Week 5 Mood and Anxiety Disorders in Children and AdolescentsRe.docx

Week 5: Mood and Anxiety Disorders in Children and
Adolescents
Reminders for WEEK 5:
· Required Readings & Media
· Assignment: Patient Education for Children and Adolescents
· To Prepare
· By Day 1, your Instructor will assign a mood or anxiety
disorder diagnosis for you to use for this Assignment.
· Assignments:
Generalized Anxiety Disorder
· Medication: duloxetine (age 7–17)
· Research signs and symptoms for your diagnosis,
pharmacological treatments, nonpharmacological treatments,
and appropriate community resources and referrals.
· The Assignment - In a 300- to 500-word blog post written for
a patient and/or caregiver audience, explain signs and symptoms
for your diagnosis, pharmacological treatments,
nonpharmacological treatments, and appropriate community
resources and referrals.
RESEARCH ARTICLE
Case rethinking: a protocol for reviewing criminal
investigations
D. Kim Rossmo*
Center for Geospatial Intelligence and Investigation, School of
Criminal Justice, Texas State

University, San Marcos, TX, USA
Mistakes in decision-making have been identified as the most
common type of error
in police investigations. Consequently, wrongful convictions
and other types of crimi-
nal investigative failure may require a complete case
‘rethinking,’ particularly when
new evidence disrupts the existing theory. A rush to judgment
resulting in a prema-
ture shift from an evidence-based to a suspect-based
investigation can produce a
number of problematic thinking errors. Faulty assumptions,
tunnel vision, groupthink,
and other cognitive biases and organizational traps hinder
evidentiary interpretation
and evaluation. This article outlines a protocol for reviewing
evidence and rethinking
a wrongful conviction or unsolved crime when the existing
investigative theory
appears to be incorrect. The protocol involves four stages: (1)
evidence; (2) interpre-
tation; (3) patterns; and (4) analysis.
Keywords: criminal investigation; police detectives; criminal
investigative failures;
cold cases; wrongful convictions
Introduction
Police agencies increasingly use sophisticated forensic
techniques and powerful
computers in their response to major crimes. What can
sometimes be forgotten in this
emphasis on technology, however, is the need to properly think
about the information

produced by these and other approaches. Police investigations
require rational analysis
and logical thinking. When cognitive biases and organizational
traps distort the
process, wrongful convictions and other types of criminal
investigative failure can
result.
This article presents a case rethinking protocol that has been
successfully used on
unsolved crimes and wrongful convictions. The focus is on
inquiries of crimes, such as
murder, where what happened remains unknown. Reviewing a
case often involves the
effort to discover new evidence; however, this method is
concerned with rethinking
existing evidence. Aspects of the protocol may also be helpful
in guiding new investiga-
tions.
The types and causes of thinking errors that can obstruct a
criminal investigation are
first discussed. A protocol for the systematic review of a
wrongful conviction or
unsolved case in which the existing investigative theory appears
incorrect is then out-
lined. The protocol involves four stages: (1) evidence; (2)
interpretation; (3) patterns;
and (4) analysis.
*Email: [email protected]
© 2014 Taylor & Francis
Police Practice and Research, 2016
Vol. 17, No. 3, 212–228,
http://dx.doi.org/10.1080/15614263.2014.978320

Criminal investigative failures
A criminal investigative failure is defined as an unsolved crime
that should have
been solved or a crime that was incorrectly ‘solved’ (i.e., a
wrongful conviction).
Many people tend to believe the criminal justice system is fair
and competent, with
police, prosecutors, and jurors logically basing their decisions
on reliable evidence.
Unfortunately, as The Innocence Project and similar groups
have shown, innocent
people are sometimes prosecuted and convicted. The reality is
the system makes
mistakes. It is difficult to determine how often such errors
happen, but estimates of
the frequency of wrongful convictions range from 0.5% (all
felonies; Huff, Rattner,
& Sagarin, 1996), to 4.1% (death row inmates; Gross, O’Brien,
Hu, & Kennedy,
2014), to 5% and higher (murder and sexual assaults; Roman,
Walsh, Lachman, &
Yahner, 2012).
Eyewitness misidentifications, improper forensic science, and
false confessions are
the major causes of wrongful convictions (Innocence Project,
2013). However, investiga-
tive thinking errors often underlie these direct causes. Mistakes
in decision-making have
been identified as the most common type of error in police
investigations (Irvine &
Dunningham, 1993). Problems can arise when police rush to

judgment during the early
stages of a case and prematurely shift the investigation from
evidence-based to
suspect-based. If this occurs before all the evidence has been
collected and analyzed,
there is a significant risk of tunnel vision.
Human beliefs are the product of expectations, desires, and
evidence; the more
ambiguous the last, the stronger the influence of expectations
and desires (Snook,
2000). ‘There can be little doubt that our beliefs influence the
processes by which we
seek out, store, and interpret relevant information’ (Ross &
Anderson, 1982, p. 149). It
is therefore important to collect as much evidence as possible
before theorizing about
the crime and suspects; once that has happened, expectations
and desires start influenc-
ing the thinking process.
The need for an alternative approach may become evident if
DNA reveals a wrong-
ful conviction or disrupts the dominant theory, or a crime is still
unsolved after a
lengthy period. However, rethinking an investigation can be
extremely difficult and
challenging. Cognitive biases and organizational traps may exist
that hinder evidence
interpretation and evaluation. As Heuer (1999, p. 125) warns,
‘once information rings a
bell, the bell cannot be unrung.’
Thinking errors
When my information changes, I alter my conclusions. What do

you do, sir? (John
Maynard Keynes)
In an ideal world, we would make the best possible decisions
after a careful evalua-
tion of all available evidence. In reality, our thinking is
frequently impaired by cognitive
biases. Within the context of a criminal investigation, such
systematic errors in thinking
can result in an unsolved crime or a wrongful conviction (Jones,
Grieve, & Milne,
2008a; Rossmo, 2009). Tunnel vision and confirmation bias are
amongst the most prob-
lematic thinking errors that can be made by detectives. Faulty
assumptions, probability
errors, and groupthink sometimes play supporting roles.
Police Practice and Research: An International Journal 213
The initiating problem is often a rush to judgment – reaching a
conclusion before all
the evidence has been considered. A premature judgment can
lead to tunnel vision and
confirmation bias, prevent subsequent evidence from being
properly evaluated, and
result in a criminal investigative failure. These errors and the
aggravating role of
groupthink are discussed below.
Premature judgment
For many reasons, detectives may jump to conclusions before an
investigation is com-
plete. Community fear, media and political pressure,

organizational stresses, personal
ego, and a compelling desire to apprehend a dangerous offender
can all result in a pre-
mature arrest. The pressures are exacerbated if the crime is
particularly heinous or the
victim is a young child.
An overreliance on intuition can lock in a premature judgment.
Humans have both
intuitive and rational decision-making processes (Kahneman,
2003); while most people
are familiar with the former, the latter is often misunderstood.
Intuition (sometimes
mistakenly referred to as ‘gut instinct’) operates at a below-
consciousness level and is
automatic, fast, and powerful (Myers, 2002). It is learned
slowly and typically involves
the use of heuristics. Because of its implicit nature, intuition is
difficult to control and is
prone to error; it exists because it helps promote survival, not
because of its accuracy.
Different situations require different types of judgment and
when a decision has to be
made quickly under conditions of uncertainty, intuition can be
useful (Wright, 2013).
But when we have reliable data and adequate time, reasoning is
the better option; com-
plex tasks, such as major crime investigations, require analysis
and logic (Jones et al.,
2008a).
‘Investigators need not be especially sure that they have the
right person to sway
their investigation toward an early suspect’ (O’Brien, 2009, p.
328). When that happens,
police prematurely move from an evidence-based to a suspect-

based investigation
(Rossmo, 2009). In the former mode, detectives have not yet
determined who the offen-
der is; they are still searching for and gathering information and
evidence to determine
what happened during the crime and who might be a suspect. In
the latter mode, detec-
tives have decided they know who the guilty person is, and their
investigation shifts to
preparing for prosecution.
The central problem therefore originates from judgments based
on only subsets
rather than the totality of evidence. Evidence discovered post-
judgment is then less
likely to be evaluated in an unbiased manner, and relevant
evidence that fails to support
the investigative conclusion may not be collected or properly
analyzed; in some
instances, it may not even be recognized as evidence.
If a flawed forensic test is done early in an investigation, it can
have a powerful
effect because of the authority given to science (Garrett &
Neufeld, 2009). However,
forensic science is not always very ‘scientific,’ and some
forensic analyses are more
subjective than objective. There have been a number of
wrongful convictions attrib-
uted to bad forensic science (National Research Council, 2009).
The uncritical accep-
tance of flawed forensic results at the beginning of a case can
result in premature
judgment and poor investigative decision-making, including the
dismissal of important
evidence later discovered simply because it conflicts with the

forensic findings. Inves-
tigations have been sent down the wrongful conviction road by
several types of
unsound forensic analyses, including time of death estimates
(Michael Morton, Texas),
214 D.K. Rossmo
sexual assault examinations (David Camm, Indiana), blood
testing (Greg Taylor, North
Carolina), bite mark comparisons (Willie Jackson, Louisiana),
and arson indicators
(Cameron Todd Willingham, Texas).
Faulty assumptions
Assumptions are often necessary in a criminal investigation,
especially during its early
stages when information tends to be limited. As evidence is
collected, however, it is
important to reevaluate all investigative assumptions and
discard those no longer valid.
Unfortunately, this does not always happen. Given enough time,
it is even possible
for an unsubstantiated assumption to reify and harden into
‘fact.’ If investigators
working from a faulty assumption develop tunnel vision, they
are unlikely to later
detect the underlying error. During the DC Sniper case, the
assumption that the killers
were driving a white van evolved into a near-certainty during
the early days of the
investigation (Horwitz & Ruane, 2003). However, this white
van – in fact, originally

