CoSeg (version 0.38)

MakeAffectedTrees: Make affected trees

Description

A function which makes trees based on historical data with affection status based on penetrance data.

Usage

MakeAffectedTrees(n = 1, g = 4, gdown = 2, frequencies.df = NULL, demographics.df = NULL, benign.boolean=FALSE)

Arguments

n

An integer giving the number of affected trees to simulate.

g

An integer giving the number of ancestral generations of the proband to simulate.

gdown

An integer giving the number of descendent generations of the proband to simulate.

frequencies.df

A dataframe giving the penetrance of the desired affection. For convenience this can be one of the included penetrance data frames (BRCA1Frequencies.df, BRCA2Frequencies.df, MLH1Frequencies.df), or a user defined penetrance similar to one of those. This defaults to BRCA1Frequencies.df.

demographics.df

A dataframe giving the demographics to use. This defaults to USDemographics.df which gives historic data on US demographics. Alternatively, the user can choose ChinaDemographics.df or input their own dataframe.

benign.boolean

A boolean variable which tells the program to simulate disease status for a benign variant when benign.boolean is TRUE.

Value

Returns a tree with affection status. The result is a dataframe with the following columns if using the defaults. If using other penetrance data, column names will differ slightly. Returns a tree with affection status. The result is a dataframe with the following columns if using the defaults. If using other penetrance data, column names will differ slightly.

See Also

See also MakeTree, AddAffectedToTree, and PlotPedigree

Examples

  ## Not run: 
# 
#     #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)