Type:	Package
Title:	Interpretation of Forensic DNA Mixtures
Version:	4.3.3
Date:	2025-05-02
Maintainer:	Maarten Kruijver <maarten.kruijver@esr.cri.nz>
Depends:	methods, tcltk,tcltk2,tkrplot
Description:	Statistical methods and simulation tools for the interpretation of forensic DNA mixtures. The methods implemented are described in Haned et al. (2011) <doi:10.1111/j.1556-4029.2010.01550.x>, Haned et al. (2012) <doi:10.1016/j.fsigen.2012.11.002> and Gill & Haned (2013) <doi:10.1016/j.fsigen.2012.08.008>.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
LazyLoad:	yes
SystemRequirements:	Tcl/Tk package TkTable
Collate:	classes_definitions.R classes_constructors.R accessors.R simufreqD.R simupopD.R AuxFunc.R changepop.R PE.R likelihood.R likestim.R mincontri.R A2.simu.R A3.simu.R A4.simu.R mastermix.R N2Exact.R N2error.R simMixSNP.R wrapdataL.R simPCR2.R simPCR2TK.R PV.R Hbsimu.R LR.R LRmixTK.R
NeedsCompilation:	yes
Packaged:	2025-05-07 15:33:43 UTC; mkruijver
Author:	Hinda Haned [aut], Oyvind Bleka [ctb], Maarten Kruijver [cre]
Repository:	CRAN
Date/Publication:	2025-05-09 10:00:02 UTC

The forensim package

Description

forensim is dedicated to the interpretation of forensic DNA mixtures through statistical methods. It relies on three S4 classes that facilitate the manipulation and the storage of genetic data produced in forensic casework: tabfreq, simugeno and simumix.
tabfreq objects are used to store allele frequencies, simugeno objects are used to store genotypes and simumix objects are used to store DNA mixtures.
For more information about these classes type 'class ?tabfreq', 'class ?simugeno' and 'class ?simumix'.

Author(s)

Hinda Haned <hinda@owlsandarrows.nl>

A Tcl/Tk graphical user interface for simple DNA mixtures resolution using allele peak heights or areas information when two alleles are observed at a given locus

Description

The A2.simu function launches a Tcl/Tk graphical interface with functionalities devoted to two-person DNA mixtures resolution, when two alleles are observed at a given locus.

Usage

A2.simu()

Details

When two alleles are observed at a given locus in the DNA stain, seven genotype combinations are possible for the two contributors: (AA,AB), (AB,AB), (AA,BB), (AB,AA), (BB,AA), (AB,BB) and (BB,AB), where A and B are the two observed alleles (in ascending order of molecular weight). Having previously obtained an estimation for the mixture proportion, it is possible to reduce the number of possible genotype combinations by keeping those only supported by the observed data. This is achieved by computing the sum of square differences between the expected allelic ratio and the observed allelic ratio, for all possible mixture combinations. The likelihood of peak heights (or areas), given the combination of genotypes, is high if the residuals are low. Genotype combinations are thus selected according to the peak heights with the highest likelihoods.

The A2.simu() function launches a dialog window with three buttons:
-Plot simulations: plot of the residuals of each possible genotype combination for varying values of the mixture proportion across the interval [0.1, 0.9]. The observed mixture proportion is also reported on the plot.
-Simulation details: a matrix containing the simulation results. Simulation details and genotype combinations with the lowest residuals can be saved as a text file by clicking the “Save" button. It is also possible to choose specific paths and names for the save files.
-Genotypes filter: a matrix giving the mixture proportion conditional on the genotype combination. This conditional mixture proportion helps filter the most plausible genotypes among the seven possible combinations. The matrix can be saved as a text file by clicking the “Save" button. It is also possible to choose a specific path and a name for the save file.

Value

No return value, called to show GUI.

Note

-Linux users may have to download the libtktable package to their system before using the A2.simu function. This is due to the Tktable widget, used in forensim, which is not (always) downloaded with the Tcl/Tk package.
-For the computational details, please see forensim tutorial at http://forensim.r-forge.r-project.org/misc/forensim-tutorial.pdf.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

References

Gill P, Sparkes P, Pinchin R, Clayton, Whitaker J, Buckleton J. Interpreting simple STR mixtures using allele peak areas. Forensic Sci Int 1998;91:41-53.

See Also

A3.simu: the three-allele model, and A4.simu: the four-allele model

Examples

A2.simu()

A Tcl/Tk graphical user interface for simple DNA mixtures resolution using allele peak heights or areas when three alleles are observed at a given locus

Description

The A3.simu function launches a Tcl/Tk graphical interface with functionalities devoted to two-person DNA mixtures resolution, when three alleles are observed at a given locus.

Usage

A3.simu()

Details

When three alleles are observed at a given locus in the DNA stain, twelve genotype combinations are possible for the two contributors: (AA,BC), (BB,AC), (CC,AB), (AB,AC), (BC,AC), (AB,BC), (BC,AA), (AC,BB), (AB,CC), (AC,AB), (AC,BC) and (BC,AB) where A, B and C are the three observed alleles (in ascending order of molecular weights). Having previously obtained an estimation for the mixture proportion, it is possible to reduce the number of possible genotype combinations by keeping those only supported by the observed data. This is achieved by computing the sum of square differences between the expected allelic ratio and the observed allelic ratio, for all possible mixture combinations. The likelihood of peak heights (or areas), given the combination of genotypes, is high if the residuals are low. Genotype combinations are thus selected according to the peak heights with the highest likelihoods.

The A3.simu() function launches a dialog window with three buttons:
-Plot simulations: plot of the residuals of each possible genotype combination for varying values of the mixture proportion across the interval [0.1, 0.9]. The observed mixture proportion is also reported on the plot.
-Simulation details: a matrix containing the simulation results. Simulation details and genotype combinations with the lowest residuals can be saved as a text file by clicking the “Save" button. It is also possible to choose specific paths and names for the save files.
-Genotypes filter: a matrix giving the mixture proportion conditional on the genotype combination. This conditional mixture proportion helps filter the most plausible genotypes among the twelve possible combinations. The matrix can be saved as a text file by clicking the “Save" button. It is also possible to choose a specific path and a name for the save file.

Value

No return value, called to show GUI.