reported as a white box truck – did not exist; the killers were
driving a blue Caprice
sedan.
Probability errors
A detective may have to estimate the probability of a particular
event in order to
decide if it is significant to the investigation or merely
coincidence. However,
humans tend to have a poor understanding of probability,
particularly at the intuitive
level (Gigerenzer, 2002). As a result, probability mistakes
commonly occur; in the
context of a criminal investigation, these mistakes can create
tunnel vision and
sustain confirmation bias. Several cases have been sabotaged by
probability errors
originating from the flawed estimates of investigators, forensic
scientists, medical
pathologists, profilers, and prosecutors. Problem areas include
violating the laws of
probability, ignoring base rates, using vague language
(‘common,’ ‘rare,’ ‘risky’),
and failing to understand the nature of randomness in large
investigations involving
many suspects (Rossmo, 2009).
The tragic case of British solicitor Sally Clark is an infamous
example of injustice
caused by a probability error. Clark was convicted of murdering
her two sons who
died from sudden infant death syndrome (SIDS). The only
evidence against her was
the assertion that the occurrence of two SIDS deaths in a single
family was highly

improbable, an estimate calculated by a pediatrician, Roy
Meadow. Unfortunately, he
made several errors in his analysis: he ignored genetic effects
and incorrectly assumed
SIDS cases are independent within families; he committed an
ecological fallacy by
equating individual-level risk to overall population risk; and he
failed to realize that
SIDS is common enough on a national level that a recurrence
happens to some unfor-
tunate family in England about once every 18 months (Hall,
1999). The Royal Statisti-
cal Society publically criticized Dr Meadow’s estimate and
Clark’s conviction was
eventually quashed on appeal, but only after she had spent more
than three years in
prison.
Tunnel vision
Tunnel vision (also called incrementalism) results from a
narrow focus on a limited
range of alternatives.
Tunnel vision is insidious …. It results in the [police] officer
becoming so focussed upon
an individual or incident that no other person or incident
registers in the officer’s thoughts.
Thus, tunnel vision can result in the elimination of other
suspects who should be investi-
gated. (Cory, 2001, p. 37)

Tunnel vision can be a product of satisficing, or the selection of
the first identified
alternative that appears ‘good enough’ (Findley & Scott, 2006;
Simon, 1956). Arrest-
ing the first likely suspect, then closing the investigation off to
alternative theories, is
a recipe for disaster. Not surprisingly, tunnel vision has been
identified as a major
cause of wrongful convictions (FPT Heads of Prosecutions
Committee Working
Group, 2004).
Gould, Carrano, Leo, and Young (2013) link tunnel vision to the
broader issue of
systemic failure in wrongful convictions: ‘tunnel vision helps
explain how one error
often leads to additional errors in an erroneous conviction. It
contributes and facilitates
system breakdown because it dismantles the rigorous testing of
evidence that makes the
investigative and adversarial processes function effectively’ (p.
xxi). They also connect
tunnel vision to the concept of escalation of commitment (also
known as sunk costs;
Brockner, 1992; Coleman, 2010; Staw, 1981) to explain why
entrenchment of beliefs
occur even when strong contradictory evidence has emerged:
As more resources – money, time, and emotions – are placed
into a narrative involving a
suspect, the actors involved are less willing or able to process
negative feedback that
refutes their conclusions. Instead, actors want to devote
additional resources in order to
recoup their original investment. As a result, evidence that
points away from a suspect is

ignored or devalued, and latent errors are overlooked. At this
point, the police are working
to rule in rather than rule out the suspect, and prosecutors have
moved from ‘inspection’
mode to ‘selling’ mode.1 Escalation of commitment contributes
and facilitates system
breakdown because it dismantles the rigorous testing of
evidence that makes the adversarial
process function effectively. (pp. 86–87)
Confirmation bias
Confirmation bias (also called verification bias) is a type of
selective thinking. Once a
hypothesis has been formed, our inclination is to confirm rather
than refute – we tend to
look for supporting information, interpret ambiguous
information as consistent with our
hypothesis, and minimize any inconsistent evidence (Koehler,
1991). Types of confirma-
tion bias include biased search for evidence, biased
interpretation of information, and
biased memory (selective recall).2
Confirmation bias can cause a detective to focus on evidence
confirming the investi-
gative theory, while ignoring or refusing to look for
contradicting evidence (Stelfox &
Pease, 2005). Existing evidence is interpreted in a biased
manner; evidence that
supports the investigative theory is taken at face value, while
contradicting evidence3
is skeptically scrutinized (Ross & Anderson, 1982). Research
has shown experienced
police investigators consider witnesses less credible if they

exonerate a preferred
suspect than if they help confirm guilt (Ask & Granhag, 2007).
Other manifestations
of confirmation bias include the failure to search for evidence
that might prove a
216 D.K. Rossmo
suspect’s alibi, not utilizing such evidence if found, and
refusing to consider alternative
hypotheses.
While the recording and documenting functions integral to a
professional criminal
investigation minimize the risk of biased memory, there have
been some instances of
detectives ‘forgetting’ to document important evidence. There
have also been several
cases where a prosecutor decided exculpatory evidence was not
relevant to the case
and failed to share it with defense counsel. It is difficult to say
whether such actions
are the result of rationalizations fed by confirmation bias or the
product of outright
unethical behavior. In the wrongful conviction of Michael
Morton for his wife’s
murder, Ken Anderson, the district attorney of Williamson
County, Texas, failed to
inform Morton’s defense counsel of key physical and witness
evidence that
eventually identified the real killer (Colloff, 2012). Anderson
was eventually
sentenced to a jail term after being found in contempt of court
and disbarred (he

had become a judge).
Confirmation bias can be detected in a criminal investigation by
identifying:
� evidence ‘missed’ post-judgment (ignored leads, unexplored
lines of inquiry, lost
evidence, unanalyzed evidence); and
� biased treatment of post-judgment evidence (disregarded,
downplayed, or distorted
evidence, illogically considered evidentiary implications).
Confirmation bias can be tested for by considering what would
happen if the order
of evidential discovery was changed. The conclusions reached
by detectives should not
depend on the particular sequence in which the evidence was
discovered. If altering the
evidential order changes the case conclusion, there is likely a
problem with the investi-
gative logic.
The strength and perseverance of confirmation bias is
dramatically illustrated by the
convoluted theories put forth by those prosecutors and police
who ‘never can admit a
mistake.’ Prosecutors are normally thankful for DNA evidence
in a criminal case;
however, their reaction can be quite different if the DNA
establishes the innocence of
someone they originally believed guilty. After the bodies of two
little girls were found
in a remote wooded area in Zion, Illinois, police arrested and
charged the father of one
of the victims with murder. When sperm recovered from the

girl’s vagina, mouth, and
anus was tested and found not to match her father’s DNA, the
prosecutor dismissed the
laboratory results, suggesting the victim contacted the sperm
while playing in the woods
where couples met to have sex (Martin, 2011). Later, after the
DNA was matched to a
convicted violent serial rapist who was a friend of the victim’s
brother, the prosecutor
again argued the sperm was unrelated to the crime. This time,
he theorized the rapist
masturbated while sleeping over at the house of the victim’s
brother. Later, while the
victim was watching a movie from the same bed, she touched
his sperm and
subsequently transferred it to her other body parts (i.e., the
inside of her vagina, mouth,
and anus).
In another example, from a Canadian case, DNA testing of
sperm found on the
victim’s clothing matched a convicted rapist from her own
neighborhood, not the
man who spent 23 years in prison for her murder. Rather than
acknowledging
the failure, some police officers tried to explain away the DNA
by suggesting the
convicted man had actually killed her. Then, after he left her
body in a back alley,
the rapist came along and had sex with her frozen corpse before
it was discovered
(Boyd & Rossmo, 1994).

Ross and Anderson (1982) observed:
it is clear that beliefs can survive potent logical or empirical
challenges. They can survive
and even be bolstered by evidence that most uncommitted
observers would agree logically
demands some weakening of such beliefs. They can even
survive the total destruction of
their original evidential bases. (p. 149)
Stubborn and irrational belief perseverance by the state in the
face of significant
conflicting new evidence is inconsistent with both justice and
reality.
Groupthink
Within organizations with strong subcultures like the police,
groupthink can enable and
exacerbate confirmation bias. Groupthink is the reluctance to
think critically and chal-
lenge the dominant theory. It occurs in highly cohesive groups
under pressure to make
important decisions. The main symptoms include (Janis, 1982):
(1) Power overestimation
(a) belief in the group’s invulnerability, resulting in
unwarranted optimism and
risk taking; and
(b) ignoring the ethical consequences of decisions because of a
belief in the
morality of the group’s purpose.
(2) Close-mindedness

(a) group rationalizations and the discrediting of warning signs;
and
(b) negative stereotyping of the group’s opponents (they are
regarded as evil,
stupid).
(3) Uniformity pressures
(a) conformity pressures (those who disagree with the dominant
views or
decisions are seen as disloyal);
(b) self-censorship (the withholding of dissenting views and
counterarguments);
(c) shared illusion of unanimity (silence is perceived as consent,
and there is an
unsubstantiated belief everyone agrees with the group’s
decision); and
(d) self-appointed mindguards (group members shield each
other from conflict-
ing information).
Groupthink has several negative outcomes (FPT Heads of
Prosecutions Committee
Working Group, 2004). Afflicted groups selectively gather
information and fail to seek
expert opinions. They neglect to critically assess their ideas,
examine few alternatives,
and fail to develop contingency plans. Groupthink in a major
crime investigation perpet-
uates confirmation bias and preserves flawed decisions.
Case rethinking protocol

