Version:	0.6
Date:	2023-11-29
Title:	Simulate Experimental Crosses
Description:	Simulate and plot general experimental crosses. The focus is on simulating genotypes with an aim towards flexibility rather than speed. Meiosis is simulated following the Stahl model, in which chiasma locations are the superposition of two processes: a proportion p coming from a process exhibiting no interference, and the remainder coming from a process following the chi-square model.
Author:	Karl W Broman [aut, cre]
Maintainer:	Karl W Broman <broman@wisc.edu>
Depends:	R (≥ 3.1.0)
Imports:	graphics, stats, Rcpp (≥ 0.12.17)
Suggests:	qtl, knitr, rmarkdown, testthat, devtools, roxygen2
License:	GPL-3
URL:	https://kbroman.org/simcross/, https://github.com/kbroman/simcross
BugReports:	https://github.com/kbroman/simcross/issues
VignetteBuilder:	knitr
LinkingTo:	Rcpp
LazyData:	true
Encoding:	UTF-8
ByteCompile:	true
RoxygenNote:	7.2.3
NeedsCompilation:	yes
Packaged:	2023-11-29 12:12:02 UTC; kbroman
Repository:	CRAN
Date/Publication:	2023-11-29 12:30:06 UTC

Example AIL pedigree

Description

Example matrix describing the pedigree for advanced intercross lines

Usage

data(AILped)

Format

A data frame with five columns: individual id, mom, dad, sex (0 for females and 1 for males) and generation.

Source

Derived from the pedF8 dataset in the QTLRel package, https://cran.r-project.org/package=QTLRel

Examples

data(AILped)
x <- sim_from_pedigree(AILped)

Collaborative Cross colors

Description

Get the vector of colors for the Collaborative Cross

Usage

CCcolors(palette = c("new", "original", "official"))

Arguments

Value

vector of eight colors

Examples

CCcolors()

Calculate adjusted chromosome length for obligate chiasma

Description

Calculate the reduced chromosome length that will give the target expected number of chiasmata when conditioning on there being at least one chiasma on the four-strand bundle.

Usage

calc_Lstar(L, m = 0, p = 0)

Arguments

Value

Adjusted length of chromosome

See Also

cross(), sim_meiosis(), sim_crossovers()

Examples

calc_Lstar(100, 0, 0)
calc_Lstar(60, 10, 0.1)

Check a pedigree for errors

Description

Perform a series of checks on the tabular data for a pedigree, checking for problems

Usage

check_pedigree(pedigree, ignore_sex = FALSE)

Arguments

Details

The parents should be listed before any of their offspring. Founders should have 0's for mother and father; all others should have non-zero values for the parents, and the parents should appear in the pedigree. Father should be male and mothers should be female (unless ignore_sex=TRUE). Individual identifiers should be unique and non-zero. There should be no missing values anywhere. (NAs are allowed in the sex column if ignore_sex=TRUE.)

Value

TRUE (invisibly) if everything is okay; otherwise gives an error.

See Also

sim_from_pedigree(), sim_ril_pedigree()

Examples

tab <- sim_ril_pedigree(7)
check_pedigree(tab)

Collapse alleles for simulated DO genotypes

Description

When simulating Diversity Outbreds, we need to specify parents 1-16, with 9-16 being the males from strains 1-8. This function collapses replaces alleles 9-16 with 1-8, to make the result ordinary DO-type data.

Usage

collapse_do_alleles(xodata)

Arguments

Value

The input object, with alleles 9-16 replaced by 1-8.

