USING BONE BIOLOGY TO ENHANCE FORENSIC AND PALEOANTHROPOLOGICAL DNA ANALYSIS.

Introduction

This project is an experiment in molecular anthropological data collection and

analysis from a human post cranial skeleton. The ultimate goal is to develop a targeted,

minimally destructive skeletal sampling method to aid forensic and biological

anthropologists, as well as DNA analysts in the decision of where to obtain bone tissue

samples for optimal DNA extraction. This will be important when dental and cranial

elements are not available in order to guide researchers toward the optimal sampling

site(s) on any given skeletal element from which to attempt extraction. Uniquely, this

project will: 1) utilize knowledge of the cellular components and processes as well as the

biochemical properties specific to the growth and maintenance of human bones in order

to target specific sites on specific skeletal elements for optimal DNA extraction, 2)

incorporate knowledge of cell types to investigate the specific type of bone (cortical or

trabecular) best for sampling at that site, 3) design and construct a visual “heat map” of

the human post-cranial skeleton for use in both forensic DNA and ancient DNA (aDNA)

laboratories.

Unlike previous studies, this project begins from a targeted cellular and

microstructure-based approach. Utilizing knowledge of the cell and tissue types,

ontogeny, and chemical properties of bone, this project seeks a new explanation for

differential DNA preservation in intra-and inter-elemental extraction sites. The project

includes traditional chemical DNA extraction, amplification, and quantitation methods, as

well as Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-

FTIR) to aid in the development of a precisely targeted, minimally destructive, approach

to DNA extraction from human or hominin bone. Finally, the results will be used to

create a sampling method that is more accurate, significantly less destructive, and thus

more cost and time efficient than methods used previously. The method can be used by

forensic practitioners as well as biological anthropologists involved with genetic and

genomic research of ancient humans and hominins. By starting from a cellular and

biochemical launching point, this project seeks to bridge the knowledge gap between

forensic anthropology, forensic DNA analysis, and biomedical understanding of human

skeletal biology and to make that knowledge accessible and applicable to those

professionals on the front lines of the real-world problem of identifying unknown human

skeletal remains.

Historically, forensic anthropological studies investigating optimal DNA

extraction sites have focused solely on the osteological element, assuming that external

taphonomic factors are behind the relative qualities and quantities of genetic material

obtained (1–8). In studies such as these, failure of an element to yield adequate DNA for

a full STR profile was blamed on environmental variables such as time since death, soil

type or pH levels, or exposure to water or sunlight, rather than the potential lack of

appropriate cellular contribution at the extraction site. Some studies discuss the potential

primacy of cellular contribution to the surviving genetic material and lament the lack of

current documentation as to how different cell types contribute their endogenous DNA to

the hydroxyapatite or dental tissue under investigation (1,2,9,10).

For example, in 2015, Higgins, et al. reported differential results between nuclear

and mitochondrial DNA (mtDNA) in extractions from different dental tissues. Dentine

yielded consistently higher amounts of mtDNA, whereas cementum was better for

nuclear DNA. This very possibly could be due to different cellular populations operative

during life and the authors state as much, adding that individual age is known to play a

role in both tissue consistency and cellular populations. Similarly, in 2019, Antinick and

Foran reported greatest success in DNA extraction results with samples taken from the

epiphyseal regions of long bones with somewhat less success in the metaphyseal regions,

and least success in the diaphyseal sites. They, too, reported intra-element differences in

DNA type success rates, further suggesting that mere osteocyte populations are not

necessarily responsible for the genetic contribution. In one of the few documented

attempts to reconcile DNA and bone cell type in 2017, Andronowski, et al. were

unsuccessful in correlating osteocyte population to nuclear DNA yield. To date, no

studies have been found that address the potential DNA contribution from either

osteoblasts (including bone-lining cells) and/or osteoclasts.

Indeed the technology behind today’s genetic analyses has advanced sufficiently

that many post-mortem influences, which in the past seemed insurmountable, can be

much more easily navigated with better sampling methods. Problems that are frequently

encountered in DNA analysis such as chemical inhibitors that are extracted with the bone

tissue and low DNA copy number can often be somewhat counteracted with enhanced

chemistry at the extraction/purification phase and with the use of better primers at the

amplification phase. Of course, some samples will be so degraded, either biochemically

or at the molecular level, that nothing can be salvaged. But by starting from a more

educated vantage point and utilizing technology that can identify the untenable samples

prior to catastrophic tissue destruction, it is possible to optimize sampling such that

taphonomic and diagenetic factors are more easily circumnavigated

This study provides a rare opportunity to investigate the role of the different

osteological cell types: osteoblasts (including their differentiation into bone lining cells),

multinucleated osteoclasts in the process of bone matrix resorption at the time of death,

and fully encapsulated osteocytes, as a potential reason for differential DNA preservation

and extraction quantity and quality in modern forensic cases (1,10–23). The use of ATR-

FTIR will help gauge which types of chemicals remain in each sample prior to traditional

chemical extraction methods (24–30). This will give a strong indication of how much

DNA remains in the sample by the presence or absence of the bond between the

hydroxyapatite and the DNA molecules. Comparing different bone tissue types, the ATR-

FTIR test will help pinpoint the cells that are responsible for observed differences in

DNA preservation. To this point, no studies have employed ATR-FTIR on human

skeletal remains processed in this way (24–26). From this launching point, detailed

analysis will be completed of the variance in DNA preservation and accessibility across a

single, recent individuals’ skeletal remains. This analysis into the number of starting

molecules for mitochondrial, autosomal, and Y chromosomal DNA, as well as the ability

to obtain a complete CODIS profile, will allow for understanding how differential DNA

presence, and therefore accessibility, can be measured across the post-crania.

This research also opens an opportunity to expand the toolkits utilized in both

forensic and aDNA studies by examining the riddle of differential DNA preservation

across bony elements and tissue types. This is a question which has lingered in the

background of many studies across forensic and biological anthropology. Perhaps most

important of all, it affords the opportunity to assist forensic DNA analysts in the pursuit

of identification of unknown human skeletal remains. Unfortunately, complete sets of

human skeletal remains are rarely recovered. Usually, all that is recovered are

disarticulated elements or sets of elements; or worse yet, mere fragments. In order to

obtain identification, investigators have to rely on forensic anthropologists and DNA

extractions from whatever skeletal material is available. Without thorough understanding

of DNA preservation across elements, identification of the deceased can be elusive. This

is not a hypothetical problem. According to the National Missing and Unidentified

Persons (NAMUS) database and the National Crime Information Center (NCIC), there

are approximately 90,000 cases of missing persons in the U.S. at any given time and

approximately 4,000 new cases of skeletal remains from unknown individuals were

located by law enforcement in 2018 (31,32). With those alarming numbers in mind,

creation of a more accurate and reliable sampling method, focusing on post-cranial

elements, allows forensic anthropologists and DNA analysts to quickly identify which

elements are the best for DNA extraction; specifically, where on a particular element is

the optimal extraction site and, if applicable, whether the dense cortical or the inner

cancellous bone tissue is optimal. Biological anthropologists will also benefit greatly

from a more precisely targeted and minimally destructive sampling ability.

Hypothesis 1 (H1): Due to the presence of multinucleated osteoclasts and the

concomitant proliferation of active osteoblasts involved in the repair and maintenance

(homeostasis) of living bone, there will be higher concentration of both nuclear and

mtDNA present in samples taken from sites on long bones and flat bones where

homeostatic bone activity (resorption) is most prevalent. These sites will occur near

articular points and any place where ante-mortem trauma has occurred.

Null hypothesis: Due to cellular apoptosis and/or the enzymatic actions involved in

autolysis after organismal death, there will be no increase in the nuclear DNA found in

sites involving in vivo homeostatic bone activity.

Test Expectations: If this hypothesis is supported, then the extractions sites on, or

adjacent to, articular surfaces will yield more complete genetic profiles of all DNA types

than mid-diaphyseal extraction sites or those sites away from articular points on flat

bones.

Hypothesis 2 (H2): In the long bones, trabecular bone tissue is directly involved with the

formation of blood cells and osteoclastogenesis. Across all element groups, trabecular

bone has a higher surface area (43,45) and thus has the potential to trap more genetic

material in the charged hydroxyapatite crystals than does the dense, outer, cortical layer.

Null hypothesis: Due to the porosity of trabecular bone, there will be no noticeable

increase, and there may even be a marked decrease, in the amount of genetic material

found in bone tissue closer to the medullary cavity.

Test Expectations: Because both H2 and H3 are so closely related in both theory and

practice, test expectations for the two hypotheses are combined and elucidated below.

Hypothesis 3 (H3): During differentiation from mature osteoblasts, both bone lining

cells and osteocytes lose most of their mitochondria and experience a marked reduction

in their nuclei (62). Due to these cellular processes, samples taken from osteonal, or

lamellar, tissue in the large, load-bearing elements will not yield as much genetic

material.

Null hypothesis: There will be no difference in the amount or quality of the genetic

material extracted from osteonal tissue sites of the large, load-bearing bones versus the

cortical and trabecular surfaces.

Test Expectations for H2 and H3: If these hypotheses are supported, then extraction

sites involving trabecular bone will consistently yield more complete genetic profiles of

both nuclear and mtDNA, than will extractions from either the cortical or osteonal layers.

Additionally, very little genetic material will be obtained at all from samples taken from

the osteonal layer of the large long bones.

Hypothesis 4 (H4): Mundorff and Davoren (2014) and Obal (2019) indicate significant

success obtaining genetic material from the bones of the hands and feet. Therefore, it is

hypothesized that due to increased osteoclastic activity, bone groups which are subjected

to a significant degree of lifetime rates of remodeling, will consistently yield more

complete genetic profiles than skeletal elements whose primary functions are more

structural than load- bearing.

Null hypothesis: According to a 2004 study by Pearson and Lieberman, Wolff’s Law

(the premise that bone experiences remodeling as a function of its role in life) is not

always true (33) . Therefore, there will be no noticeable difference in the number of

complete profiles generated from the DNA extracted from load-bearing bone groups

versus that which is extracted from structural or non-load bearing groups.

Test Expectations: If this hypothesis is supported, then extractions from load-bearing

bones and bone groups such as the vertebrae, tarsals/metatarsals, etc., will yield more

complete genetic profiles of both nuclear and mtDNA than will structural elements or

groups such as the ribs and scapulae.

This project is designed to make a significant contribution to the process of

forensic victim identification, the ethical concerns that arise from destructive analysis of

human bone tissue, and to the preservation of ancient specimens. This can be done by

designing a sampling method that begins with mindful recognition of cell populations and

bone biochemistry, and proceeds through the creation of a “heat map” that clearly shows

the best sampling sites for mitochondrial, autosomal, and Y- chromosomal DNA, if any

can be found. The results of this study will be beneficial across numerous professions and

may well retain its rigor into forthcoming technologies such as forensic genetic

genealogy (FGG).

The following chapters will delve into the history and current theories and

methods of forensic DNA analysis including a focused account of the literature of DNA

extractions from human bone. Following that is a detailed reexamination of what we

know about bone biology and biochemistry. Then comes a thorough look into the

processes of molecular taphonomy and diagenesis. All of these topics weave in and

through one another as a way to address the knowledge that was necessary for the project

as a whole. Once the background has been covered, the methods used in this project will

be covered in detail and that is followed by the results of the experiment, in- depth

discussion of those results, and finally, some thoughts on where to go from here.

CHAPTER 1: BONE CELLULAR BIOLOGY

HISTORY

A basic understanding of the history of bone biology is foundational because it

illuminates the research leading up to current trends and practices. It also shows where

there are potential gaps that invite contemporary research, such as this project. This brief

section mirrors much of the history of the biological sciences, in that a new discovery

appears, followed by a period of relative quiet until sufficient technology is developed

that allows for further advancement.

In 1691 Clopton Havers published the first known work on the microstructure of

bone (Havers 1691). The canals that carry blood, nutrients, and biochemical messages

through bones are named after him. Due to technological limitations, however, he was

unable to see the cells themselves. For the purposes of this project, the story truly begins

with a publication by Goodsir and Goodsir in 1845, in which the authors reported the

ability to see osteoblasts. The authors believed that they may be responsible for bone

formation. This was supported by the works of Tomes and DeMorgan in 1853 and Muller

in 1858. In 1864 the term “osteoblast” was officially coined and was grounded in the

hypothesis that these cells were of mesenchymal origin (34). In 1873 a German anatomist

named Albert Kölliker discovered a previously unknown type of cell, the osteoclast (nee:

osteoklast), that seemed to be responsible for the destruction of old or damaged bone

(35). As of 1916, what would eventually be known as the osteon was observed and noted

(36), yet it didn’t appear in the literature as anything other than the literal translation of

the Latin word for bone. Fourteen years later, in 1930, Weidenreich observed that the

bone tissue surrounding what we now know as osteocytes, being shot through with

Haversion canals, was different than the bone on either the periosteal or endosteal

surfaces and the trabecular bone in the medullary cavity. He could see the concentric

rings and distinct lines of demarcation between them and used the term “osteon” to

describe them, but he could not yet ascertain their significance. With the advent of

scanning electron microscopy (SEM), osteocytes were finally observed as distinct cells.

Little was known about osteonal modeling or remodeling until the 1980’s (37,38). And

according to Bonewald, it has only been since about the year 2000 that we’ve known

much about what these cells really are, what they do, and how they are formed.

For the almost 300 years between Clopton Havers’ observation and the

technological explosion of the late 20th century, new information about the structure or

function of the different bone cell types came in small bursts, a decade or two apart.

There was debate, which primed the intellectual pump, but the histochemical technology

to detect the hormones, enzymes, and other chemical factors that play an integral role in

bone growth and re- growth simply were not available. Experiments such as using

Plutonium as a stain that showed that osteoclasts do absorb the bone matrix, or Cartier’s

1951 study that confirmed osteoblastic role in bone matrix production (which had been

originally posited by Goodsir and Goodsir 100 years prior and then discounted) were the

hallmarks of progress for much of the 20th century (39,40). That is, of course, until

technology advanced enough to show that osteoblastic communication with

hematopoietic cells is necessary to induce osteclastogenesis (41) and allowed

investigators to detect the presence of biochemical factors such as Parathyroid hormone

(PTH), tumor necrosis factor (TNF), and osteoprtegrin (OPG) in the late 1990’s, and the

discovery of receptor activator of nuclear factor ƙB ligand (RANKL) in the early 2000’s

(37).

The discovery of the osteoclast revolutionized scientific understanding of bone

growth and re- growth. The echoes of Herr Kölliker’s discovery, coupled with modern

histochemical analytical methods, and an emerging picture of the communicative role

played by osteocytes, have led to the realization that bone cell types and functions are not

independent of one another. In fact, we now know that bones play vital roles in the

endocrine, immune, and the circulatory systems and are far from being static pieces of

biological lumber(38,42–44). For most of the 20th century, biological and forensic

anthropologists primarily used cellular knowledge of bone structure in qualitative

paleopathological studies until DNA was discovered and extracted from bone in the

1980’s (45,46).

Image 1- From Sims and Gooi 2008-Included here to show the complex biochemical interactions between cell types

during resorption. Osteoclasts (red) pave the way for bone lining cells (flatter blue rectangles) which derive from

osteoblasts (tall blue cells). Osteocytes are the black dots in white, stellate, lacunae. If one of the osteoblasts is

programmed for differentiation into an osteocyte, it would be the middle of the three in order to allow the

surrounding cells to encase it within the unmineralized osteoid.

For most of the history of bone biology, it was assumed that because osteoclasts

operate on bone, they must derive from the same lineage. Today, we know that

osteoblasts derive from mesenchymal stem cells, which are also responsible for the

development of chondrocytes and other tissue progenitor cells. Osteocytes are

differentiated osteoblasts that have shed their mitochondria, become completely

ensconced within the bone matrix, and taken on a new role (14,15,38,47). Additionally,

we know that osteoclasts derive from hematopoietic progenitor cells of the monocyte-

macrophage lineage rather than those of mesenchymal lineage (11,37,43). This is

relatively new information that only became available when technology was sufficiently

advanced to detect the biochemical pathways involved with macrophage differentiation

into other immunological and hematopoietic cells.

Now that science has a solid foundational knowledge, and the technological

capacity to detect, measure, and experiment with bony tissues outside of the human body

(in vitro), much of the recent research being performed in this field deals with pathology

and trauma. Conditions such as osteoarthritis (OA), osteoporosis (OP) and osteopetrosis

are being investigated further. With the discovery of tumor necrosis factor (TNF) related

to osteoclastic activity, there is much research focused on how various tumors influence,

and are influenced by, osteoblastic and osteoclastic activity. These tumors and cancers

are not only limited to the bones; since osteoclasts derive from hematopoietic cells, their

biochemical signals and pathways are systemically pervasive and can be found in

disorders involving smooth muscle cells, lung cancers, and kidney diseases, just to name

a few.

Despite the current state of our understanding of bony tissue, most forensic

anthropological research is not concerned with which cell types are yielding DNA and

the question of why their results emerge as they do are addressed simply in terms of

taphonomy. In the majority of studies there is no cellular examination to help elucidate

why the sample did not produce any viable DNA or, perhaps, produced only limited

amounts. Those studies that do involve cellular components or processes (outside of

genetic analyses) primarily involve human/non-human comparisons, the differentiation of

osseous or dental material from wood or non- biological material (48,49) or are looking

at biochemical signatures of burned remains such as the Ubelaker (2009) study (50).

Most of the introductory level textbooks in forensic anthropology do address the different

cell types and their respective roles, but little is said beyond a brief introduction (51–55).

Ever more targeted approaches to forensic analyses, especially those involved in

the identification of unknown remains, characterize the state of the research today. Even

as costs decrease somewhat from the earlier years of DNA analyses, financial concerns

still dictate a good portion of workflow in law enforcement (LE) offices, crime labs, and

so on. The ability, therefore, to target a small bone or portion of a bone for optimal DNA

analysis is a worthwhile goal. And the dissemination of this knowledge to current and

forthcoming generations of forensic anthropologists is an important aspect in our

cooperative efforts with LE officials, Medical Examiners (ME’s), coroners, and crime

labs. Increasingly stringent rules of admissibility of scientific evidence and expertise in

courts also demand more accurate and targeted scientific understandings.

BONE TISSUE TYPES

It is well understood that bone is both composed of, and responsible for, the

storage and homeostasis of molecular nutrient essentials such as calcium and phosphate.

