viterbi {qtl2} | R Documentation |
Calculate most probable sequence of genotypes
Description
Uses a hidden Markov model to calculate arg max 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
viterbi(
cross,
map = NULL,
error_prob = 0.0001,
map_function = c("haldane", "kosambi", "c-f", "morgan"),
lowmem = FALSE,
quiet = TRUE,
cores = 1
)
Arguments
cross |
Object of class |
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 |
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 |
quiet |
If |
cores |
Number of CPU cores to use, for parallel calculations.
(If |
Details
We use a hidden Markov model to find, for each individual on each chromosome, the most probable sequence of underlying genotypes given the observed marker data.
Note that we break ties at random, and our method for doing this may introduce some bias.
Consider the results with caution; the most probable sequence can
have very low probability, and can have features that are quite
unusual (for example, the number of recombination events can be too
small). In most cases, the results of a single imputation with
sim_geno()
will be more realistic.
Value
An object of class "viterbi"
: a list of two-dimensional
arrays of imputed genotypes, individuals x positions.
Also contains three attributes:
-
crosstype
- The cross type of the inputcross
. -
is_x_chr
- Logical vector indicating whether chromosomes are to be treated as the X chromosome or not, from inputcross
. -
alleles
- Vector of allele codes, from inputcross
.
See Also
sim_geno()
, maxmarg()
, cbind.viterbi()
, rbind.viterbi()
Examples
grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
map_w_pmar <- insert_pseudomarkers(grav2$gmap, step=1)
g <- viterbi(grav2, map_w_pmar, error_prob=0.002)