Nothing
Empirical testing of DNA match probabilities
In DNAtools: Tools for Analysing Forensic Genetic DNA Data
library(knitr)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "",
fig.width = 8,
fig.asp = 0.72
)
options(knitr.kable.NA = '')
Introduction
In 2001, a poster was presented by a forensic scientist from Arizona [@troyer2001]
at a scientific meeting on human identification. This poster reported a nine locus
match between two unrelated men, one white and one black [@kaye2009]. It was not a full match.
Both men had been typed at thirteen loci in total, and partially matched at
three of the remaining four loci. These partially or
non-matching loci would have excluded either man as a suspect if the other was
the true offender. However, such a match seemed to be at odds with the random
match probabilities. On one hand, these two men were in a DNA database which
consisted of approximately 65,000 profiles, and on the other hand, the random
match probabilities for the nine locus genotype were 1 in 754 million in Caucasians, 1
in 561 billion in African Americans, and 1 in 113 trillion in
Southwest Hispanics, [@troyer2001].
As we will show later this is, in effect, an example of the birthday problem
and therefore is regarded as completely predictable from a statistical perspective.
However, most us who have taught a class on the birthday problem know that our
students are initially sceptical.
DNA evidence, matches and partial matches
A pair of profiles is said to (fully) match if every allele at every
locus that occurs in one profile occurs in the other. A pair of
profiles are said to partially match if there are allelic matches at a
subset of loci. @weir2004 provided a taxonomy for describing
partial matches which depends on the number of fully, partial and
non-matching loci between a pair of profiles. For any given pair of
(full) profiles from the same multiplex there will be: $m$ loci
where both alleles match, $p$ loci where only one of the alleles
matches, and $L-m-p$ loci where none of the alleles match, where $L$ is the
total number of loci. For example, the profile that @troyer2001 found was a
9/3 partial match on 13 loci - nine fully matching loci ($m=9$), three partially
matching loci ($p=3$), and one non-matching locus.
DNA database comparison exercises
The Troyer-match came from a database matching exercise. In such an
exercise every profile is compared with every other profile in the
database. This type of comparison exercise is absolutely essential
and, in addition, can provide some interesting information about the
statistical properties of the population under consideration. We say
that database comparison is essential in the first instance for the
detection of duplicates. Duplicates may arise in a number of different
ways. For example, an offender may provide a false name or an
offender's name may be entered incorrectly. Alternatively, an offender
may have an identical twin who is already in the DNA database. There
are six pairs of identical twins in the New Zealand National DNA
Database (NZDNADB). Forensic scientists are also interested in 'very close' matches.
For example, a pair of profiles might fully match at nine loci out of ten and partially match at the
remaining locus. This may happen either because the donors of the
samples are very close relatives. It is more likely, however, that
the profiles do not match because of allelic dropout,
primer binding site mutations, nomenclature changes or somatic mutation.
The birthday problem
@weir2007 and others [@brenner2007; @curran2007; @mueller2008; @kaye2009]
note that the presence of matching profiles in a DNA database is effectively an
instance of the well-known birthday problem [@wiki_birthday] where, in a group of at
least 23 randomly chosen people, there is a greater than 50% chance
that one pair of them will have the same birthday. Early critics,
implicitly calculating the expected number of matches as $N\pi$, used
the wrong value for $N$ and the wrong value for $\pi$. Firstly, the
number of pairwise matches, not the size of the database, is the
relevant quantity. Although the database size is relatively small, the
number of pairwise comparisons is very large. The Arizona database
contained of $N=65,493$ profiles [@brenner2007]. Therefore, there are
$$
N_\text{Comparisons}=\frac{N(N-1)}{2} = 2,144,633,778
$$
or approximately two billion, possible pairwise comparisons.
The pairwise profile matching probability
Secondly, the random match probability is not the probability we need.
