BRCA1frequencies.df: BRCA1 cancer incidence data frame

Description Usage Format Details Source Examples

Description

A dataframe containing annual incidence of cancers affected by the BRCA1 gene, namely ovarian cancer and breast cancer.

Usage

1

Format

A data frame with 800 observations on the following 5 variables.

age

A numeric vector giving the age the data is relevant for.

cancer.type

A factor with levels bBRCA1 and oBRCA1 which stands for breast BRCA1 cancer and ovarian BRCA1 cancer.

female

A boolean/numeric vector indicating whether the data is for females(1) or males(0).

carrier

A boolean/numeric vector depicting whether the data is for a carrier(1) or non-carrier(0).

frequencies

A numeric vector giving the cancer incidence based on age, sex, carrier status, and gender.

Details

Data was converted to annual incidence using a spline with 3 df's. Risk of cancer for people below age 20 was set at 0.

Source

Penetrance data from PMID: 20204502.

Examples

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  ## 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)

Example output

Loading required package: kinship2
Loading required package: Matrix
Loading required package: quadprog
Loading required package: fGarch
Loading required package: timeDate
Loading required package: timeSeries
Loading required package: fBasics


Rmetrics Package fBasics
Analysing Markets and calculating Basic Statistics
Copyright (C) 2005-2014 Rmetrics Association Zurich
Educational Software for Financial Engineering and Computational Science
Rmetrics is free software and comes with ABSOLUTELY NO WARRANTY.
https://www.rmetrics.org --- Mail to: info@rmetrics.org
Loading required package: splines
Warning message:
In data(BRCA1Frequencies.df, package = "CoSeg") :
  data set 'BRCA1Frequencies.df' not found
Warning message:
In data(BRCA2Frequencies.df, package = "CoSeg") :
  data set 'BRCA2Frequencies.df' not found
Warning message:
In data(MLH1Frequencies.df, package = "CoSeg") :
  data set 'MLH1Frequencies.df' not found
Warning message:
In data(USDemographics.df, package = "CoSeg") :
  data set 'USDemographics.df' not found
Warning message:
In data(ChinaDemographics.df, package = "CoSeg") :
  data set 'ChinaDemographics.df' not found
'data.frame':	800 obs. of  5 variables:
 $ age        : int  1 2 3 4 5 6 7 8 9 10 ...
 $ cancer.type: Factor w/ 2 levels "bBRCA1","oBRCA1": 1 1 1 1 1 1 1 1 1 1 ...
 $ female     : num  0 0 0 0 0 0 0 0 0 0 ...
 $ carrier    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ frequencies: num  0 0 0 0 0 0 0 0 0 0 ...
'data.frame':	800 obs. of  5 variables:
 $ age        : int  1 2 3 4 5 6 7 8 9 10 ...
 $ cancer.type: Factor w/ 2 levels "bBRCA2","oBRCA2": 1 1 1 1 1 1 1 1 1 1 ...
 $ female     : num  0 0 0 0 0 0 0 0 0 0 ...
 $ carrier    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ frequencies: num  0 0 0 0 0 0 0 0 0 0 ...
'data.frame':	1200 obs. of  5 variables:
 $ age        : int  1 2 3 4 5 6 7 8 9 10 ...
