pedtricks (version 0.4.2)

genome_sim: A function to simulate QTL and/or SNP data.

Description

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
)

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'

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 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.

See Also

phen_sim, micro_sim

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:
genome_sim(
  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.