The random match probability for the pair of profiles in question
answers the question "What is the probability that someone other than
these two men would have this particular nine locus profile." The
probability we actually want is "What is the probability that two
randomly selected profiles would match at nine loci, partially match
at three loci, and not match at one locus." . At a given locus the two profiles
may either match ($m/p=1/0$), partially match ($m/p=0/1$) or mismatch ($m/p=0/0$).
@weir2004, @weir2007 showed that the probabilities of mismatch ($P_{0/0}$),
partial match ($P_{0/1}$) and match ($P_{1/0}$) could be expressed by
\begin{align}
P(\text{Mismatch}) &= P_{0/0} =
D^{-1}{\theta^2(1{-}\theta)(1{-}S_2) +
2\theta(1{-}\theta)^2(1{-}2S_2{+}S_3) + (1{-}\theta)^3(1{-}4S_2{+}4S_3{+}2S_2^2{-}3S_4)},\
P(\text{Partial match}) &= P_{0/1} =
D^{-1}{8\theta^2(1{-}\theta)(1{-}S_2) + 4\theta(1{-}\theta)^2(1{-}S_3)
+ 4(1{-}\theta)^3(S_2{-}S_3{-}S_2^2{+}S_4)},\
P(\text{Match}) &= P_{1/0} =
D^{-1}{6\theta^3 + \theta^2(1{-}\theta)(2{+}9S_2) +
2\theta(1{-}\theta)^2(2S_2{+}S_3) +
(1{-}\theta)^3(2S_2^2{-}S_4)},
\end{align}
where $D=(1{+}2\theta)(1{+}\theta)$ and $S_k=\sum_{i}p_i^k$, with
$p_i$ being the frequency of the $i$\textsuperscript{th} allele.
@tvedebrink2010 showed that the overall probability of matching at $m$ and partially
matching at $p$ loci, $\pi_{m/p}$, can be calculated by recursion over loci:
$$
\pi^{\ell+1}{m/p} = \left{\begin{array}{ll}
P^{\ell+1}{0/0}\pi^\ell_{m/p} +
P^{\ell+1}{0/1}\pi^\ell{m/p-1} +
P^{\ell+1}{1/0}\pi^\ell{m-1/p}& m>0~\text{and}~p>0,\
P^{\ell+1}{0/0}\pi^\ell{0/p} + P^{\ell+1}{0/1}\pi^\ell{0/p{-}1}&m=0~\text{and}~p>0,\
P^{\ell+1}{0/0}\pi^\ell{m/0} +
P^{\ell+1}{1/0}\pi^\ell{m{-}1/0}&m>0~\text{and}~p=0\
P^{\ell+1}{0/0}\pi^\ell{0/0}& m=0~\text{and}~p=0,
\end{array}\right.
$$
where the sum of the subscripts for each term on the right hand
side equals the subscript on the left hand side, e.g.\ the subscripts
of the last term in the first equation gives $1/0 + m{-}1/p = m/p$.
The initial step of the recursion has $\pi^1_{1/0} = P^1_{1/0}$,
$\pi^1_{0/1} = P^1_{0/1}$ and $\pi^1_{0/0} = P^1_{0/0}$.
The coancestry coefficient, $\theta$ or $F_{ST}$ models low levels of
relatedness between individuals in the same subpopulation, and is
typically between 0 and 0.03.
Using the package DNAtools
library(DNAtools)
The expected data format of the databases used as input for the functions in
DNAtools is a data frame, which is constituted by a column of DNA profile
identifiers (the first column) and two columns per typed DNA marker.
