Uses a pedigree with parents identified for all non-founding individuals and simulates microsatellite genotypes
Usage
micro_sim(
pedigree,
genFreqs = NULL,
genotypesSample = NULL,
knownGenotypes = NULL,
records = NULL,
eRate1 = 0,
eRate2 = 0,
eRate3 = 0
)Arguments
- pedigree
A pedigree
- genFreqs
(optional) A list of allele frequencies, can be produced with
extractA- genotypesSample
(required if
genFreqsis not supplied) a sample of genotypes from which to estimate population allele frequencies- knownGenotypes
(not yet implemented) a data frame of genotypes for (potentially a subset) of founder individuals
- records
Record availability, see details.
- eRate1
The rate of genotypic substitution errors, i.e., when a true genotype at a given locus is replaced by a pair of alleles selected at random based on the population allele frequencies
- eRate2
The rate of allelic substitution errors, i.e. when an allele is erroneously replaced at a given locus by an allele chosen at random based on the population allele frequencies
- eRate3
The rate of large allele dropouts, simulated by setting the value of the larger allele at a locus to the value of the smaller allele
Value
- trueGenotypes
A data frame of true genotypes
- observedGenotypes
A data frame of plausible observed genotypes, given specified patterns of missingness and errors.
Details
Error rates and data availability rates can be specified as either (1) single values to be applied to all individuals and all loci, (2) as a vector the same length as the number of loci, representing locus-specific rates to be applied uniformly to all individuals, or (3) as data frames with rows for each individual and columns for each locus. In the third option, observed patterns of data availability can be simulated by supplying 0s and 1s for missing and available individual genotypes, respectively.
Examples
pedigree <- as.data.frame(matrix(c(
"m1", NA, NA,
"m2", NA, NA,
"m3", NA, NA,
"d4", NA, NA,
"d5", NA, NA,
"o6", "m1", "d4",
"o7", "m1", "d4",
"o8", "m1", "d4",
"o9", "m1", "d4",
"o10", "m2", "d5",
"o11", "m2", "d5",
"o12", "m2", "d5",
"o13", "m2", "d5",
"o14", "m3", "d5",
"o15", "m3", "d5",
"o16", "m3", "d5",
"o17", "m3", "d5"
), 17, 3, byrow = TRUE))
names(pedigree) <- c("id", "dam", "sire")
for (x in 1:3) pedigree[, x] <- as.factor(pedigree[, x])
## some sample genotypes, very simple, two markers with He = 0.5
sampleGenotypes <- as.data.frame(matrix(c(
1, 2, 1, 2, 2, 1, 2, 1
), 2, 4, byrow = TRUE))
## locus names
names(sampleGenotypes) <- c("loc1a", "loc1b", "loc2a", "loc2b")
## simulate some genotypes
micro_sim(pedigree = pedigree, genotypesSample = sampleGenotypes)
#> $trueGenotypes
#> loc1b loc1b_b loc2b loc2b_b
#> m1 2 2 1 1
#> m2 1 2 1 2
#> m3 2 2 1 2
#> d4 1 1 1 1
#> d5 1 1 2 1
#> o6 2 1 1 1
#> o7 2 1 1 1
#> o8 2 1 1 1
#> o9 2 1 1 1
#> o10 2 1 1 2
#> o11 1 1 2 2
#> o12 1 1 2 2
#> o13 2 1 2 2
#> o14 2 1 2 2
#> o15 2 1 1 1
#> o16 2 1 2 2
#> o17 2 1 1 2
#>
#> $observedGenotypes
#> loc1b loc1b_b loc2b loc2b_b
#> m1 2 2 1 1
#> m2 1 2 1 2
#> m3 2 2 1 2
#> d4 1 1 1 1
#> d5 1 1 2 1
#> o6 2 1 1 1
#> o7 2 1 1 1
#> o8 2 1 1 1
#> o9 2 1 1 1
#> o10 2 1 1 2
#> o11 1 1 2 2
#> o12 1 1 2 2
#> o13 2 1 2 2
#> o14 2 1 2 2
#> o15 2 1 1 1
#> o16 2 1 2 2
#> o17 2 1 1 2
#>