FamiliasPosterior: Calculates posterior probabilities and likelihoods for...
In Familias: Probabilities for Pedigrees Given DNA Data
Description
Usage
Arguments
Value
Author(s)
Examples
Description
The calculations of the windows version of Familias 2.0
are made available in an R environment.
Usage
1
2
FamiliasPosterior(pedigrees, loci, datamatrix, prior, ref = 1,
kinship = 0, simplifyMutations = FALSE)
Arguments
pedigrees
An object of type 'FamiliasPedigree' or 'pedigree', or a list of such objects
loci
A FamiliasLocus object or a list of such objects
datamatrix
A data frame. The row names must be the names of the persons you have data for. The columns contain
the alleles, two columns for each marker, in the same order used in the loci list.
prior
A vector of non-negative probabilities summing to 1. As default a flat prior is used, with all values equal.
ref
The index in the list of pedigrees of the pedigree which should be used as reference when computing likelihood ratios.
The default value is 1.
kinship
A real in [0,1], commonly denoted theta in forensics included to model sub-population effects as departures
from Hardy-Weinberg equilibrium. The default value is zero.
simplifyMutations
In pedigrees with several generations multistep mutations may happen. If the probability of mutating to an allele
depends on which allele it mutates from, exact likelihood computations must keep track of all the possible values of
mutated alleles in such multistage mutations, and this may slow down computations considerably. Instead, one may
use in computations for multistage mutations a single allele that is not among those observed in the data.
When this approach gives exact results it is always used;
in other cases one may choose to use it as an approximation by setting
simplifyMutations to TRUE. The properties of the extra allele is the weighted average (by population frequencies)
of the alleles not observed in the data.
Value
posterior
probabilities for each pedigree
prior
prior returned for convenience and backward compatibility
LR
Likelihood ratios using the pedigree indicated with the ref parameter (default value 1) as basis
LRperMarker
Likelihood ratios per marker using the pedigree indicated with the ref parameter (default value 1) as basis
likelihoods
for each pedigree
likelihoodsPerSystem
likelihoods for each locus and each pedigree
Author(s)
Petter Mostad <mostad@chalmers.se> and Thore Egeland <Thore.Egeland@gmail.com>
Examples
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#Example 1
#Data is available for "mother", "daughter" and "AF" for two loci (systems).
#Three pedigrees are defined, with "mother" being the mother of "daughter"
#in all cases. "AF" may be the father (ped1), unrelated (ped1) or
#uncle (ped3). The posterior probabilities for the pedigrees are calculated
#and likelihoods are also given so that
#likelihood ratios can be computed. Compared to the windows version of Familias 2.0,
#it is easy to plot pedigrees and define arbitrary priors for the three alternative pedigrees.
#The below implementation uses the R package kinship2 to define and plot pedigrees
persons <- c("mother", "daughter", "AF")
ped1 <- FamiliasPedigree(id=persons, dadid=c(NA, "AF", NA), momid=c(NA, "mother", NA),
sex=c("female", "female", "male"))
ped2 <- FamiliasPedigree(id=c(persons, "TF"), dadid=c(NA, "TF", NA, NA),
momid=c(NA, "mother", NA, NA),
sex=c("female", "female", "male", "male"))
ped3 <- FamiliasPedigree(id=c(persons, "TF", "gf", "gm"), dadid = c(NA, "TF", "gf", "gf", NA, NA),
momid=c(NA, "mother", "gm", "gm", NA, NA),
sex=c("female", "female", "male", "male", "male", "female"))
par(mfrow=c(3,1))
plot(ped1);title("ped1, i.e., AF is father")
plot(ped2);title("ped2, i.e., AF is unrelated")
plot(ped3);title("ped3, i.e., AF is uncle")
par(mfrow=c(1,1))
mypedigrees <- list(isFather = ped1, unrelated=ped2, isUncle = ped3)
locus1 <- FamiliasLocus(frequencies=c(0.1, 0.2, 0.3, 0.4),
allelenames= c("A", "B", "C", "D"), name="locus1")
locus2 <- FamiliasLocus(c(0.2, 0.3, 0.5), c(17, 18, 19), "loc2", femaleMutationRate = 0.05)
myloci <- list(locus1, locus2)
datamatrix <- data.frame(locus1.1=c("A", "A", "A"), locus1.2=c("B", "B", "C"),
locus2.1=c(17, 19, 19), locus2.2=c(18, 18, 18))
rownames(datamatrix) <- persons
result=FamiliasPosterior(mypedigrees, myloci, datamatrix,ref=2)
#Example 2. Example 1 continued.