An example is given below:
data(dbExample, package = "DNAtools")
knitr::kable(head(dbExample)[,1:9])
From which we estimate allele frequencies for later use:
allele_freqs <- lapply(1:10, function(x){
al_freq <- table(c(dbExample[[x*2]], dbExample[[1+x*2]]))/(2*nrow(dbExample))
al_freq[sort.list(as.numeric(names(al_freq)))]
})
names(allele_freqs) <- sub("\\.1", "", names(dbExample)[(1:10)*2])
We use the example database provided by data(dbExample, package = "DNAtools")
to show the usage of the functions of DNAtools.
db_summary <- dbCompare(dbExample, hit = 6, trace = FALSE)
db_summary$m[!DNAtools:::up.tri(db_summary$m)] <- NA
rownames(db_summary$m) <- paste0("**",rownames(db_summary$m),"**")
kable(db_summary$m)
The hit argument returns pairs of profiles that fully match at hit (here 6)
or more loci.
There is a \code{plot} method for the returned object.
Applying the \code{plot} method to \code{db_summary} yields the dropping ball-picture below.
The right end of the distribution is interesting, due to the larger
number of coinciding loci between profile pairs.
plot(db_summary)
By using the dbExpect and dbVariance functions we can compute the expected
counts and standard deviations for the number of matching profiles at various levels.
Both functions implement the recursions formulae of @tvedebrink2012, which enables
fast and accurate evaluations of the expectations and variances.
The dbExpect-function takes the locus-wise allele frequencies and number of profiles as arguments.
Furthermore, the $\theta$-correction is adjusted for if theta > 0 and adjustment
for close relatedness can be done by the k-argument, which is a three-dimensional
vector with non-negative entries summing to one (cf. below).
db_expect <- dbExpect(allele_freqs, n = nrow(dbExample), theta = 0, vector = TRUE)
db_sd <- sqrt(diag(dbVariance(allele_freqs, n = nrow(dbExample), theta = 0)))
plot(db_summary)
points(db_expect, pch = 4)
for(x in seq_along(db_sd)) segments(x0 = x, y0 = db_expect[x]-2*db_sd[x], y1 = db_expect[x]+2*db_sd[x])
legend("topright", bty = "n", pch = c(1,4,NA), c("Observed", "Expected", "95%-CI"),
lty = c(NA,NA,1))
Superimposed are the expected counts and associated 95\% approximate confidence intervals.
The labels of the first axis denote the number of matching and partial-matching loci.
The second axis is on a $\log_{10}$-scale.
Often it is of interest to look at the number of matching alleles rather than on
the number of matching and partial matching loci. As the number of matching alleles
is given by $a = 2m+p$ it is straight forward to look at these instead. The function
dbCollapse sums over combinations of $m$ and $p$ resulting in $a$ alleles, for $a = 0, \dots, 2L$:
a_expect <- dbCollapse(dbExpect(probs = allele_freqs, n = nrow(dbExample)))
a_observed <- dbCollapse(dbCompare(dbExample, trace = FALSE)$m)
plot(seq_along(a_observed)-1, a_observed, log = "y",
xlab = "Number of matching alleles", ylab = "Count")
points(seq_along(a_expect)-1, a_expect, pch = 4)
The table below lists the k-vector for some typical types of relationsships,
where k~i~ is the probability of having i alleles Identical-by-descent (IBD).
|Relationship |Full-siblings (FS) |First-cousins (FC) |Parent-child (PC) |Avuncular (AV) |Unrelated (UN) |
|:------------------|:------------------|:------------------|:-----------------|:--------------|:--------------|
|k=(k~2~,k~1~,k~0~) |(0.25, 0.5, 0.25) |(0, 0.25, 0.75) |(0, 1, 0) |(0, 0.5, 0.5) |(0, 0, 1) |
relatives <- list(
UN = dbExpect(probs = allele_freqs, k = c(0,0,1), collapse = TRUE),
FS = dbExpect(probs = allele_freqs, k = c(1,2,1)/4, collapse = TRUE),
FC = dbExpect(probs = allele_freqs, k = c(0,1,3)/4, collapse = TRUE),
PC = dbExpect(probs = allele_freqs, k = c(0,1,0), collapse = TRUE),
AV = dbExpect(probs = allele_freqs, k = c(0,1,1)/2, collapse = TRUE)
)
pos_range <- unlist(relatives)
pos_range <- range(pos_range[pos_range>0])
plot(NA, xlim = c(0,20), ylim = pos_range, log = "y", type = "n",
ylab = "Probability", xlab = "Number of matching alleles")
for(i in seq_along(relatives)) points(0:20, relatives[[i]], col = i, type = "b")
legend("top", horiz = TRUE, col = seq_along(relatives), inset = c(0, -0.1),
legend = names(relatives), lty = 1, bty = "n", xpd = TRUE)
The function optim.relatedness can be used to estimate the fraction of comparisons
between first-cousins, avuncular, parent-child and full-subling relatives in the
database.