 $ cancer.type: Factor w/ 3 levels "crcLS","endLS",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ female     : num  0 0 0 0 0 0 0 0 0 0 ...
 $ carrier    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ frequencies: num  0 0 0 0 0 0 0 0 0 0 ...
'data.frame':	12 obs. of  7 variables:
 $ in.year            : num  1800 1900 1910 1920 1930 1940 1950 1960 1970 1980 ...
 $ out.year           : num  1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 ...
 $ female.age.marriage: num  22 21.1 21.6 21.2 21.3 ...
 $ male.age.marriage  : num  26.1 26.1 25.1 24.6 24.3 24.3 22.8 23.2 24.7 26.1 ...
 $ female.age.death   : num  60.2 62 63.8 64.9 66.5 ...
 $ male.age.death     : num  60.1 60.7 62.2 62.7 65.6 ...
 $ offspring          : num  3.15 2.93 2.88 2.74 2.21 ...
'data.frame':	12 obs. of  7 variables:
 $ in.year            : num  1800 1900 1910 1920 1930 1940 1950 1960 1970 1980 ...
 $ out.year           : num  1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 ...
 $ female.age.marriage: num  17.5 17.5 17.5 17.5 17.5 18.5 19 19.8 21.6 22.8 ...
 $ male.age.marriage  : num  20.8 22.2 22.2 22.2 20.5 23.6 24 24.4 24.8 25.2 ...
 $ female.age.death   : num  53.6 55.7 57.8 60.1 60.1 ...
 $ male.age.death     : num  57.5 58.1 58.7 59.3 60.7 ...
 $ offspring          : num  3.19 3.24 3.3 3.35 3.35 ...
[1] "No demographics given.  Using USDemographics.df"
[1] 0.01
[1] 0.02
[1] 0.03
[1] 0.04
[1] 0.05
[1] 0.06
[1] 0.07
[1] 0.08
[1] 0.09
[1] 0.1
[1] 0.11
[1] 0.12
[1] 0.13
[1] 0.14
[1] 0.15
[1] 0.16
[1] 0.17
[1] 0.18
[1] 0.19
[1] 0.2
[1] 0.21
[1] 0.22
[1] 0.23
[1] 0.24
[1] 0.25
[1] 0.26
[1] 0.27
[1] 0.28
[1] 0.29
[1] 0.3
[1] 0.31
[1] 0.32
[1] 0.33
[1] 0.34
[1] 0.35
[1] 0.36
[1] 0.37
[1] 0.38
[1] 0.39
[1] 0.4
[1] 0.41
[1] 0.42
[1] 0.43
[1] 0.44
[1] 0.45
[1] 0.46
[1] 0.47
[1] 0.48
[1] 0.49
[1] 0.5
[1] 0.51
[1] 0.52
[1] 0.53
[1] 0.54
[1] 0.55
[1] 0.56
[1] 0.57
[1] 0.58
[1] 0.59
[1] 0.6
[1] 0.61
[1] 0.62
[1] 0.63
[1] 0.64
[1] 0.65
[1] 0.66
[1] 0.67
[1] 0.68
[1] 0.69
[1] 0.7
[1] 0.71
[1] 0.72
[1] 0.73
[1] 0.74
[1] 0.75
[1] 0.76
[1] 0.77
[1] 0.78
[1] 0.79
[1] 0.8
[1] 0.81
[1] 0.82
[1] 0.83
[1] 0.84
[1] 0.85
[1] 0.86
[1] 0.87
[1] 0.88
[1] 0.89
[1] 0.9
[1] 0.91
[1] 0.92
[1] 0.93
[1] 0.94
[1] 0.95
[1] 0.96
[1] 0.97
[1] 0.98
[1] 0.99
[1] 1
[1] "No demographics given.  Using USDemographics.df"
[1] 1
[1] 0.04
[1] 0.08
[1] 0.12
[1] 0.15
[1] 0.19
[1] 0.23
[1] 0.27
[1] 0.31
[1] 0.35
[1] 0.38
[1] 0.42
[1] 0.46
[1] 0.5
[1] 0.54
[1] 0.58
[1] 0.62
[1] 0.65
[1] 0.69
[1] 0.73
[1] 0.77
[1] 0.81
[1] 0.85
[1] 0.88
[1] 0.92
[1] 0.96
[1] 1
$likelihood.ratio
[1] 1.704142

$separating.meioses
[1] 1

$number.genotypes.found
[1] 28

$unknown.genotypes
[1]  6  7  8  4  5 10

$modified.lr
          [,1]      [,2]     [,3]     [,4]     [,5]      [,6]
[1,] 0.8803743 2.5279092 2.301785 1.704213 1.862173 1.7673232
[2,] 2.5279092 0.8803743 1.106498 1.704071 1.546111 0.9801351

$original.lr
[1] 1.704142

CoSeg documentation built on Dec. 17, 2020, 3 p.m.