#Below commands (from scratch) are based on paramlink to prepare pedigrees
persons <- c("AF","mother", "daughter")
ped1=swapSex(nuclearPed(1),3)
ped1=as.data.frame(ped1)
ped1.c=ConvertPed(ped1,persons)$ped
plot(ped1.c,main="ped1")
ped1=swapSex(nuclearPed(1),3)
AF=singleton(4)
ped2=rbind(as.data.frame(ped1),as.data.frame(AF))
persons <- c("TF","mother", "daughter","AF")
ped2.c=ConvertPed(ped2,persons)$ped
plot(ped2.c,main="ped2")
ped=nuclearPed(2)
ped=addOffspring(ped,father=4,noff=1,sex=2)
persons=c("gf","gm","AF","TF","mother","daughter")
plot(ped,id.labels=persons,main="ped3")
ped=as.data.frame(ped)
ped3.c=ConvertPed(ped,persons)$ped
mypedigrees <- list(isFather = ped1.c, unrelated=ped2.c, isUncle = ped3.c)
locus1 <- FamiliasLocus(frequencies=c(0.1, 0.2, 0.3, 0.4),
allelenames= c("A", "B", "C", "D"), name="locus1")
locus2 <- FamiliasLocus(c(0.2, 0.3, 0.5), c(17, 18, 19), "loc2",
femaleMutationRate = 0.05)
myloci <- list(locus1, locus2)
datamatrix <- data.frame(locus1.1=c("A", "A", "A"), locus1.2=c("B", "B", "C"),
locus2.1=c(17, 19, 19), locus2.2=c(18, 18, 18))
rownames(datamatrix) <- c("mother", "daughter", "AF")
result=FamiliasPosterior(mypedigrees, myloci, datamatrix, ref=2)
#Example 3. Example 2 continued
#Below commands (from scratch) are based on paramlink to prepare pedigrees and datamatrix.
#The handling of datamatrix is artificial in *this* example; alternatively genotypes can be provided
#in other, perhaps more natural ways.
persons <- c("AF","mother", "daughter")
#Names in paramlink are integers 1:length(persons)={1,2,3}
ped1=swapSex(nuclearPed(1),3)
dat <- data.frame(locus1.1=c("A", "A", "A"), locus1.2=c("C","B", "B"),
locus2.1=c(19,17, 19), locus2.2=c(18, 18, 18)) #Consistent with persons above
ped1=cbind(as.data.frame(ped1),dat)
foo=ConvertPed(ped1,persons)
ped1.c=foo$ped
datamatrix=foo$datamatrix
ped1=swapSex(nuclearPed(1),3)
AF=singleton(4)
ped2=rbind(as.data.frame(ped1),as.data.frame(AF))
persons <- c("TF","mother", "daughter","AF")
ped2.c=ConvertPed(ped2,persons)$ped
ped=nuclearPed(2)
ped=addOffspring(ped,father=4,noff=1,sex=2)
persons=c("gf","gm","AF","TF","mother","daughter")
ped=as.data.frame(ped)
ped3.c=ConvertPed(ped,persons)$ped
mypedigrees <- list(isFather = ped1.c, unrelated=ped2.c, isUncle = ped3.c)
locus1 <- FamiliasLocus(frequencies=c(0.1, 0.2, 0.3, 0.4),
allelenames= c("A", "B", "C", "D"), name="locus1")
locus2 <- FamiliasLocus(c(0.2, 0.3, 0.5), c(17, 18, 19), "loc2", femaleMutationRate = 0.05)
myloci <- list(locus1, locus2)
result=FamiliasPosterior(mypedigrees, myloci, datamatrix, ref=2)
#Example 4: Using FamiliasPedigree
persons <- c("person", "AF")
sex <- c("male", "male")
ped1 <- FamiliasPedigree(id=persons, dadid=c(NA, NA), momid=c(NA, NA), sex=sex)
ped2 <- FamiliasPedigree(id=persons, dadid=c("AF", NA), momid=c(NA, NA), sex=sex)
mypedigrees <- list(unrelated = ped1, isFather=ped2)
locus1 <- FamiliasLocus(c(0.1, 0.2, 0.3, 0.4), c("A", "B", "C", "D"), "locus1",
maleMutationModel = "Equal", maleMutationRate = 0.005)
locus2 <- FamiliasLocus(c(0.2, 0.3, 0.5), c(17, 18, 19), "locus2",