References
references:
- id: brenner2007
author:
- family: Brenner
given: C
title: Arizona DNA Database Matches.
URL: https://dna-view.com/ArizonaMatch.htm
issued:
year: 2007
- id: budowle_1999
author:
- family: Budowle
given: B
- family: Moretti
given: TR
title: Genotype Profiles for Six Population Groups at the 13 CODIS Short Tandem Repeat Core Loci and Other PCR-Based Loci.
container-title: Forensic Science Communications 1(2).
URL: http://www2.fbi.gov/hq/lab/fsc/backissu/july1999/budowle.htm
issued:
year: 1999
- id: curran2010
author:
- family: Curran
given: JM
- family: Buckleton
given: JS
issued:
year: 2010
title: Re Sign mistake in allele sharing probability formulae of Curran, et al.
container-title: "Forensic Science International: Genetics, 4(3), 215--217."
- id: curran2007
author:
- family: Curran
given: JM
- family: Walsh
given: SJ
- family: Buckleton
given: J
issued:
year: 2007
title: Empirical testing of estimated DNA frequencies.
container-title: "Forensic Science International: Genetics, 1(3-4), 267--272."
- id: Rsolnp
author:
- family: Ghalanos
given: A
- family: Theussl
given: S
issued:
year: 2012
title: Rsolnp General Non-linear Optimization Using Augmented Lagrange Multiplier Method.
container-title: R package version 1.16.
- id: kaye2009
author:
- family: Kaye
given: DH
issued:
year: 2009
title: "Trawling DNA Databases for Partial Matches: What Is the FBI Afraid Of?"
container-title: Cornell Journal of Law and Public Policy, 19(1)
- id: mueller2008
author:
- family: Mueller
given: LD
issued:
year: 2008
title: Can simple population genetic models reconcile partial match frequencies observed in large forensic databases?
container-title: Journal of Genetics, 87(2), 101--108.
- id: troyer2001
author:
- family: Troyer
given: K
- family: Gilboy
given: T
- family: Koeneman
given: B
issued:
year: 2001
title: A nine STR locus match between two apparently unrelated individuals using AmFlSTR Profiler Plus and Cofiler
container-title: In Genetic Identity Conference Proceedings, 12th International Symposium on Human Identification.
- id: tvedebrink2010
author:
- family: Tvedebrink
given: T
issued:
year: 2010
title: Statistical Aspects of Forensic Genetics -- Models for Qualitative and Quantitative STR Data.
container-title: Ph.D. thesis, Department of Mathematical Sciences, Aalborg University.
- id: tvedebrink2012
author:
- family: Tvedebrink
given: T
- family: Eriksen
given: PS
- family: Curran
given: JM
- family: Mogensen
given: HS
- family: Morling
given: N
issued:
year: 2012
title: Analysis of matches and partial-matches in a Danish DNA reference profile data set.
container-title: "Forensic Science International: Genetics 6(3), 387-392."
- id: weir2004
author:
- family: Weir
given: BS
title: Matching and partially-matching DNA profiles.
container-title: Journal of Forensic Sciences, 49(5), 1009--1014.
issued:
year: 2004
- id: weir2007
author:
- family: Weir
given: BS
issued:
year: 2007
title: The rarity of DNA Profiles.
container-title: The Annals of Applied Statistics, 1(2), 358--370.