Current characterization of bone tissue types varies between two or three categories,

depending on the goal of the material, whether investigative or descriptive; and the names

of each type may also vary. For example, some authors prefer the term “cancellous” or

even “spongy” rather than “trabecular”. For the purposes of this study, it useful to break

up bone tissues into three types and the following terms, listed in order from exterior to

interior will be used: the term Cortical here refers to the very outer layer that is just below

the periosteum; Osteonal refers to the layer (found exclusively in major long bones) that

is developed in concentric rings with Haversian and Volkmann’s canals; and Trabecular

which refers to the inner- most layer of most elements, contains hematopoietic cells, and

is outside of the endosteum. A fourth type of bone, diploic, is found only in the cranium,

and because this project does not involve DNA extraction sites from the cranium or

mandible, this bone type is not thoroughly discussed. Furthermore, since this project

focuses on adult skeletal tissue, immature or woven bone is addressed only as a reference

in the section on skeletal ontogeny.

Cortical Bone

For the purposes of this study, “cortical bone” is used to refer to the

circumferential layer just below the periosteum. Often lumped together with lamellar

bone, it is created in layers of opposing direction to make it the hardest, outer layer of the

bones. It is what offers the greatest protection from most types of trauma and it is the site

of anchorage for the muscles, tendons, ligaments, cartilage, and periosteum. Structural

failure of the cortical layer results in a wide array of pathological conditions, most of

which are characterized by element deformation (19,56,57) and/or fragility. Damage to

the cortical layer often results in damage to the periosteum. This can result in various

infections such as periostitis, osteomyelitis, and so on. Not surprisingly, then, the cortical

layer is the first layer to be repaired in traumatic injury. Evidence of healing can be seen

in as little as 2-3 days after the traumatic event (17,19,43,58–63). Even in cases of

trauma, osteoclastic activity is stimulated to remodel the bone surface around the area to

prepare it for the osteoblasts to deposit new osteoid and allow for fresh hydroxyapatite

mineralization. A more detailed examination of the healing process of bone is included in

a later section that deals with the specific trauma and pathology found on the skeletal

material used in this study. Specific cellular activity is also detailed more thoroughly in

the sections below.

Osteonal Bone

For this project, the thick layer of bone between the cortical layer and the

medullary cavity/ trabecular layer is referred to as osteonal bone. As noted above, this

tissue is often called lamellar and is not always conceptually separated from the outer,

cortical layer. This tissue is laid down in concentric rings delineated by cement lines and

shot through longitudinally with Haversian canals, and latitudinally with Volkmann’s

canals. Osteons are sometimes referred to as “Haversian systems.” Also characteristic of

osteons are the presence of osteocytes in their lacunae and the dendritic process

extending from them through canaliculi. Trauma to the osteonal bone is the last tissue to

be healed once both outer and inner tables have regained structural integrity. Osteoclastic

activity in the osteons is much slower, both in initial response time and in progression

due to the energy involved in reaching the resorption site(s). See the sections on

osteoclasts and osteocytes for further specifics on cellular anatomy, processes, and

pathology.

Trabecular Bone

Trabecular bone tissue is found in the medullary cavities and is the primary bone

tissue type at the proximal and distal ends of the long bones. It also makes up the

majority of all the bony tissue in the hands and feet. Often referred to as “spongy” bone

because of its porous appearance, trabecular bone is the site of blood cell formation in the

medullary cavities and is thus the site of osteoclastogenesis (the formation, or birth, of

osteoclasts). Starting as rapidly and chaotically formed woven bone during ontogeny,

trabecular bone goes through extensive and continual remodeling during youth and

adolescence, becoming more organized and structurally functional (64). The epiphyses

and metaphyses of the long bones are primarily trabecular bone covered in a cortical shell

which allows for dramatic growth of the element(s) from birth until full skeletal maturity

when the cartilaginous growth plate that joins the epiphysis to the metaphysis is

completely ossified (40,65,66).

BONE CELL TYPES

Traditionally, bone is described in the forensic anthropological literature in terms

of three (or even four) distinct cell types. It is actually somewhat misleading, and nearly

incorrect, to separate bone cell types so much since two of the three (osteoblasts and bone

lining cells) are nearly identical and osteoclasts are more akin to immunological cells

than bone cells. Mostly, this taxonomic segregation is carried out by describing the

primary function of each cell type. However, the interdependence of the cell types cannot

be overstated. Indeed, understanding of this interdependence led researchers to coin the

term “Basic Multicellular Unit” or BMU. Still, it may be argued that, 1) due to their

hematopoietic origin (meaning that they are actually derived from the same stem cells

that produce blood cells), osteoclasts are not truly bone cells at all, 2) that because both

bone lining cells and osteocytes are actually transformed osteoblasts, that 3) there is

really only one type of bone- specific cell (osteoblasts) which performs multiple inter-

related functions. However, for the purposes of this project, the traditional distinction

between the bone cell types will be utilized, with the exception that bone lining cells are

herein classified with osteoblasts. Each individual cell type will be discussed in relevant

detail from genesis through apoptosis. The biochemical pathways involved in the

communication between the cell types will be only generally addressed, as the detailed

specifics of them are outside the scope of this project.

OSTEOBLASTS

Osteoblasts originate from mesenchymal stem cells (MSC’s) found in the

periosteum and the endosteum. MSC’s also give rise to fibroblasts, chondroblasts, and

other cells responsible for the production of tendons, ligaments, and cartilage (15,16,20).

Osteoblasts are principally responsible for the deposition of bone matrix, making them

essential for bone formation, growth, and remodeling. This also means that they are of

primary interest in pathologies of hyper- or hypo- trophic bone development, such as

osteopetrosis and osteoporosis, Paget’s disease, hyperparathyroidism, etc.

Beginning as preosteoblasts, these cells are irregularly shaped and produce

collagen types I, II, and III precursor molecules. Working with other biochemicals such

as alkaline phosphatase and osteonectin, the preosteoblast then differentiates into a

mature osteoblast and begins producing the bone matrix, or osteoid (67). Mature

osteoblasts are cuboidal in shape, contain a distinct nucleus, mitochondria, rough

endoplasmic reticulum, well- defined Golgi apparatus, and secretory vesicles. Normal

osteoblasts typically produce about 0.5 µm of bone matrix per day and persist for up to

100 days, depending on the need. After this period osteoblasts may undergo one of four

possible changes. They may differentiate into bone lining cells, differentiate into

osteocytes, they may succumb to programmed apoptosis, or undergo metaplasia and

transdifferentiate into chondroblasts (16,20,22,67,68). According to the 2006 study by

Franz-Odendaal, et al., there are multiple factors (including: species, sex, overall

organismal health, bone type, and many others) which play a role in determining what

proportion of osteoblasts follow each of the above trajectories. Estimates vary widely and

range from 50- 80% that undergo apoptosis. Parfitt’s 1990 study suggests that only about

30% of osteoblasts differentiate into osteocytes. These numbers are hard to pin down in

living organisms and are not conserved among taxa or bone types. (16,67,69).

If differentiation into bone lining cells occurs, the cells flatten and lose much of

their cytoplasm, mitochondria, and other organelles. If they come into contact with

sufficient parathyroid hormone (PTH), bone lining cells shrink even further, excreting

enzymes that remove a thin layer of osteoid from the surface of the mineralized bone

matrix and prepare it for resorption by osteoclasts (11,15,42,56,70). This shrinkage, loss

of cytoplasm and organelles, and bone surface preparation also act as a biochemical

“runway” for the osteoclasts which were activated by biochemical signaling from

osteocytes or other factors (37,43). Under normal conditions, apoptosis of osteoblasts on

bone surfaces acts as an inhibitory signal to osteoclasts when bone remodeling and

osteoid apposition need to stop, slow down, or change direction. This is different than the

inhibitory signals which modulate bone remodeling and apposition in osteonal units.

Those signals come from neighboring osteocytes (71,72). The reason for this is simply

that most osteocytic dendrites run through canaliculi that are parallel to the bone shaft

and thus intersect more with other osteonal BMU’s than with those on the surfaces of the

bones. There are dendrites that do reach the bone surfaces via canaliculi that run

transversely, or perpendicular to the long axis of the bone, and osteocyte communication

with the bone lining cells occurs at those syncytia or gap junctions. The biochemical

signals are then passed from bone lining cell to bone lining cell. Data varies regarding the

number of osteoblasts that differentiate into bone lining cells but most studies seem to

agree that it is the least common outcome, once primary osteoblastic functions have been

fulfilled.

If an osteoblast is signaled to differentiate into an osteocyte, it will cease

production of bone matrix while the neighboring cells continue to do so, effectively

burying it (38,67,73). The rates at which this occur vary depending on the location of the

cells (intramembranous, or osteonal, vs peri- or endosteal), the type of bone being

constructed or reconstructed (woven vs lamellar), and the bony element in question.

Construction and repair of the inferospinous portion of the scapula, for example, will

occur differently and at a different rate than that of diaphyseal femur tissue or cranial

bones. After the initial deposition of osteoid is in place, other osteoblasts literally line up

along the bone spicule and deposit more osteoid which encapsulates the differentiating

cell (74). This process effectively means that osteoblasts of different developmental

stages are nearly always present along the surface of bones (67,75,76). Polarization of the

osteoblasts, which influences the directionality of osteoid secretion/ matrix production,

along with the sex, age, and other organismal factors, as well as bone type, element type,

and element specific location, all influence which osteoblasts will differentiate and the

rates at which the process occurs. This has the potential for drastic implications in

forensic and ancient DNA extraction. Because of this cellular behavior, sites immediately

adjacent to peri mortem trauma should be the primary target of forensic DNA extraction

since multiple cell types would be active there. Often, these injury sites are conserved for

descriptive or educational purposes and their forensic importance is limited to

photographs. The same premise holds for sites of in vivo remodeling such as the pubic

symphysis and the auricular surface.

OSTEOCYTES

The transition from osteoblast to osteocyte is not yet fully understood. Several

studies have uncovered multiple genetic factors that play a major role systemically, but

the actual process of differentiation is still difficult to visualize, especially in vivo

(22,38,71,73). What is known is that significant alterations to the cell begin to occur very

early in differentiation. Development of dendritic processes are one of the first

observable changes to the cell as it becomes encased in osteoid. Other changes that occur

are reduction in the amount of cytoplasm and the quantity and activity of other

organelles; most notably the mitochondria. These changes are apt, given the immobile

nature and communicatory function of osteocytes. From the main lacunae, osteocytic

dendrites extend in all directions from the cell through tunnels known as canaliculi. These

tunnels and tentacles are the lines through which the cells draw nutrients and exchange

biochemical signals.

Image 2- Osteocytes in their lacunae. Image sourced from google.

Osteocytes are the longest lived of the bone cells, surviving for many decades

before programmed apoptosis. Primarily, they serve as mechanosensors for the bones but

also perform some endocrine functions such as phosphate regulation and systemic

calcium availability. During pregnancy and lactation, osteocytes in mice and rats have

been observed to remodel their own lacunae and canaliculi. Using the same methods as

osteoclasts, they dissolve bone matrix to release Ca+ into the blood (15,20,38). Most of

the time, however, osteocytes serve as coordinators of skeletal homeostasis. In his 2008

article, Skerry disputed the mechanosensory model of osteocyte involvement, noting that

normal strain and loading on bones does not show a direct response from osteocytes to

indicate that they are responsible for bone formation. However, it has been shown that

growth, remodeling, and healing are more akin to a complex, biochemical, conversation

between all cell types in the BMU rather than a single set of instructions given by one

cell type with no feedback from the others (37,38,44,60,67,73).

Trauma to the bone can cause osteocytes to initiate remodeling in different ways.

If traumatic apoptosis occurs, RANKL (receptor activator of nuclear factor kappa- β

ligand) is released which stimulates the development and proliferation of osteoclasts as

well as signaling mesenchymal stem cells to differentiate into osteoblasts. Microtrauma,

or trauma that does not result in osteocytic apoptosis, will still stimulate the above

biochemicals, but in smaller, timed, dispersals to allow for more targeted direction of

osteoclast and osteoblast proliferation. In cases of programmed apoptosis, depending on

multiple organismal factors (age, overall health, etc.), the lacuna may or may not

remodel. In healthy individuals, often empty lacuna are refilled with osteoid during the

healing process, which is the construction of a secondary osteon. Much the way a brick

wall is built, the building of secondary osteons, which often overlap the primary osteons

that were constructed in juvenile osteogenesis, allows for maintenance of full element

stability (22,66,71). However, in older individuals, or those who suffer from certain

pathologies, the empty lacuna left after osteocytic apoptosis may not be refilled. This

leads, in time, to fragility in the element and contributes to OP as well as OA.

Conversely, filling an empty lacuna without the construction of a secondary osteon can

contribute to osteopetrosis, even in an organism that does not exhibit this condition

overall; this is known as micro osteopetrosis.

Several older studies indicate that osteocytes make up approximately 90% of all

cells in any given element (14,15,20,71,73). This may lead to the assumption that they

would account for any nuclear DNA that may be extractable from bone and the possible

reason for a lack of mtDNA in certain elements such as the femur and the humerus,

especially if the sample is taken from the osteonal layer between the periosteum and the

endosteum, as most mid- diaphyseal extractions of long bones are. However, Parfitt in

1990 stated that osteoblast population in a BMU is greater than the sum of osteocytes +

bone lining cells. This would indicate that genetic material extracted from bone would

most likely be of osteoblastic origin and might account for any success in mtDNA

extractions. It is not well characterized, however, whether Parfitt’s model of cellular

populational ratio holds true in elements that are not experiencing any major remodeling

at the time of, or immediately prior to, DNA extraction. In either case, as mentioned

previously, osteocytes do undergo programmed apoptosis as well as traumatic apoptosis

in life and enzymatic apoptosis at organismal death, and thus it is not a foregone

conclusion that these cells are responsible for yielding any genetic material. This could be

especially true in cases of elderly individuals or extractions from sites of OA or OP.

Notably, according to the 2000 study by Martin, trabecular bone tissue BMU’s exhibit a

weaker inhibitory signal from osteocytes, indicating that in these specific sites, genetic

material may be osteoblastic, or even osteoclastic, in origin (71,77). This is consistent

with some aDNA studies that have favored the otic region of the petrous portion of the

temporal bone for their extractions (78,79).

OSTEOCLASTS

Osteoclasts are terminally differentiated, multinucleated, cells that derive from the

monocyte/ macrophage lineage of hematopoietic stem cells. This means that, unlike

osteoblasts, they will not differentiate into any other cell type. Additionally, despite

earlier suspicions to the contrary, it has been shown that they are derived from the same

lineage of stem cells that are responsible for blood cells. While the exact number of

nuclei in any given osteoclast may vary depending on a multiplicity of factors, they are

generally characterized by having at least 2 nuclei and as many as 20 have been recorded

in in vitro experiments (11,12,23,39,80–83). Osteoclasts are generally oblong or rounded

in shape, especially during resorption activity, and contain multiple mitochondria. Once

resorptive activity begins, the osteoclast changes shape even further. Polarization occurs

and three distinct regions become apparent: a functional secretory domain (FSD) appears

at the rounded apex of the cell opposite the surface of the bone being resorbed, a sealing

zone (SZ) appears circumferentially that creates a tight seal and a ruffled border (RB)

extends into the resorptive pit, known as a Howship’s lacuna. Actual resorption occurs by

secretion of hydrochloric acid (HCL) which dissolves the inorganic HA+ crystals and

then the enzymes tartrate- resistant acid phosphatase (TRAP), cathepsin- K, and matrix

metalloproteinase- 9 (MMP-9) degrade the organic collagen materials (16,23,80). The

metabolites are then endocytosed into the osteoclast and transcytosed to the FSD where

they are excreted into the extracellular matrix.

Image 3- From Florencio- Silva, et al 2015- Osteoclasts (Oc & Oc1) in Howship's lacunae. Note the apoptotic cell (Ap) in

a vacuole of Oc1. B = bone tissue. Ot = Osteocyte.

Osteoclast recruitment, that is, the biochemical signals which induce

osteoclastogenesis, is not of primary relevance to this project. What is relevant, however,

is the strong evidence indicating that not all populations of osteoclasts are identical. The

2011 study by de Souza Faloni, et al., showed that osteoclast populations differed in

small, yet notable ways between mouse mandibular bone versus tibial bone tissue;

“…osteoclasts at different bone sites appear to differ and the existence of bone site-

specific osteoclast heterogeneity has been proposed” (80). These cell populations differed

in nuclear number, time between osteocytic RANKL secretion resulting in

osteoclastogenesis, and the specific biochemical recipe used in resorption. This

heterogeneity extends to other skeletal elements, as well, including the calvarium and the

femur. Primary ossification in different skeletal groups occurs in different ways; that is,

they do not all ossify in identical directions at identical rates. In fact, the blueprint for

long bone ossification, for example, is quite different than that of cranial or facial bones.

Biochemically, this means that the matrices are inherently different between the

calvarium and the humerus, for example, or the sphenoid and the clavicle. Therefore, the

osteoclasts populations responsible for maintaining those elements must be able to adapt

to the specific matrix of the element they are working on. This is not only true of

different bony elements, but it applies to cortical versus trabecular bone tissues, as well.

This heterogeneity in osteoclast populations indicates another potential reason for

differential degrees of success in DNA extraction. Skeletal elements which exhibit

osteoclastic populations with fewer nuclei or mitochondria or those elements/sites where

osteoclastogenesis occurs more slowly may not yield the same amount or quality of

DNA. Multiple studies have shown that the frontal bone, for example, does not perform

well in DNA analysis (3,4,6,84,85). Assuming that osteocyte populations vary only in

number between skeletal elements, and that osteoblastic involvement in quiescent, or

non-resorbing, tissue will be minimal, then the failure to yield adequate genetic material

may very well be due to the specific population of osteoclasts that operate on or within

that element.

The application of the idea of osteoclast populational heterogeneity and elemental

site specificity may be shown in another example. Mostly, osteoclastogenesis in the long

bones occurs in the periosteum, the endosteum, or in the marrow itself, depending on the

location of the section of bone that needs remodeling. Peri- and endosteal osteoblast

populations morphologically differ somewhat from marrow- derived populations. In the

femoral head, however, osteoclastogenesis occurs from stem cells residing in the

connective tissues that surround and penetrate the joint. It is logical then, to consider that

perhaps DNA extractions involving the femoral head will be more productive in samples

taken from the inner trabeculae, rather than the external cortical matrix. The same

processes and reasoning also extend to the humerus and humeral head. Since all

osteoclasts are hematopoietic in origin, it also stands to reason that populations in closer

proximity to marrow or the endosteum, may potentially provide better, more complete,

results in genetic tests. This application of osteoclastic heterogeneity is surely operative

in all other post- cranial elements and may account for the recent popularity of various

tarsal bones in forensic analysis (1,3–5). The function and primary ossification of the

element to be tested must be taken into consideration in forensic DNA analysis.

