sim.cross.exp {twopartqtl}R Documentation

Simulate a QTL experiment

Description

Simulates data for a QTL backcross experiment using a model in which QTLs act additively.

Usage

sim.cross.exp(map, model = NULL, n.ind = 100, type = c("bc"), error.prob = 0, 
              missing.prob = 0, keep.qtlgeno = TRUE, keep.errorind = TRUE, 
              m = 0, p = 0, map.function = c("haldane", "kosambi", "c-f", "morgan"),
              dist, params, logNorm = FALSE)

Arguments

map A list whose components are vectors containing the marker locations on each of the chromosomes.
model A matrix where each row corresponds to a different QTL, and gives the chromosome number, cM position and effects of the QTL.
n.ind Number of individuals to simulate.
type Indicates type of experimental cross to simulate. Only backcross supported.
error.prob The genotyping error rate.
missing.prob The rate of missing genotypes.
keep.qtlgeno If TRUE, genotypes for the simulated QTLs will be included in the output.
keep.errorind If TRUE, and if error.prob > 0, the identity of genotyping errors will be included in the output.
m Interference parameter; a non-negative integer. 0 corresponds to no interference.
p Probability that a chiasma comes from the no-interference mechanism
map.function Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions.
dist Parametric distribution from which to generate phenotypes
params Vector of parameters for parametric distributions
logNorm If TRUE, continuous observations in point-mass mixture generated from log normal distribution. If FALSE, continuous observations are from truncated normal.

Details

This function extends sim.cross to simulate phenotypes from several additional parametric distributions including point-mass mixtures. The available distributions and their specification in dist are normal ("norm"), log normal ("lognorm"), gamma ("gamma"), truncated normal ("truncNorm"), t ("t"), Cauchy ("cauchy"), logistic ("logistic"), exponential ("expon"), Uniform ("uniform", Poisson ("pois"), and point-mass mixture ("pointmass").

Parameters for the distribution are specified as a vector in the param argument as follows: normal (mean, sd), log normal (mean, sd), gamma (shape, scale), truncated normal (mean, sd, floor) where mean and sd are the parameters of the normal distribution prior to truncation and floor is the lower value at which the distribution is truncated, t (df), Cauchy (location, scale), logistic (location, scale), exponential (rate), uniform (min, max), Poisson (lambda), point-mass mixture (proportion, mean, sd) where proportion is the proportion of observations in the point-mass and mean and sd are parameters for generating the continuous observations from a truncated normal or log normal distribution. Point-mass values are always simulated at 0.

Value

An object of class cross. See read.cross for details.
If keep.qtlgeno is TRUE, the cross object will contain a component qtlgeno which is a matrix containing the QTL genotypes (with complete data and no errors), coded as in the genotype data.
If keep.errorind is TRUE and errors were simulated, each component of geno will each contain a matrix errors, with 1's indicating simulated genotyping errors.

Author(s)

Sandra L. Taylor, sltaylor@ucdavis.edu

See Also

sim.cross

Examples


# Simulate a genetic mapp
Map <- sim.map(c(100,100), n.mar=c(22), include.x=FALSE, eq.spacing=TRUE)
num.ind <- 200

# simulate 200 backcross individuals with one qtl affecting a point-mass mixture trait
# define a qtl at 30 cm on chromosome 1 with an additive effect of 0.6
# on the mean of the continuous component and -0.1 on the proportion of 
# observations in the point-mass at 0
QTL.info <- rbind(c(1,30,0.6,-0.01))
sim <- sim.cross.exp(map=Map, model=QTL.info, n.ind=num.ind, type="bc",
          keep.qtlgeno=TRUE, map.function="haldane", dist="pointmass", params=c(0.5,1,0.6))


[Package twopartqtl version 1.0 Index]