Description Usage Arguments Value Author(s) See Also Examples
View source: R/AddAffectedToTree.R
A function that takes a tree and adds affection status using user given cancer types and penetrances.
1 | AddAffectedToTrees(tree.f, frequencies.df = NULL, g = 4, benign.boolean=FALSE)
|
tree.f |
Output from |
frequencies.df |
A dataframe giving the penetrance of the desired affection. For convenience this can be one of the included penetrance data frames ( |
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. |
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. |
John Michael O. Ranola and Brian H. Shirts
See also MakeTree
, MakeAffectedTrees
, and PlotPedigree
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ## 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)
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