relSim: Relative Simulator

Description

A set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations.

Identity by state

Description

Calculates the total number of alleles that are shared by two profiles. If the two profiles in question are indeed relatives then the matching alleles may be identical by descent, or by random chance alone, hence identity by state.

Usage

IBS(prof1, prof2, nLoci = length(prof1)/2, bPrint = FALSE)

Arguments

Value

An integer between 0 and 2*nLoci representing the total number of alleles that match in the two profiles.

Author(s)

James M. Curran

Examples

data(fbiCaucs)
P1 = randomProfile(fbiCaucs)
C1 = randomChild(P1, fbiCaucs)
IBS(P1, C1)
IBS(P1, C1, bPrint = TRUE)

Use importance sampling to determine the probability of m peaks from n contributors to a mixture

Description

WARNING: This function is experimental.

Usage

IS(
  Freqs,
  numContributors = 4,
  maxPeaks = NULL,
  numIterations = 100,
  bTail = FALSE
)

Arguments

Value

a list with as many elements as loci. If tail probabilities are selected then each locus element will be a vector of probabilities

Examples

## Not run: 
data(USCaucs)
IS(USCaucs, numContributors = 4, maxPeaks = 3, numIterations = 1e4)

## End(Not run)

CODIS STR Loci allele frequency data

Description

This data structure

Format

This data set is a list which has two sub-lists. The lists are named loci and freqs. loci is a vector of the 13 CODIS STR locus names. freqs is a list of 13 vectors, each vector contains the allele frequencies published for US Caucasians in Budowle and Moretti (1999). The raw data is available from https://www.fbi.gov/about-us/lab/forensic-science-communications/fsc/july1999/dnaloci.txt

Author(s)

James M. Curran

References

Budowle, B. and Moretti, T.R. (1999), Genotype Profiles for Six Population Groups at the 13 CODIS Short Tandem Repeat Core Loci and Other PCR Based Loci, Forensic Science Communications 1(2).

See Also

fbiCaucs

Examples

data(USCaucs)
names(USCaucs)
USCaucs$loci
names(USCaucs$freqs)
USCaucs$freqs[[1]]
names(USCaucs$freqs[[1]])
USCaucs$freqs[[1]][1]

Compute the likehood ratio for all pairs of profiles in a database

Description

This function takes every pair of profiles in a database of profiles and computes the likelihood ratio (LR) for a specific relationship given by nCode. That means there will be N(N-1)/2 LRs computed for N profiles.

Usage

allPairsLR(Profiles, listFreqs, nCode)

Arguments

Value

a NumericVector containing the LRs. They are stored in sequential order so if for example there were three profiles, then there are 3 possible LRs, and the result vector would contain the LRs for the profile pairs (1, 2), (1, 3), and (2, 3).

Author(s)

James Curran

Examples

data("USCaucs")
N = 600
profs = relSim:::.randomProfiles(USCaucs$freqs, N)
system.time({lr = relSim:::allPairsLR(profs, USCaucs$freqs, 1)})
plot(density(log10(lr)))
mean(lr > 1) ## estimate the probability that the LR is incorrectly above 1

Perform relatives simulations using large memory blocks in C

Description

Generate N pairs with a given relationship, calculate the LR for sibs, parent-child and the number of matching alleles and count the number of pairs that meet the threshold criteria.

Usage

blockSim(
  N,
  Freqs,
  rel = "UN",
  ibsthresh = NULL,
  kithresh = NULL,
  code = 1,
  falseNeg = TRUE,
  BlockSize = N/10,
  showProgress = FALSE
)

Arguments

Details

This function is used for fast accurate estimation of false positive and false negative rates. It achieves part of its speed by block exectution in C, and part by not saving the LR or IBS results. It can do 1 billion iterations in about an hour.