OSSIFICATION & ONTOGENY

Human skeletal ossification is well characterized, but a brief review is warranted

because of the site- specific nature of this project. Not all elements develop and ossify

through identical pathways and the specifics of the process may mirror osteoclastic

population heterogeneity, and osteoblastic and osteocytic proliferation. Additionally, any

structural differences in ontogenetic construction may play a role in the way in which,

and the extent to which, DNA survives in specific sites on specific skeletal elements. In

order to address the pattern of post- mortem DNA survival in skeletal tissues, then as

many factors as possible should be probed, which means understanding whether

intramembranous versus endochondral ossification allows for better DNA preservation.

The following section is divided into the two primary methods of ossification in the

human skeleton. Most elements follow either one or the other design, but some elements

exhibit both and those constitute a third group. Since this project does not test samples

from the cranium, mandible, or teeth, those elements are mentioned only as reference

points.

ENDOCHONDRAL OSSIFICATION

Primarily occurring in the long bones (including the phalanges) endochondral

ossification occurs as the cartilaginous blueprint for the element is replaced with osteoid

and hydroxyapatite crystals. MSC’s proliferate in the perichondrium (the cartilaginous

equivalent of the periosteum) and usually, just before birth, chondroblasts yield as the

MSC’s begin to differentiate into osteoblasts at the primary ossification centers located in

the mid- diaphyseal regions (86,19,56,51,66). Secondary sites at the proximal and distal

ends of the elements follow the same process, as do the metaphyses and epiphyses which

ossify from the cartilaginous growth plate.

Since ossification begins at the outer table of the element, penetrative

vascularization of the ossifying element commences and a bony ring is formed (66).

Through this vascularization, the ring is thickened and becomes dense from the

periosteum through the cortical tissue to the inside where the endosteum will eventually

reside, once the marrow and inner trabecular bone is adequately formed. This inner

tissue, and all trabecular bone, begins as unorganized, woven bone to give preliminary

strength to the element and as a platform for remodeling to occur. Similarly, primary

osteons laid down in the early stages of ontogeny will be resorbed and rebuilt into

secondary osteons in the mature bones. The marrow itself begins as “red marrow”,

primarily involved with production of blood and immunological cells, including the

macrophages and monocytes involved in osteoclastogenesis. Remodeling occurs on or

within all bony tables even during ontogeny, to allow for growth in length while still

maintaining structural integrity. This occurs at the flared ends of these elements as well,

so they can grow in thickness and length.

INTRAMEMBRANOUS OSSIFICATION

Flat bones and diploic bones primarily ossify from two membranes that build

bone between them. This occurs appositionally as the bone is laid down from the

periosteum, much the way remodeling occurs on the cortical surfaces of all elements.

Skeletal structures that develop intramembranously begin as a sandwich with two

perichondrial membranes and cartilaginous material between them. The change from

perichondrium to periosteum occurs in the same way as in endochondral ossification,

with centers that begin the process. As the perichondrium differentiates into a periosteum,

MSC’s differentiate into osteoblasts. In the cranium, ossification begins in the base of the

occipital to build structural support for the cranial vault as well as for protection for the

brain stem and the spinal cord. The sphenoid is the primary center of ossification for the

bones of the face, and also serves to build structural integrity for the sensory nerves and

the developing brain. Other elements that ossify from an intramembranous blueprint

include the sternum and manubrium and the patella.

INTRAMEMBRANOUS + ENDOCHONDRAL OSSIFICATION

Some elements ossify by utilizing a combination of the above schemes. Denser

aspects of these elements, mostly those involved in articulation with elements whose

functions require a high degree of mobility in life, ossify from a cartilaginous model

(endochondral), while the more gracile aspects ossify intramembranously. The anterior

and lateral aspects of the mandible for example, ossify intramembranously to allow for

the development of the bony crypts which hold the developing dentition, but the rami and

coronoid processes develop endochondrally. The clavicle ossifies intramembranously in

the diaphysis, and endochondrally at the medial and lateral ends. It is one of the first

bones to begin ossification and is usually the last to complete the process (87). This

combination occurs in the scapula and the ilium, as well.

HYDROXYAPATITE

Hydroxyapatite (HA+) is the inorganic, mineral, aspect of bone tissue. While the

chemical formula, Ca10(PO4)6(OH)2, has been known for decades, hydroxyapatite

formation and deposition is not definitively characterized (15,16,56,64,77). However,

recent research has indicated that osteoblasts, during osteoid production, also produce

matrix vesicles containing high concentrations of calcium (Ca2+) and phosphate (PO4-)

ions that are the constituent components of HA+ that are not found in high enough

concentrations in the extracellular matrix to satisfy the mineral requirement for bone

mineralization. After filling, these vesicles separate from the osteoblasts and rupture,

thereby “dumping” their mineral components onto the newly deposited osteoid. The OH

constituent of HA+ is found in high enough concentrations in the extracellular matrix so

that the cells need not produce it themselves. The addition of this part of the molecule

completes the formation of the hydroxyapatite crystals, which mineralizes and spreads by

accretion, joining together from the foci of deposition from other osteoblasts (64). The

resulting hydroxyapatite has a positive charge.

It is thought that HA+ crystals maintain their charge long after organismal death

and it may be this charge that attracts, binds, and preserves negatively charged DNA

molecules (88–90). Like the process of bone mineralization, this charge retention is not

definitively characterized, but may contribute to the explanation of why some bony

elements perform better in forensic (and ancient) DNA extractions than others. Elements

or bone types with higher cell populations may potentially contribute more genetic

material to the HA+ upon apoptosis or lysis after organismal death. Additionally, this

charge retention may combine with higher surface area to account for better DNA

preservation in trabecular than cortical bone tissue. However, the density of the outer

cortical layer may prove more resilient in charge retention, if cortical samples are shown

to outperform trabecular samples in forensic DNA analysis. There is also the possibility

that while the cortical layer may be stronger in original charge retention, due to exposure

to environmental conditions, it may lose its hold on DNA earlier than the protected,

trabecular, tissues, thus accounting for differences in older samples or those exposed to

certain taphonomic factors

CHAPTER 2: FORENSIC DNA ANALYSIS

In Chapter 1, human skeletal cellular biology was explored in depth with

references made to forensic DNA analysis. This section flips the lens and examines the

field of forensic DNA analysis, drawing pertinent lines of connection to human skeletal

biology. Different approaches to the forensic application of DNA analysis are examined

through time leading up to contemporary practices and briefly discussing potential future

research. While the construction of the “heat map” is primarily focused on autosomal

DNA, forensic casework often employs mitochondrial DNA (mtDNA) for its unique

properties and information, so there is discussion of such in this section. Likewise, both

X and Y chromosomal analysis is pertinent to forensic applications and will be addressed.

To a limited extent, ancient DNA (aDNA) studies are discussed as some of the

limitations encountered, methods employed, and reasons for differential DNA

preservation, extraction, and quantitation are relevant to modern forensic contexts.

Additionally, because this project is focused on the cellular components of bone as

potential reasons underlying the differential preservation of DNA, what happens to the

cells and the genetic material they hold after death needs to be understood. Therefore, the

taphonomic forces which influence the differential preservation and extractability of

DNA across the human skeleton are discussed in this section, focusing on the cellular,

biochemical and biomineral characters at play during and after decomposition.

HISTORY & THEORY

The idea that biological traits can be examined to identify an individual, whether

deceased or alive, has been explored, discussed, written about, and published on, for

centuries. Much like Clopton Havers’ early observations of bone cells, the very first cases

of forensic victim identification come from Europe during the Enlightenment Period of

the 17th and 18th centuries. During this time, there was a growing belief in Biological

Determinism. That is, that perpetrators of crime (whether actual or only potential) could

be identified by various morphological traits and victims of said crimes could be

similarly identified. Despite a resurgence in popularity during the eugenics movement in

the early 20th century, Biological Determinism, as a paradigm, has slowly lost favor over

the years. The influence of environment on behavior has become better understood and

more widely accepted, supported by acknowledgement from the scientific community

that genetics cannot always explain why an individual acted in a certain way or whether

genetics played a role in victimization. In forensics, this coalesced to an extent in the

Supreme Court ruling that forensic evidence must meet an objective, peer-reviewed,

scientific standard (Daubert v. Merrell Dow Pharmaceuticals, Inc. 1993). As time,

science, and culture have advanced, the methods used have come under greater and

greater scrutiny by the criminal justice community, and indeed, by society at large. In the

waning decades of the 20th century, the use of DNA to establish identity of both victims

and perpetrators was invented and has become the standard against which all other

methods are compared.

In forensic science, most evidence has both class characteristics and individual

characteristics, and human genetic material is no exception. Class characteristics are

those which are similar across typologies and allow for generalized identifications (for

example a projectile may exhibit class characteristics that identify it as a bullet fired from

a rifle, but not the specific make or model of rifle). In forensic genetics, mtDNA and both

X- and Y- chromosomal DNA are informative on a genealogical or phylogenetic level,

but they lack the discriminating characteristics required to identify a specific individual.

The data gathered from mtDNA can be, and is, used to make familial matches in cases

where the decedent is unknown. And Y- chromosomal analysis is similarly effective in

cases where paternity or paternal lineage is needed. For example, if an unknown decedent

is profiled as male and the nearest potential relative is the father, then the Y-

chromosomal analysis is necessary since the two males would have different mtDNA. If

the nearest potential relative is a brother, then mtDNA would be useful (assuming they

have the same mother) and the Y-chromosome would be useful as a confirmation of

relation (assuming the siblings had the same father).

Non- sex-linked autosomal DNA is what can identify individuals or maintains the

individual characteristic. Returning to the illustration of the bullet and the gun, the barrel

of a particular rifle, the firing pin, and the ejection port will all leave unique indicators on

bullets and shell casings as they are fired and allow that specific projectile to be linked

back to that specific weapon. Regardless of manufacture, no two firearms are exactly the

same and (except in cases of identical twins) no two humans have exactly the same

autosomal DNA, regardless of parentage. Therefore, in cases where a victim must be

identified to an extremely high degree of probability, or a specific perpetrator must be

linked to a specific location or piece of evidence, it is that individual’s unique autosomal

genetic profile that must be obtained.

In the United States, a rigorous benchmark has been set for the identification of a

perpetrator of crime using DNA, and victim identification investigations strive to live up

to the same standards. Using short tandem repeats (STR’s) which will be discussed in

more detail below, the genetic profile of a suspect must match at least 20 points, known

as loci, to the DNA left behind at a crime scene in order to be considered a “positive

match.” This is discussed in more detail below. The greatest goal would be to match a

complete genome from crime scene to suspect and from victim to reference sample. But,

for a multiplicity of reasons, that goal is very rarely achieved. The benchmark for

criminal prosecution is rightly very high and victim identification efforts should be no

less rigorous.

So, the question at hand is how do we obtain the greatest accuracy in our attempts

at victim identification? If there is a presumptive identification (a driver’s license, for

example, is considered a presumptive ID), then it is far easier to obtain familial reference

samples for DNA comparison, medical and dental records, and so on to try and match the

remains to an individual identity. If there are no other overt indicators of identification,

however, then investigators must rely on forensic anthropologists, odontologists, and

hope that DNA analysis results match up to records in a national database such as the

violent criminal apprehension program (VICAP) or the Armed Services database.

ACRONYM SOUP

The field has evolved over the last 30 years, becoming more accurate, less

expensive, and faster. In the beginning, long, repeating, fragments called Variable

Nucleotide Tandem Repeats (VNTR’s) were used in both victim ID and in prosecutorial

endeavors (91). VNTR testing was expensive and not nearly as reliable as modern

methods, given that genome-wide frequencies of the nucleotides being examined did not

exist at the time. The very name itself also gives a clue to this method’s greatest

shortcoming: variable. The repeat units being employed in this method vary too widely

across populations and there was no effective way to reduce allele drop-out which is a

common source of error in genetic data analysis, even today (46).

The next method that was developed involved the use of Restriction Fragment

Length Polymorphisms (RFLP’s). RFLP analysis requires the use of restriction enzymes

and probes, and typically focuses on a specific gene. This method, while effective at

discriminating variation between individuals, is laborious, time-consuming, subject to a

significant degree of human error, and is prone to other issues that are specific to

polymerase chain reaction (PCR) amplification methods, known as inhibitors (46,92–94).

While RFLP’s are more consistent across populations, and thus more reliable than

VNTR’s, today’s amplification and sequencing technology is sufficiently advanced that

this technique is no longer necessary. Additionally, RFLP analysis is not ideal in cases or

situations where the DNA is already, or is assumed to be, highly fragmented.

Single Nucleotide Polymorphisms (SNP’s) are, as the name implies, very small

polymorphisms that occur due, primarily, to mutations. The primary drawback to using

SNPs in a forensic setting is the number of specific SNPs which are needed to identify an

individual. In the last ten years, several studies have been published about the possibility

of using SNPs in forensic settings and while there are SNPs that can be individualizing

(iiSNPs), most are still in the realm of ancestry-informative, or aiSNPs (95–99). While

SNPs and Indels (insertions or deletions of small chunks of base pairs) have a lower rate

of mutation than STRs, all studies have still found that a large number, between 40 and

150, are required to even approach the same genetic information gleaned from 13- 20

STRs. The consensus is that SNPs are extremely useful in confirmatory roles, but still

lack the individualizing power of discrimination required in forensic contexts. The final

consideration that keeps SNPs and Indels from overtaking STRs in forensic settings (for

the moment, anyway) is that there are already large databases of reference STRs from

victims, perpetrators, and average citizens against which investigators can compare their

samples. However, research into the forensic utility of SNPs and Indels is gaining

considerable momentum and offers a wealth of future research potential.

The discovery of Short Tandem Repeats (STRs) in the early 1980’s greatly

increased the reliability of forensic DNA analysis and replaced VNTR’s in the 1990’s.

Multiple STRs occur throughout the human genome and knowledge of these allowed for

the creation of standards, as well as the ability to generate identification with degraded or

fragmented DNA. While a SNP could occur anywhere in the genome or get lost in either

populational redundancy during analysis or in taphonomic degradation, the ability to

predict, and thus amplify STRs was one of the defining features that led to the rise of this

method at that time. STR analysis quickly replaced all previous methods and have been

adopted by both the FBI and European forensic and law enforcement agencies. In 2017,

the FBI, aided by investigatory task forces from multiple scientific endeavors, expanded

the minimum number of STR loci to be used in criminal cases from 13 to 20. The nature

and utility of STRs is well characterized in the literature (46,84,92,100–103) and this has

become the standard method for forensic DNA analysis globally. Shewale and Liu’s 2014

textbook lays out the structure, history, and contemporary use of STR’s in detail and is an

invaluable reference for anyone interested in the topic.

FORENSIC STR ANALYSIS

STRs occur on every chromosome in the human autosomal genome. While some

of the repeating motifs in STRs are as short as 2 bp’s and some are hundreds of bp’s long,

those most useful in forensic casework, and thus the ones employed in CODIS, mostly

consist of 4 non-coding bp’s that repeat a certain number of times at a given locus. Some

CODIS STR’s are only 3 bp’s long and some are 5, but most are 4 bp’s long and are

commonly referred to as tetranucleotides. The number of repetitions vary from just over

a dozen to several dozen. Some of those that only involve a few repeats, known as

MiniSTR’s, are extremely useful in cases of degraded DNA (94,104). Commercially

available kits have been, and continue to be, developed that reduced the number of

necessary PCR cycles, increased the number of samples that can be tested simultaneously

(multiplexing), and incorporate the newer loci which include sex- discriminating STR’s

(93,102,105). Capillary electrophoresis (CE), the method used in this project and

discussed in greater detail in the methods section, is still a highly effective, and

decreasingly expensive, method of STR profile construction.

Against a backdrop of human genomes, STR “matches” are calculated simply by

multiplying the populational percentages of each STR in the sample. For example, an

individual’s STR profile is obtained from a buccal swab and the percentage of people

exhibiting the same allele at the CSF1PO locus are multiplied by the percentage of people

also exhibiting the same allele at the remaining 19 loci. The probability of multiple

people within a given population exhibiting the identical STR profile is a magnitude of

1:1x10-n. where n is the number STR loci employed. The more loci that match, obviously,

the lower the probability of a duplicate. With the addition of the 7 new loci by the FBI,

those probabilities are in the trillions to quadrillions (93). Image 1 lists the STR loci used

in the Combined DNA Index System (CODIS).

Image 4 - STR loci used in forensic DNA analysis. Reproduced from the CODIS website: fbi.gov.

Y- AND X- CHROMOSOME STR’S

The STR’s listed above, those typically used in both perpetrator and victim ID

cases, are located on the 22 autosomal chromosomes of nuclear DNA. For the most part,

sex-linked STRs, found on the X and Y chromosomes respectively, do not have the

individuation power that the others have and are better used for general kinship,

evolutionary, and haplotype/ haplogroup studies. Forensically speaking, these markers

are used almost exclusively in paternity testing and in cases of sexual assault.

New studies of STRs found on both of the sex chromosomes, however, are

beginning to show promise of the ability to confirm individuation made by other means.

Rapidly mutating Y- STRs have been found which will, no doubt, enhance the utility of

these markers in forensic casework (106,107). Newer studies of STRs on the X

chromosome are showing similar potential. Although the studies are still primarily

populationally specific, their ability to confirm or refute identity has the definite potential

for future expansion into other avenues of forensic casework (108,109).

This project does involve extraction and amplification of the Y-chromosomal

DNA in tandem with the major focus of differential DNA preservation across the human

post-cranial skeleton. In keeping with the overall goal of the project and the relative

utility of the male chromosome in forensics, Y-STR analysis in this study is primarily

research oriented with a secondary goal of haplogroup analysis and potential kinship

refence material. Sex confirmation in this case is a tertiary concern since visual analysis

of the individual at autopsy and subsequent forensic anthropological analysis both

concluded the individual was male.

MITOCHONDRIAL DNA (MTDNA)

Albert von Kölliker, the same German microbiologist who first described

osteoclasts, was also the first to describe the mitochondria in 1857. Originally coined

“bioblasts” by Kölliker, they were renamed some 40 years later by Carl Benda. It wasn’t

until the 1950’s, however, that their role as energy producers was uncovered and the term

“powerhouse of the cell” applied by Philip Siekevitz. As research progressed, it was

discovered that mitochondria were once part of a bacterium that was endocytosed by

another prokaryotic cell and developed a symbiotic relationship trading energy (in the

form of ATP) for protection. That relationship, possibly the longest standing one in

Earth’s history, survived through the development of eukaryotes and into today. Medical

and microbiologists had also learned, by the mid- 20th century, that during fertilization of

a human egg by a sperm cell, the mitochondria (located solely in the tail of the

spermatozoa) were excluded from entering the egg. This dovetailed nicely with the

discovery that mtDNA moved only along the maternal lineage. Utilizing this information,

plus the discovery that mtDNA has a much higher mutation rate than nuclear DNA,

scientists found they were able to trace human maternal lineages a long way back into the

past. In 1987 groundbreaking works were published about the utility of mtDNA in human

evolutionary studies with concepts about the molecular clock and mitochondrial Eve,

respectively (110,111).

