








































Forensic Anthropology Vol. 2, No. 2: 121–128
DOI: 10.5744/fa.2019.1019

© 2019 University of Florida Press

Large commingled assemblages present unique challenges 
for individual identification, because the segregation of 
remains into discrete individuals can be difficult and time- 
consuming. These assemblages can be problematic from data 
management and analytical perspectives, and often an 
increase in the number of casualties leads to a decrease in 
the discriminatory power of anthropological techniques such 
as visual pair matching and articulation (SWGANTH 2013). 
These challenges can lead to lengthy analytical timeframes 
and delays in identification, and a multidisciplinary approach 
to resolving commingling is both espoused and practiced 
(e.g., Salado Puerto et al. 2014; SWGANTH 2013).

Skeletal and DNA analyses are commonly employed 
together in cases of commingling (e.g., Damann & Edson 
2008; Finlayson et al. 2017; Hines et al. 2014; Mundorff et al. 
2014; Salado Puerto et al. 2014). One of the first steps in 
resolving commingling is for the anthropologist to do as 
much sorting as possible— via pair matching, articulation, 
and the biological profile— in order to determine how many 
individuals are present and then segregate those individu-
als (Damann & Edson 2008). Provenience and taphonomic 

evidence should also be used to aid in this process, as appli-
cable and available. Even in approaches that rely heavily on 
DNA, the anthropologist maintains an important role in mak-
ing and verifying associations of elements, detecting and 
resolving commingling pre-  and post- sampling, confirming 
biological profile data, and reducing the cost and burden of 
DNA analysis by associating elements (Damann & Edson 
2008; Yazedijian & Kešetović 2008).

Part of the skeletal sorting strategy entails relying on 
inter- individual differences in size, morphology, and age to 
segregate skeletal elements. It is thus important to consider 
the demographic composition of the loss population and the 
assemblage, as this can be a significant factor in the resolu-
tion of commingling. Generally, when faced with a homoge-
neous group (i.e., composed of individuals of the same sex 
and similar age, ancestry, and stature) it is more difficult to 
sort using skeletal techniques. However, as Yazedijian and 
Kešetović (2008:280) find, even in a sample composed pri-
marily of “men of fighting age” from a single ancestral back-
ground where stature is normally distributed, it is still possible 
to separate outliers.

In cases where remains are extensively commingled, 
fully skeletonized, disarticulated, and/or lacking provenience 
or other recovery information, or where the anthropologist is 
otherwise hindered in sorting remains prior to DNA sampling, 
DNA analysis may be relied on heavily to aid in the segrega-
tion of skeletal elements. In these instances, DNA analysis 
has the potential to be time- consuming and costly. What 
then becomes the best strategy to approach the analysis of 

aDefense POW/MIA Accounting Agency— Laboratory, Offutt AFB, 
NE, USA

*Correspondence to: Carrie Brown, Defense POW/MIA Accounting 
Agency— Laboratory, 106 Peacekeeper Dr., Bldg. 301, Offutt AFB,  
NE 68113, USA 

E- mail: carrie . a . brown40 . civ@mail . mil

Received 11 June 2018; Revised 28 October 2018;  
Accepted 14 January 2019

TECHNICAL NOTE

Using Biological Profile Data to Inform a DNA 
Sequencing Strategy

Carrie Browna* ● Jeffrey James Lyncha

ABSTRACT: Frequency distributions of antemortem stature and age for individuals who were casualties onboard the USS Oklahoma 
are visually compared to frequency distributions of point estimates for long bone stature and pubic symphysis age drawn from the large 
commingled assemblage that is associated with this loss incident. Based on similarities in the means and standard deviations of these 
distributions, a four- tiered DNA sequencing strategy is devised to prioritize the sampling of elements that are at least one standard devia-
tion from Oklahoma antemortem mean age and/or stature. The rationale for this approach is that elements providing estimates that are at 
least one standard deviation from antemortem stature and/or age means are more likely to be from individuals who also fall at least one 
standard deviation from the means for one or both of these categories. This prioritization strategy resulted in nearly doubling identifica-
tions during the initial phases of the project. This success demonstrates the ability to use biological profile data to aid in the DNA sequenc-
ing process and the importance of continued interdisciplinary work in resolving commingled assemblages.

