CoSeg-package {CoSeg}R Documentation

Cosegregation Analysis and Pedigree Simulation

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:

Package: CoSeg
Type: Package
Title: Cosegregation Analysis and Pedigree Simulation
Version: 0.55
Date: 2017-05-09
Author: John Michael O. Ranola and Brian H. Shirts
Depends: kinship2, fGarch, splines
Maintainer: John Michael O. Ranola <ranolaj@uw.edu>
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.
License: GPL (>= 2)
LazyData: True
Repository: R-Forge
Repository/R-Forge/Project: coseg
Repository/R-Forge/Revision: 55
Repository/R-Forge/DateTimeStamp: 2020-12-17 09:39:26
Date/Publication: 2020-12-17 09:39:26

Index of help topics:

AddAffectedToTree       Add affection status to a given tree
AddAffectedToTrees      Add affection status to a given tree
BRCA1Frequencies.df     BRCA1 cancer incidence data frame
BRCA2Frequencies.df     BRCA2 cancer incidence data frame
CalculateLikelihoodRatio
                        A function to calculate the likelihood ratio
ChinaDemographics.df    Chinese Demographics Data Frame
CoSeg-package           Cosegregation Analysis and Pedigree Simulation
FormatWebToCoSeg        Plots a tree/pedigree
MLH1Frequencies.df      Lynch Syndrome cancer incidence data frame
MakeAffectedTrees       Make affected trees
MakeTree                Make a tree
MakeTrees               Make a tree
PlotPedigree            Plots a tree/pedigree
PrunePedigree           A function to calculate the likelihood ratio
RankMembers             A function to calculate the likelihood ratio
USDemographics.df       United States Demographics Data Frame

~~ 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 <ranolaj@uw.edu>

References

~~ Literature or other references for background information ~~

Examples

  # #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")

[Package CoSeg version 0.55 Index]