In the 30+ years since those articles, biological anthropologists have successfully

extracted and typed whole mitochondrial genomes of modern humans, Neanderthals, and

Denisovans. As our technology continues to improve, phylogenetic studies continue to

become more detailed and our understanding of the human diaspora across the Earth has

come into sharper focus. Unlike nuclear DNA, mtDNA occurs in much greater numbers

within the mitochondria of a cell. This high copy number in the cells, its somewhat

protected location within the mitochondria, and its circular shape, often allows mtDNA to

survive in bone tissue better than does nuclear DNA (112–114). All of the factors

contribute to its utility in studies dealing with extremely old or degraded samples. In

forensics, we often analyze mtDNA when the autosomal material is too fragmented, or in

cases where limited skeletal elements prevent nuclear STR profiling. While it does not

have the power to identify an individual, mtDNA is useful in forensics as a means of

excluding a potential suspect or victim, and it is useful as an aid to forensic

anthropological assessments of ancestry. In cases of unknown remains, often times

mtDNA can lead to a familial match with a mother or sister or even a daughter. At that

point, other means of individuation are utilized to confirm the identity of the deceased.

ANCIENT DNA

Ancient DNA (aDNA) was originally a term indicating a general age of the origin

of a given sample. It has become, however, an umbrella term in genetic casework

referring to DNA extractions and analyses where the long, double-stranded autosomal

DNA is broken, or fragmented. This is usually because the biological tissues or fluids the

samples are taken from are less than perfectly preserved. A more detailed discussion of

the specifics of DNA fragmentation and degradation is provided in the following chapter.

Since most of the skeletal remains that are surrendered to ME’s or Coroners offices have

been subjected to unknown (and presumably less than ideal) taphonomic influences for

an unknown amount of time, the de facto assumption is that any genetic material

recovered from the samples will be degraded, or fragmented, to some extent. Working

under this assumption compels investigators to follow more stringent protocols and

allows us to be happily surprised if the results are better than expected. Further discussion

of such taphonomic processes is provided subsequently.

Ancient DNA (aDNA) studies are relevant to forensic casework because of what

has been, and continues to be, learned about genetic fragment analysis and DNA

survivability through time, across different skeletal elements, and in spite of (or perhaps,

because of) various taphonomic environments and processes. The ability to extract DNA

from megafaunal skeletal remains as old as 400,000 years (115,116) and fossil hominin

DNA as old as 45,000 years (117,118). have helped propel modern forensic DNA

extraction techniques and technologies. Finding consistently reliable results from aDNA

extractions from teeth as well as the otic portion of the tympanic region of the temporal

bone (9,79,119,120) has informed forensic investigators on the where to look for reliable

sources of genetic material, as well as how to chemically extract the DNA from bone

tissue in order to facilitate amplification. Indeed, the protocol documented by the

Dabney, et al study from 2013 is one of the methods used in this project and is discussed

further in the methods section.

The otic region of the tympanic portion of the temporal bone is made up of

trabecular bone surrounded by a layer of cortical bone that lies within the cranial vault. It

houses and protects the delicate bones of the inner ear and plays a vital in hearing. As

such, the otic region undergoes a high degree of lifetime remodeling. The cellular

processes and the general location of the region most probably account for the high level

of DNA survivability written about by biological anthropologists, specifically

documented in the Pinhasi, et al (2015) study. The teeth are the other primary sources of

aDNA due to the well documented protection afforded by layers of dentin and enamel.

Teeth survive for an extremely long time post-mortem and protect DNA from

taphonomic process that normally degrade genetic material (119,121–123).

Proteomics are becoming more and more popular in studies of ancient organisms.

This is a kind of reverse- engineering whereby, the proteins that remain in bone samples,

or even fossilized bones, are used to reconstruct the genes that coded for them. Once the

organism’s proteome has been established, some further reverse- engineering leads to a

tentative genome. In this way, investigators have been able to learn a surprising amount

from bones well over 100,000 years old (124,125). These methods, though not widely

used in forensics as of yet, have the potential to make a huge contribution to the science

in general, not just in cases of DNA degradation.

Chapter 3:MOLECULAR TAPHONOMY

Taphonomy of osseous tissues has been, and continues to be, a topic of paramount

importance in forensic anthropology. This is both a process- based understanding of how

and at what time intervals these changes occur, as well as an appreciation for what

implications these changes and processes have on the macroscopic morphology, the

microstructural composition, and the condition of endogenous DNA within the bones.

Studies of taphonomic process are abundant in the forensic anthropological literature;

historically focusing on macroscopic changes that may alter traditional forensic

anthropological construction of the biological profile. More recent studies have focused

on whether and how certain taphonomic processes and maceration techniques affect

DNA in bones. The newer taphonomic studies focus on the soil around the body at the

time of deposition or burial. Mostly, the factors examined in such studies include soil

composition, moisture and pH levels, as well as microbial constituents or other

environmental degradative agents. Maceration studies have focused on the chemicals

involved and the temperature of the solution in which the bones are processed; as both of

these certainly have some effect on the quality (strand length) of endogenous DNA when

forensic analytical methods (such as STR and SNP panels) are to be employed.

For this project, the taphonomic factor most at play is the maceration technique

employed to separate the soft tissue from the osseous. Recently, some studies have hinted

at an internal taphonomic players such as the individual’s age and relative bone density at

the time of death. Though this project does not delve into this factor, there is a great deal

of potential future research in such a proposition. While the impact of maceration

techniques on endogenous DNA in bone tissue is documented to a greater extent than the

internal factors, in neither case is there adequate understanding of the how or why. For

example, the popular opinion for some time was that, because they are embedded within

the bone tissue, osteocyte populations are responsible for the DNA that is recovered from

bone tissue. However, this is unproven and does not fit with the results obtained in

studies of differential DNA preservation patterns across the skeleton (1,2,12,113,126–

128) and also does not take into account attrition of osteocyte populations as the

individual advances in age (9,12,22,43,129). And while it was originally assumed that

chemicals such as bleach would have a negative impact on endogenous DNA levels,

studies such Steadman et al 2006 indicate that temperature has a greater effect than

detergents or chemicals. Nearly all studies today, including but not limited to those listed

above, acknowledge a lack of understanding about which cellular components of bone

are responsible for contributing to the genetic material recovered bone, as well as what

biochemical/biophysical properties of bone tissue are involved with the preservation of

DNA in spite of the various degradative processes. It is therefore crucial to understand

the types, processes, and patterns of DNA degradation.

TYPES OF DAMAGE

Base Degradation

Hydrolysis, defined as the breakdown of a compound due to interaction with

water, is the most common degradative process to which DNA is subjected. Hydrolytic

damage causes deamination, whereby a cytosine is changed to a uracil, adenine is

converted into hypoxanthine, or guanine is converted to xanthine. These switches are

known as “transitions.” Hydrolytic deamination lesions are usually repaired in life,

though such post- mortem changes can be misdiagnosed as in vivo mutations, rather than

taphonomic alterations. However, hydrolytic deamination of 5-methylcytosine to thymine

at the 5’ end CpG site converts the G to T and cannot be repaired in vivo, resulting in a

lasting mutation.

Hydrolysis also causes depurination which manifests as the elimination of either

an A or a G resulting in an abasic, or apurinic, site. Depurination also occurs at T and C

sites, although at a much slower rate. Even though the pyrimidines (Cytosine and

Thymine) are subject to this type of damage, it is known as “depurination” simply

because it occurs more quickly and easily at the purine (Adenine and Guanine) sites.

Post-mortem depurination is easier to recognize as being taphonomic than are the

deamination transitions noted above.

Most maceration methods involve the use of water, often with added solvents.

Thus, hydrolysis is the primary degradative agent encountered in studies where the bones

have been artificially defleshed. However, it is not solely the water itself that raises

concerns, but also the temperature of the water used. Increased heat has the positive

effect of increasing efficiency when removing soft tissue but the negative effect of also

increasing depurination/deamination of the DNA inside the bones. For illumination of the

damage inflicted by high temperature and high humidity, one need only look at a globe. It

is well documented that bones recovered from latitudes between the tropics of Cancer

and Capricorn rarely yield the same quantity or quality of DNA as those recovered from

cooler climates at higher latitudes (130).

Another agent of base pair degradation is oxidative damage. Similar to

deamination/ depurination by hydrolysis, oxidation causes transversions from G:C →

T:A. Oxidation is caused mostly by exposure to radiation or chemical ions, known as

radicals, such as OH-, a hydroxyl radical (131), or CO3-, a carbonate radical (132). While

an in-depth discussion of the specific chemical reactions that cause these radicals to occur

are outside the scope of this project, it is pertinent to understand that they occur in much

higher frequency in living tissue and the damage to living DNA is usually repaired,

though oxidative damage can be carcinogenic. This is the primary form of base pair

damage encountered in bone tissue recovered in drier climates. Which, while

understudied, could be due to persistent exposure to solar and cosmic radiation,

differential microbial activity, and/or chemical reactions in the soil and the immediately

adjacent atmosphere (133). This also accounts for the slower pace of oxidative damage

compared to hydrolysis. Studies such as those by Antinick and Foran (2019) and Misner,

et al (2009) seek to correlate remaining endogenous DNA to weathering patterns found in

skeletal material from various depositional environments. Yet nearly all studies indicate

that skeletal weathering is rarely a valid indicator of the conservation of DNA quality

(strand length) or quantity (copy number). Buried bone exhibited greater damage levels

than did those deposited on the surface, further indicating the possibility that oxidative

damage is a greater threat to living bone than it is taphonomically important (134).

Therefore, it is important to realize that the visual appearance of weathering, be it

bleached from subaerial exposure or stained by soil and water, may lead to erroneous

assumptions about the preservation of DNA within the elements.

Structural Degradation

Structural damage to DNA may be a more serious detriment even than oxidative

or hydrolytic degradation. Cross-linking is a form of damage that occurs when DNA is

subjected to UV radiation. It causes the double- helix strand to twist and “kink up” on

itself, like an old strand of Christmas lights. The sugar-phosphate backbone of the DNA

strand will bind in places where it normally would not. Strands that have undergone this

type of damage will not amplify in laboratory settings, and thus UV light is an effective

method for preventing contamination in aDNA labs. Cross-linked DNA is the primary

reason for low copy number data in genetic profiling. Essentially, if the sample has

undergone an extensive degree of this type of damage, no DNA will be analyzable

because once the strand is knotted up, it can’t be untangled.

Probably the easiest form of post- mortem damage to work around is strand

breakage, at least with more recent analysis methods. Strand breakage occurs by

hydrolysis or oxidation and is, quite simply, breaks in the DNA strand. Modern primers

are designed to adhere to the broken ends of the strands in the sample DNA and allow

researchers to amplify the remaining genetic material for analysis. This was not always

the case and in the early days of gel slab electrophoresis, it was seen as a dire limitation,

even though researchers could see that some DNA remained in their samples. Today, in

samples where the taphonomic damage to DNA is unknown, or is thought to be severe,

the assumption of widespread strand-breakage simply prompts researchers to design

primers that will bind to the smaller, broken, fragments in the sample. This allows for

greater success in PCR amplification whether the ensuing steps are geared toward

complete genomic assembly or, in projects such as this, STR profile generation.

Each of these different types of DNA degradation, depurination by hydrolysis,

oxidation, cross-linking, and strand breakage, require mitigation measures in the lab

when attempting extractions. Ergo, the a priori assumption for this project is that all

types of extractable DNA have undergone a level of degradation such that the methods

employed in aDNA studies are necessary to establish a baseline of the quantity (physical

amount, or copy number) and quality (strand length, number of lesions, etc.) of whatever

genetic material may remain in the bones. The Pääbo, et al (1989) article outlines the

issues and difficulties encountered in very old specimens and these are the same

conditions that are assumed to be operative when dealing with samples of unknown

provenience. By treating the specimens as though they had undergone more extensive

damage than may be the case allows for a more thorough investigation of all DNA types

across all skeletal elements and thus affords the opportunity to ascertain the cellular

components that are contributing their genetic material and the biochemical factors that

are protecting the DNA from degradation.

Rate and Patterns of Decay

Much of what is known regarding the rates and specific steps in the degradation

of DNA, whether mitochondrial or nuclear, comes from in vitro studies, or studies

involving DNA extractions taken from soft tissue. Since soft tissue decomposition

progresses through several distinct and well-documented phases, it is known that the

DNA recovered from soft tissue is subjected to damage that is not identical to the

processes that damage the DNA recovered from osseous tissue. Studies that do

investigate the patterns and rates of DNA degradation from bone often encounter issues

(especially in terms of establishing correlation, causation, and/or statistical significance)

directly related to a number of factors such as: small sample size, varied depositional

environments, extreme (and often widely ranging) post-mortem intervals (PMI’s), and

extinct and/or non- human species (2,88,135–137).

While some of these studies do offer some insight into the rates (kinetics) of DNA

degradation, there are still more variables that prevent accurate model building. For

example, there are kinetic differences between mtDNA and nuclear DNA degradation

(135), and differences in decay have also been shown in mtDNA extractions between

different dental substances (9). Brundin et al published a study in 2013 which showed in

vitro DNA molecules that are bound to hydroxyapatite crystals survive significantly

longer than those DNA molecules which are not bound to hydroxyapatite in 3 substrates

(water, sera, and DNase I). All these studies do hint at unknown cellular and biochemical

factors and the common thread is that what is still unknown makes it virtually impossible

to predict the quantity (how many chunks of base pairs) or quality (strand length, number

of lesions) of endogenous DNA that will be conserved in any given skeletal element

outside of a laboratory. Due to widely ranging depositional environments, this also means

that it is extremely difficult to ascertain a constant rate of genetic decay in specimens

outside of a laboratory setting.

Furthermore, from Pääbo’s early work in the late 1980’s through today, attempts

to obtain genetic material from ancient specimens has advanced our understanding of not

just the types or even the rates of genetic degradation, but also the patterns of how these

processes typically play out in the natural world. Modern biomedical studies of both in

vivo and in vitro samples and aDNA studies from long dead samples have provided us

with the beginnings of an understanding that, while subject to a wide array of factors and

a significant degree of stochasticity, there may be more of a pattern to DNA damage than

originally feared. It has been shown that in vivo DNA strands more commonly break at

certain locations and some base pair transitions, or misincorporations, occur more

commonly at 3’ ends and others at 5’ ends, that an overabundance of cytosines in certain

locations is indicative of post- mortem transition, and so on (9,88,130,131,135,138–140).

To that end, a computer program called mapDamage was designed that scans genetic data

for these patterns and help researchers to distinguish between what is endogenous to the

sample and what is most likely a contaminant contribution from outside sources, and to a

lesser extent, post- mortem versus ante- mortem change (141). A thorough investigation

of bioinformatic methods such as this is beyond the scope of this project. However, even

the best chemical primers, computer programs, and bioinformatic pipelines cannot repair

taphonomic damage or perfectly distinguish between post-mortem changes and in vivo

mutations.

Because it is impossible to predict the extent and nature of post-mortem damage

to DNA contained in every specific bone sample, there is need for methods that will

allow researchers an insight prior to the destructive processes of DNA extraction. Modern

extraction methods are far less destructive than in years past, due in large part to

improvements in primers, dNTP’s, and other chemical components of the extraction,

PCR, and library preparation steps. However, even more insight would be useful. If only

a tiny amount of bone powder could give researchers an indication of whether the

element (or fragment) was to be useful or should be preserved as is, more fragments or

elements could be preserved and efficiency would increase. It is for this reason that this

project uses Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) tests

to measure the chemical bonds within each element. Samples that indicate chemical

bonds consistent with those found in DNA can then be confidently used for extraction

purposes (24–26). While this test is still yet to be widely employed, preliminary results

are encouraging.

Because of the assumption of the primacy of depositional environment and,

secondarily, whatever decomposition or maceration may have occurred on the condition

of endogenous DNA in skeletal tissue, combined with the understanding that the physical

tools, chemicals, and temperatures used to remove any lingering soft tissue, there is

growing appreciation of aDNA extraction and amplification methods. Indeed, the term

“aDNA” itself no longer refers merely to the age of the sample, but the condition of the

DNA within. Some of the methods are being used even in modern forensic settings when

the endogenous genetic material has experienced unknown degrees of degradation due to

strand breakage, depurination, etc. And while analytical tools such as mapDamage

continue to improve the interpretation of genetic material by scanning read sequences for

signatures of specific damage types, it is no longer sufficient to understand merely that

some bones persist better than others through time, that different soil types have different

macrostructural effects, or merely that DNA degrades in certain ways. Contemporary

studies must be deeply involved with answering questions of why these phenomena occur

the way they do and what, exactly, is involved on a cellular and microstructural level.

Methods

The methods used in this study were chosen for their ability to effectively test the

underlying hypotheses that extractable and amplifiable DNA from the human post-

cranial skeleton will be highly dependent on cellular populations and activity and the

biochemical properties of that bone tissue. These methods were also chosen for their

applicability, as the project is keenly interested in reducing the amount of destruction to

human bone tissue necessary for STR profile construction.

Sample Selection

One of the primary goals of the project has been to develop and test a more targeted

approach to sample collection for the purposes of DNA extraction and analysis. This

involved applying the knowledge of bone cell types, their position on the elements as

indicated by both knowledge and visual assessment, and an understanding of the

biochemical properties of the different bone tissue types. Thus, samples were taken in a

very strategic manner (table 1). Flowing from the overarching hypotheses of the study,

the samples used in this analysis were selected based on the following criteria:

1) Visible bony changes to the cortical layer indicative of ante- or perimortem

cellular activity

2) Bone tissue type

3) Notable sites or elements in the known literature on forensic DNA extraction

Since the project focuses on the post-cranial skeleton, multiple samples were taken

from every element wherever possible. Due to their small size, samples taken from the

carpals, tarsals, sesamoids, 1st metacarpals, 1st metatarsals, and phalanges were collected

irrespective of tissue type. The 3rd metacarpals/ metatarsals were robust enough to

provide distinction between cortical and trabecular tissue. Additionally, since the

mechanical action of drilling into bone tissue occasionally leads to some amount of bone

powder being propelled away from the collection boats, in an effort to reduce waste, any

dispelled powder was collected and created an extra sample that is a combination of

tissue types.

Additionally, because it is well established that the petrous portion of the temporal

bone and the teeth are excellent repositories of endogenous DNA, samples were also

taken from both of the petrous regions and the right maxillary 1st molar (RM1) for

comparative purposes. Furthermore, ancient samples were obtained from elements that

had not yielded good results in DNA quantitation efforts in another study. These allowed

a baseline for biochemical analyses and will be discussed further in the ATR/FTIR

section below.