KEYWORDS: forensic anthropology, resolution of commingling, mitochondrial DNA analyses, stature, age



122 Biological Profile Data to Inform DNA Sequencing

numerous samples when individuals cannot be reliably segre-
gated prior to DNA sequencing? Simply sequencing the sam-
ples by order of receipt may not represent the best strategy.

Other researchers have provided information on the 
preservation of DNA by element (e.g., Damann & Edson 
2008; Edson et al. 2004; Hansen et al. 2017; Hines et al. 2014; 
Mundorff et al. 2009; Pinhasi et al. 2015), which is useful in 
terms of what elements should be given priority for DNA 
sampling but not for the order in which they should be 
sequenced once sampled. Currently, there is no known 
research that addresses how to prioritize samples for sequenc-
ing in large, heavily commingled assemblages that cannot 
be reliably segregated prior to DNA analysis. Having a strat-
egy to prioritize DNA analyses is potentially useful to max-
imize identifications in the early stages of an ongoing project 
where DNA is crucial to those identifications. Rather than 
waiting for all DNA to be sequenced, samples can be priori-
tized based on which are more likely to be associated with 
certain individuals (e.g., very tall or very short individuals). 
In long- term projects where a significant amount of time has 
passed since the deaths of the individuals, there are fewer 
surviving family members with each passing year and iden-
tifications become a race against time. Thus, making identi-
fications quickly is often important. A prioritization strategy 
may also be beneficial for decreasing the overall number of 
samples requiring sequencing. As outliers are identified first, 
sampled elements may be able to be pair matched and DNA 
processing halted for those elements, but it is important to 
do this in tandem with the DNA laboratory.

This study compares antemortem and postmortem data 
from a commingled assemblage with a historically known 
size (i.e., all casualties from the Oklahoma). Based on this 
investigation and on age and stature variation in the popu-
lation, it then outlines an effective and demographically 
informed strategy to prioritize DNA sequencing of postcranial 
elements using antemortem age and stature data. This strategy 
enables the identification of individuals in this assemblage 
prior to completion of all DNA analyses, reducing the pool of 
missing persons and the time families wait for notification.

Materials and Methods

This study employs skeletal elements and mitochondrial 
DNA (mtDNA) results from the USS Oklahoma assemblage. 
For information on the assemblage and analyses, including 
the extent of commingling, see Brown (2019). The identifi-
cation count given here includes only those individuals with 
postcranial elements that were identified or in analysis as of 
1 June 2018 (n = 93), and it does not include any identifica-
tions prior to project initiation in 2015.

During the inventory process, standard and supplemen-
tal measurements were taken for all elements, as applicable 

(Byrd & Adams 2003; Byrd & LeGarde 2014; Moore- Jansen 
et al. 19941), and long bone epiphyseal fusion and pubic sym-
physes were scored following McKern and Stewart (1957). 
Only those elements that were sampled for DNA analyses 
were considered in this study, and those elements that did not 
have maximum length measurements or the ability to assess 
age via fusion or the pubic symphysis were excluded. All 
cranial and dental samples were sequenced prior to the post-
cranial elements to aid with MNI estimation and dental iden-
tifications, and thus they also are excluded from this study.

For the long bones— humerus, radius, ulna, femur, tibia, 
and fibula— stature point estimates were calculated by ele-
ment using linear regression equations from Trotter’s Black 
and White Male combined data set in FORDISC 3 (Jantz & 
Ousley 2005). This process was automated in OsteoSort 
(2016) in order to create frequency distributions of point esti-
mates by element, pooling the left and right sides. A fre-
quency distribution was also created for the mean ages (point 
estimates) per pubic symphysis composite score, and again 
the sides were pooled. No distributions were created for long 
bone epiphyseal fusion, as the method following McKern and 
Stewart (1957) only provides age intervals by epiphysis per 
stage of fusion (e.g., proximal humerus, Stage 0, observed 
from 17 to 20 years). Descriptive statistics for postmortem 
estimates providing mean ages were calculated in R (R Core 
Team 2014).