- id: wiki_birthday
author: Wikipedia
issued:
year: 2010
title: Birthday problem.
URL: https://en.wikipedia.org/wiki/Birthday_problem
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Introduction
In 2001, a poster was presented by a forensic scientist from Arizona [@troyer2001] at a scientific meeting on human identification. This poster reported a nine locus match between two unrelated men, one white and one black [@kaye2009]. It was not a full match. Both men had been typed at thirteen loci in total, and partially matched at three of the remaining four loci. These partially or non-matching loci would have excluded either man as a suspect if the other was the true offender. However, such a match seemed to be at odds with the random match probabilities. On one hand, these two men were in a DNA database which consisted of approximately 65,000 profiles, and on the other hand, the random match probabilities for the nine locus genotype were 1 in 754 million in Caucasians, 1 in 561 billion in African Americans, and 1 in 113 trillion in Southwest Hispanics, [@troyer2001].
As we will show later this is, in effect, an example of the birthday problem and therefore is regarded as completely predictable from a statistical perspective. However, most us who have taught a class on the birthday problem know that our students are initially sceptical.
DNA evidence, matches and partial matches
A pair of profiles is said to (fully) match if every allele at every locus that occurs in one profile occurs in the other. A pair of profiles are said to partially match if there are allelic matches at a subset of loci. @weir2004 provided a taxonomy for describing partial matches which depends on the number of fully, partial and non-matching loci between a pair of profiles. For any given pair of (full) profiles from the same multiplex there will be: $m$ loci where both alleles match, $p$ loci where only one of the alleles matches, and $L-m-p$ loci where none of the alleles match, where $L$ is the total number of loci. For example, the profile that @troyer2001 found was a 9/3 partial match on 13 loci - nine fully matching loci ($m=9$), three partially matching loci ($p=3$), and one non-matching locus.
DNA database comparison exercises
The Troyer-match came from a database matching exercise. In such an exercise every profile is compared with every other profile in the database. This type of comparison exercise is absolutely essential and, in addition, can provide some interesting information about the statistical properties of the population under consideration. We say that database comparison is essential in the first instance for the detection of duplicates. Duplicates may arise in a number of different ways. For example, an offender may provide a false name or an offender's name may be entered incorrectly. Alternatively, an offender may have an identical twin who is already in the DNA database. There are six pairs of identical twins in the New Zealand National DNA Database (NZDNADB). Forensic scientists are also interested in 'very close' matches. For example, a pair of profiles might fully match at nine loci out of ten and partially match at the remaining locus. This may happen either because the donors of the samples are very close relatives. It is more likely, however, that the profiles do not match because of allelic dropout, primer binding site mutations, nomenclature changes or somatic mutation.
The birthday problem
@weir2007 and others [@brenner2007; @curran2007; @mueller2008; @kaye2009] note that the presence of matching profiles in a DNA database is effectively an instance of the well-known birthday problem [@wiki_birthday] where, in a group of at least 23 randomly chosen people, there is a greater than 50% chance that one pair of them will have the same birthday. Early critics, implicitly calculating the expected number of matches as $N\pi$, used the wrong value for $N$ and the wrong value for $\pi$. Firstly, the number of pairwise matches, not the size of the database, is the relevant quantity. Although the database size is relatively small, the number of pairwise comparisons is very large. The Arizona database contained of $N=65,493$ profiles [@brenner2007]. Therefore, there are $$ N_\text{Comparisons}=\frac{N(N-1)}{2} = 2,144,633,778 $$ or approximately two billion, possible pairwise comparisons.
The pairwise profile matching probability
Secondly, the random match probability is not the probability we need.
