CoSeg-package: Cosegregation Analysis and Pedigree Simulation
In CoSeg: 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 <ranolaj@uw.edu>
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.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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 <ranolaj@uw.edu>
References
~~ Literature or other references for background information ~~
Examples
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 53 54 55 | # #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
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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