The following protocol has been designed for the systematic
review of a case in which
the investigative theory appears to be wrong.4 Such a
realization may follow the discov-
ery of significant new evidence that disrupts the existing theory,
but it can also emerge
after a case has remained unsolved for an extended period of
time. Rethinking an
218 D.K. Rossmo
investigation first requires a detective to unravel the case –
wipe the slate clean, unlearn
what is believed, and abandon existing theories and suspects.
Crimes are solved by information derived from evidence
(Willmer, 1970). The
purpose of the protocol is to create a focus on the evidence, its
strengths and
weaknesses, and the overall evidentiary pattern. Facts must be
distinguished from
beliefs, certainties from suspicions, and probabilities from
possibilities. An investiga-
tor must be able to answer the question, ‘How do you know
what you think you
know?’
The protocol involves four stages: (1) evidence; (2)
interpretation; (3) patterns; and
(4) analysis. Each stage is explained in detail below, and then
briefly illustrated in a text
box using a short example based on an evidence item – the
written statement of an eye-

witness – from a sexual murder case.
Evidence
Man prefers to believe what he prefers to be true. (Francis
Bacon)
The first step in rethinking a case is to identify the evidence5 in
the investigation and
assess its reliability. Evidence is a tangible and recorded fact
relevant to the crime. In
some situations, the absence of something can be considered
evidence. Theories,
assumptions, and inferences are not evidence.
It is essential to identify the origin of each item of evidence,
whether it is a police
report, crime scene photograph, witness statement, or laboratory
analysis. Nothing
should be taken for granted. Rumors emerge in large
investigations that can solidify into
‘facts’ after sufficient retelling; ‘creeping credibility’ can turn a
hypothesis into received
knowledge over time. If a record cannot be found, then there is
no evidence. Identifying
the evidence and its supporting documentation establishes the
‘facts of the case’ and the
information content of the investigation.
There are only three ways to solve a crime: a witness; a
confession; or physical
evidence (Klockars & Mastrofski, 1991). Detectives typically
have a good
understanding of the technical nature of these evidence types,
including their benefits
and problems. However, they do not always fully understand the

probative value of
evidence (Robertson & Vignaux, 1995). Evidence has both
significance and reliability
(Griffith & Tversky, 2004). People tend to place more
importance on significant
evidence even if its reliability6 (the probability of its
truthfulness) is low. All evidence
has an error rate – eyewitnesses make misidentifications,
suspects give false confessions,
and scientific tests produce false positives. The possibility of
mistakes and human error
always exists. It is therefore necessary to estimate the reliability
of an item of evidence
in order to determine how much weight it should be given.
Source reliability, forensic
test error rates, research on evidentiary
consistency/trustworthiness, and any other
known issues should be considered. While it is usually
impossible to assign exact
probabilities, investigators can roughly evaluate evidence on an
ordinal scale from low
to high reliability. Evidence should not be considered more
reliable because the
investigator wants it to be, and it should not be deemed
unreliable simply because it is
inconsistent with the prevailing investigative theory.
Evidence
The evidence item used to illustrate the protocol is a written
statement from an
eyewitness in a sexual murder that occurred in Saskatoon,

Saskatchewan. The victim
was Gail Miller, a nursing assistant who was attacked while
walking to the bus stop
on her way to work on an icy January morning. She was raped
and stabbed; her
body was later found lying in the snow in a back alley a block
from her home
(Boyd & Rossmo, 1994).
The witness, Nichol John, was one of three Regina teenagers,
along with David
Milgaard and Ron Wilson, who set out on a road trip to buy
drugs. They drove to
Saskatoon to pick up another friend (who had money), but
ended up lost before they
found his house and got stuck in the snow. It was very cold, so
John stayed in the
car while Milgaard and Wilson went for help. John told police
she then saw Milg-
aard attack a woman on the street.
Written statement from witness Nichol John:
After we got to Saskatoon we drove around for about 10 or 15
minutes. Then
we talked to this girl. This was in the area where Sgt. Mackie
drove me around.
Ron was driving the car at this time. He drove to the curb where
Dave spoke to
this girl.
Dave was on the outside passenger side of the front seat. Dave
opened the door
to talk to this girl as she approached along the sidewalk.
Dave asked this girl for directions to either down town or

Pleasant Hill. He
offered to give her a ride to where ever she was going. She
refused the ride.
Dave closed the door and said “The stupid bitch.”
We started to drive away and only went about half a block when
we got stuck.
We ended up stuck at the entrance to the alley behind the
funeral home.
Ron and Dave got out and they tried to push the car. They
couldn’t get it out.
I recall Dave going back in the direction we had spoke to the
girl. Ron went the
other way past the funeral home.
The next thing I recall is seeing Dave in the alley on the right
side of the car.
He had a hold of the same girl we spoke to a minute before. I
saw him grab her
purse. I saw her grab for her purse again. Dave reached into one
of his pockets and
pulled out the knife. I don’t know which pocket he got the knife
from. The knife
was in his right hand. I don’t know if Dave had a hold of this
girl or not at this
time. All I recall seeing is him stabbing her with the knife.
The next I recall is him taking her around the corner of the
alley. I think I ran
after that. I think I ran in the direction Ron had gone. I recall
running down the
street. I don’t recall seeing anyone. The next thing I knew I was
sitting in the car
again. I don’t know how I got back to the car.

Reliability assessment: uncertain. It is unlikely Nichol John is
mistaken in her
identification of the killer, as she knows Milgaard and was
apparently situated near
him when the attack occurred. However, John may be deceitful.
She is a 16-year-old
street girl who uses drugs. This statement is inconsistent with
her first statement to
the police. She was under pressure by detectives to talk about
the Miller case; the
day before she gave this statement she was arrested and spent
the night in jail. She
continued to travel with Milgaard after the murder. She now
claims she cannot
remember anything about the crime.
220 D.K. Rossmo
Interpretation
Evidence must be explained and interpreted to give it meaning.
Potential inferences
from the evidence and its context are identified in the protocol’s
second stage. These are
reasonable deductions that can be based on the evidence. An
inference can be thought
of as the extrapolation from the core facts of an item of
evidence to its logical, proba-
ble, or (reasonably) possible conclusions, not all of which will
necessarily be correct.
For example, DNA recovered from the vaginal vault of the
victim in a child sex murder
leads to a strong inference it was left by the killer. However,

that inference is not as
strong if the victim was a sex trade worker.
A likely inference from an unreliable item of evidence is still
not probable; similarly,
an unlikely inference from a reliable item of evidence is also
improbable. While infer-
ences are related to theories, they are distinct; the former is an
extension from a single
item of evidence, while the latter is built up from the totality of
evidence in an investi-
gation.
The explanation for an item of evidence – the theory of its
cause – is an important
inference. While there can be a tendency to focus on the most
obvious explanation, full
consideration should be given to all feasible options. Possible
explanations can be
ranked by likelihood; as this assessment might change once an
item of evidence is con-
sidered in relationship to the other evidence in the case, it is
important not to prema-
turely eliminate possibilities (existing biases become most
treacherous at this point).
Interpretation
John’s statement is significant and highly probative. If accurate,
it identifies
Milgaard as Miller’s murderer. The following high probability
inferences can be
made from this item of evidence (lower probability inferences
are not listed in this
example):

(1) Nichol John’s statement provides an exact location for the
stabbing (a loca-
tion different from where Miller’s body was found), suggesting
there should
be blood and perhaps other physical evidence at the site.
(2) The location of the attack indicates the likely route walked
by Miller to her
bus stop the morning of the murder.
(3) As described, the attack dictates Miller should have stab
wounds and a
matching pattern of cuts in the clothing she was wearing (winter
coat and
nursing uniform).
Patterns
The evidence in an investigation is simply a collection of facts.
To understand what
happened in a crime, potential causes and inferences from the
evidence have to be con-
nected and understood as a narrative or story. The third stage of
the protocol involves
looking for patterns that can be derived from the constellation
of evidence, and deter-
mining connections (influences between items of evidence) and
sequences (what flows
from what). It is also important at this point to analyze
consistencies among inferences
as these suggest reliable narratives.

In many cases, the pattern will be patchy and incomplete. In
such instances, it is
important to refrain from ‘filling in the blanks’ beyond what
can be supported by the
available evidence. In other cases, multiple competing patterns
of varying probabilities
will emerge. Despite the human tendency to avoid uncertainty,
it is necessary to con-
sider all viable options; this often requires consideration of
contradictory possibilities.
Patterns
Part of Nichol John’s statement is consistent with the statement
of her friend,
Ron Wilson, who also said he saw Milgaard with a knife.
However, John’s description of the location of the attack is not
consistent with
the route the victim typically took to the bus stop. Miller
usually followed the short-
est path, a three-minute trip. On the morning of her murder, she
left her home five
minutes before the bus was due (the temperature was −42° F),
and it is unlikely she
would have been at the spot where John said the attack
occurred.
John’s statement is also inconsistent with some of the physical
evidence. No
blood was found near the place where she claimed the attack
happened. (It is possi-
ble she was mistaken about the location, but no blood was found
anywhere other
than by Miller’s body.) Furthermore, while the pattern of cuts in
Miller’s winter coat

matched the pattern of her stab wounds, there were no cuts in
her nursing uniform.
This meant that, at some point, Miller’s coat was taken off, her
uniform was pulled
down, and her coat was put back on again – none of which was
described by John.
Analysis
Up to this point the focus has been on the case evidence. In the
analysis stage, evi-
dence patterns are now used to generate and assess investigative
theories or hypothe-
ses (see Jones et al., 2008a). It is best to start with broad
comprehensive categories,
drilling down and becoming more specific as evidence permits.
Not all the evidence
may be helpful in this process. Caution is needed when working
with ambiguous evi-
dence, which is more vulnerable to expectations, desires, and
other biases (Snook,
2000). ‘When the evidence is ambiguous, subtle influences on
how an investigator
sees it could affect the outcome. A weak case can start to look
strong when the inves-
tigator overlooks potentially fruitful leads in another direction’
(O’Brien, 2009,
p. 329). To avoid this trap, it is important to consider the
diagnosticity of an item of
evidence.7 Diagnosticity originally referred to the ability of a
medical test to identify
a patient’s disease. In the context of a criminal investigation,
the diagnosticity of an
item of evidence is a function of its ability to distinguish
between different hypothe-
ses, such as a suspect’s guilt or innocence.