maleMutationModel = "Equal", maleMutationRate = 0.005)
myloci <- list(locus1, locus2)
datamatrix <- data.frame(locus1.1=c("A", "A"), locus1.2=c("B", "B"),
locus2.1=c(17, 19), locus2.2=c(18, 18))
rownames(datamatrix) <- persons
result <- FamiliasPosterior(mypedigrees, myloci, datamatrix)
#Example 5: User-specified mutation matrices
persons <- c("son", "mother", "AF")
sex <- c("male", "female", "male")
ped1 <- FamiliasPedigree(id=persons, dadid=c(NA, NA, NA), momid=c("mother", NA, NA), sex=sex)
ped2 <- FamiliasPedigree(id=persons, dadid=c("AF", NA, NA), momid=c("mother", NA, NA), sex=sex)
mypedigrees <- list(unrelated = ped1, isFather=ped2)
locus1 <- FamiliasLocus(c(0.1, 0.2, 0.3, 0.4), c("A", "B", "C", "D"), "locus1",
maleMutationModel = "Custom", maleMutationMatrix = matrix(
c(0.99, 0.005, 0.003, 0.002,
0.004, 0.99, 0.004, 0.002,
0.002, 0.004, 0.99, 0.004,
0.002, 0.003, 0.005, 0.99),
4, 4, byrow=TRUE), femaleMutationModel = "Custom", femaleMutationMatrix = matrix(
c(0.99, 0.005, 0.003, 0.002,
0.004, 0.99, 0.004, 0.002,
0.002, 0.004, 0.99, 0.004,
0.002, 0.003, 0.005, 0.99),
4, 4, byrow=TRUE))
datamatrix <- data.frame(locus1.1=c("A", "A", "C"), locus1.2=c("B", "A", "C"))
rownames(datamatrix) <- persons
result <- FamiliasPosterior(mypedigrees, locus1, datamatrix)
#Example 6: Using kinship:
persons <- c("AF","mother", "daughter")
ped1=swapSex(nuclearPed(1),3)
locus1=marker(ped1,1,c(1,1),2,c(2,2),3,c(1,2))
ped1=addMarker(ped1,locus1)
plot(ped1,marker=1,id.labels=persons,main="ped1") #plot function of paramlink
ped1=as.data.frame(ped1)
foo=ConvertPed(ped1,persons)
ped1.c=foo$ped
datamatrix=foo$datamatrix
locus1 <- FamiliasLocus(frequencies=c(0.1, 0.2, 0.3, 0.4), allelenames= 1:4, name="locus1")
FamiliasPosterior(ped1.c, locus1, datamatrix) #likelihood=0.1^2*0.2^2=4e-04
th=0.03;FamiliasPosterior(ped1.c, locus1, datamatrix,kinship=th) #Formula below
((th+(1-th)*0.1)*(1-th)*0.1*(th*0.2+(1-th)*0.2^2))/((1+2*th)*(1+th)) #OK
# Example 7: Exercise S11 Silent allele
# See http://arken.umb.no/~theg/alcala/familiasExercises.pdf
ped1=nuclearPed(1)
persons <- c("AF","mother", "child")
locus1=marker(ped1,1,c("B","B"),2,c("A","A"),3,c("A","A"),alleles=c("A","B","C"))
ped1=as.data.frame(addMarker(ped1,locus1))
foo=ConvertPed(ped1,persons)
ped1.c=foo$ped
datamatrix=foo$datamatrix
ped2=nuclearPed(1)
ped2=relabel(ped2,old=c(1,2,3),new=c(4,2,3))
AF=singleton(1)
ped2=rbind(as.data.frame(AF),as.data.frame(ped2))
persons <- c("AF","mother", "child","TF")
ped2.c=ConvertPed(ped2,persons)$ped
mypedigrees=list(ped1.c,ped2.c)
locus1 <- FamiliasLocus(frequencies=c(0.1, 0.1,0.75, 0.05),
allelenames= c("A","B","C", "silent"), name="locus1")
result=FamiliasPosterior(mypedigrees, locus1, datamatrix, ref=2) #LR=1.36 OK
Example output
Loading required package: kinship2
Loading required package: Matrix
Loading required package: quadprog
Loading required package: paramlink
Loading required package: Rsolnp
Did not plot the following people: AF
Did not plot the following people: AF
Mother: Creating new individual with ID 5
