CoSeg-package: Cosegregation Analysis and Pedigree Simulation

Description Details Author(s) References Examples

Description

Tools for generating and analyzing pedigrees. Specifically, this has functions that will generate realistic pedigrees for the USA and China based on historical birth rates and family sizes. It also has functions for analyzing these pedigrees when they include disease information including one based on counting meioses and another based on likelihood ratios.

Details

The DESCRIPTION file: This package was not yet installed at build time.

Index: This package was not yet installed at build time.
~~ An overview of how to use the package, including the most important functions ~~

Author(s)

John Michael O. Ranola and Brian H. Shirts

Maintainer: John Michael O. Ranola <[email protected]>

References

~~ Literature or other references for background information ~~

Examples

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  # #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")

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
Loading required package: splines
'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] 0.04
[1] 0.07
[1] 0.11
[1] 0.15
[1] 0.19
[1] 0.22
[1] 0.26
[1] 0.3
[1] 0.33
[1] 0.37
[1] 0.41
[1] 0.44
[1] 0.48
[1] 0.52
[1] 0.56
[1] 0.59
[1] 0.63
[1] 0.67
[1] 0.7
[1] 0.74
[1] 0.78
[1] 0.81
[1] 0.85
[1] 0.89
[1] 0.93
[1] 0.96
[1] 1
[1] "No demographics given.  Using USDemographics.df"
[1] 1
[1] 0.09
[1] 0.18
[1] 0.27
[1] 0.36
[1] 0.45
[1] 0.55
[1] 0.64
[1] 0.73
[1] 0.82
[1] 0.91
[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 May 29, 2017, 6:10 p.m.