Element

Site I

Site II

Site III

Manubrium

Clavicular notch (C)

Ventral surface (C,T)

N/A

Sternum

1st costal notch (C)

5th costal notch (C)

Ventral surface (C,T)

Xiphoid

Entire (C)

N/A

Clavicle

Sternal end (C,T)

Lateral end (C,T)

N/A

Scapula

Coracoid process

Acromion process

Glenoid fossa (C,T)

(C,T)

Humerus

Intertubercular sulcus

(C,T)

Deltoid tuberosity

(C,O,T)

Trochlea (C,T)

Ulna

Olecranon process

(C,T)

Brachial tuberosity

(C,O,T)

Extensor carpi ulnaris

groove (C,T)

Radius

Head (C,T)

Radial tuberosity

(C,O,T)

Ulnar notch (C,T)

Carpals

Radial/Ulnar articular

surfaces (C)

Entire Pisiform (T)

Metacarpal articular

surfaces (C)

Metacarpals

Prox. articular

Surfaces (C)

Mid-diaphyseal (C,T)

Distal articular Surfaces

(C,T)

Phalanges

Prox. Articular

surfaces (C,T)

Distal articular

surfaces (C)

Entire distal phalanges

(C)

Ribs

Head (C)

Tubercle (C)

Sternal end, where

applicable (C)

Vertebrae

Inferior aspect of

vertebral body (T)

Anterior aspect of

vertebral body (T)

Posterior aspect of

vertebral body (T)

Sacrum

Sacral promontory (T)

Auricular surface (C)

Transverse lines (C,T)

Ilium

Auricular surface (C)

Posterior inferior iliac

spine (C)

Iliac crest (C)

Ischium

Ischial spine (C)

Ischial tuberosity

(C,T)

Inferior ischial ramus

(C,T)

Pubis

Pubic symphysis

(C,T)

Dorsal surface (C)

Ventral surface of the

pubic face (C)

Femur

Fovea capitis (C,T)

Linea aspera (C,O,T)

Intercondylar fossa (C,T)

Patella

Medial articular facet

(C,T)

Lateral articular facet

(C,T)

N/A

Tibia

Tibial plateau (C,T)

Interosseus surface

(C,O,T)

Malleolar groove (C,T)

Fibula

Styloid process (C,T)

Interosseus crest

(C,O,T)

Fibular groove (C,T)

Tarsals

Sustentaculum tali &

calcaneal tuberosity of

the calcaneus (C,T)

Talar dome & head of

talus (C,T)

Anterior & posterior

articular surfaces of

other tarsals (C,T)

Metatarsals

Proximal (Tarsal)

articular surfaces

(C,T)

Distal articular

surfaces (C,T)

N/A

Phalanges

Prox. Articular

surfaces (C,T)

Distal articular

surfaces (C,T)

Entire distal phalanges

(C)

Element

Site I

Site II

Site III

Manubrium

Clavicular notch (C)

Ventral surface (C,T)

N/A

Sternum

1st costal notch (C)

5th costal notch (C)

Ventral surface (C,T)

Xiphoid

Entire (C)

N/A

Clavicle

Sternal end (C,T)

Lateral end (C,T)

N/A

Scapula

Coracoid process

(C,T)

Acromion process

(C,T)

Glenoid fossa (C,T)

Humerus

Intertubercular sulcus

(C,T)

Deltoid tuberosity

(C,O,T)

Trochlea (C,T)

Ulna

Olecranon process

(C,T)

Brachial tuberosity

(C,O,T)

Extensor carpi ulnaris

groove (C,T)

Radius

Head (C,T)

Radial tuberosity

(C,O,T)

Ulnar notch (C,T)

Carpals

Radial/Ulnar articular

surfaces (C)

Entire Pisiform (T)

Metacarpal articular

surfaces (C)

Metacarpals

Prox. articular

Surfaces (C)

Mid-diaphyseal (C,T)

Distal articular Surfaces

(C,T)

Phalanges

Prox. Articular

surfaces (C,T)

Distal articular

surfaces (C)

Entire distal phalanges

(C)

Ribs

Head (C)

Tubercle (C)

Sternal end, where

applicable (C)

Vertebrae

Inferior aspect of

vertebral body (T)

Anterior aspect of

vertebral body (T)

Posterior aspect of

vertebral body (T)

Sacrum

Sacral promontory (T)

Auricular surface (C)

Transverse lines (C,T)

Ilium

Auricular surface (C)

Posterior inferior iliac

spine (C)

Iliac crest (C)

Ischium

Ischial spine (C)

Ischial tuberosity

(C,T)

Inferior ischial ramus

(C,T)

Pubis

Pubic symphysis

(C,T)

Dorsal surface (C)

Ventral surface of the

pubic face (C)

Femur

Fovea capitis (C,T)

Linea aspera (C,O,T)

Intercondylar fossa (C,T)

Patella

Medial articular facet

(C,T)

Lateral articular facet

(C,T)

N/A

Tibia

Tibial plateau (C,T)

Interosseus surface

(C,O,T)

Malleolar groove (C,T)

Fibula

Styloid process (C,T)

Interosseus crest

(C,O,T)

Fibular groove (C,T)

Tarsals

Sustentaculum tali &

calcaneal tuberosity of

the calcaneus (C,T)

Talar dome & head of

talus (C,T)

Anterior & posterior

articular surfaces of

other tarsals (C,T)

Metatarsals

Proximal articular

surfaces (C,T)

Distal articular

surfaces (C,T)

N/A

Phalanges

Prox. Articular

Distal articular

Entire distal phalanges

surfaces (C,T)

(C)

Table 1- List of drilling sites. Applies to both LEFT and RIGHT sides. Bone tissue type from each site is listed as: C =

cortical, T = trabecular, and O = osteonal.

Sample Preparation

In an effort to reduce the risk of DNA contamination, a semi-sterile room devoted

exclusively to the purpose of bone sample collection was established (142,143). The

room is as environmentally isolated as possible and rules for the use of the room

incorporate many of the published recommendations for facilities designed for aDNA

studies (CITE). Prior to sampling, the drilling area itself was wiped down with either a

3.75% enzyme-based bleach solution or DNAway®. All tools were likewise

decontaminated. Investigators wore masks, eye protection, and nitrile gloves (also

decontaminated with bleach or DNAway®). The bones were cleaned with 3.75%

enzyme-based bleach (sodium hypochlorite) and allowed to soak for 15 minutes. They

were then rinsed 2-3x with distilled water and allowed to air dry overnight in a drawer

lined with UV sterilized paper towels.

Between each drilling session, all tools were wiped with DNAway® or bleach

solution and metal components were then autoclaved at 250°F for 25 minutes. Plastic

components such as microcentrifuge tubes and weigh boats were placed in a crosslinker

and subjected to UV-C light for 5 minutes. The room itself was decontaminated with UV-

C light for 30 minutes. Because the room is not fully climactically independent from the

rest of the Social Sciences building on the University of Montana campus, precise control

of the temperature of the room was not possible. However, relative humidity levels were

adjusted by the use of evaporators or humidifiers, depending on the need. It was

discovered that the optimal climactic environment for drilling was approximately 60-

65°F and approximately 45% relative humidity. In these conditions, there was enough

humidity to prevent static electricity from causing the bone powder to “float” inside of

the plastic collection tubes while also being dry enough to exclude excess environmental

moisture from the samples. Either extreme caused mismeasurement of sample weights

and added extra effort and time to the work of sample collection.

Sample Collection

All bone tissue samples were taken using either a rechargeable, handheld, Dremel®

7760 or an air-powered BienAir® Station S001 dental drill with a 1mm dental burr.

Power settings with both machines were deliberately kept as low as possible to avoid any

potential damage to the endogenous DNA that might occur as a result of heat generated

by the machines or their action on the bones (144). The Dremel was set to the second

power setting and the dental drill, through the use of an air pressure regulator, was kept at

or below 40psi.

Collection of cortical bone tissue samples was performed by running the drill over the

surface of the targeted region. All attempts were made to minimize the area of necessary

sampling, while still maintaining accuracy of cortical bone. In other words, since the

actual cortical layer of bone is very thin on most elements, only very light manual

pressure was applied in collection of these samples, and they are generally no larger than

1 cm2. For the osteonal samples (limited only to mid- diaphyseal drilling sites on the

humerus, radius, ulna, femur, and tibia), more pressure was applied at the same location

of the cortical sampling in order to ensure that only the osteonal tissue was being

collected. On bony elements or drilling sites where osteonal tissue was not available,

cortical sampling penetrated just deep enough to allow access to the trabecular tissue

within. At all sites, extreme care was taken to limit the size of the holes that were drilled.

In nearly all cases, the holes were no larger than 3 mm in diameter and in many cases,

cortical surface collection sites are nearly unrecognizable to the untrained eye.

The result of these drilling techniques is a powder, the particles of which are

generally uniform in size and quite small. This method precludes the need for any

additional homogenization; a step that is included in some DNA extraction protocols

where the samples are collected by scalpel or other methods. Theoretically, particle size

homogeneity is recommended by Promega and other manufacturers as it allows for less

stochasticity in actual DNA extraction across samples. The small particle size provided

by the drilling method allowed for maximization of surface area with which the

chemicals can interface during the extraction phase (145)

All bone powder was collected into UV sterilized plastic weigh boats and transferred

to 2ml microcentrifuge tubes which had also been subjected to UV light in a crosslinker.

The samples were then weighed on an electronic scale adjusted for the weight of the tube

itself, and labeled with the side of the body, the element, and drilling location. A colored

sticker indicated the tissue type. Red stickers were for cortical samples, green for

trabecular, yellow for osteonal tissue, blue for the dental sample and those samples which

were an amalgamation, received no color delineation. Because ATR/FTIR testing

requires so little powder, those samples were collected concurrently and placed in 0.2 ml

PCR tubes. All samples were then stored at 13°C in the refrigerator in the drilling room.

ATR/FTIR

Attenuated Total Reflectance Fourier Transform Infra-Red (ATR/FTIR) analysis is a

method used in biochemistry to visualize chemical components of a substance. The

technology is not new but is gaining in popularity due to its simplicity, the small amount

of sample required, the speed of the analysis, and low cost. It is sometimes used in triage

medicine to determine chemical components of blood samples and has applications in

other disciplines where such characterizations are instructive. Either liquids or solids may

be analyzed, and no fixation of solid samples is required. The equipment and software are

easy to learn, and most university chemistry departments, hospital pathology labs, and

forensic toxicology labs already have the equipment. Total testing time varies by the

number of scans per sample, but it is possible to run many samples in a very short amount

of time (approximately 5-10 minutes).

The technology fires a laser through a sample and measures the amount of IR light

that is either absorbed or reflected by the component chemicals in the sample, depending

on what analysis is desired. In this experiment, a ThermoScientific Nicolet iS5 with an

id7 ATR attachment was used. OMNIC 9.3.3.2 software was used, and the machine was

set to absorbance, with 64 scans and 4 wave resolution.

The test required approximately 1mg of bone powder and, as mentioned, no further

treatment or fixation was necessary. A background collection was run prior to adding

bone powder as a control for any background chemicals that may be on the surface of the

plate or diamond. This test followed the protocol as set forth in studies published by

Leskovar et al in 2020. Because a total of 64 scans were performed on each sample, each

sample took approximately two minutes to test.

Since the purpose of this test was to characterize both the chemical constituents of the

sample, as well confirm or refute DNA quantitation methods, an aqueous DNA sample,

that is, synthetic DNA in water, was run prior to testing bone powder. This was

informative as to where on the IR spectrum (wavelength), DNA would be found, since

the water is easily identified and subtracted from the results of the scan. The ancient

samples mentioned in the sample collection section above were tested with ATR/FTIR to

obtain a baseline indication of what DNA negative bone sample would look like on the

IR spectrum. This was also informative regarding the diagenetic changes that occur to

bone over a long (800+ years) post-mortem interval which may impede or preclude DNA

extraction. The results of all the tests were then compared to the results of DNA

quantitation methods as well as STR analysis which implicitly involves DNA

amplification by polymerase chain reaction (PCR) and is instructive of chemical

impediments thereto. The phosphate and carbonate groups shown in Figure 1 are

anticipated, as they are major chemical constituents of bone tissue. However, their

relative concentrations and ratios to one another can be indicative of diagenetic changes

that may inhibit or preclude DNA extraction and amplification. This is discussed in

greater detail in the Results section. Fatty acids, which are often found in bone tissue,

may be problematic for DNA extraction and PCR amplification. In small amounts, the

purification phase of DNA extraction can wash fatty acids out of the sample, but in too

large a concentration, they can effectively block the lysis buffer used in DNA extraction

and/or overwhelm chemical wash steps in the purification phase and be detrimental to

downstream analyses.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

Absorbance

500 1000 1500 2000 2500 3000 3500 4000

Wavenumbers (cm-1)

Image 5- Example of ATR/FTIR scan results. This was one of the ancient samples. The PURPLE arrow indicates where

DNA should be. The YELLOW arow indicates phosphate group and the RED arrow indicates a sulfate group. Fatty

tissue, which could inhibit PCR amplification of DNA, is absent from this sample but would be found at the BLACK

arrow. Water, also absent from this sample, is marked by the BLUE arrow.

DNA Extraction

This project used customized extraction kits provided by Promega® for preprocessing

and the Promega DNA IQ® system for purification. Chemical extraction methods use

demineralization buffers to dissolve the hydroxyapatite and release the DNA into

solution. This also releases unwanted contaminants into the solution and those must be

“washed” away in the purification phase to allow for a cleaner DNA free of chemicals

that will inhibit downstream quantitation and amplification. However, because this

project is interested in the optimization of methods for forensic DNA extraction from

human bone tissue, especially that which is potentially contaminated or of unknown

quality, several modifications were made to the published protocol.

Whereas the protocol calls for 100 mg of bone powder, this project used significantly

less than that. Sample weights varied between 55-75mg. Additionally, the protocol called

for the use of a vortexing incubator which both heats and agitates the samples by shaking.

Since such a device was not available, a Benchmark RotoTherm mini® was used instead.

This machine both inverts and agitates the samples within a closed, heated, chamber.

Each sample tube was closed and sealed with Parafilm immediately prior to

heating/agitation to further prevent any loss of sample. The time of incubation/agitation

was also decreased significantly as the RotoTherm mini uses more agitation than a

regular incubating vortexer. All other steps in the manufacturer’s protocol were followed

as published.

The first step in the extraction/purification phase was the creation of a lysis cocktail

which was made up of 400µL demineralization buffer, 40µL Proteinase K, and 10µL 1-

Thioglycerol. The amounts listed are for 1 sample, so the actual amounts were multiplied

by the number of samples being run +2 to accommodate for pipetting error or spillage.

Next, 400µL of the cocktail was added to each tube of bone powder and vortexed for 10

seconds. The tubes were then sealed with parafilm and added to the incubator that was set

to 56°C. Samples were incubated and agitated for 30-45 minutes. The samples were then

vortexed again for 10 seconds and added to a centrifuge set to 13,000 x g for 5 minutes to

pellet any remaining powder.

Upon removal from the centrifuge, the supernatant was carefully transferred by

pipette to new sterile 1.5mL tubes. The tubes containing the remnants of the extracted

bone powder were discarded. Next, a second lysis cocktail was prepared which consisted

of 990µL Lysis buffer and 10µL 1-Thioglycerol. 800µL of this cocktail was added to the

supernatant from the prior step and vortexed for 10 seconds.

The purification phase began with the addition of 15µL of DNA IQ® Resin magnetic

beads to each tube. Tubes were vortexed for 5 seconds and allowed to rest at room

temperature for 5 minutes, vortexing every 2 minutes. The tubes were then placed in a

magnetic rack which attracted the beads to one side. The supernatant was removed and

discarded. A final step of lysis was performed by adding 100µL of the second lysis buffer

and vortexing for 5 seconds. The tubes were placed back on the magnetic rack and the

supernatant was once again pipetted off and discarded. The beads holding the DNA were

then cleaned with a buffer solution that was a cocktail of 30µL 2xWash Buffer, 15mL

isopropyl alcohol and 15 mL 95% ethanol. The wash step, adding 100µL of the wash

buffer to the beads was repeated three times, with the supernatant being discarded after

each cleanse. Once all three washes were complete, 50 µL of Elution buffer was added to

the tubes with the beads, vortexed for 5 seconds, and incubated with no agitation at 65°C

for 5 minutes. The tubes were again vortexed for 5 seconds and placed back in the

magnetic stand. The eluted DNA solution was pipetted into new sterile 1.5 mL tubes and

stored at 4°C.

DNA Quantitation

Immediately following extraction and purification, each sample was subjected to

initial quantitation using an Invitrogen 1x dsDNA HS Assay Qubit Fluorometric

Quantification® system following manufacturer’s published protocols. This method

provides a tentative assessment of the initial molecular weight, or quantity, of DNA

contained in each sample down to the ng/µL level. However, since samples may contain

amplifiable amounts of DNA that Qubit fluorometry cannot measure, or non- target

specific DNA such as microbial or bacterial, additional quantitation of target DNA is

desired.

Therefore, real-time, or quantitative, polymerase chain reaction, or qPCR, testing was

also performed. This analysis allows for amplification of the target DNA within each

sample and is a truer measure of the staring amount of DNA that may be further

analyzed. Quantitative PCR also allows for a degree of quantification of the degree or

amount of degradation that has occurred to the DNA; usually based on varying sizes of

DNA fragments that are amplifiable and detectable with the individual kits or protocols.

Autosomal & Y- Chromosomal DNA

This project utilized the Plexor HY System® by Promega for qPCR analysis. This

product was selected for its ability to quantify as little as 6.4pg of DNA. The kit also

utilizes internal positive and negative controls, as well as employing melt curve analysis

which allows for visualization of the change in fluorescence at a given temperature range.

Failure of a sample to provide melt curve data indicates that the DNA in that sample did

not amplify with the fluorescent primers or that a contaminant prevented quenching of

the fluorescent dye at that temperature range.

This kit was also selected for its ability to discriminate between starting molecular

weights of autosomal and Y- chromosomal DNA. Forensically, this is important in cases

of potentially mixed samples, paternity testing, or biological sex determination. In cases

where the pelvis is not present for forensic anthropological analysis, or the sex

determination of the pelvis is not definitive, the ability to extract and quantify Y-

chromosomal DNA from other elements may be an invaluable asset. Even though the

presence of Y- chromosomal DNA does not necessarily definitively characterize the

biological sex or gender of the individual, it may exclude certain individuals in the set of

missing persons reports against which the DNA data is compared. Additionally, since

qPCR testing is followed by more complete genomic analysis, the presence of Y-

chromosomal DNA in qPCR results with an STR profile which does not indicate the

presence of such, may be a sign of sample contamination. However, if downstream STR

analysis does indicate the presence of a Y-chromosome in conjunction with 2 X

chromosomes, then the list of missing persons against which the results are compared is

narrowed that much more.

