A vector of indicator variables denoting founder status (1=founder, 0=non-founder)
positions
Genome locations in cM for markers
initHe
Initial levels of expected heterozygosity
mutationType
A vector of locus types - see details
mutationRate
A vector of mutation rates
founderHaplotypes
A matrix or dataframe containing founder haplotypes
phenotyped
A vector of IDs of those individuals for which to return phenotypic data
genotyped
A vector of IDs of those individuals for which to return genotypic data
returnG
If 'y' then genotypic data for all loci (including cIAM loci) will be returned.
initFreqs
A list of allele frequencies for all loci. If initFreqs is specified, it will override information from initHe. extractA from package MasterBayes can be used to obtain obtain initFreqs form a sample of genotypes. For cIAM loci, allele names in initFreqs should be allelic substitution effects.
Details
Valid mutation types are ‘Micro’, ‘Dom’, ‘dIAM’ and ‘cIAM’, for microsatellite, dominant (AFLP), discrete infinite alleles mutation model loci (SNPs), and continuous infinite alleles mutation model loci (polymorphisms effecting phenotypic variation). cIAM loci have mutational allelic substitution effects taken drawn from a normal distribution with mean 0 and variance 1.
Value
Phenotypes
A vector of phenotypes. Calculated as the sum of all allelic effects. Scaling is currently left to be done post-hoc.
MarkerData
A vector of marker genotypes, i.e. alleles at all loci except those designated ‘cIAM’
Morrissey, M.B, and A.J. Wilson, 2009. pedantics, an R package for pedigree-based genetic simulation, and pedigree manipulation, characterisation, and viewing. Molecular Ecology Resources.
See Also
phensim
Examples
testData<-as.data.frame(matrix(c(
1, NA, NA, 1, 1, 1, 2, 2,
2, NA, NA, 1, 1, 1, 2, 2,
3, NA, NA, 1, 1, 1, 2, 2,
4, NA, NA, 1, 0, 1, 2, 2,
5, NA, NA, 1, 0, 1, 2, 2,
6, 1, 4, 0, -1, 2, 3, 3,
7, 1, 4, 0, -1, 2, 3, 3,
8, 1, 4, 0, -1, 2, 3, 3,
9, 1, 4, 0, -1, 2, 3, 3,
10, 2, 5, 0, -1, 2, 3, 3,
11, 2, 5, 0, -1, 2, 3, 3,
12, 2, 5, 0, -1, 2, 3, 3,
13, 2, 5, 0, -1, 2, 3, 3,
14, 3, 5, 0, -1, 2, 3, 3,
15, 3, 5, 0, -1, 2, 3, 3,
16, 3, 5, 0, -1, 2, 3, 3,
17, 3, 5, 0, -1, 2, 3, 3),
17,8,byrow=TRUE))
names(testData)<-c("id","dam","sire","founder","sex",
"cohort","first","last")
pedigree<-as.data.frame(cbind(testData$id,testData$dam,
testData$sire))
for(x in 1:3) pedigree[,x]<-as.factor(pedigree[,x])
names(pedigree)<-c("id","dam","sire")
pedigree
##make up some microsatellite and gene allele frquencies:
sampleGenotypes<-as.data.frame(matrix(c(
1,2,-1.32,0.21,2,1,0.21,0.21),2,4,byrow=TRUE))
testFreqs<-extractA(sampleGenotypes)
## note that alleles at the gene locus are given as their
## allelic substitution effects:
testFreqs
## simulate data for these indivdiuals based on a single QTL
## with two equally alleles with balanced frequencies in the
## founders, linked (2 cM) to a highly polymorphic microsatellite:
genomesim(pedigree=pedigree,founders=testData$founder,positions=c(0,2),
mutationType=c('Micro','cIAM'),mutationRate=c(0,0),
initFreqs=testFreqs,returnG='y')
## since we specified returnG='y', we can check that
## the phenotypes add up to the
## allelic substitution effects for the second locus.