See Also

sim_do_pedigree(), sim_do_pedigree_fix_n(), sim_from_pedigree()

Examples

# simulate DO pedigree
tab <- sim_do_pedigree(8)

# simulate genotypes for that pedigree
dat <- sim_from_pedigree(tab)
# collapse to alleles 1-8
dat <- collapse_do_alleles(dat)

# also works with data on multiple chromosomes
dat <- sim_from_pedigree(tab, c("1"=100, "2"=75, "X"=100), xchr="X")
dat <- collapse_do_alleles(dat)

Convert continuous allele information into marker genotypes

Description

Convert the continuous crossover location information produced by sim_from_pedigree to marker genotypes

Usage

convert2geno(xodat, map, founder_geno = NULL, shift_map = FALSE)

Arguments

Value

If founder_geno is provided or there are just two founders, the result is a numeric matrix of genotypes, individuals x markers, with genotypes 1/2/3 codes for 11/12/22 genotypes.

If founder_geno is not provided and there are more than two founders, the result is a 3-dimensional array, individuals x markers x alleles, with the third dimensional corresponding to the maternal and paternal allele.

If the input map is a list (the components being chromosomes), then xodat and founder_geno must be lists of the same length, and the result will be a list of matrices.

See Also

get_geno(), sim_from_pedigree()

Examples

# simulate AIL pedigree
tab <- sim_ail_pedigree(12, 30)
# simulate data from that pedigree
dat <- sim_from_pedigree(tab)
# marker map (could also use sim.map in R/qtl)
map <- seq(0, 100, by=5)
names(map) <- paste0("marker", seq(along=map))
# convert data to marker genotypes
geno <- convert2geno(dat, map)


# AIL with multiple chromosomes
dat <- sim_from_pedigree(tab, c("1"=100, "2"=75, "X"=100), xchr="X")
# marker map
multmap <- list("1"=seq(0, 100, by=5),
                "2"=seq(0, 75, by=5),
                "X"=seq(0, 100, by=5))
for(i in 1:3)
  names(multmap[[i]]) <- paste0("marker", i, "_", 1:length(map[[i]]))
geno <- convert2geno(dat, multmap)

# simulate DO pedigree
tab <- sim_do_pedigree(8)
# simulate data from that pedigree
dat <- sim_from_pedigree(tab)
# simulate founder snp alleles
fg <- matrix(sample(1:2, 8*length(map), repl=TRUE), nrow=8)
# for DO, need female & male founders (to deal with X chr)
fg <- rbind(fg, fg)
# convert dat to SNP genotypes
geno <- convert2geno(dat, map, fg)
# if fg not provided, result is a 3d array
genoarray <- convert2geno(dat, map)

Convert continuous allele information into marker genotypes for multiple chromosomes

Description

Wrap up of convert2geno to adequate multiple chromosomes.

Usage

convert2geno_allchr(
  xodat,
  map,
  id = NULL,
  founder_geno = NULL,
  return.matrix = TRUE,
  shift_map = FALSE
)

Arguments

Value

If founder_geno is provided or there are just two founders, the result is a numeric matrix of genotypes, individuals x markers, with genotypes 1/2/3 codes for 11/12/22 genotypes. If there are more than two founders and founder_geno are letters, the result is a character matrix, too.

If founder_geno is not provided and there are more than two founders, the result is a 3-dimensional array, individuals x markers x alleles, with the third dimensional corresponding to the maternal and paternal allele.

See Also

convert2geno()

Examples

library(qtl)
# marker map
map <- sim.map(len=rep(100, 19), n.mar=10, include.x=FALSE)
# simulate AIL pedigree
tab <- sim_ail_pedigree(12, 30)
# simulate data from that pedigree
dat <- sim_from_pedigree_allchr(tab, map)
names(map) <- paste0("marker", seq(along=map))
# convert data to marker genotypes
id <- which(tab[, "gen"]==12)
geno <- convert2geno_allchr(dat, map, id)

Create a parent object

Description

Create a parent object

Usage

create_parent(L, allele = 1)

Arguments

Value

A list with two components, for the individual's two chromosomes. Each is a list with alleles in chromosome intervals (as integers) and locations of the right endpoints of those intervals.