All qPCR analysis requires running several standards along with the experimental

samples. The Plexor HY® kit requires 7 standards of known DNA concentrations

ranging from 50 ng/µL at the most concentrated to 0.0032 ng/µL at the most diluted.

These are duplicated for a total of 14 standards for every experiment. To enable

visualization of potential contamination, two negative controls are also run on every

experiment. During the analysis phase, these known concentrations of DNA provide a

standard curve against which sample results are compared.

Setup for qPCR amplification and quantitation began with the creation of the

standards against which the samples would be compared. Using undiluted concentrate,

dilutions were made using 40 µL of Promega TE-4Buffer and 10µL of the next most

concentrated solution. For example, for the 10ng/µL dilution, 10µL of undiluted

concentration was added to 40µL of TE-4 buffer. For the next dilution, 10µL of the

10ng/µL dilution was added to 40µL of TE-4 buffer, and so on to arrive at concentrations

of 50, 10, 2, 0.4, 0.08, 0.016, and 0.0032 ng/µL respectively. A negative control was also

created using the TE-4 buffer and amplification grade water in order to attempt to detect

potential contamination to the reagents.

The reaction mix for the analysis began with the formulation of a cocktail containing

10µL Plexor® HY 2x Master Mix, 7µL amplification grade water, and 1µL Plexor® HY

20X Primer/IPC Mix. As in the extraction and purification stage, the actual amounts of

chemicals were multiplied by the number of samples to be run +2 to account for pipetting

error or loss. The cocktail was vortexed for 10 seconds and then 18µL of the cocktail was

added to each well along with 2µL of sample DNA or TE-4 buffer which created an NTC,

or No- Template Control. Two NTC wells were run with every plate.

The plate was then taken to the UM Genomics Core for analysis on the Stratagene

Mx3000P® Quantitative PCR System. The experimental design was input into the

software following manufacturer's recommendations. For full details, see Promega

TM294.

Mitochondrial DNA

Because mtDNA is carried within the mitochondria of the cell, is circular in shape,

and is generally more plentiful in the cells than nuclear DNA, quantitative analysis of

mtDNA differs from nuclear DNA quantitation in a few practical ways. The theoretical

underpinnings and the processes of activation, denaturation, annealing and extension are

the same, but the molecules themselves are different enough that different primers and

dyes are employed. Quantitative analysis of mtDNA is important to this project as it

helps determines the nature of cell types present at the sampling sites at the time of death.

For this project, PowerUp®SYBR Green system from Applied Biosystems was used

as it is amenable for visualization on the MX3000P Genetic Analyzer at the UM

Genomics Core, because the kit allows for the use of customized standards for sample

comparison, and finally, because The PowerUp® SYBER® Green kit also allows

investigators the option of integrating a dissociation curve into the qPCR, just as there is

with the Plexor®HY nuclear DNA kit from Promega. This is helpful for the current

project because it aids in determining the degree of degradation sustained by the DNA

molecules. In this experiment, GBlocks® from Integrated DNA Technologies were

created for use as standards against which the experimental samples were compared. A

GBlock®, just like the standards used in autosomal and Y-chromosomal quantitation, is

an artificially created sequence of DNA with a one base pair difference from normal

human mtDNA found in the hypervariable region 1 (HVR1) of the mitochondrial DNA

molecule. The GBlock® standards in this experiment ranged from 3.35nM at the most

concentrated to 0.000000335nM at the most diluted.

Mitochondrial DNA quantitation followed the manufacturer’s published protocol for

10µL volumes and began with creation of a loading cocktail. The cocktail called for 5µL

PowerUp®SYBR® Green Master Mix, 1µL each of forward and reverse primers and

2µL of amplification grade water. Actual amounts were multiplied by the number of

reaction wells in each run plus 2 to account for pipetting error or environmental loss. The

cocktail mix was vortexed for 10 seconds and spun in a microcentrifuge to eliminate air

bubbles. Nine (9) µL of the cocktail were added to each of the reaction wells followed by

1µL of either sample DNA or GBlock® artificial DNA as standards. Every plate was

accompanied by a no template control (NTC) well which substituted an additional 1µL of

amplification grade water for either the sample or control DNA. The NTC was used to

visualize any potential contamination. Both the thermal cycling and dissociation set-ups

followed the manufacturer’s published protocols for standard cycling mode (primer Tm <

60°).

DNA Analysis

Since the primary goal of this study has been to develop a sampling method that will

aid forensic DNA analysts and other investigators involved in the identification of

unknown human post-cranial skeletal remains, the primary method of analysis lies in the

establishment of an STR profile which can be uploaded into CODIS and NaMUS and

compared to other known profiles. To that end, the eluted and purified DNA samples

were taken to the Montana State Crime Lab for STR analysis. The Qiagen Investigator

24Plex QS kit was used with the following specifications (PCR was performed prior to

CE injection following the manufacturer’s protocol):

Injection on the Applied Biosystems 3500 CE with run specs:

Application Type: HID

Capillary Length: 36cm

Polymer: POP4

Dye Set: Qiagen BT6

Run Module: HID36_POP4

Protocol Name: Qiagen 24 Plex_1.2kV30sec_DEFAULT

Oven Temp (°C): 60

Run Voltage (kVolts): 13.0

PreRun Voltage (kVolts): 15

Injection Voltage (kVolts): 1.2

Run Time (sec): 1550

PreRun Time (sec): 180

Injection Time (sec): 30

Data Delay (sec): 1

Statistical Analysis

PAST 4.03 was utilized as the analysis software for this project. This software

was chosen due to experience with it as well as the general ease of use. One- way

analysis of variance, or ANOVA, was employed when comparing the results of the three

types of DNA, as well as the three tissue types, in both inter- and intra- element

comparisons. ANOVA testing in PAST 4.03 automatically incorporates several tests

concurrently, including Levene’s test of homogeneity of variance and Welch’s F. One of

the known limitations of ANOVA is its assumption of homogeneity of variance within

the sample population (146–151). In this project, that was often not applicable as the

numbers themselves could have anywhere from two decimal places to 11 depending on

the sample types. For example, ATR/FTIR analyses involved IR wavelength

measurements which were in the hundreds, if not thousands. Results of mtDNA

quantitation, on the other hand, were several decimal places in the opposite direction.

Thus, since Welch’s F does not make the assumption of data normality, it was often the

more accurate and dependable of the tests. The Kruskall- Wallis test of medians is also

included in the PAST software ANOVA analysis, as is Turkey’s Pairwise and Dunn’s

post- hoc. While the former is not instructive for data of the type used in this study, the

latter two tests were often useful to determine where the variance actually was when

ANOVA or Welch’s F indicated significance (152–155).

During comparisons of ATR/FTIR results to either tissue type or DNA type,

correlation tests were performed. This was carried out in an effort to determine if it was

possible to ascertain if/whether the biochemical components of bone tissue types

influenced DNA extractability. PAST 4.03 allows for several types of correlation tests to

be run depending on what kind of data is being analyzed. Because this project deals

exclusively with interval/ratio data, Pearson’s R and Spearman’s Rho were compared. It

was quickly evident that, in most cases, Pearson’s R was less desirable as it is a test of

linear correlation and the data simply did not fit that assumption. However, because

Spearman’s Rho is designed more for ordinal data than interval data, it was decided that

Pearson’s R was the more logical choice (156–160).

In order to determine which sites on which elements are best to use for DNA

analysis, both inter- and intra- element comparisons were performed. Inter- element

group testing provided a picture of whether there is a significant difference between the

DNA quantities obtained from, for example, the legs versus the arms, or the ribs versus

the vertebrae. Intra- element testing, then, allowed for determination of where on each

bone is the optimal site. This proceeded along each side, where applicable, and then the

elements or groups from each side were combined.

Statistical analysis proceeded from the broad to the more specific. Initially, all

tissue type data and element group data were combined and only the DNA type was

specified. For example, all data from the bones of the right arm were compared by DNA

type to all the bones from right leg. Then, all cortical tissue samples from each side were

compared to all trabecular tissue samples and all osteonal tissue samples for each DNA

type. Then, where applicable, proximal sites were compared to mid- diaphyseal and distal

sites on each element and compared by both tissue and DNA type.

Using starting molecular weight data obtained from qPCR testing, inter- group

variability testing was performed to assess the significance, if any, between the different

bone groups. Both sides of the body were tested in this way. Groups were assigned based

on anatomical position of the bones. In other words, the bones of the arm, starting with

the humerus and then proceeding inferiorly to the ulna and radius were compared to the

legs from femur through tibia and fibula. Similarly, the clavicles and the scapulae were

compared to the sternum and manubrium. The vertebrae were compared to the sacrum,

the wrists and hands compared to the ankles and feet, and the patellas were compared to

the other sesamoid bones. Due to their unique anatomical positions, the os coxae were

compared to each other and the sacrum.

In order to further enhance understanding of the actual cellular contributions to

endogenous DNA preservation, it was necessary to compare data taken from at least 2

sampling sites on each element to each other. For example, cortical bone tissue samples

from the proximal ends of all of the long bones were compared to mid- diaphyseal sites

as well as to distal sampling sites. This was repeated for trabecular and osteonal (where

possible) tissues. Furthermore, this was done for all 3 types of qPCR data as well as the

percentage of complete STR profiles. This was repeated in intra- element testing.

Meaning that all cortical and trabecular samples from a single bone were compared to

themselves and then to each other for all DNA types. In this way, we can begin to see if,

and to what extent, cellular populations differ within each sampling point and across

element groups as well as each individual bone. Since only 1 sample of osteonal bone

tissue was taken from each relevant bone, those samples were compared by element by

side (right humeral osteonal samples compared to each other) and then to those from

other elements as well as the mid- diaphyseal cortical and trabecular sample data.

Results

The tables included in this section show which tests indicated significant

differences or variation. Because the study is focused on finding the optimal sites for

DNA extraction and not generalizations of DNA preservation, ANOVA read- outs

showing no significant differences are omitted. However, results of all testing can be

found in Appendix 1. As indicated in the Methods section, the alpha level for all testing

was set at p < 0.05.

While all samples were tested with ATR/FTIR spectroscopy, and statistically

analyzed for significant variance within or between samples those samples that exhibited

low starting molecular weight of any or all DNA types, and/or failed to produce at least

75% of the STR loci of a full forensic profile, were further scrutinized for potential

biochemical reasons for the low performance. As discussed in the background section,

lack of amino acid presence, overabundance of either phosphate or carbonate ions, or the

presence of PCR inhibitors such as fats or humic acids, are all possible reasons for a

sample’s failure to provide adequate DNA in qPCR or fragment analysis (161,162).

At the end of this section is a table showing the highest DNA yielding location for

each bone by 1) tissue type and 2) DNA type. The specific drilling site for each bone is

included. In this way, those interested in real-world questions of where to sample for

DNA analysis can find the data quickly.

Inter- Group Variability

All Tissue Types

First, only autosomal DNA data was compared between drilling locations. Then the Y-

chromosome data only, and then the mtDNA data only. Following this, each DNA

analysis type was compared to each other type. This was repeated for both sides of the

body. Lastly, the percentage of full STR profile construction was compared to the starting

mtDNA molecular weight.

APPENDICULAR SKELETON

LEFT SIDE

Left Side Element Group Comparisons

First, averages were taken of the qPCR raw scores from sampling sites on each

element for each DNA type alone. In this way, all of the autosomal, Y chromosome, and

mtDNA data from each element could be compared to other elements irrespective of the

tissue type. Then, each DNA type average was taken for each particular tissue type.

Comparison between the element groups involved raw qPCR data, rather than

averages of such. No significance was noted on the starting DNA quantification values of

the left side between any element groups other than the hands and feet. In other words,

there was no statistically significant difference between the arm and the leg, but ANOVA

testing did show significant difference between the means of the left hand (the carpals,

metacarpals and phalanges) and the left foot (tarsals, metatarsals and phalanges) in raw

qPCR values for autosomal and Y-chromosomal DNA. No significant difference was

found in mtDNA data. In the outputs from PAST 4.03 which follow, relevant p values are

highlighted in yellow.

Autosomal DNA:

LEFT hand versus LEFT foot:

Sum of sqrs df Mean square F p (same)

Between groups: 0.246507 1 0.246507 6.398 0.01873

Within groups: 0.8861 23 0.0385261 Permutation p

(n=99999)

Total: 1.13261 24 0.01514

Components of variance (only for random effects):

Var(group): 0.0168816 Var(error): 0.0385261 ICC: 0.304679

omega2: 0.1776

Levene´s test for homogeneity of variance, from means p (same): 0.106

Levene´s test, from medians p (same): 0.1208

Welch F test in the case of unequal variances: F=7.315, df=20.91, p=0.0133

Y- Chromosome

Sum of sqrs df Mean square F p (same)

Between groups: 0.0224674 1 0.0224674 7.418 0.01211

Within groups: 0.0696592 23 0.00302866 Permutation p

(n=99999)

Total: 0.0921265 24 0.00255

Components of variance (only for random effects):

Var(group): 0.00157782 Var(error): 0.00302866 ICC: 0.342521

omega2: 0.2043

Levene´s test for homogeneity of variance, from means p (same): 0.006095

Levene´s test, from medians p (same): 0.02025

Welch F test in the case of unequal variances: F=9.483, df=13.32, p=0.008572

In these cases, the Bonferroni corrected p values were identical to the raw p

values. Despite homogeneity of variance in the autosomal DNA tests, there is significant

difference between the hand and foot samples at p < 0.05. The averages of raw data from

the groups are shown in the table below. Note that the tarsals outperformed the carpals in

all three analyses, while the metacarpals outperformed the metatarsals in autosomal and

mtDNA, but not in Y-chromosome analysis. The phalanges of the foot outperformed

those of the hand in both autosomal and Y- chromosome analysis, but not in

mitochondrial DNA. STR results

Autosomal

Y- Chromosome

Mitochondrial

Carpals

0.08179

0.01650

5.95E-05

Metacarpals

0.14257

0.00988

8.52E-04*

Phalanges

0.02854

0.00699

3.24E-05

Tarsals

0.42633*

0.09376*

2.50E-04

Metatarsals

0.09758

0.01799

5.13E-07

Phalanges

0.34852

0.08249

1.393E-07

Table 2- Raw qPCR data scores for the left hands and feet. Asterisks indicate highest performing element groups.

Tissue Type Comparisons

When combining all the data from each tissue type and then comparing each to

the others, no statistically significant difference was found between the averages of

autosomal or Y- chromosomal DNA.

Mitochondrial DNA comparisons across tissue types on the left side, however, did

indicate some statistical significance in Dunn’s post-hoc testing, (shown below) which

can be useful in tests of small sample size like this one. Especially since it makes no

assumption of normality and because ANOVA is known to be prone to error in tests of

small sample populations. In this case, the greatest variance was seen between starting

DNA molecular weights of mtDNA between cortical and osteonal tissue across the whole

of the left side of the skeleton.

Cort Mito

Trab Mito

Osteo Mito

Cort Mito

0.116

0.0152

Trab Mito

0.116

0.236

Osteo Mito

0.0152

0.236

Table 3- Post hoc testing results. Significant variance is highlighted in yellow.

It is when the three DNA types were compared within the tissue types that the

greatest amount of difference is seen statistically. This testing is the most important for

this project because it is the most indicative of differential cellular populations at the time

of death in the post-cranial skeleton.

ALL left side Cortical Tissue: Autosomal vs Y- Chromosome vs

mtDNA

Test for equal means

Sum of sqrs df Mean square F p (same)

Between groups: 1.1279 2 0.56395 5.417 0.009805

Within groups: 3.12315 30 0.104105 Permutation p

(n=99999)

Total: 4.25105 32 1E-05

Components of variance (only for random effects):

Var(group): 0.0418041 Var(error): 0.104105 ICC: 0.286507

omega2: 0.2112

Levene´s test for homogeneity of variance, from means p (same): 0.01556

Levene´s test, from medians p (same): 0.1201

Welch F test in the case of unequal variances: F=8.445, df=13.33, p=0.004271

Given the lack of homogeneity of variance seen in Levene’s test, Welch’s F is more

likely the truer test since it makes no assumption of normality. Welch’s F detected a

significant difference at p < 0.05.

Cortical A

Cortical Y

Cortical Mito

Cortical A

0.03427

4.179E-07

Cortical Y

0.03427

0.003243

Cortical Mito

4.179E-07

0.003243

Table 4- Post hoc testing. Significant variance, in yellow, was most pronounced between cortical autosomal and

cortical mtDNA scores.

Post- hoc testing shows that the variance is most pronounced between autosomal and

mtDNA in the cortical tissue. The next most pronounced degree of variation occurred

between the Y- chromosome and mtDNA. The least, though still significant, was between

autosomal and Y- chromosome data.

ALL left side Trabecular Tissue: Autosomal vs Y- Chromosome vs

mtDNA

Test for equal means:

Sum of sqrs df Mean square F p (same)

Between groups: 0.150962 2 0.0754811 14.23 4.948E-05

Within groups: 0.15385 29 0.00530516 Permutation p

(n=99999)

Total: 0.304812 31 0.00018

Components of variance (only for random effects):

Var(group): 0.00658543 Var(error): 0.00530516 ICC: 0.553835

omega2: 0.4526

Levene´s test for homogeneity of variance, from means p (same): 0.000337

Levene´s test, from medians p (same): 0.004344

Welch F test in the case of unequal variances: F=15.74, df=12.63, p=0.0003716

Again, since there was no homogeneity of variance, Welch’s F test is more reliable than

ANOVA. It detected significant difference at p < 0.05.

Trabec A

Trab Y

Trab Mito

Trabec A

0.05322

8.808E-07

Trab Y

0.05322

0.002235

Trab Mito

8.808E-07

0.002235

Table 5- Post- hoc testing. As with cortical bone tissue, trabecular tissue varied most between autosomal and mtDNA

scores.

Post- hoc testing showed the highest variance, again between autosomal and mtDNA in

the trabecular tissue, followed by Y- chromosome versus mtDNA. The difference

between the variance of Y- chromosome and autosomal DNA was nearly significant.

LEFT side tissue types by location

Cortical Tissue

Statistically significant difference was noted in autosomal DNA levels between cortical

samples from the proximal versus distal ends as well as between the mid-shaft versus

distal ends of long bones.