Value

A vector containing the number of profile pairs that satisfied the threshold conditions

Author(s)

James M. Curran

See Also

sim

Examples

## not run
## this counts the number of unrelated pairs that are falsely identified
## as siblings using the policy that there are 16 or more matching
## alleles, and the LR/KI is greater than 100,000
## this is a very rare event for the FBI Caucasians with a frequency of
## about 4-5 times in 10 million pairs
## Not run: 
data(fbiCaucs)
N = 1e8
ki = 1e5
ibs = 16
code = 5
BlockSize = 1e6
blockSim(N, fbiCaucs, rel = "UN", ibsthresh = ibs, kithresh = ki,
         code = code, falseNeg = FALSE, BlockSize = BlockSize)

## End(Not run)

Breed a population with an approximate level of \theta(F_{ST})

Description

This function simulates a population with an approximate level of population substructure. This is achieved by subdividing a population into equal sized subpopulations and allowing them to breed within themselves for

t =

\lceil{\frac{\log_e(1-\theta)}{\log\left(1-\frac{1}{2N_s}\right)}}\rceil

generations, where N_s is the number of individuals in each subpopulation. This will produce a population with an estimated coancestry coefficient approximately equal to \theta

Usage

breedFst(Freqs, theta = 0.01, N = 10000, ns = 10, DNAtools = FALSE)

Arguments

Value

An object of class 'population' which is a list with the following elements

• profiles - a vector of profiles where the level of inbreeding is approximately equal to \theta

• nProfiles - the total number of individuals in the population

• nSubpops - the number of sub-populations in the population

• nLoci - the number of loci each individual is typed at

• theta - the desired level of substructure in the population. The actual value will be near to this.

• Freqs - a Freq object representing the ancestral frequencies of the population

Author(s)

James M. Curran

Examples

data(USCaucs)
pop = breedFst(USCaucs)

Calculate locus-wise and population \theta = F_{ST}, F = F_{IT}, and f = F_{IS} values

Description

This procedure uses the method of Weir and Cockerham to estimate \theta = F_{ST}, F = F_{IT}, and f = F_{IS} for a population with known substructure

Usage

calcFStats(Pop, subPopIdx = NULL)

Arguments

Value

A vector of length n_{loci}+1 with locus-wise \theta values and an overall \theta value for the population

Author(s)

James M. Curran

References

Weir, B.S., Genetic Data Analysis II, (1996) p.173–179, Sinauer, Sunderland, MA.

See Also

breedFst

Examples

data(USCaucs)
set.seed(123)
p = breedFst(USCaucs)
fstats = calcFStats(p)
fstats

Caculate locus-wise and population F_{ST} values

Description

This procedure uses the method of Weir and Cockerham to estimate \theta (F_{ST}) for a population with substructure

Usage

calcFst(Pop, subPopIdx = NULL)

Arguments

Value

A vector of length n_{loci}+1 with locus-wise \theta values and an overall \theta value for the population

Author(s)

James M. Curran

References

Weir, B.S., Genetic Data Analysis II, (1996) p.173–179, Sinauer, Sunderland, MA.

See Also

breedFst

Examples

data(USCaucs)
p = breedFst(USCaucs)
fst = calcFst(p)
fst

Make sure that the frequencies are such

Description

Checks whether a list of frequencies at a series of genetic loci both sum to one and lie between 0 and 1.

Usage

checkFreqs(Freqs)

Arguments

Details

If a locus fails to sum to one, or there are alleles which fall below zero or above one, then a warning message will be returned for each item in error.

Author(s)

James M. Curran

See Also

normalizeFreqs

Examples

data(fbiCaucs)
checkFreqs(fbiCaucs)

## induce an error
fbiCaucs$freqs[[1]] = runif(10)
checkFreqs(fbiCaucs)

Returns the false positive or false negative rates for a set of IBS and/or KI thresholds

Description

This function is used to calcalate the various tables in the work of Ge et al. and Balding et al. Specifically it can be used to calculate the false positive rate for unrelated pairs being identified as full-sibs or parent-child pairs under differing levels of IBS or KI (or both) thresholds. It can also be used to calculate the false negative rates for full-sib, or parent-child, pairs being identified as unrelated, again with differing levels of IBS, KI or both.

Usage

errorRate(
  simResults,
  bIBS = TRUE,
  bKI = FALSE,
  rel = "UN",
  IBSthresh = 14:17,
  KIthresh = c(1000, 10000, 1e+05, 1e+06),
  nLoci = 13
)

Arguments

Value

A vector (or a two-column matrix) of false negative or false positive rates. If the relationship is 'UN' then false positive rates are returned for parent-child and full-sibs, with parent-child being in column 1 and full-sibs in column 2. If the relationship is 'PC' then the false negative rate is returned for parent-child pairs, and if it is 'FS' then the false negative rate for full-sibs.