See Also

cross(), sim_meiosis()

Examples

create_parent(100, 1)
create_parent(100, 1:2)

Cross two individuals

Description

Simulate the cross of two individuals to create a single progeny

Usage

cross(
  mom,
  dad,
  m = 10,
  p = 0,
  xchr = FALSE,
  male = FALSE,
  obligate_chiasma = FALSE,
  Lstar = NULL
)

Arguments

Details

Simulations are under the Stahl model with the interference parameter being an integer. This is an extension of the chi-square model, but with chiasmata being the superposition of two processes, one following the chi-square model and the other exhibiting no interference.

Value

A list with two components, for the individual's two chromosomes. Each is a list with alleles in chromosome intervals (as integers) and locations of the right endpoints of those intervals.

See Also

create_parent(), sim_meiosis(), sim_crossovers(), calc_Lstar()

Examples

mom <- create_parent(100, 1:2)
dad <- create_parent(100, 1:2)
child <- cross(mom, dad)

Get genotype at a single position

Description

With data on the continuous crossover location information produced by sim_from_pedigree, grab the genotype at a given position.

Usage

get_geno(xodat, position)

Arguments

Value

A numeric matrix with two columns: the maternal and paternal allele for each individual.

See Also

sim_from_pedigree(), convert2geno()

Examples

# simulate AIL pedigree
tab <- sim_ail_pedigree(12, 30)
# simulate data from that pedigree
dat <- sim_from_pedigree(tab)
# get genotype at position 30 cM
geno <- get_geno(dat, 30)

Mouse chromosome lengths

Description

Mouse chromosome lengths in cM from the Cox et al. map

Usage

data(mouseL_cox)

Format

A numeric vector with lengths in cM for the 20 mouse chromosomes.

Source

Taken from Table 1 of Cox et al. (2009) A new standard genetic map for the laboratory mouse. Genetics 182:1335-1344. doi:10.1534/genetics.109.105486

See Also

mouseL_mgi

Examples

data(mouseL_cox)

Mouse chromosome lengths

Description

Mouse chromosome lengths in cM from the Mouse Genome Informatics (MGI) standard map.

Usage

data(mouseL_mgi)

Format

A numeric vector with lengths in cM for the 20 mouse chromosomes.

Source

Taken from Table 1 of Cox et al. (2009) A new standard genetic map for the laboratory mouse. Genetics 182:1335-1344. doi:10.1534/genetics.109.105486

See Also

mouseL_cox

Examples

data(mouseL_mgi)

Plot cross lines

Description

Add lines for a cross

Usage

plot_crosslines(
  momloc,
  dadloc,
  kidsloc,
  gap = 3,
  chrlength = 30,
  cex = 1.5,
  lwd = 2,
  arrow_length = 0.1,
  col = "white",
  ...
)

Arguments

Value

None.

See Also

plot_ind()

Examples

mom <- create_parent(100, 1:2)
dad <- create_parent(100, 3:4)
kids <- lapply(1:4, function(junk) cross(mom, dad))
plot(0,0, type="n", xlim=c(0, 100), ylim=c(0,100),
     xaxt="n", yaxt="n", xlab="", ylab="")
loc <- list(c(25,75), c(75,75), c(12.5,25), c(37.5,25), c(62.5, 25), c(87.5,25))
plot_ind(mom, loc[[1]])
plot_ind(dad, loc[[2]])
for(i in 1:4) plot_ind(kids[[i]], loc[[i+2]])
plot_crosslines(loc[[1]], loc[[2]], loc[3:6])

Plot an individual

Description

Add an individual, as a pair of chromosomes, to a plot

Usage

plot_ind(
  ind,
  center,
  chrlength = 30,
  chrwidth = 3,
  gap = 3,
  col = CCcolors(),
  border = "black",
  lend = 1,
  ljoin = 1,
  allborders = FALSE,
  ...
)

Arguments

Value

None.