Mother: Creating new individual with ID 5
$posterior
[1] 1
$prior
[1] 1
$LR
[1] 1
$LRperMarker
[,1]
locus1 1
$likelihoods
[1] 4e-04
$likelihoodsPerSystem
[,1]
locus1 4e-04
$posterior
[1] 1
$prior
[1] 1
$LR
[1] 1
$LRperMarker
[,1]
locus1 1
$likelihoods
[1] 0.0005054875
$likelihoodsPerSystem
[,1]
locus1 0.0005054875
[1] 0.0005054875
Familias documentation built on May 2, 2019, 6:33 a.m.
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Description Usage Arguments Value Author(s) Examples
Description
The calculations of the windows version of Familias 2.0 are made available in an R environment.
Usage
1 2 | FamiliasPosterior(pedigrees, loci, datamatrix, prior, ref = 1,
kinship = 0, simplifyMutations = FALSE)
|
Arguments
pedigrees |
An object of type 'FamiliasPedigree' or 'pedigree', or a list of such objects |
loci |
A FamiliasLocus object or a list of such objects |
datamatrix |
A data frame. The row names must be the names of the persons you have data for. The columns contain the alleles, two columns for each marker, in the same order used in the loci list. |
prior |
A vector of non-negative probabilities summing to 1. As default a flat prior is used, with all values equal. |
ref |
The index in the list of pedigrees of the pedigree which should be used as reference when computing likelihood ratios. The default value is 1. |
kinship |
A real in [0,1], commonly denoted theta in forensics included to model sub-population effects as departures from Hardy-Weinberg equilibrium. The default value is zero. |
simplifyMutations |
In pedigrees with several generations multistep mutations may happen. If the probability of mutating to an allele depends on which allele it mutates from, exact likelihood computations must keep track of all the possible values of mutated alleles in such multistage mutations, and this may slow down computations considerably. Instead, one may use in computations for multistage mutations a single allele that is not among those observed in the data. When this approach gives exact results it is always used; in other cases one may choose to use it as an approximation by setting simplifyMutations to TRUE. The properties of the extra allele is the weighted average (by population frequencies) of the alleles not observed in the data. |
Value
posterior |
probabilities for each pedigree |
prior |
prior returned for convenience and backward compatibility |
LR |
Likelihood ratios using the pedigree indicated with the ref parameter (default value 1) as basis |
LRperMarker |
Likelihood ratios per marker using the pedigree indicated with the ref parameter (default value 1) as basis |
likelihoods |
for each pedigree |
likelihoodsPerSystem |
likelihoods for each locus and each pedigree |
Author(s)
Petter Mostad <mostad@chalmers.se> and Thore Egeland <Thore.Egeland@gmail.com>
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 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 | #Example 1
#Data is available for "mother", "daughter" and "AF" for two loci (systems).