The random match probability for the pair of profiles in question
answers the question "What is the probability that someone other than
these two men would have this particular nine locus profile." The
probability we actually want is "What is the probability that two
randomly selected profiles would match at nine loci, partially match
at three loci, and not match at one locus." . At a given locus the two profiles
may either match ($m/p=1/0$), partially match ($m/p=0/1$) or mismatch ($m/p=0/0$).
@weir2004, @weir2007 showed that the probabilities of mismatch ($P_{0/0}$),
partial match ($P_{0/1}$) and match ($P_{1/0}$) could be expressed by
\begin{align}
P(\text{Mismatch}) &= P_{0/0} =
D^{-1}{\theta^2(1{-}\theta)(1{-}S_2) +
2\theta(1{-}\theta)^2(1{-}2S_2{+}S_3) + (1{-}\theta)^3(1{-}4S_2{+}4S_3{+}2S_2^2{-}3S_4)},\
P(\text{Partial match}) &= P_{0/1} =
D^{-1}{8\theta^2(1{-}\theta)(1{-}S_2) + 4\theta(1{-}\theta)^2(1{-}S_3)
+ 4(1{-}\theta)^3(S_2{-}S_3{-}S_2^2{+}S_4)},\
P(\text{Match}) &= P_{1/0} =
D^{-1}{6\theta^3 + \theta^2(1{-}\theta)(2{+}9S_2) +
2\theta(1{-}\theta)^2(2S_2{+}S_3) +
(1{-}\theta)^3(2S_2^2{-}S_4)},
\end{align}
where $D=(1{+}2\theta)(1{+}\theta)$ and $S_k=\sum_{i}p_i^k$, with
$p_i$ being the frequency of the $i$\textsuperscript{th} allele.
@tvedebrink2010 showed that the overall probability of matching at $m$ and partially matching at $p$ loci, $\pi_{m/p}$, can be calculated by recursion over loci:
$$
\pi^{\ell+1}{m/p} = \left{\begin{array}{ll}
P^{\ell+1}{0/0}\pi^\ell_{m/p} +
P^{\ell+1}{0/1}\pi^\ell{m/p-1} +
P^{\ell+1}{1/0}\pi^\ell{m-1/p}& m>0~\text{and}~p>0,\
P^{\ell+1}{0/0}\pi^\ell{0/p} + P^{\ell+1}{0/1}\pi^\ell{0/p{-}1}&m=0~\text{and}~p>0,\
P^{\ell+1}{0/0}\pi^\ell{m/0} +
P^{\ell+1}{1/0}\pi^\ell{m{-}1/0}&m>0~\text{and}~p=0\
P^{\ell+1}{0/0}\pi^\ell{0/0}& m=0~\text{and}~p=0,
\end{array}\right.
$$
where the sum of the subscripts for each term on the right hand
side equals the subscript on the left hand side, e.g.\ the subscripts
of the last term in the first equation gives $1/0 + m{-}1/p = m/p$.
The initial step of the recursion has $\pi^1_{1/0} = P^1_{1/0}$,
$\pi^1_{0/1} = P^1_{0/1}$ and $\pi^1_{0/0} = P^1_{0/0}$.
The coancestry coefficient, $\theta$ or $F_{ST}$ models low levels of relatedness between individuals in the same subpopulation, and is typically between 0 and 0.03.
Using the package DNAtools
library(DNAtools)
The expected data format of the databases used as input for the functions in
DNAtools is a data frame, which is constituted by a column of DNA profile
identifiers (the first column) and two columns per typed DNA marker.
