AddAffectedToTrees: Add affection status to a given tree

Description Usage Arguments Value Author(s) See Also Examples

View source: R/AddAffectedToTree.R

Description

A function that takes a tree and adds affection status using user given cancer types and penetrances.

Usage

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AddAffectedToTrees(tree.f, frequencies.df = NULL, g = 4, benign.boolean=FALSE)

Arguments

tree.f

Output from MakeTree function.

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.

g

A parameter giving the generation to select a proband in.

benign.boolean

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

Value

Adds the affection status the inputted tree. The added columns are based on the inputted frequencies.df. For the default BRCA1Frequencies.df the extra columns are listed below.

famid

An integer id for the current tree. This is only useful for multiple trees.

bBRCA1.d

A boolean/integer depicting whether the individual has breast cancer(1) or not(0).

oBRCA1.d

A boolean/integer depicting whether the individual has ovarian cancer(1) or not(0)

bBRCA1.aoo

An integer giving the age of onset of the breast cancer if the individual is affected or NA if not.

oBRCA1.aoo

An integer giving the age of onset of the ovarian cancer if the individual is affected or NA if not.

proband

A boolean/integer labeling the proband(one with the variant and the disease) with a 1 and everyone else with 0. Note that if no suitable proband is found, all members will receive a -1.

Author(s)

John Michael O. Ranola and Brian H. Shirts

See Also

See also MakeTree, MakeAffectedTrees, and PlotPedigree

Examples

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  ## 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)

CoSeg documentation built on May 29, 2017, 6:10 p.m.