Antemortem ages and statures were drawn from mili-
tary records. When more than one stature was listed in the 
records, an average of reported statures was produced and 
used for comparative purposes. Frequency distributions and 
descriptive statistics for antemortem stature and age were 
calculated in R (R Core Team 2014).

Antemortem and postmortem distributions were com-
pared visually and using descriptive statistics (e.g., similar 
means and standard deviations). This formed the basis for the 
tiered prioritization strategy of the postcranial samples sub-
mitted for mtDNA analyses (See Results, below). All sam-
ples were sent to the Armed Forces DNA Identification 
Laboratory (AFDIL) for sequencing.

Results

Antemortem stature and age distributions are given in Fig-
ures 1 and 2; postmortem stature point estimate distributions 
for all six limb bones and pubic symphysis age distributions 
are given in Figures 3 through 9. Table 1 provides the mean, 
standard deviation, and within and outside one standard devi-
ation intervals per distribution.

1. At the time data collection started for the Oklahoma project, the 
updated data collection procedures (Langley et al. 2016) had not yet been 
released, so all measurements were taken following the Moore- Jansen et al. 
(1994) standards for consistency throughout the duration of the project.



Brown and Lynch 123

Comparing Figures 1 through 9, there is a clear similar-
ity between distributions of antemortem and postmortem 
stature and age, though it is noted that the use of McKern 
and Stewart (1957) demonstrates a regression to the mean 
for pubic symphysis age point estimates as compared to the 

antemortem data. Additionally, the average of tibiae point 
estimates is an inch shorter than the averages for all other 
long bones in the postmortem assemblage and for the aver-
age antemortem stature of Oklahoma crew members. How-
ever, because the standard deviation for stature based on the 

FIG. 1—Distribution of antemortem statures for the Oklahoma sample; 
mean = 68.56 inches, SD = 2.31 inches, n = 374. The black, dashed, and 
dotted lines are mean, one standard deviation, and two standard 
deviations, respectively.

FIG. 2—Distribution of antemortem ages for the Oklahoma sample; 
mean = 24.49 years, SD = 6.4 years, n = 394. The dashed line indicates 
the median, and the solid line indicates the mean.

FIG. 3—Distribution of stature point estimates for the humerus; 
mean = 68.26 inches, SD = 1.86 inches, n = 507. The solid, dashed, and 
dotted lines are mean, one standard deviation, and two standard 
deviations, respectively.

FIG. 4—Distribution of stature point estimates for the ulna; 
mean = 68.35 inches, standard deviation = 1.49 inches, n = 328. The 
solid, dashed, and dotted lines are mean, one standard deviation, and 
two standard deviations, respectively.



124 Biological Profile Data to Inform DNA Sequencing

tibia point estimates is similar to the other long bones and 
the distribution of point estimates based on the tibia is nor-
mal, the tibia data are still used in the prioritization strategy. 
Because of the similar standard deviations, it was deter-
mined that the issue is with how condylo- malleolar length 
is being translated into stature and not because of great 

variation in tibia length.2 Of 394 individuals, 160 are at least 
one standard deviation from the antemortem mean stature 

2. For a detailed analysis of this issue, see Lynch et al. (2019). The 
condylo- malleolar length of the tibia was measured following the 
description in Moore- Jansen et al. (1994).

FIG. 5—Distribution of stature point estimates for the radius; 
mean = 68.44 inches, SD = 1.47 inches, n = 325. The solid, dashed, and 
dotted lines are mean, one standard deviation, and two standard 
deviations, respectively.

FIG. 6—Distribution of stature point estimates for the femur; 
mean = 68.17 inches, SD = 1.97 inches, n = 642. The solid, dashed, and 
dotted lines are mean, one standard deviation, and two standard 
deviations, respectively.

FIG. 7—Distribution of stature point estimates for the tibia; 
mean = 67.08 inches, SD = 1.88 inches, n = 573. The solid, dashed, and 
dotted lines are mean, one standard deviation, and two standard 
deviations, respectively.

FIG. 8—Distribution of stature point estimates for the fibula; 
mean = 68.45 inches, SD = 1.97 inches, n = 360. The solid, dashed, and 
dotted lines are mean, one standard deviation, and two standard 
deviations, respectively.