See Also

plot_crosslines()

Examples

mom <- create_parent(100, 1:2)
dad <- create_parent(100, 3:4)
kid <- cross(mom, dad)
plot(0,0, type="n", xlim=c(0, 100), ylim=c(0,100),
     xaxt="n", yaxt="n", xlab="", ylab="")
loc <- list(c(25,75), c(75,75), c(50,25))
plot_ind(mom, loc[[1]])
plot_ind(dad, loc[[2]])
plot_ind(kid, loc[[3]])
plot_crosslines(loc[[1]], loc[[2]], loc[[3]])

Simulate pedigree for 4-way intercross

Description

Simulate a 4-way cross, among four inbred lines (a table of individual, mom, dad, sex)

Usage

sim_4way_pedigree(ngen = 1, nsibs = 100)

Arguments

Details

We start with a set of 4 individuals (representing four inbred lines), and make a pair of crosses to generate a pair of heterozygous individuals. These are then crosses to generate a set of F1 individuals. If ngen==1, we stop there, with sum(nsibs) individuals in this last generation. If gen==2, we generate length(nsibs) male/female pairs of F1 offspring; these are intercrossed to generate a set of sibships, with lengths defined by the values in nsibs. Individuals in the last generation are alternating female/male.

Value

A data frame with five columns: individual ID, mother ID, father ID, sex, and generation. Founders have 0 for mother and father ID. Sex is coded 0 for female and 1 for male.

See Also

sim_from_pedigree(), sim_ril_pedigree(), sim_do_pedigree(), sim_ail_pedigree()

Examples

# 100 F1s between heterozygous parents
tab <- sim_4way_pedigree(1, 100)
# could also do this
tab2 <- sim_4way_pedigree(1, rep(10, 10))

# 120 F2s in 10 sibships each of size 12
tab3 <- sim_4way_pedigree(ngen=2, rep(12, 10))

Simulate AIL pedigree

Description

Simulate a pedigree for advanced intercross lines (a table of individual, mom, dad, sex)

Usage

sim_ail_pedigree(
  ngen = 12,
  npairs = 30,
  nkids_per = 5,
  design = c("nosib", "random")
)

Arguments

Details

Advanced intercross lines (AIL) are generated from a pair of inbred lines. We cross them and then cross the F1 to generate npair breeding pairs. The subsequent ngen outbreeding generations then proceed by crossing a male and female from the preceding generation (mated completely at random, with design="random", or avoiding siblings, with design="nosib"). Each breeding pair gives a single female and a single male to the next generation, except at the last generation nkids_per offspring are mated, in equal numbers male and female. (If nkids_per is an odd number, the number of males and females in each sibship will differ by one, alternating between sibships, with one additional female and then one additional male.

Value

A data frame with five columns: individual ID, mother ID, father ID, sex, and generation. Founders have 0 for mother and father ID. Sex is coded 0 for female and 1 for male.

See Also

sim_from_pedigree(), sim_ril_pedigree(), sim_do_pedigree(), sim_4way_pedigree()

Examples

tab <- sim_ail_pedigree(12, 30)

Simulate AIL pedigree with fixed n

Description

Simulate a pedigree for advanced intercross lines (a table of individual, mom, dad, sex) so that the last generation reaches a desired sample size n

Usage

sim_ail_pedigree_fix_n(
  ngen = 12,
  nkids_per = 5,
  nsample_ngen = 150,
  npairs = NULL,
  method = c("last2", "sub2"),
  design = c("nosib", "random")
)

Arguments

Details

The default value for npairs depends on the choice of method. For method="last2", we use a default of npairs=30; for method="sub2", we use a default of npairs=300.

Value

A data frame with five columns: individual ID, mother ID, father ID, sex, and generation. Founders have 0 for mother and father ID. Sex is coded 0 for female and 1 for male.