When developing case theories, investigators should remember
Occam’s razor, the
Principle of Parsimony – if more than one explanation for the
evidence is possible, it is
preferable to start with the simplest (the one with the fewest
assumptions). The narrative
of the crime should not be any more complicated than necessary
to properly explain the
evidence. An important principle in the physical sciences,
Occam’s razor can also be a
helpful investigative concept. While many things are possible in
a crime, only some are
probable.
222 D.K. Rossmo
In the special case of only two competing theories, the available
evidence can be
used to determine which is the most likely. The probability of
an item of evidence given
the first theory is compared to the probability of the evidence
given the second theory.8
This process is then repeated for all items of evidence and the
results combined. The
final product indicates which theory is the more likely based on
the totality of the evi-
dence (Blair & Rossmo, 2010).
The last step involves determining how particular suspects fit
into the various crime
theories and identifying any suspect-related information that
might link back to the

crime, such as a relationship with the victim or prior criminal
history. It should be
remembered, however, that a suspect’s bad character is not
direct evidence of a crime,
absent information that provides a connecting link (e.g., a
motive).
If it becomes necessary to use assumptions at this point, they
should be written
down and all the conclusions derived from them so identified.
To avoid tunnel vision,
the alternatives to an assumption (i.e., competing assumptions)
must also be identified
and considered.
Analysis
There are significant inconsistencies between Nichol John’s
statement and other
reliable evidence in the case. Consequently, the reliability of
her statement should be
reduced to low. John most likely lied in order to get the police
to leave her alone.
Her statement should not be relied upon for theory generation or
suspect prioritiza-
tion.
Unfortunately, considerable weight was given to John’s
statement during the ori-
ginal investigation and trial, even though she continued to claim
she could not
remember anything. Milgaard was convicted of Miller’s murder
and spent over two
decades in prison. DNA testing eventually identified the real
killer, a man who used
the same bus stop as the victim every morning (Commission of

Inquiry Into the
Wrongful Conviction of David Milgaard, 2005).
Visualization
It is important to be thorough when rethinking a case and the
results for each stage of
the protocol should be comprehensively recorded. It can be
helpful to graphically depict
the evidence in a diagram. Figure 1 shows an example of such a
diagram based on a tri-
ple murder case in Indiana. The wife and two children of David
Camm, a former Indi-
ana State Police trooper, were shot in the garage of their home.
Despite having an alibi,
David Camm was tried and convicted of their murders, the case
relying heavily on
blood spatter evidence. Following a successful appeal, DNA
found on a sweatshirt
recovered from the crime scene was linked to Charles Boney, a
violent felon with multi-
ple convictions for attacks against women. His palm print was
also found on the side of
the victims’ vehicle. Boney gave various conflicting
explanations for his presence at the
Camm house, the sixth and final version of which had him there
to sell David Camm a
handgun. The district attorney pursued murder charges against
Boney, but he also chose
to retry Camm. Figure 1 shows a simplified version of the
evidentiary support for each
of the three theories of the case: (1) David Camm did the
murders alone, the first
prosecution theory; (2) David Camm and Charles Boney did the
murders together, the

second prosecution theory; and (3) Charles Boney did the
murders alone, the defense
theory. Key evidence items are shown in small boxes situated
under the particular the-
ory they support (interpretations are not shown); the stronger
the reliability of the evi-
dence, the heavier the outline of the square. Clusters of
consistent evidence are circled.
The third theory won out in court – in separate trials, juries
found Boney guilty and
David Camm not guilty.
Discussion
It is easy for detectives to become overwhelmed by the details
in a major crime investi-
gation. The signal-to-noise ratio is often low and larger patterns
can be missed because
of a focus on minutiae. There is a tendency to use cognitive
heuristics (‘mental short-
cuts’) under such conditions, even though these can lead to
cognitive biases and then to
errors. One need look no further than conspiracy theorists to
find examples of how
biases can cause individuals to interpret and distort evidence to
support the most amaz-
ing conclusions.
The purpose of this protocol is to assist investigators rethink a
case when the exist-
ing theory appears incorrect. It requires a detective to start over
by reexamining the

structure of the case, beginning with its foundation – the
evidence. Investigative hypoth-
eses are generated only after the evidence has been inventoried
and assessed, explained
and interpreted, and connections and patterns identified. By
focusing on and working
from the evidence, the investigation becomes grounded in
reality instead of being based
Figure 1. Case evidence pattern in the Camm triple murder
investigation.
224 D.K. Rossmo
on beliefs, hunches, or biases. This method can help correct
thinking errors resulting
from a rush to judgment, tunnel vision, confirmation bias, and
similar cognitive and
organizational traps.
The rethinking approach requires an investigator to abandon the
existing theory and
adopt a completely open mind. However, this is much easier
said than done. Research
has shown that awareness of cognitive bias does not make it
easier to avoid (Heuer,
1999). Consequently, rethinking a case is more difficult for
those detectives with prior
involvement in the investigation. The protocol works best when
followed by someone
with no previous connection to the crime, ideally, an
investigator from an outside
agency. This is the policy in England; unsolved murders are
reviewed at prescribed time

periods (e.g., 28 days, 12 months) by a senior investigating
officer (SIO) who is not
involved in the case. In high profile, complex, or sensitive
investigations, it is recom-
mended the SIO be from another police force (ACPO, 2006).
External peer reviewers,
for all the psychological and organizational reasons discussed
above, are more apt to
notice mistakes and omissions and much more likely to point
them out.
Conclusion
The purpose of the case rethinking protocol is to shift the focus
of an investigation from
the prevailing theory/suspect to the evidence. Existing theories
and assumptions can cre-
ate cognitive traps that prevent a crime from being solved.
There is a tendency to
‘explain away’ inconsistent evidence discovered post-theory,
rather than letting it con-
tribute to the development and prioritization of investigative
hypotheses. This is confir-
mation bias.
The protocol does not provide a magic equation for the right
answer; it is nothing
more than a systematic thinking tool, an analytic framework to
help investigators objec-
tively rethink a case. It is still up to the detective to solve the
crime. However, pilots
and surgeons have found the use of checklists to be helpful in
minimizing mistakes –
and their subsequent tragedies (Gawande, 2011). Criminal
investigative failures that
result in innocent people being convicted or guilty parties going

free are no less tragic.
Following this protocol can help reduce the number of such
outcomes through a system-
atic and evidence-based approach to case rethinking.
Acknowledgements
I wish to thank Dr David Stubbins, Central Intelligence Agency
(ret.), Deputy Chief Constable
Doug LePard, Vancouver Police Department, Detective James
Trainum, DC Metropolitan Police
Department (ret.), Special Agent Gregg McCrary, Federal
Bureau of Investigation (ret.), and the
anonymous reviewers for their helpful comments and
suggestions.
Notes
1. This is similar to moving from an evidence-based to a
suspect-based investigation (Rossmo,
2009), or from information generation to case building (Stelfox
& Pease, 2005).
2. A study on bias in criminal investigations found participants
who were asked to identify a
suspect early in the process showed a greater tendency to
confirm that hypothesis, and later
‘remembered’ the case evidence as being consistent with their
particular suspect’s guilt
(O’Brien, 2009).
3. Police investigators rarely test their theories by searching for
disconfirming evidence even
though it is more probative than confirmative evidence (e.g.,
exclusionary DNA). Once a
narrative of the crime has been adopted, police tend to focus
only on gathering confirmatory

evidence (Stubbins & Stubbins, 2009).
4. Templates for reviewing major crimes and evaluating cold
cases have been previously
proposed (e.g., Adcock & Stein, 2013; Jones, Grieve, & Milne,
2008b). The present protocol
differs from these tools in its focus on how to rethink a case.
5. It may not be feasible to reanalyze every item of evidence in
an investigation. The goal is to
list all the nontrivial items that might have an influence on the
theory of the crime and its
suspects. Trivial, however, does not mean inconsistent or
contradictory.
6. Reliability is used here to refer to the accuracy or
‘truthfulness’ of evidence, consistent with
most of the legal literature. However, the correct scientific term
is validity.
7. Heuer’s (1999) analysis of competing hypotheses matrix can
be a useful tool here.
8. The probability of evidence given a theory is not equal to the
probability of the theory given
the evidence; confusing cause given effect with effect given
cause is known as the prosecu-
tor’s fallacy (Robertson & Vignaux, 1995). Bayes’ theorem
provides the mathematical rela-
tionship between the two probabilities (Taroni, Aitken,
Garbolino, & Biedermann, 2006).

Notes on contributor
D. Kim Rossmo is the University Endowed Chair in
Criminology and the director of the Center
for Geospatial Intelligence and Investigation in the School of
Criminal Justice at Texas State
University. He has researched and published in the areas of
environmental criminology, the
geography of crime, and criminal investigations. He is a
member of the International Association
of Chiefs of Police Advisory Committee for Police Investigative
Operations and is a full fellow of
the International Criminal Investigative Analysis Fellowship.
He has written books on criminal
investigative failures and geographic profiling.
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Contents lists available at ScienceDirect
Forensic Science International
journal homepage: www.elsevier.com/locate/forsciint
Forensic genetic genealogy using microarrays for the

identification of
human remains: The need for good quality samples – A pilot
study
A. Davawalaa, A. Stocka, M. Spidena, R. Danielc, J. McBaind,
D. Hartmana,b,⁎
a Victorian Institute of Forensic Medicine, Victoria, Australia
b Department of Forensic Medicine, Monash University,
Victoria, Australia
c Office of the Chief Forensic Scientist, Victoria Police
Forensic Services Department, Victoria, Australia
d totheletterDNA, Brisbane, Queensland, Australia
a r t i c l e i n f o
Article history:
Received 2 December 2021
Received in revised form 31 January 2022
Accepted 23 February 2022
Available online 25 February 2022
Keywords:
DNA analysis
Forensic
Genetic Genealogy
Missing persons
a b s t r a c t
The successful application of forensic genetic genealogy (FGG)
to identify Jane and John Doe cases in the
United States has raised the prospect of using the technique in
Australia to assist in the reconciliation of
unidentified human remains (UHRs) with long term missing
persons. A study was conducted to explore the

feasibility of FGG using whole genome array (WGA) data from
both pristine control samples as well as
compromised casework samples, with the view to explore how
DNA quantity and quality impacted on the
ability to generate search results when compared to a genetic
genealogy database, such as GEDmatch. From
this study, several insights were gained as to the impact DNA
quantity and degradation had on the per-
centage of SNPs genotyped and heterozygote/homozygote ratio
– which are critical for successful matching
outcomes. It was noted in this study (using a control sample)
that successful matching occurred when
genotyping errors were 5% or less. Two UHR cases were
matched to kits on GEDmatch PRO, which provided
investigative leads for identification purposes. The
effectiveness of the FGG approach to match casework
samples (as well as volunteer samples used in the study) is
indicative of the usage of ‘direct-to-consumer’
(DTC) genetic testing by Australians. Given the (often) limited
availability of casework samples, findings
from this study will assist Australian agencies considering the
use of FGG, to determine if WGA is a suitable
method for their application.
Crown Copyright © 2022 Published by Elsevier B.V. All rights
reserved.
1. Introduction
The identification of a deceased person is an integral part of the
medico-legal death investigation process in Victoria, Australia,
with
unidentified human remains (UHR) cases reported to the
Coroner for
investigation [1]. For these cases, the use of DNA matching is
reliant