Author(s)

James M. Curran

See Also

sim, readResults

Examples

## not run
## Not run: data(fbiCaucs)
unrel = sim(10000)
errorRate(unrel)

## End(Not run)

Calculate the exclusion power of a multiplex by locus

Description

Calculates the exclusion power

1-2\left(\sum_{i=1}^{n_l}p_i^2\right)^2-4\sum_{i=1}^{n_l}p_i^4

at each locus for a set of allele frequencies.

Usage

exclusionPower(Freqs)

Arguments

Value

The exclusion power for each locus.

Author(s)

James M. Curran

References

NRC II, Evaluation of Forensic Evidence, (1996), p.96, National Academy Press.

Examples

data(USCaucs)
ep(USCaucs)

## get the multiplex wide exclusion power
1 - prod(1-ep(USCaucs))

Search a database for siblings or children

Description

This function searches a database of profiles for either a sibling or a child

Usage

famSearch(profiles, siblings, children, listFreqs, step)

Arguments

Value

a List containing two dataframes, one called sibs and one called children. Each dataframe has results from searching for either the sibling or the child in the database. For each entry there is a record of which profile gave the highest LR (and its value), and the position of the actual sibling or parent/child in the database (and its respective LR).

Author(s)

James Curran

CODIS STR Loci allele frequency data

Description

This data structure

Format

This data set is a list which has two sub-lists. The lists are named loci and freqs. loci is a vector of the 13 CODIS STR locus names. freqs is a list of 13 vectors, each vector contains the allele frequencies published for US Caucasians in Budowle et al. (2001).

Author(s)

James M. Curran

References

Budowle B, Shea B, Niezgoda S, Chakraborty R. (2001), CODIS STR loci data from 41 sample populations, J. Forensic Sci. 46:453-89.

See Also

USCaucs

Examples

data(fbiCaucs)
names(fbiCaucs)
fbiCaucs$loci
names(fbiCaucs$freqs)
fbiCaucs$freqs[[1]]
names(fbiCaucs$freqs[[1]])
fbiCaucs$freqs[[1]][1]

Retrieve data from Budowle and Moretti (1999) from the web

Description

Retreives the Budowle and Moretti (1999) and compiles the allele frequency tables needed for the other parts of this package such as sim.

Usage

fetchBMdata(url = NULL, id = NULL)

Arguments

Details

The first three populations have data on 20 loci, the second three on 13 loci. The missing values (0's in the raw data) have been dropped and are not used in calculating the frequencies. This function will not work if you are not connected to the internet, or access to the internet is blocked.

Value

A list consisting of six elements corresponding to the six populations detailed in the data set. Each of the list elements is a list in itself with three further elements named loci, profiles and freqs. loci is a vector of the 13-20 STR locus names. freqs is a list of 13-20 vectors, each vector contains the allele frequencies. profiles contains the raw profiles that the allele frequency tables were constructed from.

Author(s)

James M. Curran

References

Budowle, B. and Moretti, T.R. (1999), Genotype Profiles for Six Population Groups at the 13 CODIS Short Tandem Repeat Core Loci and Other PCR Based Loci, Forensic Science Communications 1(2).

See Also

fbiCaucs, USCaucs

Examples

## not run
## Not run: 
db = fetchBMdata()
names(db)
f = db[["TRINIDADIAN"]]$freqs
dbExpect(f, k = "UN", collapse = TRUE)

## End(Not run)

Identity by state at a locus

Description

Calculates the number of alleles that are shared by two profiles at a single locus. If the two profiles in question are indeed relatives then the matching alleles may be identical by descent, or by random chance alone, hence identity by state.

Usage

locusIBS(profMat)

Arguments

Value

A vector of length N containing values 0, 1, or 2 depending on how many alleles each pair of profiles share at a locus.

Author(s)

James M. Curran

Examples

data(fbiCaucs)
G = randomSample(1, fbiCaucs, rel = 'FS', N = 1000)
ibs = locusIBS(G)
barplot(tabulate(ibs+1, nbins = 3))

Calculate locuswise likelihood ratios for two person victim/suspect mixtures

Description

Calculates the likelihood ratio for pairs of profiles under the propositions H_p:\qquad V+S and H_d:\qquad V+U, where V, S and U are the victim, the suspect and someone unrelated to the suspect respectively. The calculation does not employ \theta so there are no assumptions about the subpopulations of the contributors.