#Three pedigrees are defined, with "mother" being the mother of "daughter"
#in all cases. "AF" may be the father (ped1), unrelated (ped1) or
#uncle (ped3). The posterior probabilities for the pedigrees are calculated
#and likelihoods are also given so that
#likelihood ratios can be computed. Compared to the windows version of Familias 2.0,
#it is easy to plot pedigrees and define arbitrary priors for the three alternative pedigrees.
#The below implementation uses the R package kinship2 to define and plot pedigrees
persons <- c("mother", "daughter", "AF")
ped1 <- FamiliasPedigree(id=persons, dadid=c(NA, "AF", NA), momid=c(NA, "mother", NA),
sex=c("female", "female", "male"))
ped2 <- FamiliasPedigree(id=c(persons, "TF"), dadid=c(NA, "TF", NA, NA),
momid=c(NA, "mother", NA, NA),
sex=c("female", "female", "male", "male"))
ped3 <- FamiliasPedigree(id=c(persons, "TF", "gf", "gm"), dadid = c(NA, "TF", "gf", "gf", NA, NA),
momid=c(NA, "mother", "gm", "gm", NA, NA),
sex=c("female", "female", "male", "male", "male", "female"))
par(mfrow=c(3,1))
plot(ped1);title("ped1, i.e., AF is father")
plot(ped2);title("ped2, i.e., AF is unrelated")
plot(ped3);title("ped3, i.e., AF is uncle")
par(mfrow=c(1,1))
mypedigrees <- list(isFather = ped1, unrelated=ped2, isUncle = ped3)
locus1 <- FamiliasLocus(frequencies=c(0.1, 0.2, 0.3, 0.4),
allelenames= c("A", "B", "C", "D"), name="locus1")
locus2 <- FamiliasLocus(c(0.2, 0.3, 0.5), c(17, 18, 19), "loc2", femaleMutationRate = 0.05)
myloci <- list(locus1, locus2)
datamatrix <- data.frame(locus1.1=c("A", "A", "A"), locus1.2=c("B", "B", "C"),
locus2.1=c(17, 19, 19), locus2.2=c(18, 18, 18))
rownames(datamatrix) <- persons
result=FamiliasPosterior(mypedigrees, myloci, datamatrix,ref=2)
#Example 2. Example 1 continued.
#Below commands (from scratch) are based on paramlink to prepare pedigrees
persons <- c("AF","mother", "daughter")
ped1=swapSex(nuclearPed(1),3)
ped1=as.data.frame(ped1)
ped1.c=ConvertPed(ped1,persons)$ped
plot(ped1.c,main="ped1")
ped1=swapSex(nuclearPed(1),3)
AF=singleton(4)
ped2=rbind(as.data.frame(ped1),as.data.frame(AF))
persons <- c("TF","mother", "daughter","AF")
ped2.c=ConvertPed(ped2,persons)$ped
plot(ped2.c,main="ped2")
ped=nuclearPed(2)
ped=addOffspring(ped,father=4,noff=1,sex=2)
persons=c("gf","gm","AF","TF","mother","daughter")
plot(ped,id.labels=persons,main="ped3")
ped=as.data.frame(ped)
ped3.c=ConvertPed(ped,persons)$ped
mypedigrees <- list(isFather = ped1.c, unrelated=ped2.c, isUncle = ped3.c)
locus1 <- FamiliasLocus(frequencies=c(0.1, 0.2, 0.3, 0.4),
allelenames= c("A", "B", "C", "D"), name="locus1")
locus2 <- FamiliasLocus(c(0.2, 0.3, 0.5), c(17, 18, 19), "loc2",
femaleMutationRate = 0.05)
myloci <- list(locus1, locus2)
datamatrix <- data.frame(locus1.1=c("A", "A", "A"), locus1.2=c("B", "B", "C"),
locus2.1=c(17, 19, 19), locus2.2=c(18, 18, 18))
rownames(datamatrix) <- c("mother", "daughter", "AF")
result=FamiliasPosterior(mypedigrees, myloci, datamatrix, ref=2)
#Example 3. Example 2 continued
#Below commands (from scratch) are based on paramlink to prepare pedigrees and datamatrix.
