polySegratio-package {polySegratio} | R Documentation |
Segregation Ratios for Autoployploids
Description
These functions provide tools for computing expected segregation ratios (or more correctly segregation proportions) for dominant markers in regular autopolyploids and simulating such marker data as well as conducting standard Chi squared tests and Binomial confidance intervals for assigning marker dosage.
Details
Package: | polySegratio |
Type: | Package |
Version: | 0.2-5 |
Date: | 2018-03-22 |
License: | GPL-3 |
Use expected.segRatio
to compute expected segregation
proportions for regular autopolyploids
Use segregationRatios
to compute segregation ratios for
a matrix of markers
Use test.segRatio
to assignmarker dosage via Chi squared
tests or Binomial CIs
Use sim.autoMarkers
and sim.autoCross
to
simulate marker data under various scenarios
Use addMisclass
and addMissing
make some
markers misclassified or missing at random
Author(s)
Peter Baker p.baker1@uq.edu.au
References
J B S Haldane (1930) Theoretical genetics of autopolyploids. Journal of genetics 22 359–372
Ripol, M I et al(1999) Statistical aspects of genetic mapping in autopolyploids. Gene 235 31–41
Examples
## expected segregation proportions heterogeneous parents
expected.segRatio(4)
expected.segRatio("Tetraploid")
expected.segRatio("Octa")
## expected segregation proportions homogeneous parents
expected.segRatio("Octa",type="heter")
## generate dominant markers for autotetraploids
a1 <- sim.autoMarkers(4,c(0.8,0.2))
print(a1)
plot(a1)
## generate crosses for different parental types
p2 <- sim.autoCross(4, dose.proportion=list(p01=c(0.7,0.3),
p10=c(0.7,0.3),p11=c(0.6,0.2,0.2)))
print(p2)
plot(p2)
## simulate and test some markers, printing out a summary table of
## no.s of correct marker dosages
a <- sim.autoMarkers(ploidy = 8, c(0.7,0.2,0.09,0.01),
type="hetero", n.markers=500,n.individuals=100)
a <- addMissing(a, 0.07) # make seven percent missing at random
at <- test.segRatio(a$seg.ratios, ploidy=8, type.parents="het",
method="bin")
print(addmargins(table(a$true.doses$dosage, at$dosage, exclude=NULL)))