L Prox Cort

Auto

L Mid Cort

Auto

L Dist Cort

Auto

L Prox Cort

Auto

0.6653

0.004926

L Mid Cort

Auto

0.6653

0.01735

L Dist Cort

Auto

0.004926

0.01735

Table 6- Post- hoc testing showed the variance was most pronounced between proximal and distal sampling sites

among cortical tissue samples.

Post- hoc testing shows that the most significant variance in qPCR data scores lies

between cortical tissue samples from the proximal and the distal ends of the long bones.

The next most significant degree of variance was between mid-diaphyseal and distal

cortical samples. It's not that the scores themselves were variable, per se, but that there

was significant difference (or, variance) between 1-scores from the proximal and distal

sites and 2- between the mid- diaphyseal and distal sites. Essentially meaning that the

distal sites were either statistically better or worse (ANOVA doesn't distinguish between

higher and lower) than the proximal sites and the mid- diaphyseal sites, but there wasn't

much difference between proximal and mid- diaphyseal.

Comparison between cortical levels of Y- Chromosomal DNA also showed significant

difference between proximal and distal samples.

L Prox Cort Y

L Mid Cort Y

L Dist Cort Y

L Prox Cort Y

0.3042

0.01735

L Mid Cort Y

0.3042

0.1764

L Dist Cort Y

0.01735

0.1764

Table 7- Post- hoc testing showed significant variance between proximal and distal sampling sites.

There was no significant difference detected in mtDNA levels between cortical samples

from proximal, mid- shaft, and distal samples of the long bones.

Trabecular Tissue

No significant difference was detected in autosomal, Y-chromosomal, or

mitochondrial DNA results from trabecular tissue samples of the proximal ends versus

midshaft versus distal ends of the long bones from the left side of the body.

Osteonal Tissue

Because there was only one osteonal sample taken from the mid-diaphyseal

regions of each long bone, the results of such are included here, rather than in the intra-

element testing section.

Sum of sqrs df Mean square F p (same)

Between groups: 0.244599 2 0.1223 3.844 0.05126

Within groups: 0.381764 12 0.0318136 Permutation p

(n=99999)

Total: 0.626363 14 0.00527

Components of variance (only for random effects):

Var(group): 0.0180972 Var(error): 0.0318136 ICC: 0.362591

omega2: 0.275

Levene´s test for homogeneity of variance, from means p (same): 0.03314

Levene´s test, from medians p (same): 0.06082

Welch F test in the case of unequal variances: F=3.707, df=5.333, p=0.09794

Given the small sample size and heterogeneity of variance, the lack of significance shown

by Welch’s F and the near significance of ANOVA may be the result of a type II error.

Osteo Auto

Osteo Y

Osteo Mito

Osteo Auto

0.1791

0.0008893

Osteo Y

0.1791

0.04771

Osteo Mito

0.0008893

0.04771

Table 8- Post- hoc testing shows significant the most variance occurred between autosomal and mtDNA scores.

Post- hoc testing shows the greatest variance occurs between autosomal and mtDNA in

the osteonal tissue and lends credibility to the idea that a false negative may have

occurred. However, given the nature of the data, it is not surprising that a significance

may have been detected between autosomal DNA and mtDNA. Whether or not this is the

case, further study with larger sample population is warranted.

Figure 1- Averages of the three DNA types across the tissue types on the left side of the body. Y- axis is DNA levels in

ng/uL.

RIGHT SIDE

Element group comparisons

Right Arm versus Right Leg

No significance was detected between the elements of the right arm (humerus,

ulna and radius) and the right leg (femur, tibia, and fibula) in autosomal DNA. However,

significant difference was found between the right arm and leg in Y chromosome

analysis.

R Hum Y

R Rad Y

R Ulna Y

R Fem Y

R Tib Y

R Fib Y

R Hum Y

0.6244

0.7312

0.01763

0.1799

0.89

R Rad Y

0.6244

0.8712

0.0596

0.3945

0.5301

R Ulna Y

0.7312

0.8712

0.03506

0.2977

0.6267

R Fem Y

0.01763

0.0596

0.03506

0.3019

0.01202

R Tib Y

0.1799

0.3945

0.2977

0.3019

0.1391

R Fib Y

0.89

0.5301

0.6267

0.01202

0.1391

Table 9- Post- hoc testing indicated that significant variance occurred between the right femur and fibula, the right

ulna, and the right humerus.

Dunn’s post- hoc shows the difference between the means from the humerus & femur,

the femur & ulna, and the femur & the fibula.

Additionally, a significant difference was detected between mitochondrial DNA means of

the elements of the right arm and the right leg.

R Hum

mtDNA

R Rad

mtDNA

R Ulna

mtDNA

R Fem

mtDNA

R Tib

mtDNA

R Fib

mtDNA

R Hum

mtDNA

0.2464

0.1928

0.5622

0.6368

0.1566

R Rad

mtDNA

0.2464

0.9161

0.08211

0.1028

0.01001

R Ulna

0.1928

0.9161

0.05732

0.07345

0.005685

mtDNA

R Fem

mtDNA

0.5622

0.08211

0.05732

0.9145

0.4025

R Tib

mtDNA

0.6368

0.1028

0.07345

0.9145

0.345

R Fib

mtDNA

0.1566

0.01001

0.005685

0.4025

0.345

Table 10- Post- hoc testing indicating the majority of variance occurred between the right fibula and ulna for mtDNA

scores.

Dunn’s post- hoc shows the variance is mostly between the radius and fibula and between

the ulna and the fibula.

Right Hand vs Right Foot

ANOVA and post-hoc testing show the greatest variance is between the carpals

and the metatarsals. As with the same elements on the left side, raw data indicates that the

bones of the foot outperformed those of the hand in both autosomal and Y- chromosomal

DNA and mtDNA generally, although the metatarsals underperformed in this DNA type.

Autosomal DNA

Y-Chromosome

mtDNA

Carpals

0.01777

0.00768

9.39E-05

Metacarpals

0.08750

0.01006

1.02E-05

Hand Phalanges

0.23162

0.03139

1.39E-05

Tarsals

0.42073

0.05237

6.08E-04*

Metatarsals

0.04518

0.01330

1.98E-06

Foot Phalanges

1.24256*

0.12397*

4.62E-04

Table 11- Raw qPCR score averages from the right hands and feet. Asterisks indicate the highest performing groups.

Right Shoulder (Clavicle vs Scapula)

Comparisons of the upper elements of the right arm (the scapula, clavicle,

humerus, radius, and ulna) yielded no statistically significant differences across DNA

types.

Right Side Tissue Type Comparisons

Cortical versus Trabecular versus Osteonal- Autosomal DNA

ANOVA detected significant difference in DNA starting molecular weight

variance between tissue types. However, in spite of heterogeneity of variance, Welch’s F

did not. Dunn’s post- hoc, below, shows that the variance is primarily between the

cortical and the trabecular and the cortical and the osteonal samples. Raw data seems to

indicate that it is more likely a type II error in Welch’s F, rather than a type I error in

ANVOA.

Cortical Auto.

Trabecular Auto.

Osteonal Auto.

Cortical Auto.

0.009264

0.004876

Trabecular Auto.

0.009264

0.2888

Osteonal Auto.

0.004876

0.2888

Table 12- Post- hoc testing showing greatest variance between cortical vs trabecular and cortical vs osteonal tissue.

This is most likely a type II error in Welch's F test.

Cortical versus Trabecular versus Osteonal- Y- Chromosome

As with autosomal DNA scores, ANOVA detected significant difference between the

variance in Y- Chromosome data.

Cortical Y

Trabecular Y

Osteonal Y

Cortical Y

0.002045

0.00375

Trabecular Y

0.002045

0.4125

Osteonal Y

0.00375

0.4125

Table 13- Post- hoc testing showing greatest variance between cortical vs trabecular and cortical vs osteonal tissue.

Post- hoc testing shows that the variance is greatest between the cortical and trabecular,

followed by the cortical versus osteonal tissues.

No significant difference was detected between the tissue types by mtDNA data.

Figure 2- Averages of the three DNA types in each of the three tissue types across the right side of the body. DNA

amounts are in ng/uL.

Right side tissue type by location

No statistically significant difference was detected in comparative analyses of the

proximal, mid- diaphyseal, and distal ends of the long bones across all tissue and DNA

types.

THE AXIAL SKELETON

One-way ANOVA testing did not detect any significant difference between the

ribs of either side and the sternum and manubrium. Note that there is no trabecular

sample from the clavicular notch, nor a cortical sample from the inferior articular margin.

The reason is that the bone tissue at these locations was not conducive to sampling in that

manner.

Sample

Autosomal DNA

Y- Chromosome

mtDNA

Clavicular Notch Cort.

0.84411*

0.22409*

0.00E+00

Inferior Articular Margin Trab.

0.53527

0.12502

7.01E-04*

Prox. Corpus Sterni Cort.

0.13609

0.03377

1.33E-04

Prox. Corpus Sterni Trab.

0.01181

0.00105

9.77E-06

Dist. Corpus Sterni Cort.

0.00292

0.00266

0.00E+00

Dist. Corpus Sterni Trab.

0.45001

0.05253

3.17E-03

Table 14- Raw qPCR averages from the sternum and manubrium. Asterisks indicate highest perform sites.

It should also be noted that there were significant outliers in the data in autosomal

and Y-chromosomal DNA in cortical samples taken from the 8th ribs bilaterally, but most

especially on the right side. Additionally, five of the samples taken from the right ribs

failed to yield any mtDNA, whereas only one sample from the left failed.

Sample

L Ribs

Auto.

L Ribs Y

Chromo.

L Ribs

mtDNA

R Ribs

Auto.

R Ribs Y

Chromo.

R Ribs

mtDNA

Rib 1

Cort.

0.04506

0.03737

2.92E-04

0.11375

0.03888

0.00+E00

Rib 1

Trab.

0.00961

0.00387

2.11E-05

0.01686

0.00870

2.41E-05*

Rib 4

Cort.

6.06043

0.60374

0.00+E00

0.26937

0.09781

0.00E+00

Rib 4

Trab.

0.01070

0.00254

6.58E-05

0.00734

0.00171

5.25E-06

Rib 4

Frag. Cort.

0.30980

0.06586

2.41E-04

0.07765

0.01092

0.00E+00

Rib 4

Frag.

Trab.

0.01180

0.00122

7.67E-07

0.05148

0.01220

6.38E-09

L Rib 8

Cort.

1.73860*

0.78290*

6.10E-04*

6.28993*

1.9013*

0.00E+00

L Rib 8

Trab.

0.19730

0.03754

3.46E-06

1.29446

0.07055

0.00E+00

Table 15- Raw qPCR averages for the ribs. Asterisks indicate highest performing elements.

Vertebrae vs Sacrum vs Os Coxae

Statistical comparison of the three vertebral types indicated no statistical

differences in autosomal or Y- chromosomal DNA. However, because the lumbar

vertebrae failed to yield any mtDNA whatsoever, ANOVA analysis of all three tissue

types failed. ANOVA analysis is inherently a measure of variance and if all samples from

one group = 0, then there is no variance to compare. Comparison of the cervical and

thoracic vertebrae by mtDNA indicated no statistically significant difference. Nor was

there any significant difference found in comparisons of the cervical and thoracic

vertebrae to the sacrum, and either/both Os Coxae.

Intra- Group Variability

Intra-group variability testing was performed by combining left and right-side

data for each for each sampling location (proximal, mis-diaphyseal, and distal) on each

element and comparing the data for each DNA type. In other words, all of the data from

the sampling sites at the proximal ends of the long bones were combined and compared

against all of the data from the mid-diaphyseal, and distal sampling sites on each element

individually. Since there were very few actual data points to compare (four each), and

because the osteonal data was included in the inter-element comparisons, raw data scores

may be more instructive. In fact, some ANOVA calculators require at least five data

points and in tissue type comparisons, there were only two for each sampling location.

Shapiro-Wilk tests of normality were run along with ANOVA, simply as a measure of

certainty and visualization of the data.

Image 6- Fun picture illustrating some of the relevant anatomical directions used in the study.

Humeri:

Autosomal DNA:

Prox.

Hum Cort.

Prox. Hum

Trab.

Mid Hum

Cort.

Mid Hum

Trab.

Dist. Hum

Cort.

Dist. Hum

Trab.

Right

0.0231

0.2662

0.0297

0.0415

4.6646*

0.1145

Left

0.0267

0.0340

0.1507

0.0457

1.0905*

0.1637

Table 16- Raw autosomal qPCR averages of the humeri by sampling site and tissue type. Asterisks indicate highest

performing sites.

Most likely due to small sample size, one-way ANOVA and Welch’s F both

failed to detect any statistical significance in the variance of the humeri. However, the

table above clearly shows a marked difference between the raw autosomal DNA qPCR

data scores of the cortical samples taken from the distal humeri versus all of the others.

Not surprisingly, this pattern repeated for the Y- chromosome.

Y- Chromosome:

Prox. Hum.

Cort.

Prox. Hum.

Trab.

Mid. Hum.

Cort.

Mid. Hum.

Trab.

Dist. Hum.

Cort.

Dist. Hum.

Trab.

Right

0.00898

0.05601

0.08978

0.00832

0.15056*

0.01086

Left

0.00411

0.01474

0.06603

0.00483

0.14296*

0.02493

Table 17- Raw Y- Chromosome averages for the humeri by sampling site and tissue type. Asterisks indicate highest

performing sites/tissues.

mtDNA:

Prox. Hum.

Cort.

Prox. Hum.

Trab.

Mid. Hum.

Cort.

Mid. Hum.

Trab.

Dist. Hum.

Cort.

Dist. Hum.

Trab.

Right

2.68E-05

5.21E-08

1.98E-05

2.70E-05*

0.00E+00

2.11E-05

Left

7.29E-05

4.07E-07

3.49E-04*

7.84E-05

1.74E-04

3.82E-05

Table 18- Raw mtDNA qPCR averages for the humeri by sampling site and tissue type. Asterisks indicate highest

performing sites.

Generally, the left humerus outperformed the right in mtDNA scores, and

interestingly, the sampling site which the best for autosomal and Y-chromosome, was the

worst for mtDNA. Cortical samples from the distal humerus were the highest in

100

autosomal DNA and Y- chromosome, while the mid- diaphyseal cortical and trabecular

were the highest in mtDNA.

Ulnae:

Sum of sqrs df Mean square F p (same)

Between groups: 0.603911 2 0.301955 4.971 0.02898

Within groups: 0.668179 11 0.0607436 Permutation p

(n=99999)

Total: 1.27209 13 0.02745

Components of variance (only for random effects):

Var(group): 0.0527651 Var(error): 0.0607436 ICC: 0.464855

omega2: 0.362

Levene´s test for homogeneity of variance, from means p (same): 0.01221

Levene´s test, from medians p (same): 0.01651

Welch F test in the case of unequal variances: F=6.663, df=4.551, p=0.04446

Both ANOVA and Welch’s F detected significance at p < 0.05.

101

Post-hoc testing showed the greatest variation occurred between the mid- diaphyseal and

distal sampling sites. The outliers in this case were the proximal trabecular sample from

the right ulna at the high end and the mid- diaphyseal trabecular samples from both sides

at the low end.

L Ulna Prox Auto

L Ulna Mid Auto

L Ulna Dist Auto

L Ulna Prox Auto

0.05187

0.5541

L Ulna Mid Auto

0.05187

0.009534

L Ulna Dist Auto

0.5541

0.009534

Table 19- Post- hoc testing shows greatest variance between distal and mid- diaphyseal sites.

No significant difference was detected between sampling sites for either the Y-

chromosome or mtDNA.

Radii:

Autosomal DNA

Sum of sqrs df Mean square F p (same)

Between groups: 0.629961 2 0.31498 5.156 0.02632

Within groups: 0.672 11 0.0610909 Permutation p

(n=99999)

Total: 1.30196 13 0.02984

102

Components of variance (only for random effects):

Var(group): 0.0555383 Var(error): 0.0610909 ICC: 0.476196

omega2: 0.3725

Levene´s test for homogeneity of variance, from means p (same): 0.1594

Levene´s test, from medians p (same): 0.2369

Welch F test in the case of unequal variances: F=3.446, df=5.892, p=0.1021

ANOVA detected significance, whereas Welch’s F did not.

Post- hoc testing indicates that the greatest variance, as with the ulnae, occurs between

the mid- diaphyseal and distal sampling sites. However, given the results of Levene’s and

Welch’s F, the ANOVA results may well be a type I error. Outliers in this case occurred

on the right side with highest scores being the distal sites and nearly equally low scores

from the mid- diaphyseal sites.

Prox Rad Auto

Mid Rad Auto

Dist Rad Auto

Prox Rad Auto

0.8774

0.09097

Mid Rad Auto

0.8774

0.04486

Dist Rad Auto

0.09097

0.04486

Table 20- Post- hoc testing shows greatest variance between distal and mid- diaphyseal sites of the radius.

103

No significant difference was detected between either Y- chromosomal or mtDNA

among sampling sites of the radii.

Femora

Despite the lack of any mtDNA from several sites in the femora, no significant

difference was detected between sampling sites across the three DNA types. The raw data

from the femora is more instructive.

Sampling Site

Left

Autosomal

Left Y-

Chromosome

Left

MtDNA

Right

Autosomal

Right Y-

Chromosome

Right

mtDNA

Fovea Capitis Cort. 0.15387 0.04409 4.21E-05 1.31470 0.53454 0.00E+00

Fovea Capitis Trab. 0.24068 0.03996 7.09E-05 1.98432 0.52505 0.00E+00

Nutrient Foramen Cort. 0.16600 0.08380 4.83E-05 0.60443 0.34736 0.00E+00

Nutrient Foramen Ost. 0.28897 0.02743 4.66E-10 0.56923 0.20587 1.97E-03

Nutrient Foramen Trab. 0.03646 0.00441 9.37E-06 0.12636 0.09317 0.00E+00

Inter Condylar Fossa Cort. 0.47736 0.16870 4.98E-06 1.53072 0.63708 1.69E-03

Inter Condylar Fossa Trab. 0.04574 0.01794 5.47E-06 0.00337 0.00000 3.95E-06

Table 21- Raw qPCR averages from the femora. Note the lack mtDNA and Y- chromosome data from the right side.

Also of note is the discrepancy between the mtDNA values from the osteonal sampling sites between sides.

Tibias

No statistically significant difference was detected in the tibias across DNA type

and sampling site. However, raw qPCR data for these elements looks very similar to that

of the femora with several sites from both sides failing to produce any mtDNA data.