#The handling of datamatrix is artificial in *this* example; alternatively genotypes can be provided
#in other, perhaps more natural ways.
persons <- c("AF","mother", "daughter")
#Names in paramlink are integers 1:length(persons)={1,2,3}
ped1=swapSex(nuclearPed(1),3)
dat <- data.frame(locus1.1=c("A", "A", "A"), locus1.2=c("C","B", "B"),
locus2.1=c(19,17, 19), locus2.2=c(18, 18, 18)) #Consistent with persons above
ped1=cbind(as.data.frame(ped1),dat)
foo=ConvertPed(ped1,persons)
ped1.c=foo$ped
datamatrix=foo$datamatrix
ped1=swapSex(nuclearPed(1),3)
AF=singleton(4)
ped2=rbind(as.data.frame(ped1),as.data.frame(AF))
persons <- c("TF","mother", "daughter","AF")
ped2.c=ConvertPed(ped2,persons)$ped
ped=nuclearPed(2)
ped=addOffspring(ped,father=4,noff=1,sex=2)
persons=c("gf","gm","AF","TF","mother","daughter")
ped=as.data.frame(ped)
ped3.c=ConvertPed(ped,persons)$ped
mypedigrees <- list(isFather = ped1.c, unrelated=ped2.c, isUncle = ped3.c)
locus1 <- FamiliasLocus(frequencies=c(0.1, 0.2, 0.3, 0.4),
allelenames= c("A", "B", "C", "D"), name="locus1")
locus2 <- FamiliasLocus(c(0.2, 0.3, 0.5), c(17, 18, 19), "loc2", femaleMutationRate = 0.05)
myloci <- list(locus1, locus2)
result=FamiliasPosterior(mypedigrees, myloci, datamatrix, ref=2)
#Example 4: Using FamiliasPedigree
persons <- c("person", "AF")
sex <- c("male", "male")
ped1 <- FamiliasPedigree(id=persons, dadid=c(NA, NA), momid=c(NA, NA), sex=sex)
ped2 <- FamiliasPedigree(id=persons, dadid=c("AF", NA), momid=c(NA, NA), sex=sex)
mypedigrees <- list(unrelated = ped1, isFather=ped2)
locus1 <- FamiliasLocus(c(0.1, 0.2, 0.3, 0.4), c("A", "B", "C", "D"), "locus1",
maleMutationModel = "Equal", maleMutationRate = 0.005)
locus2 <- FamiliasLocus(c(0.2, 0.3, 0.5), c(17, 18, 19), "locus2",
maleMutationModel = "Equal", maleMutationRate = 0.005)
myloci <- list(locus1, locus2)
datamatrix <- data.frame(locus1.1=c("A", "A"), locus1.2=c("B", "B"),
locus2.1=c(17, 19), locus2.2=c(18, 18))
rownames(datamatrix) <- persons
result <- FamiliasPosterior(mypedigrees, myloci, datamatrix)
#Example 5: User-specified mutation matrices
persons <- c("son", "mother", "AF")
sex <- c("male", "female", "male")
ped1 <- FamiliasPedigree(id=persons, dadid=c(NA, NA, NA), momid=c("mother", NA, NA), sex=sex)
ped2 <- FamiliasPedigree(id=persons, dadid=c("AF", NA, NA), momid=c("mother", NA, NA), sex=sex)
mypedigrees <- list(unrelated = ped1, isFather=ped2)
locus1 <- FamiliasLocus(c(0.1, 0.2, 0.3, 0.4), c("A", "B", "C", "D"), "locus1",
maleMutationModel = "Custom", maleMutationMatrix = matrix(
c(0.99, 0.005, 0.003, 0.002,
0.004, 0.99, 0.004, 0.002,