An example is given below:
data(dbExample, package = "DNAtools") knitr::kable(head(dbExample)[,1:9])
From which we estimate allele frequencies for later use:
allele_freqs <- lapply(1:10, function(x){ al_freq <- table(c(dbExample[[x*2]], dbExample[[1+x*2]]))/(2*nrow(dbExample)) al_freq[sort.list(as.numeric(names(al_freq)))] }) names(allele_freqs) <- sub("\\.1", "", names(dbExample)[(1:10)*2])
We use the example database provided by data(dbExample, package = "DNAtools")
to show the usage of the functions of DNAtools.
db_summary <- dbCompare(dbExample, hit = 6, trace = FALSE)
db_summary$m[!DNAtools:::up.tri(db_summary$m)] <- NA rownames(db_summary$m) <- paste0("**",rownames(db_summary$m),"**") kable(db_summary$m)
The hit argument returns pairs of profiles that fully match at hit (here 6)
or more loci.
There is a \code{plot} method for the returned object. Applying the \code{plot} method to \code{db_summary} yields the dropping ball-picture below. The right end of the distribution is interesting, due to the larger number of coinciding loci between profile pairs.
plot(db_summary)
By using the dbExpect and dbVariance functions we can compute the expected
counts and standard deviations for the number of matching profiles at various levels.
Both functions implement the recursions formulae of @tvedebrink2012, which enables
fast and accurate evaluations of the expectations and variances.
The dbExpect-function takes the locus-wise allele frequencies and number of profiles as arguments.
Furthermore, the $\theta$-correction is adjusted for if theta > 0 and adjustment
for close relatedness can be done by the k-argument, which is a three-dimensional
vector with non-negative entries summing to one (cf. below).
db_expect <- dbExpect(allele_freqs, n = nrow(dbExample), theta = 0, vector = TRUE) db_sd <- sqrt(diag(dbVariance(allele_freqs, n = nrow(dbExample), theta = 0))) plot(db_summary) points(db_expect, pch = 4) for(x in seq_along(db_sd)) segments(x0 = x, y0 = db_expect[x]-2*db_sd[x], y1 = db_expect[x]+2*db_sd[x]) legend("topright", bty = "n", pch = c(1,4,NA), c("Observed", "Expected", "95%-CI"), lty = c(NA,NA,1))
Superimposed are the expected counts and associated 95\% approximate confidence intervals. The labels of the first axis denote the number of matching and partial-matching loci. The second axis is on a $\log_{10}$-scale.
Often it is of interest to look at the number of matching alleles rather than on
the number of matching and partial matching loci. As the number of matching alleles
is given by $a = 2m+p$ it is straight forward to look at these instead. The function
dbCollapse sums over combinations of $m$ and $p$ resulting in $a$ alleles, for $a = 0, \dots, 2L$:
a_expect <- dbCollapse(dbExpect(probs = allele_freqs, n = nrow(dbExample))) a_observed <- dbCollapse(dbCompare(dbExample, trace = FALSE)$m) plot(seq_along(a_observed)-1, a_observed, log = "y", xlab = "Number of matching alleles", ylab = "Count") points(seq_along(a_expect)-1, a_expect, pch = 4)
The table below lists the k-vector for some typical types of relationsships, where k~i~ is the probability of having i alleles Identical-by-descent (IBD).
|Relationship |Full-siblings (FS) |First-cousins (FC) |Parent-child (PC) |Avuncular (AV) |Unrelated (UN) | |:------------------|:------------------|:------------------|:-----------------|:--------------|:--------------| |k=(k~2~,k~1~,k~0~) |(0.25, 0.5, 0.25) |(0, 0.25, 0.75) |(0, 1, 0) |(0, 0.5, 0.5) |(0, 0, 1) |
relatives <- list( UN = dbExpect(probs = allele_freqs, k = c(0,0,1), collapse = TRUE), FS = dbExpect(probs = allele_freqs, k = c(1,2,1)/4, collapse = TRUE), FC = dbExpect(probs = allele_freqs, k = c(0,1,3)/4, collapse = TRUE), PC = dbExpect(probs = allele_freqs, k = c(0,1,0), collapse = TRUE), AV = dbExpect(probs = allele_freqs, k = c(0,1,1)/2, collapse = TRUE) )
pos_range <- unlist(relatives) pos_range <- range(pos_range[pos_range>0]) plot(NA, xlim = c(0,20), ylim = pos_range, log = "y", type = "n", ylab = "Probability", xlab = "Number of matching alleles") for(i in seq_along(relatives)) points(0:20, relatives[[i]], col = i, type = "b") legend("top", horiz = TRUE, col = seq_along(relatives), inset = c(0, -0.1), legend = names(relatives), lty = 1, bty = "n", xpd = TRUE)
The function optim.relatedness can be used to estimate the fraction of comparisons
between first-cousins, avuncular, parent-child and full-subling relatives in the
database.