See Also

sim_from_pedigree(), sim_ril_pedigree(), sim_ail_pedigree(), sim_do_pedigree(), sim_4way_pedigree(), sim_do_pedigree_fix_n()

Examples

tab <- sim_ail_pedigree_fix_n(12)

Simulate crossover locations using the Stahl model

Description

Simulate crossover locations on a single meiotic product using the Stahl model.

Usage

sim_crossovers(L, m = 10, p = 0, obligate_chiasma = FALSE, Lstar = NULL)

Arguments

Details

Chiasma locations are a superposition of two processes: a proportion p exhibiting no interference, and a proportion (1-p) following the chi-square model with interference parameter m. Crossover locations are derived by thinning the chiasma locations with probability 1/2.

Simulations are under the Stahl model with the interference parameter being an integer. This is an extension of the chi-square model, but with chiasmata being the superposition of two processes, one following the chi-square model and the other exhibiting no interference.

Value

Numeric vector of crossover locations, in cM

References

Copenhaver, G. P., Housworth, E. A. and Stahl, F. W. (2002) Crossover interference in arabidopsis. Genetics 160, 1631–1639.

Foss, E., Lande, R., Stahl, F. W. and Steinberg, C. M. (1993) Chiasma interference as a function of genetic distance. Genetics 133, 681–691.

Zhao, H., Speed, T. P. and McPeek, M. S. (1995) Statistical analysis of crossover interference using the chi-square model. Genetics 139, 1045–1056.

Examples

x <- sim_crossovers(200, 10, 0)
x <- sim_crossovers(200, 10, 0.04)
x <- sim_crossovers(100, 0, 0, obligate_chiasma=TRUE)

Simulate a pedigree for Diversity Outbred mice

Description

Simulate a pedigree for generating Diversity Outbred (DO) mice (a table of individual, mom, dad, sex).

Usage

sim_do_pedigree(
  ngen = 12,
  npairs = 144,
  ccgen = rep(4:12, c(21, 64, 24, 10, 5, 9, 5, 3, 3)),
  nkids_per = 5,
  design = c("nosib", "random")
)

Arguments

Details

Diversity outbred (DO) mice are generated from a set of 8 inbred lines. We need two individuals from each line (one female and one male) as the order of the initial crosses will be randomized; for example, sometimes the individual from line 1 will be a mother and sometimes a father. The founders are numbered 1-8 for the females from the 8 lines, and 9-16 for the corresponding males.

Diversity Outbred mice are generated by first creating a panel of partially-inbred 8-way RIL (the so-called pre-CC, for pre-Collaborative Cross). The ccgen argument specifies the number of inbreeding generations for each of the CC lines. We generate a pre-CC line for each of the npairs breeding pairs, and generate a sibling pair from each as the starting material.

The subsequent ngen outbreeding generations then proceed by crossing a male and female from the preceding generation (mated completely at random, with design="random", or avoiding siblings, with design="nosib"). Each breeding pair gives a single female and a single male to the next generation, except at the last generation nkids_per offspring are mated, in equal numbers male and female. (If nkids_per is an odd number, the number of males and females in each sibship will differ by one, alternating between sibships, with one additional female and then one additional male.

The default for ccgen is taken from Figure 1 of Svenson et al. (2012).

Value

A data frame with six columns: individual ID, mother ID, father ID, sex, generation, and TRUE/FALSE indicator for whether DO or pre-DO. Founders have 0 for mother and father ID. Sex is coded 0 for female and 1 for male.

References

Svenson KL, Gatti DM, Valdar W, Welsh CE, Cheng R, Chesler EJ, Palmer AA, McMillan L, Churchill GA (2012) High-resolution genetic mapping using the mouse Diversity Outbred population. Genetics 190:437-447

See Also

sim_from_pedigree(), sim_ril_pedigree(), sim_ail_pedigree(), sim_4way_pedigree()

Examples

tab <- sim_do_pedigree(8)

Simulate a pedigree for Diversity Outbreds for a target sample size

Description

Simulate a pedigree for Diversity Outbred (DO) mice (a table of individual, mom, dad, sex) so that the last generation reaches a desired sample size.

