ghap.freq(object = phase, type = "maf")
# 
# # Heritability   = 0.3
# # Number of QTLs = 1000
# # Major QTLs     = 0
# # Records per id = 1
# nqtl <- 1000
# h2 <- 0.3
# mkr <- sample(names(freq[which(freq > 0.05)]), size = nqtl)
# eff <- runif(n = nqtl, min = 0, max = 1)
# eff <- h2*eff/sum(eff)
# names(eff) <- mkr
# df1 <- ghap.simpheno(object = phase, h2 = eff)
# 
# # Heritability   = 0.5
# # Number of QTLs = 100
# # Major QTLs     = 1
# # Records per id = 5 (balanced)
# # Repeatability = 0.2
# nqtl <- 100
# h2 <- 0.4
# r2 <- 0.2
# reps <- 5
# mkr <- sample(names(freq[which(freq > 0.05)]), size = nqtl)
# eff <- runif(n = nqtl, min = 0, max = 1)
# eff <- h2*eff/sum(eff)
# eff[which(eff == min(eff))] <- 0.1
# names(eff) <- mkr
# df2 <- ghap.simpheno(object = phase, h2 = eff,
#                      r2 = r2, nrep = reps)
# 
# # Heritability   = 0.5
# # Number of QTLs = 100
# # Major QTLs     = 1
# # Records per id = 5 (unbalanced)
# # Repeatability = 0.2
# nqtl <- 100
# h2 <- 0.4
# r2 <- 0.2
# reps <- 5
# mkr <- sample(names(freq[which(freq > 0.05)]), size = nqtl)
# eff <- runif(n = nqtl, min = 0, max = 1)
# eff <- h2*eff/sum(eff)
# eff[which(eff == min(eff))] <- 0.1
# names(eff) <- mkr
# df3 <- ghap.simpheno(object = phase, h2 = eff, r2 = r2,
#                      nrep = reps, balanced = FALSE)

GHap documentation built on July 2, 2022, 1:07 a.m.