Brown and Lynch 125

FIG. 9—Distribution of age estimates based on the public symphysis; 
mean = 24.31 years, SD = 5.21 years, n = 405. The dashed line indicates 
the median, and the solid line indicates the mean.

TABLE 1—Descriptive statistics for antemortem and postmortem stature and age distributions, including 
within and outside one standard deviation intervals.

Biological parameter Mean
Standard 
Deviation

Within 1 Standard 
Deviation

Outside 1 Standard 
Deviation

Antemortem stature (inches) 68.56 2.31 66.25– 70.87 <66.25; >70.87
Postmortem stature (inches)— humerus 68.26 1.86 66.40– 70.12 <66.40; >70.12
Postmortem stature (inches)— ulna 68.35 1.49 66.86– 69.84 <66.86; >69.84
Postmortem stature (inches)— radius 68.44 1.47 66.97– 69.91 <66.97; >69.91
Postmortem stature (inches)— femur 68.17 1.97 66.20– 70.14 <66.20; >70.14
Postmortem stature (inches)— tibia 67.08 1.88 65.20– 68.96 <65.20; >68.96
Postmortem stature (inches)— fibula 68.45 1.97 66.48– 70.42 <66.48; >70.42
Antemortem age (years) 24.49 6.40 18.09– 30.89 <18.09; >30.89
Postmortem age (years)— pubic symphysis 24.31 5.21 19.10– 29.52 <19.10; >29.52

and/or age; this represents 40.6% of the Oklahoma loss pop-
ulation that can potentially be segregated based on stature 
and/or age.

It is important to note that biological profile character-
istics are not the only means by which segregation occurs in 
the Oklahoma project. Dental records, the presence of a DNA 
reference sample, and the commonality of the mtDNA 
sequence are also important factors in segregation and even-
tual identification. For example, an individual may be of 
average height and age but have a rare mtDNA sequence and 
thus is easily segregated and identified because no other indi-
viduals share that mtDNA sequence, while an individual 
who is an outlier for age and/or stature has no DNA refer-
ence on file or a common DNA sequence and thus takes lon-
ger to segregate and identify. For additional information 
on DNA reference samples and the identification process 
for Oklahoma crew members, see Brown (2019). The priori-
tization strategy does not address the overall identification 

success for the project, which is unknown at this time, but 
outlines a method by which elements can be moved forward 
in the DNA sequencing queue using height and age estima-
tions, rather than waiting for all DNA samples to be sequenced 
by order of receipt. It is important to note that due to the 
extensive commingling in this assemblage, very few of the 
identifications are made without DNA data.

Based on the composition of the loss population and the 
commingled assemblage and similarities in means and stan-
dard deviations, postcranial samples were prioritized in four 
tiers, representing 32.6% of the total samples submitted to 
AFDIL (Table 2). The prioritization strategy targeted ele-
ments with stature and/or age estimates that fell at least one 
standard deviation from the Oklahoma (antemortem) means. 
The rationale was to prioritize elements that were more likely 
to come from individuals at least one standard deviation from 
antemortem mean height and/or age, as these elements are 
the easiest to segregate from the commingled assemblage; 
however, the strategy was not find elements that belong to the 
same individual. The first tier to be prioritized was long 
bones with stature point estimates greater than or less than 
two standard deviations from the point estimate mean per 
element, as these likely represented the tallest and shortest 
individuals in the assemblage, respectively. The second tier 
included those elements that produced age estimates less than 
or equal to 20 years and greater than or equal to 30 years 
(Stage 0, 1, and 2 fusion for the long bones, and composite 
scores of 0– 5 and 14– 15 for the pubic symphysis), in order 
to capture those individuals at or outside one standard devi-
ation from the antemortem mean age. The third tier consisted 
of elements exhibiting Stage 3 fusion, as this generally occurs 
in the early twenties, and the fourth tier was long bones pro-
viding point estimates between one and two standard devia-
tions from the point estimate mean per element. This 
particular order was chosen to attempt to sequence elements 
from individuals furthest from the mean first and then prog-
ress through the remains from greatest to least deviation from 
the mean. Due to the antemortem age distribution for the loss 
population, epiphyses fusing in the mid-  to late twenties were 
not prioritized as part of the strategy. If an element was placed 
in multiple levels (e.g., femur with unfused epiphyses and 



126 Biological Profile Data to Inform DNA Sequencing

TABLE 2—Prioritization of samples: descriptions and counts. Total 
DNA samples submitted from June 2015 to October 2017, N = 4,749.