on 1) the ability to recover a DNA profile from the deceased;
and 2)
the availability of a comparison sample (either from the missing
person or a relative). From a DNA point of view, if the DNA
profile
from a UHR case is not matched in a local, state or national
missing
persons DNA database, an identification cannot be achieved and
must wait for the appropriate Ante-Mortem (AM) data to be
made
available. In these instances, forensic genetic genealogy (FGG)
– an
emerging field for forensic investigation – has the potential to
pro-
vide an alternate avenue for identification [2–6].
FGG, also known as Investigative Genetic Genealogy (IGG),
combines DNA testing – using Whole Genome Array (WGA) or
Whole Genome Sequencing (WGS) data – with traditional
genealo-
gical methods to infer (familial) relationships between
individuals
[3]. Unlike conventional Short Tandem Repeat (STR) analysis,
which
is only useful for matching to close relatives, FGG will enable
mat-
ches extending to more distant relatives (such as 1st, 2nd, or
3rd
cousins) due to the large number of DNA sites that are available
for
comparison. This is achieved by determining the DNA segments
shared by individuals – shared centimorgan (cM) – with the
more
segments you share with someone, the more closely related you
are.
The familial connections can then be used to provide leads for

the
purpose of identification.
Our current awareness regarding the use of FGG for
identification
purposes has come from law enforcement (LE) and other
agencies
and/or projects in the United States (U.S.). From a human
identifi-
cation point of view, most notable is the DNA Doe project, a
non-
profit organisation, which aims to identify Jane and John Does
in the
U.S. using FGG through donations and volunteer work of
genetic
https://doi.org/10.1016/j.forsciint.2022.111242
0379-0738/Crown Copyright © 2022 Published by Elsevier B.V.
All rights reserved.
]]]]
]]]]]]
⁎ Corresponding author at: Victorian Institute of Forensic
Medicine, Victoria,
Australia.
E-mail address: [email protected] (D. Hartman).
Forensic Science International 334 (2022) 111242
genealogists [7]; with early success stories including the an-
nouncement in April 2018 that a Jane Doe discovered in Ohio
U.S.,

who became known as the Buckskin Girl, had been identified
using
this approach [8]. However, it was the use of FGG to identify
the
Golden State Killer [6] that really thrust FGG into the public
domain.
Such is the interest in the use of FGG, that companies (such as
Parabon NanoLabs [9], Bode [10], and Othram [11]) are
offering FGG
as a service to assist with cold case and missing persons
investiga-
tions. Nevertheless, further work is still required not only to
review
the scientific and technical aspects of FGG, such as in de Vries
et. al.
[12], but also consider the ethical, privacy and legal factors sur-
rounding FGG, given the use of public database held by direct-
to-
consumer (DTC) companies, and the creation of large data sets
[13–15]. All these factors will impact on how FGG would
function
once operationalised, with forensic agencies needing to consider
all
of them in the context of their legislative frameworks.
FGG is in its infancy in Australia, with cases being considered
for
FGG particularly when all other avenues for identification have
been
exhausted [16]. Given that there is usually a finite amount of
DNA
available for analysis, it is imperative that the likelihood of
success
based on this approach is understood, as well as the limitations
that
compromised casework samples present. To this end, a pilot

study
was undertaken to evaluate FGG using both good quality control
samples as well as casework samples from UHR cases, with the
aim
of providing preliminary information regarding DNA quality
and
ability to generate a match list when searched against a
genealogy
database, such as GEDmatch/GEDmatch PRO.
GEDmatch/GEDmatch
PRO enables the comparison of DNA data from control and
casework
samples to DNA data from users that have utilised one of
several DTC
companies and have uploaded their data to GEDmatch. Matches
are
categorised based on the total shared DNA segments (shared cM
values) between the questioned sample to the nearest matches,
with
the greater amount of shared DNA indicative of a closer
familial
relationship. DNA samples recovered from the control and
casework
samples were subjected to WGA using the OmniExpress
microarray
(previously routinely used by DTC companies). Data was
uploaded to
GEDmatch (control samples) or GEDmatch PRO (casework
samples),
with one casework sample further investigated using Family-
TreeDNA. The impact of DNA quantity and quality on the
ability to
generate SNP data suitable for upload and comparison on
genetic
genealogy databases were evaluated.

2. Methods
2.1. Sample Selection
Samples were collected (and analysed) with approval from the
Victorian Institute of Forensic Medicine Ethics Committee, EC
11–2019. As part of the approval process considerations were
given
as to the use of casework samples – for which all current
avenues for
identification had been exhausted – including data storage and
analysis, as well as privacy and ethical matters.
2.1.1. Ideal (Positive Control) Sample
A control sample (buccal swab or toenail clipping) was obtained
from a living donor who has known kits on GEDmatch. This
sample,
herein referred to as ‘ideal sample’, was expected to yield good
quality DNA (based on the degradation index (DI)) in enough
quantity for optimal processing on the microarray. Consent was
provided by the donor for their DNA to be genotyped and
uploaded
to GEDmatch.
2.1.2. Casework Samples
Eight unidentified human remains cases were selected for FGG
(Table 1). The post-mortem interval (PMI) could only be
estimated
for two cases, as most (75%) were skeletal remains of unknown
antiquity. All cases had conventional nuclear DNA profiling
(using
Identifiler™ Plus or GlobalFiler™ on a 3500 genetic analyser,
Ther-

moFisher Scientific) and mitochondrial DNA profiling (using
Sanger
sequencing on a 3500 genetic analyser) with either complete or
partial DNA profiles available for identification purposes
(Table 1).
Comparisons to the Victorian Missing Persons DNA Database
(VMPDD) – which at present holds more than 350 reference
samples
for missing persons – failed to produce a familial match in these
cases. Where possible, biogeographical ancestry (BGA)
predictions
(previously derived using the Precision ID Ancestry Panel,
Thermo-
Fisher Scientific) were used to assist with case selection (Table
1), as
subjects of European ancestry have a higher probability of
success
due to their over-representation in genetic genealogy databases
[4].
Sample types were mostly bone samples, with one sample being
a
bloodstain sample (Table 1).
2.1.3. Control Samples
Four control samples were collected from laboratory staff in-
volved in the processing of the casework samples for
elimination
purposes. These samples were either toenail clipping (x3) or
skin
(x1) samples. Toenail clippings were collected as a sample type,
as
this is a sample often analysed by the laboratory for casework,
while
the skin sample provided an additional sample type to analyse.
Consent was provided by the donors for their DNA to be

genotyped
and uploaded to GEDmatch.
2.2. DNA Extractions
DNA was extracted from the ideal sample (buccal swab or
toenail
clipping) and other control samples (toenail clipping or skin)
sam-
ples using a QIAamp® DNA investigator kit (Qiagen) on a
QIAcube
platform following the manufacturer’s protocols, with an
elution
volume of 100 μL.
Table 1
Case selection – unidentified human remains.
Case No. Age of
case (yrs)a
Body condition PMI Sample type for DNA
extraction
nDNA profile
available (No. loci)b
mtDNA profile available
(HVI and/or HVII)
BGA prediction
availability
1 17 Intact Days Bloodstain 15/15 HVI & HVII Yes, European
2 14 Mandible with teeth Unknown Bone 15/15 HVI & HVII
Yes, European

3 31 Skeletal (complete) Unknown Bone 13/15 HVI & HVII
Yes, European
4 15 Skeletal (skull and other
bones)
Unknown Bone 13/15 HVI & HVII Yes, European
5 26 Skeletal (skull and other
bones)
Unknown Bone 15/15 HVI & HVII Yes, European
6 4 Skeletal (complete) Unknown Bone 21/21 HVI & HVII Yes,
European
7 11 Skeletal (humerus) Unknown Bone 12/15 HVI & HVII
Yes, European
8 1 Decomposed (complete) Days-month Bone 21/21 HVI &
HVII No
a time since case reported – does not reflect the age of the
deceased or the time since death.
b Number of loci (not including sex determination markers) for
Identifiler™ Plus or GlobalFiler™ out of 15 or 21 respectively.
A. Davawala, A. Stock, M. Spiden et al. Forensic Science
International 334 (2022) 111242
2
DNA was extracted from the bloodstain casework sample (Case
1)
using a QIAamp® DNA investigator kit (Qiagen) completed
manually,
rather than on a robotic platform, following the manufacturer’s

protocols, with a total elution volume of 100 μL (4 ×25 μL).
For the bone casework samples, for the first extraction run,
DNA
was extracted from casework samples (cases 2–7) using a
phenol/
chloroform DNA extraction method [17] using an input amount
of
1–4 g of bone powder. For the second extraction run, four of the
bone samples (cases 3, 4, 5 and 6), as well as Case 8, were
extracted
using higher quantities of bone powder (15–30 g of bone
powder)
and including a demineralisation step (demineralisation buffer -
0.5 M EDTA, pH 8.0, 1% (w/v) N-Lauroylsarcosine) prior to the
phenol/chloroform extraction. The demineralisation buffer was
used
at a ratio of 15 mL for every 1 g of bone and the sample was
placed in
a rotating incubator at 56 °C for 12–24 hrs.
All sample extractions included extraction blanks to detect any
laboratory contamination.
2.3. DNA Quantification and DNA Genotyping
Nuclear DNA concentration was determined using Quantifiler™
HP (Life Technologies) using a 7500 real-time PCR instrument
(ThermoFisher Scientific) under conditions specified by the
manu-
facturer [18].
DNA extracts were genotyped using an Illumina OmniExpress-
24 [19] at the Australian Genome Research Facility (AGRF)
under
conditions specified by the manufacturer. Samples were run on

two
chips with varying input amounts (Table 2). Two analyses were
conducted, the first consisted of running samples VIFM-01 -
VIFM-
24 on an OmniEpress-24 microarray (chip 1), and the second
con-
sisting of samples VIFM-25 - VIFM-48 on a second
OmniExpress-24
microarray (chip 2). Cases 1-7 were run in duplicate on chip 1,
while cases 3–6 as well as Case 8 were run in duplicate on chip
2,
resulting in cases 3–6 not only having intra-chips duplicates,
but
also inter-chip duplicates (Table 2). VIFM-01 was a positive
control
Table 2
SNP analysis – Samples analysed on the Illumina OmniExpress-
24 microarray. Samples VIFM-01 - VIFM-24 were run on the
chip 1 and samples VIFM-25 - VIFM-48 were run on
chip 2. Gender and call rate obtained from the SNP analysis is
noted.
Sample ID Sample Sample Type Concentration (ng//μL) Deg
Index Input (ng) Gender Call Rate
VIFM-01 Ideal Sample Buccal swab 38.4096 0.83 153.6384 √
0.99
0.99
0.99
0.98
VIFM-05 Ideal Sample Buccal swab 38.4096 0.83 0.4000
Unknown 0.94