Usage

sim_do_pedigree_fix_n(
  ngen = 12,
  nkids_per = 5,
  nccgen = 15,
  nsample_ngen = 150,
  npairs = NULL,
  method = c("last2", "sub2", "fixcc"),
  design = c("nosib", "random"),
  selc.method = c("byfamily", "byindiv")
)

Arguments

Details

The default number of breeding pairs depends on the chosen method. With method="last2", the default is npairs=30; with method="sub2", the default is npairs=300; with method="fixcc", npairs is ignored and is fixed at 144.

Value

A data frame with six columns: individual ID, mother ID, father ID, sex, generation, and TRUE/FALSE indicator for whether DO or pre-DO. Founders have 0 for mother and father ID. Sex is coded 0 for female and 1 for male.

See Also

sim_from_pedigree(), sim_ril_pedigree(), sim_ail_pedigree(), sim_do_pedigree(), sim_4way_pedigree(), sim_ail_pedigree_fix_n()

Examples

tab <- sim_do_pedigree_fix_n(8)

Simulate pedigree for F1 between diversity outbreds and another inbred line

Description

Simulate a pedigree for a set of DOF1 individuals: the F1 offspring of a set of diversity outbred mice and another inbred strain (such as a mutant line).

Usage

sim_dof1_pedigree(
  ngen = 12,
  npairs = 144,
  ccgen = rep(4:12, c(21, 64, 24, 10, 5, 9, 5, 3, 3)),
  nkids_per = 5,
  design = c("nosib", "random")
)

Arguments

Details

Diversity outbred (DO) mice are generated from a set of 8 inbred lines. We need two individuals from each line (one female and one male) as the order of the initial crosses will be randomized; for example, sometimes the individual from line 1 will be a mother and sometimes a father. The founders are numbered 1-8 for the females from the 8 lines, and 9-16 for the corresponding males.

Diversity Outbred mice are generated by first creating a panel of partially-inbred 8-way RIL (the so-called pre-CC, for pre-Collaborative Cross). The ccgen argument specifies the number of inbreeding generations for each of the CC lines. We generate a pre-CC line for each of the npairs breeding pairs, and generate a sibling pair from each as the starting material.

The subsequent ngen outbreeding generations then proceed by crossing a male and female from the preceding generation (mated completely at random, with design="random", or avoiding siblings, with design="nosib"). Each breeding pair gives a single female and a single male to the next generation, except at the last generation nkids_per offspring are mated, in equal numbers male and female. (If nkids_per is an odd number, the number of males and females in each sibship will differ by one, alternating between sibships, with one additional female and then one additional male.

The default for ccgen is taken from Figure 1 of Svenson et al. (2012).

We assume that the F1 offspring are all from a cross DO female x line 17 male, and so the last generation of the DO is taken to be all females.

Value

A data frame with seven columns: individual ID, mother ID, father ID, sex, generation, a TRUE/FALSE indicator for whether DO or pre-DO, and a TRUE/FALSE indicator for whether DOF1. Founders have 0 for mother and father ID. Sex is coded 0 for female and 1 for male.

References

Svenson KL, Gatti DM, Valdar W, Welsh CE, Cheng R, Chesler EJ, Palmer AA, McMillan L, Churchill GA (2012) High-resolution genetic mapping using the mouse Diversity Outbred population. Genetics 190:437-447

See Also

sim_from_pedigree(), sim_ril_pedigree(), sim_ail_pedigree(), sim_4way_pedigree()

Examples

tab <- sim_dof1_pedigree(8)

Simulate genotypes for pedigree

Description

Simulate genotypes along one chromosome for a pedigree

Usage

sim_from_pedigree(
  pedigree,
  L = 100,
  xchr = FALSE,
  m = 10,
  p = 0,
  obligate_chiasma = FALSE
)

Arguments

Value

A list with each component being the data for one individual, as produced by the cross() function. Those results are a list with two components, corresponding to the maternal and paternal chromosomes. The chromosomes are represented as lists with two components: an integer vector of alleles in chromosome intervals, and a numeric vector of locations of the right-endpoints of those intervals; these two vectors should have the same length.

