Simulates a chromosome of arbitrary length with arbitrary numbers, types, and spacings of genetic loci over arbitrary pedigrees.
Usage
genome_sim(
pedigree,
founders = NULL,
positions = NULL,
initHe = NULL,
mutationType = NULL,
mutationRate = NULL,
phenotyped = NULL,
founderHaplotypes = NULL,
genotyped = NULL,
returnG = "n",
initFreqs = NULL
)Arguments
- pedigree
A pedigree
- founders
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
- phenotyped
A vector of IDs of those individuals for which to return phenotypic data
- founderHaplotypes
A matrix or dataframe containing founder haplotypes
- genotyped
A vector of IDs of those individuals for which to return genotypic data
- returnG
If 'y' then genotypic data for all loci (including
cIAMloci) will be returned.- initFreqs
A list of allele frequencies for all loci. If
initFreqsis specified, it will override information frominitHe.extractAfrom packageMasterBayescan be used to obtain obtaininitFreqsform a sample of genotypes. ForcIAMloci, allele names ininitFreqsshould be allelic substitution effects.
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'
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.
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
#> id dam sire
#> 1 1 <NA> <NA>
#> 2 2 <NA> <NA>
#> 3 3 <NA> <NA>
#> 4 4 <NA> <NA>
#> 5 5 <NA> <NA>
#> 6 6 1 4
#> 7 7 1 4
#> 8 8 1 4
#> 9 9 1 4
#> 10 10 2 5
#> 11 11 2 5
#> 12 12 2 5
#> 13 13 2 5
#> 14 14 3 5
#> 15 15 3 5
#> 16 16 3 5
#> 17 17 3 5
## 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
#> $V2
#> 2 1
#> 0.5 0.5
#>
#> $V4
#> 0.21 -1.32
#> 0.75 0.25
#>
## simulate data for these indivdiuals based on a single QTL
## with two equally alleles with balanced frequencies in the
## founders, linked (2 cM) to a highly polymorphic microsatellite:
genome_sim(
pedigree = pedigree, founders = testData$founder, positions = c(0, 2),
mutationType = c("Micro", "cIAM"), mutationRate = c(0, 0),
initFreqs = testFreqs, returnG = "y"
)
#> Processing pedigree...
#> 0% 50% 100%
#> | | |
#> -
#> -
#> -
#> -
#> -
#> -
#> -
#> -
#> -
#> -
#> -
#> -
#> -
#> -
#> -
#> -
#> -
#>
#> ...done.
#>
#> Calculating phenotypes...
#> done.
#>
#> Tabulating marker genotypes...
#> done.
#> $Phenotypes
#> V1 V2
#> 1 1 -1.11
#> 2 2 0.42
#> 3 3 -1.11
#> 4 4 0.42
#> 5 5 -1.11
#> 6 6 -1.11
#> 7 7 0.42
#> 8 8 0.42
#> 9 9 0.42
#> 10 10 0.42
#> 11 11 0.42
#> 12 12 -1.11
#> 13 13 -1.11
#> 14 14 0.42
#> 15 15 -1.11
#> 16 16 -2.64
#> 17 17 -1.11
#>
#> $MarkerData
#> [1] doubleids markers
#> <0 rows> (or 0-length row.names)
#>
#> $genomes
#> doubleids V2 V3
#> 1 1 2 -1.32
#> 2 1 1 0.21
#> 3 2 2 0.21
#> 4 2 2 0.21
#> 5 3 1 -1.32
#> 6 3 1 0.21
#> 7 4 2 0.21
#> 8 4 2 0.21
#> 9 5 2 0.21
#> 10 5 2 -1.32
#> 11 6 2 -1.32
#> 12 6 2 0.21
#> 13 7 2 0.21
#> 14 7 2 0.21
#> 15 8 1 0.21
#> 16 8 2 0.21
#> 17 9 1 0.21
#> 18 9 2 0.21
#> 19 10 2 0.21
#> 20 10 2 0.21
#> 21 11 2 0.21
#> 22 11 2 0.21
#> 23 12 2 0.21
#> 24 12 2 -1.32
#> 25 13 2 0.21
#> 26 13 2 -1.32
#> 27 14 1 0.21
#> 28 14 2 0.21
#> 29 15 1 0.21
#> 30 15 2 -1.32
#> 31 16 1 -1.32
#> 32 16 2 -1.32
#> 33 17 1 0.21
#> 34 17 2 -1.32
#>
## since we specified returnG='y', we can check that
## the phenotypes add up to the
## allelic substitution effects for the second locus.