Usage

lrMix(profiles, Freqs)

Arguments

Value

A matrix of LRs calculated at each locus for every pair of profiles. Note this is the set of N profile pairs supplied in profiles, not a pairwise comparison.

Author(s)

James M. Curran

Examples

data(USCaucs)
p = randomProfilePairs(USCaucs, 10000)
log.lrs = log10(lrMix(p, USCaucs))
boxplot(log.lrs, las = 2)

Likelihood Ratio for Parent-Child / Paternity Index

Description

Calculates Likelihood Ratio comparing the probability of two profiles if they are indeed parent-child compared to unrelated. This is the paternity index or PI.

Usage

lrPC(parent, child, Freqs = NULL, nLoci = length(parent)/2, f = NULL, n = NULL)

Arguments

Value

A value between 0 and infinity representing support (or lack of support if the value is less than 1) for the hypothesis that the two profiles are parent and child. There is no mutation built into this calculation. This means that the LR will be zero if the profiles do not share at least one allele in common at each locus in the multiplex.

Author(s)

James M. Curran

References

Buckleton, J, Triggs, C.M., and Walsh, S.J. (2005)Forensic DNA Evidence Interpretation, CRC Press., Boca Raton, FL. p.410

See Also

lrSib, IBS

Examples

data(fbiCaucs)
P1 = randomProfile(fbiCaucs)
C1 = randomChild(P1, fbiCaucs)
lrPC(P1, C1, fbiCaucs)

Likelihood Ratio / Kinship Index for full-siblings

Description

Calculates Likelihood Ratio comparing the probability of two profiles if they are indeed full-sibs compared to unrelated. This is sometimes called the kinship index (KI) for full-sibs.

Usage

lrSib(sib1, sib2, Freqs = NULL, nLoci = length(sib1)/2, f = NULL, n = NULL)

Arguments

Value

A value between 0 and infinity representing support (or lack of support if the value is less than 1) for the hypothesis that the two profiles are full-siblings. There is no mutation built into this calculation.

Author(s)

James M. Curran

References

Buckleton, J, Triggs, C.M., and Walsh, S.J. (2005)Forensic DNA Evidence Interpretation, CRC Press., Boca Raton, FL. p.411

See Also

lrSibDebug, lrPC, IBS

Examples

data(fbiCaucs)
P1 = randomProfile(fbiCaucs)
S1 = randomSib(P1, fbiCaucs)
P2 = randomProfile(fbiCaucs)
lrSib(P1, S1, fbiCaucs)
lrSib(P1, P2, fbiCaucs)

Likelihood Ratio / Kinship Index for full-siblings

Description

Calculates Likelihood Ratio comparing the probability of two profiles if they are indeed full-sibs compared to unrelated. This is sometimes called the kinship index (KI) for full-sibs. This function is identical to lrSib except that the calculation is performed in R, and provides full calculation detail at each locus. It exists primarily to check that the correct formula and logic is being applied in the LR calculation so that the result can be manually verified.

Usage

lrSibDebug(sib1, sib2, Freqs)

Arguments

Value

A list containing three elements Lines, lr, and Cases. Lines is a list of strings containing the calculation at each locus so that the result can be written to file for example. Cases is a numeric code listing which logical case (1-11) the locus falls into for the profiles in question. lr is the KI for full-sibs for the two profiles.

Author(s)

James M. Curran

References

Buckleton, J, Triggs, C.M., and Walsh, S.J. (2005)Forensic DNA Evidence Interpretation, CRC Press., Boca Raton, FL. p.411

See Also

lrSib, lrPC, IBS

Examples

data(fbiCaucs)
P1 = randomProfile(fbiCaucs)
S1 = randomSib(P1, fbiCaucs)
P2 = randomProfile(fbiCaucs)
cat(paste(lrSibDebug(P1, S1, fbiCaucs)$Lines))
cat(paste(lrSibDebug(P1, P2, fbiCaucs)$Lines))

Normalize frequencies to 1

Description

Normalize a list of frequencies at a series of genetic loci both sum to one. Not that this does not deal with the problem of values larger than one or smaller than zero.

Usage

normalizeFreqs(Freqs)

Arguments

Details

Divides vector in Freqs$freqs by the vector sum.

Value

A list containg elements loci and freqs. freqs is a list of vectors containing the frequencies at the given loci.