Note

-Linux users may have to download the libtktable package to their system before using the A3.simu function. This is due to the Tktable widget, used in forensim, which is not (always) downloaded with the Tcl/Tk package.
-For the computational details, please see forensim tutorial at http://forensim.r-forge.r-project.org/misc/forensim-tutorial.pdf.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

References

Gill P, Sparkes P, Pinchin R, Clayton, Whitaker J, Buckleton J. Interpreting simple STR mixtures using allele peak areas. Forensic Sci Int 1998;91:41-53.

See Also

A2.simu: the two-allele model, and A4.simu: the four-allele model

Examples

A3.simu()

A Tcl/Tk graphical user interface for simple DNA mixtures resolution using allele peak heights or areas when four alleles are observed at a given locus

Description

The A4.simu function launches a Tcl/Tk graphical interface with functionalities devoted to two-person DNA mixtures resolution, when four alleles are observed at a given locus.

Usage

A4.simu()

Details

When four alleles are observed at a given locus in the DNA stain, six genotype combinations are possible for the two contributors: (AB,CD),(AC,BD),(AD,BC),(BC,AD),(BD,AC) and (CD,AB) where A, B, C and D are the four observed alleles (in ascending order of molecular weights). Having previously obtained an estimation for the mixture proportion, it is possible to reduce the number of possible genotype combinations by keeping those only supported by the observed data. This is achieved by computing the sum of square differences between the expected allelic ratio and the observed allelic ratio, for all possible mixture combinations. The likelihood of peak heights (or areas), given the combination of genotypes, is high if the residuals are low. Genotype combinations are thus selected according to the peak heights with the highest likelihoods.

The A4.simu() function launches a dialog window with three buttons:
-Plot simulations: plot of the residuals of each possible genotype combination for varying values of the mixture proportion across the interval [0.1, 0.9]. The observed mixture proportion is also reported on the plot.
-Simulation details: a matrix containing the simulation results. Simulation details and genotype combinations with the lowest residuals can be saved as a text file by clicking the “Save" button. It is also possible to choose specific paths and names for the save files.
-Genotypes filter: a matrix giving the mixture proportion conditional on the genotype combination. This conditional mixture proportion helps filter the most plausible genotypes among the six possible combinations. The matrix can be saved as a text file by clicking the “Save" button. It is also possible to choose a specific path and a name for the save file.

Value

No return value, called to show GUI.

Note

-Linux users may have to download the libtktable package to their system before using the A4.simu function. This is due to the Tktable widget, used in forensim, which is not (always) downloaded with the Tcl/Tk package.
-For the computational details, please see forensim tutorial at http://forensim.r-forge.r-project.org/misc/forensim-tutorial.pdf.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

References

Gill P, Sparkes P, Pinchin R, Clayton, Whitaker J, Buckleton J. Interpreting simple STR mixtures using allele peak areas. Forensic Sci Int 1998;91:41-53.

See Also

A2.simu: the two-allele model, and A3.simu: the three-allele model

Examples

A4.simu()

Accessors for forensim objects

Description

Accessors for forensim objects: simugeno, simumix and tabfreq. "$" and "$<-" are used to access the slots of an object, they are equivalent to "@" and "@<-".

Value

A simugeno, a simumix or a tabfreq object.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

Examples

data(strusa)
class(strusa)

strusa@pop.names
#equivalent
strusa$pop.names

The number of all possible combinations of m elements among n with repetitions

Description

The number of all possible combinations of m elements among n with repetitions.

Usage

Cmn(m, n)

Arguments

Details

There are (n+m-1)!/(m!(n-1)!) ways to combine m elements among n with repetitions.

Value

Numeric with the number of combinations.

Note

Cmn was implemented as an auxiliary function for the dataL function which computes the likelihood of the observed alleles in a mixed DNA stain conditional on the number of contributors.

Author(s)

Hinda Haned <hinda@owlsandarrows.nl>

See Also

comb for all possible combinations of m elements among n with repetitions

Examples

Cmn(2,3)
comb(2,3)

A Tcl/Tk simulator of the heterozygous balance

Description

Hbsimu is a user-friendly graphical interface simulating the heterozygous balance of heterozygous profiles generated according to the simulation model described in Gill et al. (2005)

Usage

Hbsimu()

Value

No return value, called to show GUI.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

References

Gill P, Curran J and Elliot K. A graphical simulation model of the entire DNA process associated with the analysis of short tandem repeat loci. Nucleic Acids Research 2005, 33(2): 632-643.

Examples

Hbsimu()

Likelihood ratio for DNA evidence interpretation (2): a sophistacted version of function LR()

Description

LR Allows the calculation of likelihood ratios for a piece of DNA evidence, for any number of replicates, any number of contributors, and when drop-in and drop-out are possible.

Usage

LR(Repliste, Tp, Td, Vp, Vd, xp, xd, theta, prDHet, prDHom, prC, freq)

Arguments

Value

List with named elements for numerator likelihood (num), denominator likelihood (deno) and likelihood ratio (LR)

Author(s)

Hinda Haned hinda@owlsandarrows.nl

References

Gill, P.; Kirkham, A. & Curran, J. LoComatioN: A software tool for the analysis of low copy number DNA profiles Forensic Science International, 2007, 166(2-3), 128-138

Curran, J. M.; Gill, P. & Bill, M. R. Interpretation of repeat measurement DNA evidence allowing for multiple contributors and population substructure Forensic Science International, 2005, 148, 47-53

See Also

LRmixTK

Examples

#load allele frequencies
library(forensim)
data(ngm)
#create vector of allele frequencies
d10<-ngm$tab$D10
# heterozygote dropout probability (resp. homozygote) is set to 0.2 for all
# contributors (0.04 for homozygotes)
LR(Repliste=c(12,13,14),Tp=c(12,13),Td=0,Vp=0,Vd=0,xd=2,xp=1,theta=0,prDHet=c(0.2,0.2),
prDHom=c(0.04,0.04),prC=0,freq=d10)

GUI for the LR function

Description

User-friendly graphical user interface for the LR calculator LR.

