simPhyloQTL: Simulate a set of intercrosses for a single diallelic QTL

Description

Simulate a set of intercrosses with a single diallelic QTL.

Usage

simPhyloQTL(n.taxa=3, partition, crosses, map, n.ind=100, model, error.prob=0, missing.prob=0, partial.missing.prob=0, keep.qtlgeno=FALSE, keep.errorind=TRUE, m=0, p=0, 	    map.function=c("haldane","kosambi","c-f","morgan"))

Arguments

n.taxa

Number of taxa (i.e., strains).

partition

A vector of character strings of the form "AB|CD" or "A|BCD" indicating, for each QTL, which taxa have which allele. If missing, simulate under the null hypothesis of no QTL.

crosses

A vector of character strings indicating the crosses to do (for the form "AB", "AC", etc.). These will be sorted and then only unique ones used. If missing, all crosses will be simulated.

map

A list whose components are vectors containing the marker locations on each of the chromosomes.

n.ind

The number of individuals in each cross. If length 1, all crosses will have the same number of individuals; otherwise the length should be the same as crosses.

model

A matrix where each row corresponds to a different QTL, and gives the chromosome number, cM position and effects of the QTL (assumed to be the same in each cross in which the QTL is segregating).

error.prob

The genotyping error rate.

missing.prob

The rate of missing genotypes.

partial.missing.prob

When simulating an intercross or 4-way cross, this gives the rate at which markers will be incompletely informative (i.e., dominant or recessive).

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.

Interference parameter; a non-negative integer. 0 corresponds to no interference.

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.

Value

A list with each component being an object of class cross. See read.cross for details. The names (e.g. "AB", "AC", "BC") indicate the crosses.If keep.qtlgeno is TRUE, each 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 in each cross will each contain a matrix errors, with 1's indicating simulated genotyping errors.

Details

Meiosis is assumed to follow the Stahl model for crossover interference (see the references, below), of which the no interference model and the chi-square model are special cases. Chiasmata on the four-strand bundle are a superposition of chiasmata from two different mechanisms. With probability p, they arise by a mechanism exhibiting no interference; the remainder come from a chi-square model with inteference parameter m. Note that m=0 corresponds to no interference, and with p=0, one gets a pure chi-square model. QTLs are assumed to act additively, and the residual phenotypic variation is assumed to be normally distributed with variance 1.

The effect of a QTL is a pair of numbers, ($a,d$), where $a$ is the additive effect (half the difference between the homozygotes) and $d$ is the dominance deviation (the difference between the heterozygote and the midpoint between the homozygotes).

References

Broman, K. W., Kim, S., An\'e, C. and Payseur, B. A. Mapping quantitative trait loci to a phylogenetic tree. In preparation.

Examples

Run this code

## Not run: 
# # example map; drop X chromosome
# data(map10)           
# map10 <- map10[1:19]
# 
# # simulate data
# x <- simPhyloQTL(4, partition="AB|CD", crosses=c("AB", "AC", "AD"),
#                  map=map10, n.ind=150,
#                  model=c(1, 50, 0.5, 0))
# 
# # run calc.genoprob on each cross
# x <- lapply(x, calc.genoprob, step=2)
# 
# # scan genome, at each position trying all possible partitions
# out <- scanPhyloQTL(x, method="hk")
# 
# # maximum peak
# max(out, format="lod")
# 
# # approximate posterior probabilities at peak
# max(out, format="postprob")
# 
# # all peaks above a threshold for LOD(best) - LOD(2nd best)
# summary(out, threshold=1, format="lod")
# 
# # all peaks above a threshold for LOD(best), showing approx post'r prob
# summary(out, format="postprob", threshold=3)
# 
# # plot of results
# plot(out)
# ## End(Not run)

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