Unknown 0.66
Unknown 0.26
Unknown 0.74
VIFM-09 Case 1 Bloodstain 3.1455 0.62 12.5820 √ 0.99
VIFM-10 Case 1 Bloodstain 3.1455 0.62 12.5820 √ 0.98
VIFM-11 Case 2 Tooth/mandible 0.0091 2.09 0.0364 Unknown
0.71
VIFM-12 Case 2 Tooth/mandible 0.0091 2.09 0.0364 Unknown
0.20
VIFM-13 Case 3 Bone 0.0617 1.15 0.2468 Unknown 0.52
VIFM-14 Case 3 Bone 0.0617 1.15 0.2468 NR NR
VIFM-23 Mixture 5.8570 N/A 23.4280 Unknown 0.75
VIFM-24 Control -ve Unknown 0.22
0.98
VIFM-26 Ideal Sample Buccal swab 5.7000 1.01 0.9000 √ 0.97
Unknown 0.96
Unknown 0.95
Unknown 0.88

Unknown 0.79
Unknown 0.64
VIFM-34 Case 5 Bone 0.1533 VD 0.6132 Unknown 0.88
VIFM-35 Case 5 Bone 0.1533 VD 0.6132 Unknown 0.89
VIFM-44 Ideal Sample Toenail 5.7277 2.64 22.9108 √ 0.97
VIFM-45 C-sample 1 Toenail 8.9482 2.30 35.7929 √ 0.98
VIFM-46 C-sample 2 Toenail 1.6843 4.96 6.7373 Unknown
0.64
VIFM-47 C-sample 3 Toenail 10.4638 1.88 41.8553 √ 0.98
VIFM-48 C-sample 4 Skin 16.2860 1.55 65.1439 Unknown
0.97
NR: no result obtained
√: gender call as expected for the donor
VD: very degraded
3
sample while VIFM-24 was a negative control (no DNA added)
sample.
Data analysis for the Illumina OmniExpress-24 runs was con-

ducted by AGRF using Illumina’s GenomeStudio 2.0.3 with
Genotyping module 2.0.3, using the default Illumina settings
and
Illumina InfiniumOmniExpress-24v1–3_A1 manifest and
Infinium
OmniExpress-24v1–3-A1_ClusterFIle cluster files.
2.4. GEDmatch and GEDmatch PRO Uploads
Data files were prepared for upload to GEDmatch or GEDmatch
PRO using Illumina’s GenomeStudio 2.0. An account was
created
under GEDmatch and GEDmatch PRO, respectively, with ideal
and
control samples uploaded to GEDmatch, and although casework
samples were originally uploaded to GEDmatch these were
moved to
GEDmatch PRO when the terms and conditions for GEDmatch
were
updated in January 2021. While the VIFM is not a LE agency,
GEDmatch now requires all kits uploaded for the purpose of
iden-
tifying human remains to be processed using GEDmatch PRO.
Each
data file uploaded to GEDmatch was assigned a unique kit
identifier
for subsequent searching, with all kits having a status of
‘research’ to
ensure that the kit’s DNA data would not be included in match
re-
sults of other users [20]. Uploads to GEDmatch PRO were
similarly
assigned a unique kit identifier for subsequent searching.
For the casework samples that returned a poor match result –
defined as either returning no matches or matching known

‘junk’
(artificial) kits – on GEDmatch PRO, additional data files were
pre-
pared (as described above) for upload following data clean up
(see
section below).
2.5. GEDmatch and GEDmatch PRO Searching
For GEDmatch, the subscription fee was paid to enable access
to a
number of on-line search tools. On GEDmatch, kits derived
from the
ideal and control samples were queried against the entire data
set on
the database (‘one-to-many’ comparison). More detailed one-to-
one
comparisons were done using the ‘one-to-one’ comparison tool;
where the kit with the highest DNA input amount (150 ng) of
the ideal
sample was designated as having the correct SNP calls, to which
all
other ideal samples (with DNA input of 80 ng or less) would be
compared to evaluate match outcomes. Population admixture
pro-
portions were estimated using the Eurogenes K13 model [21]
offered
as part of the GEDmatch tool kit. On GEDmatch PRO, kits
derived from
casework samples were queried against all kits that have opted-
in for
law-enforcement searches (‘one-to-many’ segment based
comparison)
with population admixture proportions estimated using the
Eurogenes
K13 model [21] offered as part of the GEDmatch PRO tool kit.

For all matches observed on GEDmatch and GEDmatch PRO,
the
shared cM values were used to determine likely relationship to
the
kit/sample in question using the Shared cM project v4 tool in
DNA
Painter (https://dnapainter.com/tools/sharedcmv4). This was the
preferred method for determining relationships based on current
best practice (as advised by genetic genealogist) rather than
using
the ‘Gen’ value provided by GEDmatch or GEDmatch PRO.
Matches
from the ‘one-to-many’ comparisons in GEDmatch Pro were
cate-
gorised based on the total shared DNA segments (shared cM
values)
between the sample and the nearest matches, with the greater
amount of shared DNA indicative of a closer relationship.
At the completion of the study, 13th October 2021, the kits for
the
ideal and control samples were removed from GEDmatch.
2.6. FamilyTreeDNA Upload
The data file for Case 1 (VIFM-09) was submitted to a DTC da-
tabase – FamilyTreeDNA – for comparison using the Gene By
Gene /
FamilyTreeDNA investigative Genetic Genealogy (IGG)
Services [22].
2.7. Bioinformatics
2.7.1. Data Processing
Data analysed using Illumina’s GenomeStudio 2.0.3 as

described
in Section 2.3 Section 2.4 was further processed using Python
pro-
gramming language-based scripts for both data exploration and
generation of composite SNP profiles.
2.7.2. Heterozygote/Homozygote Ratio
The Heterozygote SNPs/Homozygote SNPs (Het/Hom) ratios
were
calculated as a ratio of the number of SNPs that were
heterozygous
and those that were homozygous, using all SNPs that yielded
gen-
otyping data for all the samples.
2.7.3. SNP Concordance, Drop-out and Drop-in
To evaluate concordance of SNP calls, a comparison was under-
taken for all dilutions of the ideal sample that yielded
genotyping
data. VIFM-01, with the highest DNA input amount (150 ng)
was
designated as the ideal sample, to which all other samples (with
DNA input of 80 ng or less) would be compared to evaluate
SNP call
concordance, drop-out, and drop-in. Any SNPs which had no
data
available were removed prior to the concordance or drop-out
eva-
luations.
2.7.4. Genotyping Errors
The impact of DNA input on types of genotyping errors was

evaluated. The types of genotyping errors considered were: i)
false
genotype – homozygote (both reference and comparison
genotypes
homozygote with different allele calls); ii) allele drop-out (false
homozygote); and iii) allele drop-in (false heterozygote). The
geno-
typing error was calculated as a percentage of total SNP calls
for the
sample.
2.7.5. Composite Profiles
Composite profiles were generated for the casework samples
that
failed to yield a search outcome with the aim of cleaning and
en-
riching data for upload and searching. Described below are the
manipulations of the SNP data undertaken. SNP data for cases 3
(VIFM-13, VIFM-14, VIFM-36 and VIFM-37), 4 (VIFM-15,
VIFM-16,
VIFM-38 and VIFM-39), 5 (VIFM-17, VIFM-34 and VIFM-35)
and 6
(VIFM-20, VIFM-40 and VIFM-41) were interrogated and three
new
data files were created which had: (i) the removal non-
concordant
SNPs, as well as SNPs which had no data available, (ii) as (i)
with the
additional removal of all homozygote SNPs, and (iii) as (i) with
the
additional removal of all homozygote SNPs not seen in at least
two of
the replicates, created for each case.
3. Results