Usage

LRmixTK(verbose)

Arguments

Value

No return value, called to show GUI.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

Calculates exact allele distribution for 2 contributors

Description

The distribution of N, the number of alleles showing is calculated exactly assuming 2 contributors. Theta-correction is not implemented. The function may be used to check accuracy of simulations and indicate required number of simulations for one example.

Usage

N2Exact(p)

Arguments

Value

Returns(P(N=i) for i=1,2,3,4

Author(s)

Thore Egeland Thore.Egeland@medisin.uio.no

Examples

#Distribution for a marker with 20 alles of equal frequency
N2Exact(p=rep(0.05,20))

Calculates exact error for maximum allele count for two markers

Description

The maximum allele count principle leads to wrong conclusion for two contributors if only a maximum of one or two alleles is seen. This probability of error is calculated.

Usage

N2error(dat)

Arguments

Value

The probability of error is returned.

Author(s)

Thore Egeland Thore.Egeland@medisin.uio.no

Examples

#Example based on 15 markers of Tu data
library(forensim)
data(Tu)
N2error(Tu)

The random man exclusion probability

Description

Computes the random man exclusion probability of a mixture stored in a simumix object

Usage

PE(mix, freq, refpop = NULL, theta = 0, byloc = FALSE)

Arguments

Details

PE gives the exclusion probability at a locus, or at several loci when conditions for Hardy Weinberg are met. If this condition is not met in the population, than a value for theta must be supplied to take into account dependencies between alleles. The formula of the exclusion probability that allows taking into account departure from Hardy Weinberg proportions due to population subdivision was provided by Bruce Weir, please see the references section.

Value

Numeric vector with exclusion probability (by locus if byloc = TRUE).

Author(s)

Hinda Haned <hinda@owlsandarrows.nl>

References

Clayton T, Buckleton JS. Mixtures. In: Buckleton JS, Triggs CM, Walsh SJ, editors. Forensic DNA Interpretation. CRC Press 2005;217-74

Examples

data(strusa)
geno1<-simugeno(strusa,n=c(0,0,100))
mix2 <-simumix(geno1,ncontri=c(0,0,2))
PE(mix2,strusa,"Hisp",byloc=TRUE)

Predictive value of the maximum likelihood estimator of the number of contributors to a DNA mixture

Description

The PV function implements the predictive value of the maximum likelihood estimator of the number of contributors to a DNA mixture

Usage

PV(mat, prior)

Arguments

Value

Vector of the predictive values

Author(s)

Hinda Haned <hinda@owlsandarrows.nl>

References

Haned H., Pene L., Sauvage F., Pontier D., The predictive value of the maximum likelihood estimator of the number of contributors to a DNA mixture, submitted, 2010.

See Also

maximum likelihood estimator likestim

Examples

# the following examples reproduce some of the calculations appearing
# in the article cited above, for illustrative purpose, the maximum 
#number of contributors is set here to 5 
#matcondi: Table 2 in Haned et al. (2010)
matcondi<-matrix(c(1,rep(0,4),0,0.998,0.005,0,0,0,0.002,0.937,0.067,0,0,0,0.058,
0.805,0.131,rep(0,3),0.127,0.662,rep(0,3),0.001,0.207),ncol=6)
#prior defined by a forensic expert (Table 3 in Haned et al., 2010)
prior1<-c(0.45,0.04,0.30,0.15,0.06)
#uniform prior, for each mixture type, the probability of occurrence is 1/5,
#5 being the threshold for the number of contributors
prior2<-c(rep(1/5,5))
#predictive values for prior1
PV(matcondi,prior1)
#for prior2
PV(matcondi, prior2)

Allele frequencies of 15 autosomal short tandem repeats loci on Chinese Tu ethnic minority group

Description

Population genetic analysis of 15 STR loci of Chinese Tu ethnic minority group.

Usage

data(Tu)

Format

a data frame presented in the format of the Journal of Forensic Sciences for genetic data: allele names are given in the first column, and frequencies for a given allele are read in rows for the different markers. When a given allele is not observed, value is coded NA (rather than "-" in the original format).

Details

CSF1PO, FGA, TH01, TPOX, vWA, D3S1358, D5S818, D7S820, D8S1179, D13S317, D16S539, D18S51 and D21S11, belong to the core CODIS loci used in the US, whereas D2S1338 and D19S433 belong to the European core loci.

References

Zhu B, Yan J, Shen C, Li T, Li Y, Yu X, Xiong X, Muf H, Huang Y, Deng Y. (2008). Population genetic analysis of 15 STR loci of Chinese Tu ethnic minority group. Forensic Sci Int; 174: 255-258.

Examples

data(Tu)
tabfreq(Tu)

Function to change population-related information in forensim objects

Description

The changepop function changes population-related information in tabfreq, simugeno and simumix objects

Usage

changepop(obj, oldpop, newpop)

Arguments

Value

a forensim object where the slots containing population-related information have been modified

Author(s)

Hinda Haned hinda@owlsandarrows.nl

Examples

data(strveneto)
tab1 <- simugeno(strveneto,n=100)
tab2 <- changepop(tab1,"Veneto","VENE")
tab1$pop.names
tab2$pop.names

Generate all possible combinations of m elements among n with repetitions

Description

Generate all possible combinations of m elements among n with repetitions.

Usage

comb(m, n)

Arguments

Details

There are (n+m-1)!/(m!(n-1)!) ways to combine m elements among n with repetitions, combn generates all these possible combinations.

Value

A matrix of (n+m-1)!/(m!(n-1)!) rows, and n columns, each row is a possible combination of m elements among n .

Author(s)

Hinda Haned hinda@owlsandarrows.nl

See Also

Cmn for the calculation of the number of all possible combinations of m elements among n with repetitions

Examples

#combine 2 objects among 3 with repetitions
Cmn(2,3)
comb(2,3)

Generic formula of the likelihood of the observed alleles in a mixture conditional on the number of contributors for a specific locus

Description

The function dataL gives the likelihood of a set of alleles observed at a specific locus conditional on the number of contributors that gave these alleles. Calculation is based upon the frequencies of the observed alleles.

Usage

dataL(x = 1, p, theta = 0)

Arguments

Value

Numeric likelihood value.