References
references: - id: brenner2007 author: - family: Brenner given: C title: Arizona DNA Database Matches. URL: https://dna-view.com/ArizonaMatch.htm issued: year: 2007 - id: budowle_1999 author: - family: Budowle given: B - family: Moretti given: TR title: Genotype Profiles for Six Population Groups at the 13 CODIS Short Tandem Repeat Core Loci and Other PCR-Based Loci. container-title: Forensic Science Communications 1(2). URL: http://www2.fbi.gov/hq/lab/fsc/backissu/july1999/budowle.htm issued: year: 1999 - id: curran2010 author: - family: Curran given: JM - family: Buckleton given: JS issued: year: 2010 title: Re Sign mistake in allele sharing probability formulae of Curran, et al. container-title: "Forensic Science International: Genetics, 4(3), 215--217." - id: curran2007 author: - family: Curran given: JM - family: Walsh given: SJ - family: Buckleton given: J issued: year: 2007 title: Empirical testing of estimated DNA frequencies. container-title: "Forensic Science International: Genetics, 1(3-4), 267--272." - id: Rsolnp author: - family: Ghalanos given: A - family: Theussl given: S issued: year: 2012 title: Rsolnp General Non-linear Optimization Using Augmented Lagrange Multiplier Method. container-title: R package version 1.16. - id: kaye2009 author: - family: Kaye given: DH issued: year: 2009 title: "Trawling DNA Databases for Partial Matches: What Is the FBI Afraid Of?" container-title: Cornell Journal of Law and Public Policy, 19(1) - id: mueller2008 author: - family: Mueller given: LD issued: year: 2008 title: Can simple population genetic models reconcile partial match frequencies observed in large forensic databases? container-title: Journal of Genetics, 87(2), 101--108. - id: troyer2001 author: - family: Troyer given: K - family: Gilboy given: T - family: Koeneman given: B issued: year: 2001 title: A nine STR locus match between two apparently unrelated individuals using AmFlSTR Profiler Plus and Cofiler container-title: In Genetic Identity Conference Proceedings, 12th International Symposium on Human Identification. - id: tvedebrink2010 author: - family: Tvedebrink given: T issued: year: 2010 title: Statistical Aspects of Forensic Genetics -- Models for Qualitative and Quantitative STR Data. container-title: Ph.D. thesis, Department of Mathematical Sciences, Aalborg University. - id: tvedebrink2012 author: - family: Tvedebrink given: T - family: Eriksen given: PS - family: Curran given: JM - family: Mogensen given: HS - family: Morling given: N issued: year: 2012 title: Analysis of matches and partial-matches in a Danish DNA reference profile data set. container-title: "Forensic Science International: Genetics 6(3), 387-392." - id: weir2004 author: - family: Weir given: BS title: Matching and partially-matching DNA profiles. container-title: Journal of Forensic Sciences, 49(5), 1009--1014. issued: year: 2004 - id: weir2007 author: - family: Weir given: BS issued: year: 2007 title: The rarity of DNA Profiles. container-title: The Annals of Applied Statistics, 1(2), 358--370. - id: wiki_birthday author: Wikipedia issued: year: 2010 title: Birthday problem. URL: https://en.wikipedia.org/wiki/Birthday_problem
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