3.1. DNA Extracts & Genotyping
For the DNA extracts described below, SNP genotyping on mi-
croarrays was conducted by an external service provider
(AGRF).
Reports detailing the performance of the two microarray runs
were
provided, with both runs passing all the required controls
including
the non-specific binding controls that target bacterial sequences
(data not shown).
3.1.1. Ideal (Positive Control) Sample
Two DNA extracts were prepared from buccal swabs with con-
centration (ng/μL)/ DI values of 38.4/0.83 (extract 1) and
5.70/1.01
(extract 2) respectively. An additional DNA extract was
prepared
from a toenail clipping with a concentration of 5.73 ng/μL and
DI
value of 2.64. Results from the SNP analysis are shown in Table
2.
Extract 1 was used to prepare samples VIFM-01 to VIFM-08 for
the
first microarray run, with DNA input ranging in amounts from
4
approximately 150 ng to 0.4 pg. Extract 2 was used to prepare

samples VIFM-25-VIFM-33 for the second microarray run
ranging in
input amounts from approximately 22 ng to 0.02 ng. The DNA
ex-
tract derived from the toenail clipping was analysed on the
second
microarray run with an input amount of 23 ng. Samples with an
input amount ranging from 150 ng to 0.7 ng all had call rates >
0.97
and were able to provide the expected gender (data not shown).
Samples with 0.6 ng to 0.4 ng input amounts had call rates >
0.93
and were not able to provide the expected gender. While
samples
with 0.1 ng to 0.04 ng input had call rates between 0.66 and
0.88,
and samples with 0.02 ng or less input having call rates < 0.74.
3.1.2. Control Samples
Three of the four control samples (C-samples) were DNA
extracts
recovered from toenail samples with concentration (ng/μL)/DI
values
of: 8.9/2.30 (C-sample 1), 1.7/4.96 (C-sample 2) and 10.5/1.88
(C-
sample 3); whereas the fourth DNA extract was recovered from
a
skin sample with a concentration of 16.3 ng/μL and DI value of
1.55.
Results from the SNP analysis are shown in Table 2. Three of
the four
samples had call rates > 0.97 (C-samples 1, 3 and 4), with one
sample (C-sample 2) having a call rate of 0.64. Two of the four
samples (C-samples 1 and 3) with call rates > 0.976 were able
to

provide the expected gender for those samples, with the other
two
samples (C-samples 2 and 4) yielded an ‘unknown’ gender
result.
3.1.3. Casework Samples
The DNA extracts for the casework samples ranged in con-
centration from 0.009 ng/μL to 3.5 ng/μL with DI values
ranging from
0.6 to 3.6, with results from the SNP analysis shown in Table 2.
In the first SNP analysis with Case 1-7, only one case (Case 1)
had
a call rate > 0.9, with some returning poorer call rates of <
0.73 in
either one (cases 3, 5 and 6) or both replicates (cases 2 and 4),
and
some failing to yield a result in either one or both replicates,
such as
case 7. Only one case (Case 1) provided gender determination,
all
other cases yielded an ‘unknown’ gender result.
In the second SNP analysis, where samples of additional extrac-
tions for cases 3–5 and an extra case not previously analysed
(Case 8)
were run, two cases (cases 3 and 6) had a call rate > 0.9; with
the
other cases yielding call rates between 0.7 and 0.8. None of the
cases
were able to provide gender determination and were ‘unknown’.
For
casework samples, the expected gender was known from STR
pro-
filing (data not shown).

3.1.4. Sample Quality
For the samples that yielded SNP data, an analysis of how the
samples performed – the percentage of SNPs genotyped and
Het/
Hom ratios – was undertaken. Of the non-degraded samples
(ideal
sample dilution series), DNA input amount was seen to correlate
with percentage of SNPs genotyped and Het/Hom ratio (Fig. 1).
When evaluating the combined impact of sample input amount
and
degradation on Het/Hom ratios, for samples with an input
amount > 1 ng of DNA in the SNP assay degradation did not
appear
to have an adverse impact on the Het/Hom ratio; while for
samples
with an input amount of < 1 ng, degradation did appear to have
an
adverse impact on the Het/Hom ratio (Fig. 2). Evaluation of the
impact of DNA input amount and degradation on the percentage
of
SNPs genotyped showed two clusters with a small overlap; one
of
samples that yielded > 85% SNPs genotyped, and one of
samples that
yielded < 85% SNPs genotyped (Fig. 2). Furthermore, when
evalu-
ating DNA input amounts to GEDmatch outcomes based on the
percentage of SNPs genotyped or Het/Hom ratios, thresholds of
percentage SNPs genotyped and Het/Hom ratio of approximately
85% and range within 0.3–0.45, respectively, were observed in
order
to predict a GEDmatch search outcome (Fig. 1). Three samples
gave a
Het/Hom ratio > 1, likely due to genotyping error causing false

heterozygote SNP calls. All three samples also gave low SNP
call rates
(< 52%). While increased Het/Hom ratio has been observed for
cer-
tain populations [23,24], all three samples were processed in
du-
plicate or triplicate with all associated repeats yielding a
Het/Hom
ratio < 1. Possible reasons considered for genotyping errors are
in-
sufficient DNA (input range 0.25 ng-0.04 ng), poor quality
DNA
(degradation index range 1.15–2.09) [25,26], artifacts
introduced due
to the deamination [27], contamination [28] or a combination of
these.
To evaluate SNP call concordance, drop-out and drop-in, a pair-
wise comparison was undertaken of all the ideal samples with
an
input of 80 ng or less to VIFM-01 (150 ng input). A positive
correla-
tion (Pearson correlation coefficient r = 0.94) was observed
between
concordance and a logarithmic increase in DNA input for
samples
with DNA input of 0.9 ng and under (Fig. 3). This relationship
did not
sustain for samples with a DNA input of 4 ng and over, as these
samples reached close to maximum concordance. For drop-out,
the
comparisons showed that the number of drop-out decreased with
an
increasing DNA input; however, some drop-out (numbering in
the

10 s) were observed in samples with 0.4 ng DNA input up to 80
ng
DNA input (Fig. 4). Similarly for drop-in, the comparisons
showed
that the number of drop-ins decreased with an increasing DNA
input; however, some drop-ins (numbering in the 10–100 s)
were
observed in samples with 0.6 ng DNA input up to 80 ng input
DNA (Fig. 5).
3.2. Genetic Genealogy: Ideal sample – Dilution Series
The donor of the ideal sample identified as having Scottish,
Dutch
and Ashkenazi Jewish ancestry (Supplementary Fig. S1); with
Dutch
heritage going back two generations on the donor’s maternal
side,
and Australian/Scottish heritage on the paternal side. The
donor’s
ancestry was also determined using the admixture proportions
provided by Eurogenes K13 modelling (Fig. S1), with North
Atlantic
(45%), Baltic (20%), West Mediterranean (15%), West Asian
(10%) and
East Mediterranean (7%) making up the bulk of the admixture.
The
DNA groups that make up the North Atlantic population
include:
Danish; French Basque; Irish; North Dutch; Norwegian;
Orcadian;
Southeast English; Southwest English and West Scottish. The
ad-
mixture prediction for this sample using the Eurogenes K13
mod-
elling appeared to align with the donor’s reported ancestry.

The kits derived from the ideal sample – having incrementally
less input DNA (from 80 ng down to 0.4 pg) – were compared to
the
kit obtained when 150 ng was used for the SNP analysis using
the
‘one-to-one’ comparison tool in GEDmatch (Fig. S2). Kits down
from
80 ng to 0.1 ng shared > 99% of their SNPs with VIFM-01 (150
ng)
with greater than 550,000 SNPs used in the comparisons. For
the
two kits having 0.04 ng input (VIFM-06 and VIFM-32), they
shared
81% and 95% respectively with VIFM-01 with 422,862 and
505,953
SNPs used in the comparisons. All the kits from 80 ng to 0.1 ng
yielded Total Half-Match segments (HIR) > 3500 cM with a
most-
recent-common-ancestor (MRCA) of 1.0; while the two kits
having
0.4 ng (VIFM-06 and VIFM-32) yielding HIR/MRCA values of
3027/1.1
and 3560/1.0 respectively. Kits with less than 0.02 ng failed to
yield
any results using the ‘one-to-one’ comparisons.
VIFM-01 (150 ng), when searched against all kits available on
GEDmatch using the ‘one-to-many’ comparison, returned the
ex-
pected matches to other kits (five at the time of this
comparison)
held by the donor on GEDmatch (data not shown). Kits from 80
ng to
0.1 ng yield the same top five hits compared to VIFM-01 (150
ng)

(Fig. S2) and while the top match was identical, the order of the
other four matches varied slightly – with the top match having >
3500 shared cM and MRCA of 1. For the two 0.4 ng kits
(VIFM-06 and
VIFM-32), only the top match was the same as that of VIFM-01
(150 ng) with shared cM/MRCA values of 3161/1.1 and
3577/1.0 re-
spectively. Kits with less than 0.02 ng returned what were con-
sidered ‘junk’ matches (matches to known artificial junk kits
present
5
in GEDmatch at the time) or no matches at all, likely as a result
of
having low SNP call rates (approximately 60% or less) in
combination
with an increase in genotyping errors due to insufficient and/or
poor
quality DNA available for typing.
3.3. Genetic Genealogy: Control samples
The donor ancestry for the C-samples was noted at the com-
mencement of the project (data not shown), with the admixture
proportions for the kits derived from the C-samples obtained
using
the Eurogenes K13 modelling (Fig. S3). While the bulk of the
ad-
mixtures were composed of populations in the North Atlantic,

Baltic
and West Mediterranean, their proportions varied particularly
be-
tween C-sample 1 and the others – with C-sample 1 having a
sig-
nificant Amerindian contribution (27%). This is not surprising
as the
donor of this sample reported a South American and European
an-
cestry. The DNA groups that make up the Amerindian
population
include: Karitiana; Mayan; North Amerindian; and Pima. Hence
the
admixture prediction for this sample using the Eurogenes K13
modelling appeared to align with the donor’s reported ancestry.
In
Fig. 1. Quality – An assessment of input amount and GEDmatch
outcomes for non-degraded samples that yielded SNP data.
Panel A: DNA input to Het/Hom ratio and GEDmatch
outcomes. Panel B: DNA input to % SNP genotyped and
GEDmatch outcomes.
6
Fig. 2. Quality – assessment of input amount and degradation on
Het/Hom ratio and % SNPs genotyped for samples that yielded
SNP data. Shown is DNA input and degradation to
Het/Hom ratio and % SNPs genotyped.
Fig. 3. Quality – SNP call concordance for samples that yielded

SNP data. Panel A: DNA input to the number of identical SNP
calls between both samples. Panel B: DNA input to the
number of non-identical SNP calls between both samples.
7
Fig. 4. Quality – SNP drop-out for samples that yielded SNP
data. DNA input to drop-outs.
Fig. 5. Quality – SNP drop-in for samples that yielded SNP
data. DNA input to drop-ins.
8
addition, C-sample 3 and C-sample 4, (which were derived from
the
same donor) generated almost identical admixture proportions
(data
not shown).
Of the ‘one-to-many’ comparisons, all kits for the C-samples
generated matches on GEDmatch which could be further in-
vestigated, these are detailed in Table 3. Kits for C-samples 3
and 4
(which originated from the same donor) produced the same
match

outcomes.
3.4. Genetic Genealogy: Casework Samples
Of the ‘one-to-many’ comparisons, only two of the eight case-
work samples analysed generated matches on GEDmatch PRO
which
could be further investigated, in both instances the duplicate
kits
produced the same match outcomes. Of the remaining six cases,
kits
returned what were considered ‘junk’ matches or no matches at
all,
likely as a result of having poor SNP call rates and erroneous
geno-
typing due to insufficient DNA available for typing in some
instances,
or the DNA being degraded in others. This may indicate sample
quality as the reason to matching ‘junk’ kits on the database (as
different samples matched the same ‘junk’ kits) rather than not
having any suitable matching kits on the database.
3.4. .1. Case 1 (VIFM-09 & VIFM-10)
The admixture proportions for this case (Fig. S4) consisted of
populations in East Mediterranean (37%), West Mediterranean
(19%),
North Atlantic (17%), Baltic (9%), West Asian 8%) and Red Sea
(6%)
comprising the majority of the admixture. In addition, kits for
VIFM-
09 and VIFM-10 generated almost identical admixture
proportions
(data not shown).
Of the ‘one-to-many’ comparison in GEDmatch Pro, Case 1

