sim_geno: Simulate genotypes given observed marker data

Description

Uses a hidden Markov model to simulate from the joint distribution Pr(g | O) where g is the underlying sequence of true genotypes and O is the observed multipoint marker data, with possible allowance for genotyping errors.

Usage

sim_geno(
  cross,
  map = NULL,
  n_draws = 1,
  error_prob = 0.0001,
  map_function = c("haldane", "kosambi", "c-f", "morgan"),
  lowmem = FALSE,
  quiet = TRUE,
  cores = 1
)

Value

An object of class "sim_geno": a list of three-dimensional arrays of imputed genotypes, individuals x positions x draws. Also contains three attributes:

crosstype - The cross type of the input cross.
is_x_chr - Logical vector indicating whether chromosomes are to be treated as the X chromosome or not, from input cross.
alleles - Vector of allele codes, from input cross.

Arguments

cross: Object of class "cross2". For details, see the R/qtl2 developer guide.
map: Genetic map of markers. May include pseudomarker locations (that is, locations that are not within the marker genotype data). If NULL, the genetic map in cross is used.
n_draws: Number of simulations to perform.
error_prob: Assumed genotyping error probability
map_function: Character string indicating the map function to use to convert genetic distances to recombination fractions.
lowmem: If FALSE, split individuals into groups with common sex and crossinfo and then precalculate the transition matrices for a chromosome; potentially a lot faster but using more memory.
quiet: If FALSE, print progress messages.
cores: Number of CPU cores to use, for parallel calculations. (If 0, use parallel::detectCores().) Alternatively, this can be links to a set of cluster sockets, as produced by parallel::makeCluster().

Details

After performing the backward equations, we draw from $P r (g_{1} = v | O)$ and then $P r (g_{k + 1} = v | O, g_{k} = u)$ .

Examples

Run this code

grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
map_w_pmar <- insert_pseudomarkers(grav2$gmap, step=1)
draws <- sim_geno(grav2, map_w_pmar, n_draws=4, error_prob=0.002)

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