Note

dataL function has several similarities with the Pevid.gen function of the forensic package which computes the probability of the DNA evidence, dataL implements a particular case of this probability. Please see https://cran.r-project.org/package=forensic

Author(s)

Hinda Haned hinda@owlsandarrows.nl

References

Haned H, Pene L, Lobry JR, Dufour AB, Pontier D. Estimating the number of contributors to forensic DNA mixtures: Does maximum likelihood perform better than maximum allele count? J Forensic Sci, accepted 2010.

Curran JM, Triggs CM, Buckleton J, Weir BS. Interpreting DNA Mixtures in Structured Populations. J Forensic Sci 1999;44(5): 987-995

See Also

lik.loc and lik for calculating the likelihood of a given simumix object

Examples

#likelihood of observing two alleles at frequencies 0.1 and 0.01 when the number of
#contributors is 2, in two cases:  theta=0 and theta=0.03
dataL(x=2,p=c(0.1,0.01), theta=0)
dataL(x=2,p=c(0.1,0.01), theta=0.03)

Finds the allele frequencies of a mixture from a tabfreq object

Description

The findfreq function finds the allele frequencies of a mixture stored in a simumix object, form a given tabfreq object. If the tabfreq object contains multiple populations, a reference population from which to extract the frequencies must be specified.

Usage

findfreq(mix, freq, refpop = NULL)

Arguments

Value

A list giving the allele frequencies for each locus.

Author(s)

Hinda Haned <hinda@owlsandarrows.nl>

See Also

simumix

Examples

data(strusa)
s2<-simumix(simugeno(strusa,n=c(0,2000,0)),ncontri=c(0,2,0))
findfreq(s2,strusa,refpop="Cauc")

Function to find the maximum of a vector and its position

Description

The findmax function finds the maximum of a vector and its position.

Usage

findmax(vec)

Arguments

Details

findmax finds the maximum value of a vector and its position.

Value

A matrix of two columns:
max the position of the maximum in vec
maxval the maximum

Note

findmax is an auxiliary function for the dataL function, used to compute the likelihood of the observed alleles in a mixed DNA stain given the number of contributors.

Author(s)

Hinda Haned <hinda@owlsandarrows.nl>

Examples

findmax(1:10)

Likelihood of the observed alleles at different loci in a DNA mixture conditional on the number of contributors to the mixture

Description

The lik function computes the likelihood of the observed alleles in a forensic DNA mixture, for a set of loci, conditional on the number of contributors to the mixture. The overall likelihood is computed as the product of loci likelihoods.

Usage

lik(x = 1, mix, freq, refpop = NULL, theta = NULL, loc=NULL)

Arguments

Details

lik computes the likelihood of the alleles observed at all loci conditional on the number of contributors. This function implements the general formula for the interpretation of DNA mixtures in case of population subdivision (Curran et al, 1999), in the particular case where all contributors are unknown and belong to the same subpopulation.
The likelihood for multiple loci is computed as the product of loci likelihoods.

Value

Numeric likelihood value.

Author(s)

Hinda Haned <hinda@owlsandarrows.nl>

References

Haned H, Pene L, Lobry JR, Dufour AB, Pontier D. Estimating the number of contributors to forensic DNA mixtures: Does maximum likelihood perform better than maximum allele count? J Forensic Sci, accepted 2010.
Curran JM, Triggs CM, Buckleton J, Weir BS. Interpreting DNA Mixtures in Structured Populations. J Forensic Sci 1999;44(5): 987-995

See Also

lik.loc for the likelihood per locus, likestim and likestim.loc for the estimation of the number of contributors to a DNA mixture through likelihood maximization

Examples

data(strusa)
#simulation of 1000 genotypes from the African American allele frequencies
gen<-simugeno(strusa,n=c(1000,0,0))
#3-person mixture
mix3<-simumix(gen,ncontri=c(3,0,0))
sapply(1:3, function(i) lik(x=i,mix3, strusa, refpop="Afri"))

Likelihood per locus of the observed alleles in a DNA mixture conditional on the number of contributors to the mixture

Description

The lik.loc function computes the likelihood of the observed data in a forensic DNA mixture, for each of the loci involved, conditional on the number of contributors to the mixture.

Usage

lik.loc(x = 1, mix, freq, refpop = NULL, theta = NULL, loc=NULL) 

Arguments

Details

lik.loc computes the likelihood per locus of the observed alleles. This function implements the general formula for the interpretation of DNA mixtures in case of subdivided populations (Curran et al, 1999), in the particular case where all contributors are unknown and belong to the same subpopulation.
The Fst coefficient given in the theta argument allows accounting for population subdivision when all contributors belong to the same subpopulation.

Value

The function lik.loc returns a vector, of length the number of loci in loc, giving the likelihood of the data for each locus.

Author(s)

Hinda Haned <hinda@owlsandarrows.nl>

References

Haned H, Pene L, Lobry JR, Dufour AB, Pontier D. Estimating the number of contributors to forensic DNA mixtures: Does maximum likelihood perform better than maximum allele count? J Forensic Sci, accepted 2010.

Curran JM, Triggs CM, Buckleton J, Weir BS. Interpreting DNA Mixtures in Structured Populations. J Forensic Sci 1999;44(5): 987-995

See Also

lik for the overall loci likelihood, likestim and likestim.loc for the estimation of the number of contributors to a DNA mixture through likelihood maximization

Examples

data(strusa)
#simulation of 1000 genotypes from the Caucasian allele frequencies
gen<-simugeno(strusa,n=c(0,100,0))

#4-person mixture
mix4 <- simumix(gen,ncontri=c(0,4,0))
lik.loc(x=2,mix4, strusa, refpop="Cauc")
lik.loc(x=2,mix4, strusa, refpop="Afri")
#You may also want to try:
#likestim(mix4,strusa,refpop="Cauc")

Likelihood of DNA evidence conditioned on a given hypothesis

Description

likEvid allows the calculation of likelihood for a piece of DNA evidence, for any number of replicates, any number of contributors, and when drop-in and drop-out are possible.