(VIFM-09 and VIFM-10) had the closest matches in the 80–500
range
of total shared cM, indicative of second cousin (2 C) - third
cousin
(3 C) or first cousin once removed (1C1R) relationships (Table
3). The
comparison performed on FamilyTreeDNA yielded a closer
match
(616 cM) placing this in the 550–1200 range indicative of first
cousin
(1 C)− 1C1R (data not shown).
3.4.2. . Case 8. (VIFM-42 & VIFM-43)
The admixture proportions for this case (Fig. S4) consisted of
population in North Atlantic (49%), Baltic (22%), West
Mediterranean
(19%), West Asian (5%) and East Mediterranean (4%)
comprising the
majority of the admixture. In addition, kits for VIFM-42 and
VIFM-43
gave almost identical admixture proportions (data not shown).
Of the ‘one-to-many’ comparison in GEDmatch PRO, Case 8
(VIFM-42 and VIFM-43) had a match in the 1200–2200 range
in-
dicative of half siblings, aunt, uncles, grand-parents/child
relation-
ships; as well as a further matches in the 80–500 range
indicative of
2–3 C or 1C1R (Table 3).
3.5. Genetic Genealogy: Pristine Versus Compromised Samples
A comparison of GEDmatch and GEDmatch PRO outcomes for
all

samples that were successfully uploaded was undertaken. When
evaluating Het/Hom ratio and the percentage of SNPs genotyped
to
GEDmatch or GEDmatch PRO outcomes, a Het/Hom ratio in the
range of 0.3–0.45 was required to successfully match – even
when
the percentage of SNPs genotyped dropped below 90% (Fig. 6).
Fur-
thermore, the Het/Hom ratio appears to be a better predictor of
match success rather than the percentage of SNPs genotyped
(Fig. 7).
Furthermore, the impact that DNA input has on three types of
genotyping errors (false allele call rate (homozygote); allele
drop-
out rate; and allele drop-in rate) was evaluated using the ideal
sample series (see Fig. 8). When considering the three typing
errors
combined, it was noted that samples with an input of 0.02 ng or
less
had an observed genotyping error ranging from 19% to 63%;
whereas
samples with 0.1 ng or more had less than 1% genotyping
errors, of
which the majority were allele drop-ins. When looking at the
0.02 ng
or less samples in more detail, allele drop-out is more prevalent
for
samples with 0.02 ng and 0.004 ng input, whereas the false
allele
rate at 32% and allele drop-out rate at 28%, are similar with the
smallest DNA input of 0.0004 ng. For the duplicate samples
with an
input of 0.04 ng, each had genotyping errors of 18% and 5%
respec-
tively.

3.6. Bioinformatic Analysis: Casework Samples
In order to improve the search outcome for casework samples,
bioinformatic analysis of the SNP data was undertaken to
remove
any non-concordant and no-data SNPs from the combined SNP
data
for those cases. The removal of all homozygote SNPs, or those
not
seen in at least two replicates was also performed. While the
bioinformatic treatment improved the number of SNPs available
for
comparison for these casework samples (and thus the call rate)
– as
exemplified for case 4 (VIFM-15, VIFM-16, VIFM-38 and
VIFM-39)
(Table 4) – none of the kits generated from the upload of the
treated
data resulted in any match data (data not shown), as they all had
Het/Hom ratios outside the range of 0.3–0.4 and failed this
quality
measure.
4. Discussion
While the operationalisation of FGG outside of the U.S. is
limited,
others have commenced evaluation of FGG for cold case
investiga-
tions and human identification purposes [15,29]. Tillmar et.al.
2021
[29], for example, described the successful use of FGG to solve
a 16-
year old double murder case in Sweden; demonstrating the
utility of

genetic genealogy databases for countries other than the U.S. As
more countries consider the use of FGG, concerns have been
raised
regarding the ethical use of public databases for criminal
Table 3
One-to-many – control and casework samples. For control
samples (VIFM-45, −46, −47 and −48) where a match list was
returned from GEDmatch, and for casework samples
(VIFM-09, −10, −42, and −43) where a match list was returned
from GEDmatch PRO, the number of close matches based on cM
is shown – with numbers capped to 500. Adapted
from Thomson et. al. 2020 [34]
Number of close matches
Range cM VIFM-09 VIFM-10 VIFM-42 VIFM-43 VIFM-45
VIFM-46 VIFM-47 VIFM-48 Likely relationship
~ 3570 0 0 0 0 0 0 0 0 Parent or child
2200–3300 0 0 0 0 0 0 0 0 Full sibling
1200–2200 0 0 1 1 0 0 0 0 Half siblings, aunts, uncles,
grand-parents/children
550–1200 0 0 0 0 0 0 0 0 1–1 C1R
80–500 7 6 4 4 0 0 0 0 approximately 2–3 C or 1C1R
50–80 500 500 0 0 0 0 1 1 approximately 3–4 C
30–50 N/A N/A N/A N/A 5 4 10 9 approximately 4 C or more
distant (could be closer)
N/A: not applicable
9

investigations, as well as the validity of methodology(ies) used
to
infer relationships [30–32]. Thus, case studies (using
forensically
relevant samples) to evaluate FGG are required to improve our
awareness.
In order to better understand the impact of parameters such as
DNA input and degradation levels as well as SNP call rates on
the
ability to deliver enough DNA data for a kit to yield matches
when
uploaded to a genealogy database, an ideal sample (at various
input
amounts), as well as control samples (varying degradation
levels)
were analysed. For a good quality DNA sample with a low de-
gradation index (~ 1), as little as 0.1 ng was able to yield the
expected
match profile when performing the ‘one-to-many’ comparisons,
however, an input of 0.04 ng resulted in the identification of the
same top match only. While inputs of 0.7 ng or greater were
required
to achieve the correct gender (as well as call rates above 0.97)
this
was not a requirement for success in yielding matches. Failure
to
correctly call the gender may be a result of not enough SNP
markers
on the X and/or Y chromosomes being typed to enable gender
de-
termination. However, this observation requires further

investiga-
tion. Hence, the ability to predict the gender of the donor from
the
SNP analysis was not a requirement to successfully yield
matches
when searched against GEDmatch, with samples returning
‘unknown’ for gender able to yield the expected matches.
Samples
with slightly worse degradation levels (ranging from 1.6 to 5.0)
with
Fig. 7. GEDmatch and GEDmatch PRO outcomes. For all
samples that were successfully uploaded to GEDmatch and
GEDmatch Pro, an assessment of Het/Hom ratio and per-
centage of SNPs genotyped to GEDmatch outcomes, following
the removal of three outlier samples with Het/Hom ratios > 1.5.
Fig. 6. GEDmatch and GEDmatch PRO outcomes. For all
samples that were successful uploaded to GEDmatch and
GEDmatch Pro, an assessment of Het/Hom ratio and percentage
of SNPs genotyped to GEDmatch outcomes.
10
inputs between 7 and 65 ng all generated matches when
performing
the ‘one-to-many’ comparisons. These results were encouraging
as
many of the samples received by the laboratory from UHR cases
often yield low levels of DNA which are degraded. It was noted,
however, that for samples with an input < 1 ng, degradation ap-

peared to have an adverse impact on the Het/Hom ratio. For the
ideal
sample, it was observed that with DNA inputs less than 0.04 ng
more
than 5% genotyping errors were detected. Furthermore, the
occur-
rence of these genotyping errors coincided with the loss of a
correct
match list when searched in GEDmatch. The presence of 5% or
more
of genotyping errors had a detrimental impact on the matching
outcome.
FGG has been used successfully in the identification of UHR
cases,
particularly in the U.S., leading to the reconciliation of long-
term
missing persons with unidentified deceased [7]. This approach
is
dependent on having the appropriate data set (size and composi-
tion) in the genealogy database, such as GEDmatch and Family-
TreeDNA, to ensure matches occur [33]. In order to evaluate the
usefulness of FGG for the identification of UHR cases in an
Australian
context, eight cases were selected for analysis. These cases
typically
represent the sample type (bone) and DNA yields and quality
(low
concentration and degraded) encountered for UHR in the
laboratory.
Furthermore, as it was expected that cases with European
ancestry
should yield results when searched on GEDmatch PRO, case
selec-
tion considered those cases which had BGA predictions

available,
with seven of the eight cases predicted to have European
ancestry
based on BGA analysis. Although Case 8 did not have any BGA
pre-
dictions available, mitochondrial haplotyping indicated
European
ancestry on the maternal side. The admixture predictions
obtained
using the Eurogenes K13 modelling appeared to be a good
indicator
of the ancestry for the known samples and could potentially be
used
as an indicator as to the likelihood of matches on the genealogy
database for the unknown samples. However, further assessment
of
the accuracy of Eurogenes K13 modelling would be required.
For all the casework samples, DNA input amounts fell within
the
range of 0.1–14 ng, with all but one having degradation values
of
0.6–3.6. Based on the results for the ideal sample, it was
anticipated
that most would return a match result using ‘one-to-many’
Fig. 8. GEDmatch and GEDmatch PRO outcomes. An
assessment of genotyping error (%) as a function of input DNA
for all samples that were successfully uploaded to GEDmatch
and GEDmatch Pro.
Table 4
Bioinformatic – Case 4. Number of SNPs available for
searching following three treatments: the removal (i) non-
concordant and no-data SNPs; (ii) as (i) and any homozygote
SNPs;

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