If the input L is a vector, in order to simulate multiple chromosomes at once, then the output will be a list with length length(L), each component being a chromosome and having the form described above.

See Also

check_pedigree(), sim_ril_pedigree(), sim_ail_pedigree(), sim_from_pedigree_allchr()

Examples

# simulate AIL pedigree
tab <- sim_ail_pedigree(12, 30)
# simulate data from that pedigree
dat <- sim_from_pedigree(tab)
# simulate multiple chromosomes
dat <- sim_from_pedigree(tab, c("1"=100, "2"=75, "X"=100), xchr="X")

Simulate genotypes for pedigree for multiple chromosomes

Description

Simulate genotypes along all chromosomes for a pedigree. This is a wrap up of sim_from_pedigree.

Usage

sim_from_pedigree_allchr(
  pedigree,
  map,
  m = 10,
  p = 0,
  obligate_chiasma = FALSE
)

Arguments

Value

A list with each component being the result from sim_from_pedigree, of length same as map.

See Also

check_pedigree(), sim_ril_pedigree(), sim_ail_pedigree() sim_from_pedigree()

Examples

library(qtl)
# marker map
map <- sim.map(len=rep(100, 19), n.mar=10, include.x=FALSE)
# simulate AIL pedigree
tab <- sim_ail_pedigree(12, 30)
# simulate data from that pedigree
dat <- sim_from_pedigree_allchr(tab, map)

Simulate meiosis

Description

Output a random meiotic product from an input individual.

Usage

sim_meiosis(parent, m = 10, p = 0, obligate_chiasma = FALSE, Lstar = NULL)

Arguments

Details

Simulations are under the Stahl model with the interference parameter being an integer. This is an extension of the chi-square model, but with chiasmata being the superposition of two processes, one following the chi-square model and the other exhibiting no interference.

Value

A list with alleles in chromosome intervals (as integers) and locations of the right endpoints of those intervals.

References

Copenhaver, G. P., Housworth, E. A. and Stahl, F. W. (2002) Crossover interference in arabidopsis. Genetics 160, 1631–1639.

Foss, E., Lande, R., Stahl, F. W. and Steinberg, C. M. (1993) Chiasma interference as a function of genetic distance. Genetics 133, 681–691.

Zhao, H., Speed, T. P. and McPeek, M. S. (1995) Statistical analysis of crossover interference using the chi-square model. Genetics 139, 1045–1056.

See Also

create_parent(), cross(), sim_crossovers(), calc_Lstar()

Examples

ind <- create_parent(100, 1:2)
prod <- sim_meiosis(ind)

Generate a ril pedigree

Description

Generate a pedigree for multi-way recombinant inbred lines (a table of individual, mom, dad, sex)

Usage

sim_ril_pedigree(
  ngen = 20,
  selfing = FALSE,
  parents = 1:2,
  firstind = max(parents) + 1
)

Arguments

Value

A data frame with five columns: individual ID, mother ID, father ID, sex, and generation. Founders have 0 for mother and father ID. Sex is coded 0 for female and 1 for male.

See Also

sim_from_pedigree(), sim_ail_pedigree(), sim_do_pedigree(), sim_4way_pedigree()

Examples

tab <- sim_ril_pedigree(7)

Find heterozygous regions

Description

Find regions of heterozygosity in an individual

Usage

where_het(ind)

Arguments

Value

A matrix with two columns; each row indicates the start and end of a region where the individual is heterozygous

See Also

sim_from_pedigree(), convert2geno()

Examples

mom <- create_parent(100, 1:2)
dad <- create_parent(100, 1:2)
child <- cross(mom, dad)
where_het(child)