Author(s)

James M. Curran

See Also

checkFreqs

Examples

data(fbiCaucs)

## induce an error
fbiCaucs$freqs[[1]] = rgamma(10,1,1)
checkFreqs(fbiCaucs)
fbiCaucs = normalizeFreqs(fbiCaucs)
checkFreqs(fbiCaucs)

Print summary details of a substructed population

Description

Nicely prints summary information about a substructured population created using breedFst

Usage

## S3 method for class 'population'
print(x, ...)

Arguments

Author(s)

James M. Curran

See Also

breedFst

Examples

data(fbiCaucs)
p = breedFst(fbiCaucs)
print(p)

Print a DNA profile

Description

Nicely prints a profile object out in genotype pairs

Usage

## S3 method for class 'profile'
print(x, horizontal = FALSE, ...)

Arguments

Author(s)

James M. Curran

Examples

data(fbiCaucs)
P1 = randomProfile(fbiCaucs)
P2 = randomProfile(fbiCaucs)
P1
print(P1, horizontal = TRUE)
print(P2, horizontal = TRUE)

Generate a random child from a given DNA profile and a given set of allele frequencies

Description

Generates a random child (or parent) from a given DNA profile from a given set of allele frequencies. At each locus, the child inherits the first allele of the given profile with one half, or the second allele with probability one half. The second allele is chosen at random with probability proportional to the allele frequencies.

Usage

randomChild(profile, Freqs)

Arguments

Details

The alleles are simply integers rather than the STR repeat numbers. This speeds up computation immensely when calculating any of the LRs or IBS.

Value

A vector with 2*nLoci elements. Each pair of elements represents the genotpe of the random individual at that locus. The genotype alleles are always ordered so that allele1 <= allele2.

Author(s)

James M. Curran

See Also

randomChild, randomSample, randomSib

Examples

data(fbiCaucs)
P1 = randomProfile(fbiCaucs)
C1 = randomChild(P1,fbiCaucs)
P1
C1

Generate one or more random parent/child pairs from a given set of allele frequencies

Description

Generates one or more pairs random parent/child pairs from a given set of allele frequencies.

Usage

randomPCPairs(Freqs, BlockSize = 1)

Arguments

Details

The alleles are simply integers rather than the STR repeat numbers. This speeds up computation immensely when calculating any of the LRs or IBS.

Value

A list of length BlockSize. Each element of the list has a sublist containing two profiles called parent and child

Author(s)

James M. Curran

See Also

randomSibPairs, randomProfilePairs

Examples

data(fbiCaucs)
P = randomPCPairs(fbiCaucs)
P$parent
P$child

Generate a random DNA profile from a given set of allele frequencies

Description

Generates a random DNA profile from a given set of allele frequencies.

Usage

randomProfile(Freqs)

Arguments

Details

The alleles are simply integers rather than the STR repeat numbers. This speeds up computation immensely when calculating any of the LRs or IBS.

Value

A vector with 2*nLoci elements. Each pair of elements represents the genotpe of the random individual at that locus. The genotype alleles are always ordered so that allele1 <= allele2.

Author(s)

James M. Curran

See Also

randomChild, randomSample, randomSib

Examples

data(fbiCaucs)
P1 = randomProfile(fbiCaucs)

Generate one or more random DNA profile pairs from a given set of allele frequencies

Description

Generates one or more random DNA profile pairs from a given set of allele frequencies.

Usage

randomProfilePairs(Freqs, BlockSize = 1)

Arguments

Details

The alleles are simply integers rather than the STR repeat numbers. This speeds up computation immensely when calculating any of the LRs or IBS.

Value

A list of length BlockSize. Each element of the list has a sublist containing two profiles called prof1 and prof2

Author(s)

James M. Curran

See Also

randomPCPairs, randomSibPairs

Examples

data(fbiCaucs)
P = randomProfilePairs(fbiCaucs)
P$prof1
P$prof2

Generate a random sample of related (or unrelated) pairs of people

Description

Generate a random sample of unrelated, full-sib, or parent/child pairs of profiles at a single locus.

Usage

randomSample(nLoc, Freqs, rel = "UN", N = 10000)

Arguments

Value

An N by 4 matrix of random profiles. The first two columns represent the genotype of person one and the second two columns represent the genotype of column two. Note that the random profiles do not use the orginal allele designations.

