fillX.G: Mendelian Transition Probabilities

Description Usage Arguments Value Note Author(s) References See Also Examples

Description

This function is primarily intended for use within getXlist, and fills in the design matrices of the model with the genetic likelihoods.

Usage

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fillX.G(X.list, A, G, E1=0.005, E2=0.005, marker.type="MSW")

Arguments

X.list

list of design matrices for each offspring derived using getXlist

A

list of allele frequencies

G

list of genotype objects; rows must correspond to individuals in the vector X.list$id

E1

if Wang's (2004) model of genotyping error for co-dominant markers is used this is the probability of an allele dropping out. If CERVUS's (Kalinowski, 2006; Marshall, 1998) model of genotyping error for co-dominant markers is used this parameter is not used. If Hadfield's (2009) model of genotyping error for dominant markers is used this is the probability of a dominant allele being scored as a recessive allele.

E2

if Wang's (2004) or CERVUS's (Kalinowski, 2006; Marshall, 1998) model of genotyping error for co-dominant markers are used this is the probability of an allele being miss-scored. In the CERVUS model errors are not independent for the two alleles within a genotype and so if a genotyping error has occurred at one allele then a genotyping error occurs at the other allele with probability one. Accordingly, E2(2-E2) is the per-genotype rate defined in CERVUS. If Hadfield's (2009) model of genotyping error for dominant markers is used this is the probability of a recessive allele being scored as a dominant allele.

marker.type

"MSW" or "MSC" for co-dominant markers with Wang's (2004) model of genotyping error or CERVUS's model of genotyping error (Kalinowski, 2006; Marshall, 1998) or "AFLP" for dominant markers (Hadfield, 2009).

Value

list of design matrices of the form X.list containing genetic likelihoods for each offspring.

Note

If a GdataPed object is passed to getXlist then the genetic likelihoods will be calculated by default.

Author(s)

Jarrod Hadfield j.hadfield@ed.ac.uk

References

Marshall, T. C. et al (1998) Molecular Ecology 7 5 639-655 Kalinowski S.T. et al (2007) Molecular Ecology 16 5 1099-1106 Hadfield J. D. et al (2009) in prep

See Also

getXlist

Examples

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## Not run: 
data(WarblerG)
A<-extractA(WarblerG)

ped<-matrix(NA, 5,3)
ped[,1]<-1:5
ped[,2]<-c(rep(NA, 4), 1)
ped[,3]<-c(rep(NA, 4), 2)

genotypes<-simgenotypes(A, ped=ped)

sex<-c("Female", "Male", "Female", "Male","Female")
offspring<-c(0,0,0,0,1)

data<-data.frame(id=ped[,1], sex, offspring)

res1<-expression(varPed(x="offspring", restrict=0))

PdP<-PdataPed(formula=list(res1), data=data)
GdP<-GdataPed(G=genotypes$Gobs, id=genotypes$id)

X.list<-getXlist(PdP)
# creates design matrices for offspring (in this case indivdiual "5")

X.list.G<-fillX.G(X.list, A=A, G=genotypes$Gobs, E2=0.005)
# genetic likelihoods are arranged sires within dams 

X.list.G$X$"5"$dam.id
X.list.G$X$"5"$sire.id

# so for this example we have parental combinations 
# ("1","2"), ("1","4"), ("3","2"), ("2","4"):

X.list.G$X$"5"$G

# The true parents have the highest likelihood in this case

## End(Not run)


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