Usage

likEvid(Repliste, Tg, Vg, x, theta, prDHet, prDHom, prC, freq)

Arguments

Value

Numeric likelihood value.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

References

Gill, P.; Kirkham, A. & Curran, J. LoComatioN: A software tool for the analysis of low copy number DNA profiles Forensic Science International, 2007, 166(2-3), 128-138

Curran, J. M.; Gill, P. & Bill, M. R. Interpretation of repeat measurement DNA evidence allowing for multiple contributors and population substructure Forensic Science International, 2005, 148, 47-53

See Also

LRmixTK

Examples

#load allele frequencies
library(forensim)
data(ngm)
#create vector of allele frequencies
d10<-ngm$tab$D10
# evaluate the evidence under Hp; contributors are the suspect and one unknown,
# dropout probabilities for the suspect and the unknown are the same: 0.2 for heterozygotes,
# and 0.1 for homozygotes.
likEvid(Repliste=c(12,13,14),Tg=c(12,13),Vg=0,x=1,theta=0,prDHet=c(0.2,0.2),
prDHom=c(0.04,0.04),prC=0,
freq=d10)
#  evaluate the evidence under Hd; contributors are two unknown people, the dropout
# probabilities for the unknowns is kept the same under Hd
likEvid(Repliste=c(12,13,14),Tg=0,Vg=0,x=2,theta=0,prDHet=c(0.2,0.2),
prDHom=c(0.04,0.04),prC=0,freq=d10)

Maximum likelihood estimation of the number of contributors to a forensic DNA mixture for a set of loci

Description

The likestim function gives multiloci estimation of the number of contributors to a forensic DNA mixture using likelihood maximization.

Usage

likestim(mix, freq, refpop = NULL, theta = NULL, loc=NULL)

Arguments

Details

The number of contributors which maximizes the likelihood of the data observed in the mixture is searched in the discrete interval [1,6]. In most cases this interval is a plausible range for the number of contributors.

Value

A matrix of dimension 1 x 2, the first column, max, gives the maximum likelihood estimation of the number of contributors, the second column gives the corresponding likelihood value maxvalue.

Author(s)

Hinda Haned <hinda@owlsandarrows.nl>

References

Haned H, Pene L, Lobry JR, Dufour AB, Pontier D. Estimating the number of contributors to forensic DNA mixtures: Does maximum likelihood perform better than maximum allele count? J Forensic Sci, accepted 2010.

Egeland T, Dalen I, Mostad PF. Estimating the number of contributors to a DNA profile. Int J Legal Med 2003, 117: 271-275

Curran JM, Triggs CM, Buckleton J, Weir BS. Interpreting DNA Mixtures in Structured Populations. J Forensic Sci 1999, 44(5): 987-995

See Also

likestim.loc for maximum of likelihood estimations per locus

Examples

data(strusa)
#simulation of 1000 genotypes from the Hispanic allele frequencies
gen<-simugeno(strusa,n=c(0,0,100))
#4-person mixture
mix4 <- simumix(gen,ncontri=c(0,0,4))
likestim(mix4,strusa,refpop="Hisp")

Maximum likelihood estimation per locus of the number of contributors to forensic DNA mixtures.

Description

The likestim.loc function returns the estimation of the number of contributors, at each locus, obtained by maximizing the likelihood.

Usage

likestim.loc(mix, freq, refpop = NULL, theta = NULL, loc = NULL)

Arguments

Details

The number of contributors which maximizes the likelihood of the data observed in the mixture is searched in the discrete interval [1,6]. In most cases this interval is a plausible range for the number of contributors.

Value

A matrix of dimension loc x 2. The first colum, max, gives the maximum likelihood estimation of the number of contributors for each locus in row. The second column, maxvalue, gives the corresponding likelihood value.

Author(s)

Hinda Haned <hinda@owlsandarrows.nl>

References

Haned H, Pene L, Lobry JR, Dufour AB, Pontier D. Estimating the number of contributors to forensic DNA mixtures: Does maximum likelihood perform better than maximum allele count? J Forensic Sci, accepted 2010.

Egeland T , Dalen I, Mostad PF. Estimating the number of contributors to a DNA profile. Int J Legal Med 2003, 117: 271-275

Curran, JM , Triggs CM, Buckleton J , Weir BS. Interpreting DNA Mixtures in Structured Populations. J Forensic Sci 1999, 44(5): 987-995

See Also

likestim for multiloci estimations

Examples

data(strusa)
#simulation of 1000 genotypes from the Hispanic allele frequencies
gen<-simugeno(strusa,n=c(0,0,100))
#4-person mixture
mix4 <- simumix(gen,ncontri=c(0,0,4))
likestim.loc(mix4,strusa,refpop="Hisp")

A Tcl/Tk graphical user interface for simple DNA mixtures resolution using allele peak heights/ or areas information

Description

The mastermix function launches a Tcl/Tk graphical user interface dedicated to the resolution of two-person DNA mixtures using allele peak heights/ or areas information. mastermix is the implementation of a method developed by Gill et al (see the references section), and previously programmed into an Excel macro by Dr. Peter Gill.

Usage

mastermix()

Details

mastermix is a Tcl/Tk graphical user interface implementing a method developed by Gill et al (1998) for simple mixtures resolution, using allele peak heights or areas information.

This method searches through simulation the most likely combination(s) of the contributors' genotypes. Having previously obtained an estimation for the mixture proportion, it is possible to reduce the number of possible genotype combinations by keeping only those supported by the observed data. This is achieved by computing the sum of square differences between the expected allelic ratio and the observed allelic ratio, for all possible mixture combinations. The likelihood of peak heights (or areas), conditional on the combination of genotypes, is high if the residuals are low. Genotype combinations are thus selected according to the peak heights with the highest (conditioned) likelihoods.

mastermix offers a graphical representation of the simulation for three models:
-The two allele model: at a given locus, two alleles are observed in the DNA stain.
-The three allele model: at a given locus, three alleles are observed in the DNA stain.
-The four allele model: at a given locus, four alleles are observed in the DNA stain.

A left-click on each button launches a simulation dialog window for the corresponding model, while a right-click opens the corresponding help page.

Value

No return value, called to show GUI.

Note

-Each implemented model can either be launched using the mastermix interface, or the A2.simu, A3.simu and A4.simu functions, depending on the considered model.
-For the computational details, please see forensim tutorial at http://forensim.r-forge.r-project.org/misc/forensim-tutorial.pdf.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

References

Gill P, Sparkes P, Pinchin R, Clayton, Whitaker J, Buckleton J. Interpreting simple STR mixtures using allele peak areas. Forensic Sci Int 1998;91:41-5.