Level Description n % of total

0 All cranial and dental 604 12.7
1 Stature point estimate ± 2 standard 

deviations (in inches)
89 1.9

2 Age estimate ≤ 20, ≥ 30 (in years) 247 5.2
3 Age estimate ~21 (in years) 106 2.2
4 Stature point estimate ± 1– 2 standard 

deviations (in inches)
501 10.5

Total All prioritized 1,547 32.6

point estimate of 72 inches), it was given the higher of the 
ranked priorities.

As of 1 June 2018, 44 individuals of the 93 individuals 
identified, approved for identification, or currently segre-
gated from the assemblage and undergoing analysis were 
at least one standard deviation from mean stature and/or 
age. This represents 47.3% of the study sample. Figure 10 
displays the breakdown of these 44 by status: identified, 
approved for identification, or currently undergoing analysis 
and segregated from the commingled assemblage. These 44 
individuals represent 27.5% of the 160 Oklahoma casualties 
that are at least one standard deviation from mean stature 
and/or age.

The prioritization strategy resulted in the mtDNA 
sequencing of 943 postcranial samples. When combined with 
the cranial and dental samples that were processed first, this 
results in a total of 1,547 samples processed as priority, out 
of a total 4,749 submitted (32.6%). The completion of the 
postcranial priority tiers took approximately 18 months, 
though it should be noted that during this time AFDIL also 
processed many other cases and projects as well as additional 
nuclear testing for the Oklahoma project.

Discussion

As of 1 June 2018, mtDNA sequencing was just over 65% 
complete for the entire project, and all priority samples were 
completed. Project analyses have resulted in the identifica-
tion or segregation of 93 individuals, and nearly half of them 
are at least one standard deviation from the loss population 
mean antemortem stature and/or age. This indicates that the 
biological- profile- informed strategy to target specific sam-
ples for sequencing, rather than relying on order of submis-
sion, can be considered a success in this assemblage in terms 
of segregating and identifying individuals earlier than if wait-
ing for all DNA to be sequenced, because it resulted in 
nearly double the number of identifications in the initial phase 
of the project. This strategy does not consider overall proj-
ect success in terms of identifications, since the project is 
ongoing, and identifications rely heavily on DNA. Due to this 
reliance on DNA, ultimately identification is related largely 
to having a reference sample on file and the relative rarity of 
that sequence in the Oklahoma sample as well as the ability 
to use other methods to segregate and associate elements and 
individuals.

Biological anthropologists use their knowledge of human 
variation to segregate individuals. In commingled assem-
blages this variation enables anthropologists to employ 
smart sampling strategies that can reduce the burden on the 
DNA testing laboratory by associating elements prior to 
DNA analyses. In this case, given the size of the assemblage 
and high degree of commingling, sorting was not possible 
prior to DNA testing, so variations in size and morphology 
were employed to inform the order of sequencing. Addition-
ally, once an element on a priority list has been sequenced, 
analysts worked to associate contralateral elements via osteo-
metric sorting (Byrd & Adams 2003; Byrd & LeGarde 
2014) and visual pair matching in order to reduce the burden 
on AFDIL by canceling elements that were sampled but sub-
sequently successfully associated to sequenced elements.

The prioritization strategy employed here has not 
resulted in a dramatic decrease in the overall number of sam-
ples requiring analysis at AFDIL. However, even several 
dozen cancellations enables AFDIL to put time and resources 
toward other samples. Where this process likely will provide 
some efficiency is in the association of additional remains to 
previously identified individuals. Because identifications are 
made prior to all DNA samples being sequenced, there are 
instances in which an element or elements are associated to 
an individual following identification. In these cases, addi-
tional skeletal, DNA, and dental reports must be written, as 
applicable, in order to associate the additional remains. By 
prioritizing the sequencing of elements that are likely from 
individuals who are outside one standard deviation in height 
and/or age, it is more likely that all elements for a single 
individual will be sequenced first, then segregated, and, 

FIG. 10—Identification and analysis of individuals at least one standard 
deviation from antemortem stature and age means compared to 
individuals similar to the mean.