Author(s)

James M. Curran

See Also

randomProfile, randomSib, randomChild

Examples

data(fbiCaucs)
G = randomSample(1, fbiCaucs, "FS", 100)

Generate a random sibling from a given DNA profile and a given set of allele frequencies

Description

Generates a random sibling from a given DNA profile from a given set of allele frequencies. At each locus, the sibling inherits the first allele of the given profile with one quarter, or the second allele with probability one quarter, both alleles with probability one quarter, or neither with probability one quarter. If the sibling inherits zero or one identical alleles, the missing alleles are chosen at random with probability proportional to the allele frequencies.

Usage

randomSib(profile, Freqs)

Arguments

Details

The alleles are simply integers rather than the STR repeat numbers. This speeds up computation immensely when calculating any of the LRs or IBS.

Value

A vector with 2*nLoci elements. Each pair of elements represents the genotpe of the random individual at that locus. The genotype alleles are always ordered so that allele1 <= allele2.

Author(s)

James M. Curran

See Also

randomChild, randomSample

Examples

data(fbiCaucs)
P1 = randomProfile(fbiCaucs)
S1 = randomSib(P1,fbiCaucs)
P1
S1

Generate one or more pairs of random siblings from a given set of allele frequencies

Description

Generates one or more pairs of random siblings from a given set of allele frequencies.

Usage

randomSibPairs(Freqs, BlockSize = 1)

Arguments

Details

The alleles are simply integers rather than the STR repeat numbers. This speeds up computation immensely when calculating any of the LRs or IBS.

Value

A list of length BlockSize. Each element of the list has a sublist containing two profiles called sib1 and sib2

Author(s)

James M. Curran

See Also

randomPCPairs, randomProfilePairs

Examples

data(fbiCaucs)
P = randomSibPairs(fbiCaucs)
P$sib1
P$sib2

Read in a file of allele frequencies

Description

Reads in a file of alleles in a particular format.

Usage

readFreqs(strPath, FSIGenFormat = TRUE, delim = ",")

Arguments

Details

This function reads frequencies in the rectangular allele freqency table format used by FSI Genetics and other journals. This file format assumes a comma separated value file (CSV) (although the column delimeter can be specified). The first column should be labelled 'Allele' and contain the STR allele designations that are used in the data set. The remaining columns will have the locus name as a header, and frequencies that are either blank, zero, or non-zero. Blanks or zeros are used to specify that the allele is not observed (and not used) at the locus. The final row of the file should start with 'N' or 'n' in the first column and give the number of individuals typed (or the number of alleles recorded) in assessing the frequency of the alleles.

The second format is a very particular 'Curran' text format. The first line contains the number of loci in the multiplex. The next line will contain the name of the first locus and the number of alleles, nA, the locus separated by a comma. The next nA lines contain the allele number (from 1 to nA), the STR designation of the allele, and the frequency separated by commas. This pattern is repeated for each locus. In the future this function will read the rectangular allele freqency table used by FSI Genetics and other journals.

Value

a list containing two vectors and a list, loci, counts, and freqs. The vector loci is a vector of the locus names in the frequency file. The vector counts is a vector of the number of individuals (or sometimes alleles) typed at each locus. This will null if the 'Curran' format is used. The list freqs, is a list of vectors with each vector containing the frequencies of the alleles at the locus. The names of the elements of the vectors are the STR allele designations.

Author(s)

James M. Curran

Read a set of profiles from a file

Description

Reads a set of profiles from a file

Usage

readProfiles(
  fileName,
  freqs = NULL,
  sep = "\t",
  header = FALSE,
  id = 1,
  discardMissing = TRUE
)

Arguments

Details

The alleles are recorded integers rather than the STR repeat numbers. This speeds up computation immensely when calculating any of the LRs or IBS.

Value

a list containing a data.frame of profiles where the alleles have been recoded to the allele index number, rather than the allele itself, and a set of frequencies in the same format as you would get from readFreqs. If freqs have been supplied, then this will just be the same set of frequencies, if they have not, then this will be calculated from the profiles. Given that the profiles generally do not have any locus name information the loci will just be labelled Locus1, Locus2, .... If there are missing values then the raw missing profiles are returned

Author(s)

James M. Curran

Read a simulation result set from file

Description

This function will read the output from sim that has been saved to disk

Usage

readResults(
  N = 0,
  rel = "UN",
  gzip = TRUE,
  strPath = "",
  strVer = "",
  fileName = NULL
)

Arguments

Details

The arguments to this file are used to generate the input file name. The format is very rigid, being 'results-sim-rel-N(-strVer).csv(.gz)' That is, if strVer is something than an empty string then it is included after the number of interations. Similarly if gzip == TRUE then the filename is assumed to end with '.gz'

Value

a data frame with three columns labelled sib, pc, and ibs. These represent the LRs for sibs and parent-child calculated on each simulated profile pair, and the number of matching alleles (IBS).