See Also

A2.simu, A3.simu and A4.simu

Examples

mastermix()

Minimum number of contributors required to explain a forensic DNA mixture

Description

mincontri gives the minimum number of contributors required to explain a forensic DNA mixture. This method is also known as the maximum allele count as it relies on the maximum number of alleles showed through all available loci

Usage

mincontri(mix, loc = NULL)

Arguments

Value

Integer with minium number of contibutors.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

See Also

likestim for the estimation of the number of contributors through likelihood maximization

Examples

data(strusa)
#simulation of 1000 genotypes from the African American allele frequencies
gen<-simugeno(strusa,n=c(1000,0,0))
#5-person mixture
mix5<-simumix(gen,ncontri=c(5,0,0))
#compare
likestim(mix5, strusa, refpop="Afri")
mincontri(mix5)

Handling of missing values in a data frame

Description

naomitab handles missing values (NA) in a data frame: it returns a list of the columns where NAs have been removed.

Usage

naomitab(tab)

Arguments

Value

Returns a list of length the number of columns in tab where each component is a column of tab, and the values are the corresponding rows where NAs have been removed.

Note

This function was designed to handle missing values in data frames in the format of the Journal of Forensic Sciences for population genetic data: allele names are given in the first column, and frequencies for a given allele are read in rows for different loci. When a given allele is not observed, the value is coded NA (originally coded "-" in the journal).

Author(s)

Hinda Haned <hinda@owlsandarrows.nl>

See Also

tabfreq

Examples

data(Tu)
naomitab(Tu)

Number of alleles in a mixture

Description

nball gives the number of alleles of a simumix object.

Usage

nball(mix, byloc = FALSE)

Arguments

Value

If byloc=TRUE, the number of alleles by locus; otherwise the sum.

Author(s)

Hinda Haned <hinda@owlsandarrows.nl>

See Also

simumix

Examples

data(strusa)
#simulating 100 genotypes with allele frequencies from the African American population
gaa<-simugeno(strusa,n=c(100,0,0))
#simulating a 4-person mixture
maa4<-simumix(gaa,ncontri=c(4,0,0))
nball(maa4,byloc=TRUE)

Allele frequencies for the new generation markers NGM, for the Caucasian US population

Description

Allele frequencies for 15 autosomal short tandem repeats loci in the American Caucasian population.

Usage

data(ngm)

References

Budowle, B.; Ge, J.; Chakraborty, R.; Eisenberg, A.; Green, R.; Mulero, J.; Lagace, R. & Hennessy, L. Population genetic analyses of the NGM STR loci International Journal of Legal Medicine, 2011, 1-9

Examples

library(forensim)
data(ngm)
boxplot(ngm$tab)

Allele frequencies for the new generation markers sgm, for the Norwegian population

Description

Allele frequencies for 10 autosomal short tandem repeats loci in the Norwegian population.

Usage

data(sgmNorway)

References

Andreassen, R., S. Jakobsen, and Mevaag, B., Norwegian population data for the 10 autosomal STR loci in the AMPFlSTR(R) SGM Plus(TM) system. Forensic Science International, 2007. 170(1): p. 59-61.

Simulates SNP mixtures

Description

Simulates SNP mixtures and outputs optionally file suitable for wrapdataL function for estimation of number of contributors

Usage

simMixSNP(nSNP , p , ncont, writeFile, outfile , id )

Arguments

Value

Returns a data frame with columns Id, marker, allele, frequency and height (=1 for now)

Author(s)

Thore Egeland <Thore.Egeland@medisin.uio.no>

Examples

simMixSNP()

Polymorphism chain reaction simulation model

Description

simPCR2 implements a simulation model for the polymorphism chain reaction (Gill et al., 2005). Giving several input parameters, simPCR2 outputs the number of amplified DNA molecules and their corresponding peak heights (in RFUs).

Usage

simPCR2(ncells,probEx,probAlq, probPCR, cyc = 28, Tdrop = 2 * 10^7,
probSperm = 0.5, dip = TRUE,KH=55)

Arguments

Details

A threshold of Tdrop (must be a multiple of 10^7) is needed to generate a signal, then, a log-linear relationship is used to determine the intensity of the signal with respect to the number of successfully amplified DNA molecules. Dropout events occur whenever less than Tdorp molecules are generated.

Value

A matrix with the following components:

Author(s)

Hinda Haned hinda@owlsandarrows.nl

References

Jeffreys AJ, Wilson V, Neumann R and Keyte J. Amplification of human minisatellites by the polymerase chain reaction: towards DNA fingerprinting of single cells. Nucleic Acids Res 1988;16: 10953_10971.

Gill P, Curran J and Elliot K. A graphical simulation model of the entire DNA process associated with the analysis of short tandem repeat loci. Nucleic Acids Research 2005, 33(2): 632-643.

See Also

simPCR2TK

Examples

#simulation of a 28 cycles PCR, with the initial stain containing 5 cells 
simPCR2(ncells=5,probEx=0.6,probAlq=0.30,probPCR=0.8,cyc=28, Tdrop=2*10^7,dip=TRUE,KH=55)

A Tcl/Tk graphical interface for the polymorphism chain reaction simulation model

Description

simPCR2TK is a user-friendly graphical interface for the simPCR2 function that implements a simulation model for the polymorphism chain reaction.

Usage

simPCR2TK()

Value

No return value, called to show GUI.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

References

Gill P, Curran J and Elliot K. A graphical simulation model of the entire DNA process associated with the analysis of short tandem repeat loci. Nucleic Acids Research 2005, 33(2): 632-643.

See Also

simPCR2

Examples

#launch the graphical interface
simPCR2TK()

Function to simulate allele frequencies for independent loci from a Dirichlet model

Description

The simufreqD function simulate single population allele frequencies for independent loci. Allele frequencies are generated as random deviates from a Dirichlet distribution, whose parameters control the mean and the variance of the simulated allele frequencies.

Usage

simufreqD(nloc = 1, nal = 2, alpha = 1)

Arguments

Details

Allele frequencies for independent loci are simulated using a Dirichlet distribution with parameter alpha. At a given locus L with n alleles, the allele frequencies are modeled as a vector of random variables p=(p1, ..., pn), following a Dirichlet distribution with parameters:
alpha = (alpha1, ..., alphan) where p1+...+pn=1 and alpha1,..., alphan > 0.

