linkage: Calculate recombination frequency, LOD and phase

In polymapR: Linkage Analysis in Outcrossing Polyploids

Calculate recombination frequency, LOD and phase

Description

linkage is used to calculate recombination frequency, LOD and phase within one type of marker or between two types of markers.

Usage

linkage(
  dosage_matrix,
  markertype1 = c(1, 0),
  markertype2 = NULL,
  target_parent = "P1",
  other_parent = "P2",
  G2_test = FALSE,
  convert_palindrome_markers = TRUE,
  LOD_threshold = 0,
  ploidy,
  ploidy2 = NULL,
  pairing = c("random", "preferential"),
  prefPars = c(0, 0),
  combinations_per_iter = NULL,
  iter_RAM = 500,
  ncores = 1,
  verbose = TRUE,
  full_output = FALSE,
  log = NULL
)

Arguments

dosage_matrix	An integer matrix with markers in rows and individuals in columns.
markertype1	A vector of length 2 specifying the first markertype to compare. The first element specifies the dosage in target_parent, the second in other_parent.
markertype2	A vector of length 2 specifying the first markertype to compare. This argument is optional. If not specified, the function will calculate linkage within the markertype as specified by markertype1. The first element specifies the dosage in target_parent, the second in other_parent.
target_parent	Character string specifying the target parent as provided in the column-names of dosage_matrix. Target in this context means that you wish to estimate linkage between markers with an allele that is polymorphic and segregating in this parent.
other_parent	Character string specifying the other parent as provided in the column-names of dosage_matrix
G2_test	Apply a G2 test (LOD of independence) in addition to the LOD of linkage.
convert_palindrome_markers	Logical. Should markers that behave the same for both parents be converted to a workable format for that parent? E.g.: should 3.1 markers be converted to 1.3? If unsure, set to TRUE.
LOD_threshold	Minimum LOD score of linkages to report. Recommended to use for large number (> millions) of marker comparisons in order to reduce memory usage.
ploidy	Integer. The ploidy of the target parent (parent 1). If parent 2 has the same ploidy level, then also the ploidy level of parent 2.
ploidy2	Integer, by default NULL. If parental ploidies differ, use this to specify the ploidy of parent 2. Note that in cross-ploidy situations, ploidy2 must be smaller than ploidy.
pairing	Type of pairing. "random" or "preferential".
prefPars	The estimates for preferential pairing parameters for the target and other parent, respectively, in range 0 <= p < 2/3. By default this is c(0,0) (so, no preferential pairing). See the function test_prefpairing and the vignette for more details.
combinations_per_iter	Optional integer. Number of marker combinations per iteration.
iter_RAM	A (very) conservative estimate of working memory in megabytes used per core. It only takes the size frequency matrices into account. Actual usage is more, especially with large number of linkages that are reported. Reduce memory usage by using a higher LOD_threshold.
ncores	Number of cores to use. Works both for Windows and UNIX (using doParallel). Use parallel::detectCores() to find out how many cores you have available.
verbose	Should messages be sent to stdout?
full_output	Logical, by default FALSE. If TRUE, the complete output over all phases and showing marker combination counts is returned.
log	Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

Returns a data.frame with columns:

marker_a

first marker of comparison. If markertype2 is specified, it has the type of markertype1.

marker_b

second marker of comparison. It has the type of markertype2 if specified.

r

(estimated) recombinations frequency

LOD

(estimated) LOD score

phase

phase between markers

Examples

data("screened_data3")
SN_SN_P1 <- linkage(dosage_matrix = screened_data3,
                   markertype1 = c(1,0),
                   target_parent = "P1",
                   other_parent = "P2",
                   ploidy = 4,
                   pairing = "random",
                   ncores = 1
                   )

polymapR documentation built on Nov. 5, 2023, 1:09 a.m.