AddAffectedToTrees {CoSeg} | R Documentation |
A function that takes a tree and adds affection status using user given cancer types and penetrances.
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
## 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)