Value

A matrix containing the simulated allele frequencies. The data is presented in the format of the Journal of Forensic Sciences for genetic data: allele names are given in the first column, and frequencies for a given allele are read in rows for the different markers in columns. When an allele is not observed for a given locus, the value is coded NA (instead of "-" in the original format).

Note

The code used here for the generation of random Dirichlet deviates was previously implemented in the gtools library.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

References

Johnson NL, Kotz S, Balakrishnan N. Continuous Univariate Distributions, vol 2. John Wiley & Sons, 1995.

Wright S. The genetical structure of populations. Ann Eugen 1951;15:323-354.

See Also

simupopD

Examples

#simulate alleles frequencies for 5 markers with respectively 2, 3, 4, 5, and 6 alleles

simufreqD(nloc=5,na=c(2,3,4,5,6) , alpha=1)

forensim class for simluated genotypes

Description

The S4 simugeno class is used to store existing or simulated genotypes.

Slots

tab.freq:

a list giving allele frequencies for each locus. If there are several populations, tab.freq gives allele frequencies in each population

nind:

integer vector giving the number of individuals. If there are several populations, nind gives the numbers of individuals per population

pop.names:

factor of populations names

popind:

factor giving the population of each individual

which.loc:

character vector giving the locus names

tab.geno:

matrix giving the genotypes (in rows) for each locus (in columns). The genotype of a homozygous individual carrying the allele "12" is coded "12/12". A heterozygous individual carrying alleles "12" and "13" is coded "12/13" or "13/12".

indID:

character vector giving the individuals ID

Methods

names

signature(x = "simugeno"): gives the names of the attributes of a simugeno object

show

signature(object = "simugeno"): shows a simugeno object

print

signature(object = "simugeno"): prints a simugeno object

Author(s)

Hinda Haned hinda@owlsandarrows.nl

See Also

as.simugeno for the simugeno class constructor, is.simugeno, simumix and tabfreq

Examples

showClass("simugeno")

simugeno constructor

Description

Constructor for simugeno objects.
The function simugeno creates a simugeno object from a tabfreq object.

The function as.simugeno is an alias for simugeno function.

is.simugeno tests if an object is a valid simugeno object.

Note: to get the manpage about simugeno, please type 'class ? simugeno'.

Usage

simugeno(tab,which.loc=NULL,n=1)
as.simugeno(tab,which.loc=NULL,n=1)
is.simugeno(x)

Arguments

Details

At a given locus, an individual's genotype is simulated by randomly drawing two alleles (with replacement) at their respective allele frequencies in the target population.

Value

For simugeno and as.simugeno, a simugeno object. For is.simugeno, a logical.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

See Also

"simugeno", and tabfreq for creating a tabfreq object from a data file.

Examples

data(Tu)
tab<-tabfreq(Tu)
#simulation of 3 individual genotypes for the STR  marker FGA
geno1 <- simugeno(tab,which.loc='FGA', n =100)
geno1@tab.geno

forensim class for DNA mixtures

Description

The S4 simumix class is used to store DNA mixtures of individual genotypes along with informations about the individuals poulations and the loci used to simulate the genotypes.

Slots

ncontri:

integer vector giving the number of contributors to the DNA mixture. If there are several populations, ncontri gives the number of contributors per population

mix.prof:

matrix giving the contributors genotypes (in rows) for each locus (in columns). The genotype of a homozygous individual carrying the allele "12" is coded "12/12". A heterozygous individual carrying alleles "12" and "13" is coded "12/13" or "13/12".

mix.all:

list giving the alleles present in the mixture for each locus

which.loc:

character vector giving the locus names

popinfo:

factor giving the population of each contributor

Methods

names

signature(x = "simumix"): gives the names of the attributes of a simumix object

show

signature(object = "simumix"): shows a simumix object

print

signature(object = "simumix"): prints a simumix object

Author(s)

Hinda Haned hinda@owlsandarrows.nl

See Also

simugeno, as.simumix, is.simumix, simugeno and tabfreq

Examples

showClass("simumix")
data(strusa)

simumix constructor

Description

Constructor for simumix objects.
The function simumix creates a simumix object from a tabfreq object.

The function as.simumix is an alias for simumix function.

is.simumix tests if an object is a valid simumix object.

Note: to get the manpage about simumix, please type 'class ? simumix'.

Usage

simumix(tab,which.loc=NULL,ncontri=1)
as.simumix(tab,which.loc=NULL,ncontri=1)
is.simumix(x)

Arguments

Details

DNA mixtures are created by randomly drawing individual genotypes with a uniform probability. If there are N individuals in the sample (the simugeno object), then each individual has a probability of 1/N to be selected.

Value

For simumix and as.simumix, a simumix object. For is.simumix, a logical.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

See Also

"simumix", simugeno for creating a simugeno object.

Examples

data(Tu)
tab<-simugeno(tabfreq(Tu),n=1200)
#simulation of a 3-person mixture characterized with markers FGA, TH01 and TPOX 
simumix(tab,which.loc=c('FGA','TH01', 'TPOX') , n =3)

Simulate multi-population allele frequencies for independent loci from a reference population, following a Dirichlet model

Description

Simulate multi-population allele frequencies for independent loci, from a given reference population, following a Dirichlet model. Allele frequencies in the populations are generated as random deviates from a Dirichlet distribution, whose parameters control the deviation of allele frequencies from the values in the reference population.

Usage

simupopD(npop = 1, nloc = 1, na = 2, globalfreq = NULL, which.loc = NULL,
alpha1, alpha2 = 1)

Arguments

Details

In the reference population, allele frequencies for independent loci are simulated using a Dirichlet distribution with parameter alpha2.
At a given locus L with n alleles, the allele frequencies are modeled as a vector of random variables p=(p1, ..., pn) following a Dirichlet distribution with a parameter vector of length n, where each component is equal to alpha2, p1+...+pn=1 and alpha2 > 0.
Note that a more sophisticated generation of global allele frequencies is possible using the simufreqD function. Similarly, allele frequencies in the independent populations are simulated using a Dirichlet Distribution. For example, for the first population to simulate, at a given locus L with n alleles, the allele frequencies are modeled as a vector of random variables p=(p1, ..., pn) following a Dirichlet distribution with a parameter vector of length n:
(p1(1-a1)/alpha1[1], ..., pn(1-alpha1[1])/alpha1[1]), where p1+...+pn=1 and alpha1[1] > 0.
alpha1[1] is the variance parameter for population 1 and is equivalent to Wright's Fst. The closest this parameter is to one, the more the population allele frequencies are different from the values of the reference population.

