PrunePedigree: A function to calculate the likelihood ratio
In CoSeg: Cosegregation Analysis and Pedigree Simulation

Description Usage Arguments Value Author(s) Examples

This function calculates the likelihood ratio for an allele causing a disease asssuming that the allele is extremely rare so that all family members who have the allele got it directly from a common ancestor in the pedigree.

1	PrunePedigree(ped, affected.vector, pruning.level=1)

`ped`	A dataframe of a pedigree containing id, momid, dadid, age(Current age or age affected), y.born(year born), female(a logical where 1 is female and 0 is male), and genotype(0 is non-carrier, 1 carrier, and 2 unknown) in any order but with these specific column names.
`affected.vector`	A vector giving the affection status of the individual. 0 is unaffected, 1 is affected, and 2 is unknown.
`pruning.level`	An integer with value 1 or 2. A value of 1 will include the first degree relatives of all affecteds and carriers in the resultant pedigree. A value of 2 will only include individuals who are affected, carriers, or both and the individuals connecting them. Note that if the prband is the only individual left after pruning, this will return only the proband and their parents.

prunedped

A subset of the input pedigree with low information individuals removed. Note that low information individuals are individuals who are not first degree relatives of affecteds or mutation carriers and also do not connect individuals who are affected or mutation carriers. This dataframe of a pedigree contains id, momid, dadid, age(Current age or age affected), y.born(year born), female(a logical where 1 is female and 0 is male), and genotype(0 is non-carrier, 1 carrier, and 2 unknown) with these specific column names.

John Michael O. Ranola and Brian H. Shirts

  ## Not run: 
    #Load all the data included in the CoSeg package.
    data(BRCA1Frequencies.df, package="CoSeg")
    data(BRCA2Frequencies.df, package="CoSeg")
    data(MLH1Frequencies.df, package="CoSeg")
    data(USDemographics.df, package="CoSeg")
    data(ChinaDemographics.df, package="CoSeg")

    #summaries of all the data
    str(BRCA1Frequencies.df)
    str(BRCA2Frequencies.df)
    str(MLH1Frequencies.df)
    str(USDemographics.df)
    str(ChinaDemographics.df)

    #Make a tree with no affection status, g=4 generations above, gdown=2 generations below,
    #seed.age=50, and demographics.df=NULL which defaults to USDemographics.df.
    tree1=MakeTree()

    #Make a tree using Chinese demographics instead.
    tree2=MakeTree(demographics.df=ChinaDemographics.df)

    #Add affection statust to tree2 using BRCA1Frequencies.df which gives the BRCA1
    #penetrance function
    tree1a=AddAffectedToTree(tree.f=tree1,frequencies.df=BRCA1Frequencies.df)

    #make a tree with affection status (same as running MakeTree() and then AddAffectedToTree())
    tree3=MakeAffectedTrees(n=1,g=2,gdown=2,frequencies.df=MLH1Frequencies.df)
    #tree4=MakeAffectedTrees(n=1,g=2,gdown=2,frequencies.df=BRCA2Frequencies.df)


    #Depending on the size of the pedigree generated, probands (defined here as members of the
    #pedigree who are carriers of the genotype with the disease) may not always be present in
    #the pedigree.  To alleviate this problem in this example we manually generate a pedigree.
    #Note that this is from the Mohammadi paper where the Likelihood method originates from.
    ped=data.frame(degree=c(3,2,2,3,3,1,1,2,2,3), momid=c(3,NA,7,3,3,NA,NA,7,NA,8),
      dadid=c(2,NA,6,2,2,NA,NA,6,NA,9), id=1:10, age=c(45,60,50,31,41,68,65,55,62,43),
      female=c(1,0,1,0,1,0,1,1,0,1), y.born=0, dead=0, geno=2, famid=1, bBRCA1.d=0, oBRCA1.d=0,
      bBRCA1.aoo=NA, oBRCA1.aoo=NA, proband=0)
    ped$y.born=2010-ped$age
    ped$geno[c(1,3)]=1
    ped$bBRCA1.d[c(1,3)]=1
    ped$bBRCA1.aoo[1]=45
    ped$bBRCA1.aoo[3]=50
    ped$proband[1]=1

    ped=ped[c(6,7,2,3,8,9,1,4,5,10),]

    #Calculate the likelihood ratio
    CalculateLikelihoodRatio(ped=ped, affected.vector={ped$bBRCA1.d|ped$oBRCA1.d}, gene="BRCA1")

    #Plot the pedigree
    PlotPedigree(ped, affected.vector={ped$bBRCA1.d|ped$oBRCA1.d})

    #Rank and plot the members of the pedigree with unknown genotypes
    RankMembers(ped=ped, affected.vector={ped$bBRCA1.d|ped$oBRCA1.d}, gene="BRCA1")
  
## End(Not run)