Brown and Lynch 127

finally, identified at the same time, thus reducing the work-
load involved in writing multiple reports later. However, this 
efficiency has not yet been proven or disproven in the Okla-
homa project.

Prioritizing the samples is important given the five- year 
timeline for the Oklahoma project and the amount of time 
that has passed since the incident. While prioritizing likely 
will not affect the overall number of identifications, it is 
important in terms of identifying individuals earlier on, 
thereby reducing the missing persons pool. Identification 
now, rather than once all DNA has been processed, is also 
important for family members. December 2018 marked the 
77th anniversary of the bombing of Pearl Harbor and while 
there are still living siblings of the Oklahoma crew members, 
there are fewer with each passing year.

By employing this strategy, an inherent statement is 
being made about the usefulness of each element in terms of 
segregation potential. In this study, elements that do not 
appear in the priority list are those that do not have informa-
tion that is considered probative to distinguishing individuals 
based on the distribution of age and/or stature in the loss pop-
ulation. However, as detailed above, this does not necessarily 
affect the ultimate identification, as other lines of evidence 
are also used to support the association of a set of segregated 
remains to an individual. But, the prioritization strategy can 
and should be customized based on the demographic compo-
sition of the loss population. For example, ancestry was not 
used for this project, because all crania were sequenced prior 
to the development of the tiered list. However, had this not 
been the case, ancestry assessment of the crania would have 
been included in the tiers, since the Oklahoma loss popula-
tion included only a small number of individuals of Asian 
and African ancestry that would likely have been able to be 
segregated based on morphology and osteometry.

The prioritization strategy outlined here relies on stan-
dardized data collection of the entire assemblage. This step 
is crucial because it provides the baseline for understanding 
the assemblage in terms of demographic composition and 
number of individuals represented. In projects where multi-
ple analysts are collecting data on the skeletal remains, it is 
also important to outline standard protocols, such as what age 
estimation methods are used, so that data collection is con-
sistent across the assemblage.

Additional limitations to this method include the avail-
ability of antemortem anthropological data, the preservation 
of skeletal elements, and the recovery of elements. The ante-
mortem and postmortem frequency distributions are only as 
good as the data available. If biological profile data for the 
loss population are not available or measurements and esti-
mates cannot be made on the remains due to preservation or 
absence of elements, it is not possible to create frequency 
distributions and sort individuals or elements using this 
strategy.

The use of the McKern and Stewart (1957) pubic sym-
physis ages demonstrates a well- known phenomenon in bio-
logical anthropology: age mimicry of the study sample to the 
method reference sample (Bocquet- Appel & Masset 1982). 
While the Oklahoma assemblage is theoretically similar to the 
McKern and Stewart (1957) sample— young, White military 
males— the samples are not identical. When the McKern and 
Stewart (1957) pubic symphysis is used, it underages the 
upper end of the Oklahoma distribution, pushing individuals 
closer to the sample mean.

Finally, this study demonstrates that interdisciplinary 
work continues to be vital to the resolution of commingling. 
DNA and skeletal analysis should be used in tandem, in a 
type of feedback loop where each informs the other. In this 
case, working relationships between anthropologists and 
DNA analysts are vital to the success of the prioritization 
strategy as well as the project as a whole.

Conclusion

This study demonstrates the utility of using demographic 
data to prioritize the DNA sequencing of elements in a large 
commingled assemblage. This research focused on isolating 
potential outliers in both the antemortem and postmortem 
data and focusing on these individuals for earlier segrega-
tions and identifications. Because of the demographic 
makeup of the loss population, the clavicle and innominates 
were not targeted in the initial priority sequencing strategy. 
However, they have now been added as the fifth tier in order 
to aid with age estimates during the association to known 
individuals phase of the project.

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