Value

The result is stored in a list with two elements :

Note

The code used here for the generation of random Dirichlet deviates was previously implemented in the gtools library.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

References

Nicholson G, Smith AV, Jonsson F, Gustafsson O, Stefansson K, Donnelly P. Assessing population differentiation and isolation from single-nucleotide polymorphism data. J Roy Stat Soc B 2002;64:695–715

Marchini J, Cardon LR. Discussion on the meeting on "Statistical modelling and analysis of genetic data" J Roy Stat Soc B, 2002;64:740-741

Wright S. The genetical structure of populations. Ann Eugen 1951;15:323-354

See Also

simufreqD

Examples

# simulate allele frequencies for two populations
data(Tu)
simupopD(npop=2,globalfreq=Tu, which.loc=c("FGA","TH01","TPOX"),
alpha1=c(0.2,0.3),alpha2=1)
  

Allele frequencies for 15 autosomal short tandem repeats core loci on U.S. Caucasian, African American, and Hispanic populations.

Description

Allele frequencies for 15 autosomal short tandem repeats loci on three American populations : Caucasians, African Americans and Hispanics. Among the 15 loci, 13 belong to the core Combined DNA Index System (CODIS) loci used by the Federal Bureau of Investigation (USA), in forensic DNA analysis, and two supplementary loci are more commonly used in Europe, see details.

Usage

data(strusa)

Format

strusa is a tabfreq object giving allele frequencies of 15 loci in three American populations.

Details

CSF1PO, FGA, TH01, TPOX, vWA, D3S1358, D5S818, D7S820, D8S1179, D13S317, D16S539, D18S51 and D21S11, belong to the core CODIS loci used in the US, whereas D2S1338 and D19S433 belong to the European core loci.

References

Butler JM, Reeder DJ. http://www.cstl.nist.gov/strbase/index.htm, last visited: May 11th 2009

Butler JM, Schoske R, Vallone MP, Redman JW, Kline MC. Allele frequencies for 15 autosomal STR loci on U.S. Caucasian, African American, and Hispanic populations. J Forensic Sci 2003;48(8):908-911.

Examples

data(strusa)
strusa
#genotypes simulations from each population
geno<- simugeno(strusa,n=c(100,100,100))
geno
#3-person mixture simulation with the contributors from the 3 populations
mix3<- simumix(geno,ncontri=c(1,1,1))
mix3

Population study of three miniSTR loci in Veneto (Italy)

Description

Allele frequencies for three short tandem repeats loci D10S1248, D2S441 and D22S1045 in a sample of 198 individuals born in Veneto, Italy. These loci are commonly used in forensic DNA characterization.

Usage

data(strveneto)

Format

strveneto is a tabfreq object

References

Turrina S, Atzei R, De Leo D. Population study of three miniSTR loci in Veneto (Italy). Forensic Sci Int Genetics 2008; 1(1);378-379

Examples

data(strveneto)
#allele frequencies
strveneto@tab

forensim class for population allele frequencies

Description

The S4 tabfreq class is used to store allele frequencies, from either one or several populations.

Slots

tab:

a list giving allele frequencies for each locus. If there are several populations, tab gives allele frequencies in each population

which.loc:

character vector giving the names of the loci

pop.names:

factor of populations names (optional)

Methods

names

signature(x = "tabfreq")

: gives the names of the attributes of a tabfreq object

show

signature(object = "tabfreq")

: shows a tabfreq object

print

signature(object="tabfreq")

: prints a tabfreq object

Author(s)

Hinda Haned hinda@owlsandarrows.nl

See Also

as.tabfreq, is.tabfreq and simugeno for genotypes simulation from allele frequencies stored in a tabfreq object

Examples

showClass("tabfreq")

tabfreq constructor

Description

Constructor for tabfreq objects.
The function tabfreq creates a tabfreq object from a data frame or a matrix giving allele frequencies for a single population in the Journal of Forensic Sciences (JFS) format for population genetic data. Whene multiple populations are considered, data shall be given as a list, where each element is either a matrix or a data frame in the JFS format, and the populations names must be specified.

The function as.tabfreq is an alias for the tabfreq function.

is.tabfreq tests if an object is a valid tabfreq object.

Note: to get the manpage about tabfreq, please type 'class ? tabfreq'.

Usage

tabfreq(tab,pop.names=NULL)
as.tabfreq(tab,pop.names=NULL)
is.tabfreq(x)

Arguments

Value

For tabfreq and as.tabfreq, a tabfreq object. For is.tabfreq, a logical.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

See Also

"tabfreq", simugeno for creating a simugeno object from a tabfreq object.

Examples

data(Tu)
tabfreq(Tu,pop.names=factor("Tu"))

Virtual classes for forensim

Description

Virtual classes that are only for internal use in forensim

Objects from the Class

A virtual Class: programming tool, not intended for objects creation.

Author(s)

Hinda Haned hinda@owlsandarrows.nl

ML estimate of number of contributors for SNPs

Description

Wrap up of dataL in forensim. Given file with columns: "No, Marker, Allele, Frequency and Height" the log likelihood for requested number of contributors is calculated. For now only "Frequency" column is used.

Usage

wrapdataL(fil , plotte , nInMixture , tit )

Arguments

Value

Plot (optional) and log likelihoods

Author(s)

Thore Egeland Thore.Egeland@medisin.uio.no

Examples

aa<-simMixSNP(nSNP=5,writeFile=FALSE,outfile="sim.txt",ncont=3) #Simulates data

# run with writeFile = TRUE for plot
# aa<-simMixSNP(nSNP=5,writeFile=TRUE,outfile="sim.txt",ncont=3)
# res<-wrapdataL(fil="sim.txt") # Calculates and plots