0.002, 0.004, 0.99, 0.004,
0.002, 0.003, 0.005, 0.99),
4, 4, byrow=TRUE), femaleMutationModel = "Custom", femaleMutationMatrix = matrix(
c(0.99, 0.005, 0.003, 0.002,
0.004, 0.99, 0.004, 0.002,
0.002, 0.004, 0.99, 0.004,
0.002, 0.003, 0.005, 0.99),
4, 4, byrow=TRUE))
datamatrix <- data.frame(locus1.1=c("A", "A", "C"), locus1.2=c("B", "A", "C"))
rownames(datamatrix) <- persons
result <- FamiliasPosterior(mypedigrees, locus1, datamatrix)
#Example 6: Using kinship:
persons <- c("AF","mother", "daughter")
ped1=swapSex(nuclearPed(1),3)
locus1=marker(ped1,1,c(1,1),2,c(2,2),3,c(1,2))
ped1=addMarker(ped1,locus1)
plot(ped1,marker=1,id.labels=persons,main="ped1") #plot function of paramlink
ped1=as.data.frame(ped1)
foo=ConvertPed(ped1,persons)
ped1.c=foo$ped
datamatrix=foo$datamatrix
locus1 <- FamiliasLocus(frequencies=c(0.1, 0.2, 0.3, 0.4), allelenames= 1:4, name="locus1")
FamiliasPosterior(ped1.c, locus1, datamatrix) #likelihood=0.1^2*0.2^2=4e-04
th=0.03;FamiliasPosterior(ped1.c, locus1, datamatrix,kinship=th) #Formula below
((th+(1-th)*0.1)*(1-th)*0.1*(th*0.2+(1-th)*0.2^2))/((1+2*th)*(1+th)) #OK
# Example 7: Exercise S11 Silent allele
# See http://arken.umb.no/~theg/alcala/familiasExercises.pdf
ped1=nuclearPed(1)
persons <- c("AF","mother", "child")
locus1=marker(ped1,1,c("B","B"),2,c("A","A"),3,c("A","A"),alleles=c("A","B","C"))
ped1=as.data.frame(addMarker(ped1,locus1))
foo=ConvertPed(ped1,persons)
ped1.c=foo$ped
datamatrix=foo$datamatrix
ped2=nuclearPed(1)
ped2=relabel(ped2,old=c(1,2,3),new=c(4,2,3))
AF=singleton(1)
ped2=rbind(as.data.frame(AF),as.data.frame(ped2))
persons <- c("AF","mother", "child","TF")
ped2.c=ConvertPed(ped2,persons)$ped
mypedigrees=list(ped1.c,ped2.c)
locus1 <- FamiliasLocus(frequencies=c(0.1, 0.1,0.75, 0.05),
allelenames= c("A","B","C", "silent"), name="locus1")
result=FamiliasPosterior(mypedigrees, locus1, datamatrix, ref=2) #LR=1.36 OK
|
Example output
Loading required package: kinship2
Loading required package: Matrix
Loading required package: quadprog
Loading required package: paramlink
Loading required package: Rsolnp
Did not plot the following people: AF
Did not plot the following people: AF
Mother: Creating new individual with ID 5
Mother: Creating new individual with ID 5
$posterior
[1] 1
$prior
[1] 1
$LR
[1] 1
$LRperMarker
[,1]
locus1 1
$likelihoods
[1] 4e-04
$likelihoodsPerSystem
[,1]
locus1 4e-04
$posterior
[1] 1
$prior
[1] 1
$LR
[1] 1
$LRperMarker
[,1]
locus1 1
$likelihoods
[1] 0.0005054875
$likelihoodsPerSystem
[,1]
locus1 0.0005054875
[1] 0.0005054875
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