sim.autoMarkers: Simulates dominant markers from an autopolyploid cross

In polySegratio: Simulate and test marker dosage for dominant markers in autopolyploids

Description

Dominant markers are simulated from an autopolyploid cross given the ploidy level, expected segregation ratios and the proportions in each dosage marker class. This may be chosen from tetraploid to heccaidecaploid and the segregation ratios may be specified explicitly or generated automatically.

Usage

sim.autoMarkers(ploidy.level, dose.proportion, n.markers = 500,
n.individuals = 200, seg.ratios, no.dosage.classes,
type.parents = c("heterogeneous", "homozygous"),
marker.names = paste("M", 1:n.markers, sep = "."),
individual.names = paste("X", 1:n.individuals, sep = "."),
overdispersion=FALSE, shape1=50, seed)

Arguments

ploidy.level	the number of homologous chromosomes, either as numeric (single value) or as a character string containing type tetraploid, hexaploid, octoploid, ...
dose.proportion	the proportion of markers to be simulated in each dosage class. Note that the exact number will be randomly generated from the multinomial distribution
n.markers	number of markers (Default: 500)
n.individuals	number of individuals in the cross (Default: 200)
seg.ratios	numeric vector containing segregation proportion to be supplied if you wish to override automatic calculations using ploidy.level
no.dosage.classes	only generate markers for the first no.dosage.classes classes (if set)
type.parents	heterogeneous for (1,0) or (0,1) homozygous for (1,1) (default: heterogeneous)
marker.names	labels for markers (Default: M.1 ... M.n.markers)
individual.names	labels for offspring (Default: ... X.j ... )
overdispersion	logical indicating overdispersion (Default: FALSE)
shape1	shape1 parameter(s) for the beta distribution used to generate the Binomial probability p, either of length 1 or no.dosage.classes. Default: 50 which implies very little overdispersion. NB: 'shape2' is calculated from shape 1 and expected segregation ratios
seed	integer used to set seed for random number generator (RNG) which (if set) may be used to reproduce results

Value

Returns an object of class simAutoMarkers containing

markers	matrix of 0,1 dominant markers with individuals as cols and rows as markers
E.segRatio	expected segregation proportions, list with components ratiosegregation proportions, ploidy.levellevel of ploidy 4,6,8, ... ploidy.nametetraploid, ... , unknown
type.parents	heterogeneous for (1,0) or (0,1) homozygous for (1,1)
dose.proportion	proportions of markers set for each dosage class
n.markers	number of markers (Default: 500)
n.individuals	number of individuals in the cross (Default: 200)
true.doses	list containing dosage doses generated for each marker for simulation table.dosagessummary of no.s in each dosage namesnames for each dosage such as SD (single dose), DD (double dose), SDxSD etc
seg.ratios	object of class segRatio containing segregation ratios
time.generated	time/date when data set generated
seed	seed for random number generator seed which could be used to reproduce results (I hope)
overdispersion	either a list with components 'overdispersion': logical for whether overdispersion is set or not and if TRUE then two extra components 'shape1' and 'shape2' contain parameters for the beta distribution employed to generate Binomial probabilities
call	matches arguments when function called

Note

For use in simulation studies, other parameters such as the true dosage of each marker are also returned. Also, if extra binomial variation or overdispersion is requested then a beta-binomial distribution is employed to simulate marker data.Note that as the 'shape1' parameter becomes larger, the resulting marker data are less overdispersed.

Author(s)

Peter Baker [e-mail]

See Also

simAutoMarkers, print.simAutoMarkers, plot.simAutoMarkers, segRatio

Examples

## generate autopolyploid markers
a1 <- sim.autoMarkers(4,c(0.8,0.2),n.markers=20,n.individuals=10)
print(a1)

a2 <-
sim.autoMarkers(8,c(0.7,0.2,0.09,0.01),type.parents="homo",n.markers=20,n.individuals=10)
print(a2)

polySegratio documentation built on May 2, 2019, 6:09 p.m.