Author(s)

James M. Curran

See Also

sim

Examples

data(fbiCaucs)
## not run
## write the results of 100 unrelated profile pairs to
## results-sim-UN-100.csv.gz
## and read it back in
## Not run: 
sim(100, save = T)
unrel = readResults(100)
sim(100, rel = "FS", strVer = "01", save = T)
sibs = readResults(100, rel = "FS", strVer = "01")

## End(Not run)

Perform the relatives simulation

Description

Generate N pairs with a given relationship and calculate the LR for sibs, parent-child and the number of matching alleles

Usage

sim(
  N,
  Freqs,
  rel = "UN",
  save = FALSE,
  strPath = "",
  strVer = "",
  BlockSize = N/100,
  fileName = NULL
)

Arguments

Details

This is the function that generates all the data for the results in the paper. WARNING: this function is not especially fast. To achieve the 100 million iterations used in the paper, 30 instances of R were launched on a multicore server. Each instance represented one relationship with 10 million iterations. The compute time for this arrangement was approximately 1 hours, meaning a full serial run would have taken over 30 hours to achieve the same result.

Value

a data frame with three columns: sib, pc, ibs containing the LRs for full-siblings, parent-child, and the number of matching alleles for each generated pair of profiles.

Author(s)

James M. Curran

See Also

readResults, errorRate

Examples

## not run
## this replicates Ge et al.'s experiment and takes about 45 minutes
## to run (I think)
## Not run: 
data(fbiCaucs)
N = 1000000
sim(N, fbiCaucs, save = T)
sim(N, fbiCaucs, 'FS', save = T)
sim(N, fbiCaucs, 'PC', save = T)

## End(Not run)

Simulate and count unique alleles in N person mixtures

Description

This function simulates N persons mixtures using the supplied frequencies and records the number of times they share 1, 2, ..., 2N alleles locus by locus.

Usage

simNpersonMixture(freqs, numContributors, numIterations = 10000)

Arguments

Value

an object of class npmresult

Export a population with substructure to a Nexus file

Description

Exports a population with population substructure to a Nexus formatted file so that GDA can be used to check the Fst calculations

Usage

toNexus(Pop, fileName = "output.nex")

Arguments

Author(s)

James M. Curran

References

Maddison DR, Swofford DL, Maddison WP (1997), NEXUS: An extensible file format for systematic information, Systematic Biology 46 (4): 590–621.

Zaykin, D. and Lewis, P., GDA - software to accompany Genetic Data Analysis II, <http://phylogeny.uconn.edu/software/>.

See Also

breedFst

Examples

## Don't run
## Not run: 
data(USCaucs)
p = breedFst(USCaucs)
toNexus(p)

## End(Not run)

Saves/writes population frequencies to disk

Description

Writes a population of profiles to disk using the original allele designations rather than the internal integer representations that are used for the other functions.

Usage

writeCSV(fileName, pop, n = 100, delim = ",")

Arguments

Value

a matrix which contains the table written to file.

Note

Rare alleles are recoded to 108.1. This is unlikely to do the right thing when you have things like <5 or >20 in your allele names. Given it is impossible to predict what a user would like to do, I suggest you recode them yourself before using this function.

Author(s)

James M. Curran

See Also

breedFst USCaucs

Examples

data(USCaucs)
## Not run: 
  writeCSV("USCaucs.csv", USCaucs)

## End(Not run)

Saves/writes population profiles to disk

Description

Writes a population of profiles to disk using the original allele designations rather than the internal integer representations that are used for the other functions.

Usage

writePop(fileName, pop, addAmelo = FALSE, delim = ",", dupLoci = TRUE)

Arguments

Note

Rare alleles are recoded to 108.1.

Author(s)

James M. Curran

See Also

breedFst

Examples

data(USCaucs)
pop = breedFst(USCaucs)
## Not run: 
  writePop("USCaucs.csv", pop)
  
## End(Not run)