104

Sampling Site

Left

Autosomal

Left Y-

Chromosome

Left Mito

Right

Autosomal

Right Y-

Chromosome

Right

Mito

Tibial Plateau Cort. 0.06259 0.02534 1.31E-05 1.41029 0.37741 2.56E-06

Tibial Plateau Trab. 0.00380 0.00147 0.00E+00 0.09577 0.02189 3.90E-06

Mid-diaph. Cort. 0.05063 0.00880 7.85E-06 0.98559 0.44617 2.42E-03

Mid-diaph. Ost. 0.07628 0.01914 0.00E+00 0.05901 0.02012 4.84E-06

Mid-diaphy Trab. 0.04214 0.02384 0.00E+00 0.01769 0.01161 0.00E+00

Malleolar Groove Cort. 0.66057 0.07306 8.25E-09 0.29396 0.12147 6.15E-06

Malleolar Groove Trab. 0.05711 0.01195 0.00E+00 0.21526 0.05322 0.00E+00

Table 22- Raw qPCR averages from the tibias. Note the lack of mtDNA data from trabecular samples on both sides and

the osteonal sample on the left.

Fibulas

ANOVA detected significance between the scores of sampling sites on the

fibulas. However, this may be another type I error due to small population size. Welsch’s

F is nearly significant, but this also indicates support for the above statement. Post- hoc

testing shows the greatest variance was between proximal and distal sites. This is

consistent with the raw data which shows higher performance of distal samples than

proximal with intermediate performance of mid- diaphyseal samples.

Prox Fib Auto

Mid Fib Auto

Dist Fib Auto

Prox Fib Auto

0.2807

0.01423

Mid Fib Auto

0.2807

0.1698

Dist Fib Auto

0.01423

0.1698

Table 23- Post- hoc testing shows greatest variance between proximal and distal sampling sites of the fibulae.

No significant difference was detected in Y- Chromosome or mtDNA between the

proximal, mid- diaphyseal, and distal fibula samples.

105

Irregular Bones

No statistically significant differences were noted within in the clavicles or

scapulae or the os coxae. The qPCR data from the clavicles indicates that the medial end

performed better than the lateral end on both sides. Data from the scapulae were mixed

and showed no clear pattern.

Sampling Site

Left

Autosomal

Left Y-

Chromosome

Left

MtDNA

Right

Autosomal

Right Y-

Chromosome

Right

mtDNA

Clavicles

Medial End Cort. 0.52210 0.15167 6.36E-03 1.60835 0.55749 2.05E-04

Medial End Trab. 0.34800 0.01471 0.00E+00 0.06831 0.00492 5.59E-09

Lateral End Cort. 0.13830 0.06363 0.00E+00 0.64781 0.24278 2.62E-05

Lateral End Trab. 0.08975 0.01548 8.88E-09 0.03534 0.00466 4.39E-05

Scapulae

Glenoid Fossa Cort. 0.27374 0.14407 1.64E-04 0.73983 0.19428 0.00E+00

Glenoid Fossa Trab. 0.01384 0.00474 2.60E-05 0.03607 0.06603 0.00E+00

Acromion Process Cort. 0.01805 0.18988 4.05E-10 0.71162 0.15759 4.83E-08

Acromion Process Trab. 0.56170 0.05292 1.37E-04 0.18870 0.03741 2.65E-05

Table 24- Raw qPCR averages for the clavicles and scapulae

While no statistically significant difference was detected between the right and

left Os Coxae, examining the raw data may be more instructive as to which sampling

sites are optimal for DNA extraction. The auricular surface and the cortical tissue of the

pubic symphysis yielded the highest amounts of autosomal DNA, and the pubic

symphysis was among the best for mtDNA, as well. Results from trabecular samples

from the os coxae were mixed across DNA types.

106

Sampling Site

Left

Autosomal

Left Y-

Chromosome

Left mtDNA

Right

Autosomal

Right Y-

Chromosome

Right

mtDNA

Iliac Crest 0.11510 0.02488 4.81E-04 0.17752 0.02880 1.72E-05

Auricular Surface 2.12180 0.06066 0.00E+00 0.25182 0.06008 0.00E+00

Posterior Inferior Spine 1.44270 0.00092 0.00E+00 0.01534 0.00142 8.08E-07

Ischial Spine 0.02870 0.00091 2.97E-05 0.01074 0.00111 8.92E-06

Ischial Tuberosity Cort. 0.04316 0.01206 4.06E-05 0.03402 0.00868 5.24E-08

Ischial Tuberosity Trab. 0.05825 0.00547 5.55E-06 0.04727 0.00732 3.74E-08

Ischio-Pubic Ramus Cort. 0.05030 0.00552 8.58E-05 0.03751 0.00456 1.15E-05

Ischio-Pubic Ramus Trab. 0.61590 0.00244 0.00E+00 0.01148 0.00086 2.55E-08

Pubic Symphysis Cort. 0.61088 0.09139 7.49E-05 0.75087 0.08042 3.59E-05

Pubic Symphysis Trab. 0.01283 0.00278 1.61E-05 0.00756 0.00086 5.26E-06

Dorsal Surface 0.43939 0.00548 1.48E-09 0.09917 0.00682 5.97E-07

Ventral Surface 0.35614 0.02571 0.00E+00 0.23351 0.05299 0.00E+00

Table 25- Raw qPCR averages for the os coxae.

ATR/FTIR

ANOVA testing of ATR/FTIR results were first run by the absorbance levels of

amino acids, phosphates and carbonates by tissue type, irrespective of DNA type. No

statistically significant differences were noted. Then, ANOVA was run by tissue type and

showed no statistically significant difference in amino acid absorbance across tissue type.

The tissue types did, however, exhibit some significant differences in phosphate and

carbonate absorption. With these results in mind, correlation tests were conducted first in

aggregate, then across tissue type, for all DNA types.

Phosphate Absorbance by tissue type

Sum of sqrs df Mean square F p (same)

Between groups: 0.0716473 2 0.0358237 4.676 0.01357

107

Within groups: 0.398369 52 0.00766094 Permutation p

(n=99999)

Total: 0.470016 54 0.01307

Components of variance (only for random effects):

Var(group): 0.00170027 Var(error): 0.00766094 ICC: 0.18163

omega2: 0.1179

Levene´s test for homogeneity of variance, from means p (same): 0.1731

Levene´s test, from medians p (same): 0.252

Welch F test in the case of unequal variances: F=4.343, df=15.1, p=0.03237

ANOVA and Welch’s F both detected significance at p < 0.05.

Dunn’s post- hoc indicates the greatest variance is between the phosphate absorbance

levels in the trabecular and the osteonal tissues.

Ph Abs (Cort)

Ph Abs (Trab)

Ph Abs (Ost)

Ph. Abs. Cort.

0.01195

0.6593

Ph. Abs. Trab.

0.01195

0.03407

Ph. Abs.Ost.

0.6593

0.03407

Table 26- Post- hoc testing shows greatest variance between phosphate absorbance levels of the trabecular vs cortical

samples with somewhat less between trabecular and osteonal samples.

108

Carbonate absorbance by tissue type

Sum of sqrs df Mean square F p (same)

Between groups: 0.0787873 2 0.0393936 6.413 0.003239

Within groups: 0.319402 52 0.00614234 Permutation p

(n=99999)

Total: 0.398189 54 0.00318

Components of variance (only for random effects):

Var(group): 0.00200749 Var(error): 0.00614234 ICC: 0.246323

omega2: 0.1645

Levene´s test for homogeneity of variance, from means p (same): 0.2217

Levene´s test, from medians p (same): 0.284

Welch F test in the case of unequal variances: F=6.353, df=15.13, p=0.009933

Both ANOVA and Welch’s F detected significance at p < 0.05.

109

Post- hoc testing in this case shows that the greatest variation in Carbonate absorbance is

between the cortical and the trabecular tissue with only somewhat less variance between

trabecular and osteonal tissue.

Car. Abs. Cort.

Car. Abs. Trab.

Car. Abs. Ost.

Car. Abs. Cort.

0.003059

0.6693

Car. Abs. Trab.

0.003059

0.01615

Car. Abs. Ost.

0.6693

0.01615

Table 27- Post- hoc testing shows greatest variance between carbonate absorption in the trabecular vs cortical tissue

with somewhat less between trabecular and osteonal tissue.

Correlation Testing

In an effort to ascertain more details about the nature of the variance seen in

ANOVA testing, correlation testing was performed between DNA concentration values

and the ATR results. First, phosphate absorption was run against carbonate absorption

and the results indicate a strong, linear, correlation with a p = 4.087E-40 where α = 0.05.

Then, aggregate correlation testing, that is, comparing all phosphate and

carbonate absorption scores to all scores from each DNA type suggested a weak,

negative, monotonic, correlation between phosphate absorption and autosomal DNA.

Both Y-chromosome and mtDNA were weakly, positively correlated. Pearson’s R,

shown in the table below, indicated that none of the p values were statistically significant.

As with the ANOVA data, relevant p values are highlighted in yellow and relevant

correlation coefficients are in blue.

110

Phosphate

Autosomal

Phosphate

Y Chromo

Phosphate

Mito

Phosphate

0.19305

Phosphate

0.80702

Phosphate

0.71717

Autosomal

-0.17819

Y Chromo

0.033702

Mito

-0.049962

Table 28- Pearson's R Correlation test results of phosphate absorption by DNA type. None of the p values (yellow)

showed significant correlation. Correlation coefficients (blue) indicated negative relationships.

Figure 3- Example of an output from PAST 4.03 showing correlation strength/weakness between Phosphate

absorbance levels and mtDNA Arrows point to the small, pink, circles indicating a very weak, negative, correlation.

Cortical Bone Tissue

Pearson’s R detected a weak, negative, monotonic, correlation between amino

acid absorption and autosomal DNA (p = 0.13701). A weak, positive correlation was

111

detected between amino acid absorbance and both Y-chromosome data (p = 0.69626) and

mtDNA (p = 0.24743).

Both phosphate (p = 0.15592) and carbonate (p = 0.14663) were weakly,

negatively, correlated with autosomal DNA and Y-chromosome data. A weak, positive

correlation was detected between both phosphate (p = 0.47651) and carbonate (p =

0.47651) absorbance and mtDNA.

Ph Abs

Y Chromo

Car Abs

Y Chromo

Ph Abs

0.51472

Car Abs

0.49512

Y Chromo

-0.13668

Y Chromo

-0.14306

Table 29- Correlation testing results between phosphate and Y chromosome and carbonate and Y chromosome DNA.

Trabecular Bone Tissue

Amino

Acid

Auto

Amino

Acid

Chromo

Amino

Acid

mtDNA

Amino

Acid

0.79112

Amino

Acid

0.77478

Amino

Acid

0.93026

Auto.

-0.058437

Chromo

-0.063123

mtDNA

-0.019325

Table 30- Correlation testing results comparing amino acid absorption levels by DNA type in trabecular tissue.

112

Phos.

Auto

Phos.

Chromo

Phos.

mtDNA

Phos.

0.34747

Phos.

0.31151

Phos.

0.88409

Auto.

-0.20525

Chromo

0.22072

mtDNA

-0.032186

Table 31- Correlation testing results comparing phosphate absorption levels by DNA type in trabecular tissue.

Car.

Auto.

Car.

Chromo

Car.

mtDNA

Car.

0.29079

Car.

0.30894

Car.

0.77917

Auto.

-0.23013

Chromo

0.22186

mtDNA

-0.06186

Table 32- Correlation testing results comparing carbonate absorption levels by DNA type in trabecular tissue.

While none of the p values met the requirements for significance, there was a

more consistently negative relationship between biochemical properties of trabecular

tissue and DNA.

Osteonal Bone Tissue

Amino

Acid

Auto.

Amino

Acid

Chromo

Amino

Acid

mtDNA

113

Amino

Acid

0.2423

Amino

Acid

0.94658

Amino

Acid

0.68898

Auto.

0.50996

Chromo

-0.03148

mtDNA

0.18643

Table 33- Correlation testing results comparing amino acid absorption by DNA type in osteonal tissue.

Phos.

Auto.

Phos.

Chrom

Phos.

mtDNA

Phos.

0.73005

Phos.

0.8289

Phos.

0.42004

Auto.

0.16109

Chromo

-0.10125

mtDNA

0.35714

Table 34- Correlation testing between phosphate absorption by DNA type in osteonal tissue.

Carb.

Auto.

Carb.

Chromo

Carb.

mtDNA

Carb.

0.47619

Carb.

0.88054

Carb.

0.51452

Auto.

0.14168

Chromo

-0.07054

mtDNA

0.29918

Table 35- Correlation testing between carbonate absorbance by DNA type in osteonal tissue.

114

With the exception of Y-chromosome data, which was negative in all three

correlation tests, osteonal tissue was more positively correlated with amino acid,

phosphate, and carbonate levels.

To elaborate on the relationship between carbonate and phosphate levels and

DNA, those samples which displayed the lowest levels of mtDNA were combined in a

table with the ancient samples that were run through ATR/FTIR analysis as a baseline.

Since there were only four ancient samples, with only mtDNA results in an unrelated

study, no correlation or other statistical analysis could be run on those samples. Raw data

does not appear to support, or be supported by, the findings from the correlation tests.

This could well be due to smaller sample size, but whatever the reason, the raw data table

are provided below.

115

Sample

Tissue

Type

Amino Acid

Absorbance

Phosphate

Absorbance

Carbonate

Absorbance

Mito

LTIB 6 1

0.098 0.331 0.300 0.00E+00

LTIB 2 2

0.093 0.370 0.329 0.00E+00

R2A 1 0.088 0.310 0.266 0.00E+00

MAN 1 1 0.084 0.330 0.340 0.00E+00

C2A 1 0.083 0.207 0.193 0.00E+00

C2B 2 0.068 0.120 0.107 0.00E+00

LRib 2A 1 0.066 0.191 0.181 0.00E+00

R3A 1 0.062 0.148 0.129 0.00E+00

L2B 2

0.062 0.135 0.110 0.00E+00

C4A 1 0.060 0.351 0.340 0.00E+00

C1A 1 0.059 0.235 0.241 0.00E+00

LFP 4 1

0.043 0.160 0.162 0.00E+00

L2A 1

0.042 0.138 0.136 0.00E+00

LTIB 5 2 0.039 0.145 0.150 0.00E+00

LTAR 1 1

0.029 0.156 0.134 0.00E+00

LSC 3 1 0.143 0.409 0.354 4.05E-10

LFEM 4 3

0.064 0.237 0.225 4.66E-10

LRad 5 2

0.045 0.153 0.152 6.81E-10

T10B 2

0.060 0.700 0.510 0.00E+00

Ancient A 2 0.03 0.5 0.39 0.00E+00

Ancient B 2 0.05 0.75 0.5 0.00E+00

Ancient C 2 0.05 0.85 0.64 0.00E+00

Ancient D 2 0.05 0.8 0.56 0.00E+00

Table 36- Samples with zero, or E-10 mtDNA results are listed alongside Amino Acid, Phosphate, and carbonate levels

as measured by ATR/FTIR spectroscopy. The bottom 4 samples are the ancient samples which were run as a baseline

for the ATR/FTIR analyses.

116

Figure 4- Graph of mtDNA in relation to Amino Acid, Phosphate, and Carbonate absorbance.

117

Figure 5- Graph showing the correlation between amino acid, phosphate, and carbonate absorbance from ATR/FTIR

testing and autosomal DNA.

STR Results

Due to budgetary restraints, only 62 of the samples were run through CE for

fragment analysis and STR profile construction. While this may seem to be a serious

limit to the study, it is illustrative of real- world issues facing forensic DNA analysts. The

samples that were chosen for fragment analysis were selected to be a truly representative

118

subset of the overall sample population. The criteria used were 1) tissue type, 2) skeletal

element grouping, and 3) the highest, middle, and lowest performing samples by qPCR

data. The samples were taken to the Montana State Crime Lab and testing was performed

by Joe Pasternak. Ten of the eluted DNA samples had evaporated beyond the point that

re- hydration with deionized water was possible. Results from the remaining 51 are as

follows:

Sample

# Loci

% Profile

Sample

# Loci

% Profile

LOS1

23.0

100%

MAN2

23.0

100%

LOS7

11.0

48%

MC3B

9.0

39%

23.0

100%

MC3A

8.5

37%

LMC3A

14.0

61%

MC1B

3.5

15%

LMC1B

16.0

70%

MC1A

5.0

22%

LMC1A

0.0

RAD6

3.0

13%

LH4

0.0

RAD1

23.0

100%

LH1

23.0

100%

23.0

100%

R4B

23.0

100%

23.0

100%

R4A

0.0

HUM4

11.5

50%

R1B

7.0

30%

HUM2

22.0

96%

119

R1A

23.0

100%

HUM1

11.0

48%

L1B

0.0

FEM2

14.0

61%

L1A

0.0

FEM1

23.0

100%

T1B

5.0

22%

OS9

21.0

91%

T1A

3.0

13%

OS7

12.0

52%

C1B

10.5

45%

OS2

17.0

74%

C1A

23.0

100%

OS1

23.0

100%

MT1B

19.0

83%

STE2

6.5

28%

MT1A

14.0

61%

STE1

23.0

100%

TAR4

0.0

TIB3

22.0

96%

TAR3

23.0

100%

TIB1

19.0

83%

TAR2

23.0

100%

FEM6

23.0

100%

TAR1

22.0

96%

FEM5

23.0

100%

TIB7

22.0

96%

FEM4

23.0

100%

TIB6

23.0

100%

TIB4

22.0

96%

Table 37- Fragment analysis indicating the # of STR loci for each sample and the % of complete profile represented. A

profile is considered complete that contains all 23 of the CODIS loci. Red = cortical tissuesamples, green =

trabecular, and yellow = osteonal.

120

Figure 6- Average percentage of complete STR profiles by tissue type.

121

Sample

# Loci

Sample

# Loci

Sample

# Loci

LOS1

23.0

FEM1

23.0

TAR3

23.0

LOS7

11.0

OS9

21.0

TAR2

23.0

LMC3A

14.0

OS7

12.0

TAR1

22.0

LMC1A

0.0

OS2

17.0

TIB6

23.0

LH1

23.0

OS1

23.0

FEM6

23.0

R4A

0.0

STE1

23.0

HUM1

11.0

R1A

23.0

MC3A

8.5

FEM1

23.0

L1A

0.0

MC1A

5.0

OS9

21.0

T1A

3.0

RAD6

3.0

OS7

12.0

C1A

23.0

RAD1

23.0

OS2

17.0

MT1A

14.0

23.0

OS1

23.0

TAR4

0.0

23.0

Table 38- # STR loci by cortical samples.

Figure 7- STR loci by cortical tissue.23 loci is the maximum that can be amplified by the Qiagen Investigator kit and

constitutes a 100% profile. The chart goes to 25 to aid in legibility.

122

Sample

# Loci

23.0

LMC1B

16.0

R4B

23.0

R1B

7.0

L1B

0.0

T1B

5.0

C1B

10.5

MT1B

19.0

TIB2

19.0

FEM5

23.0

FEM2

14.0

STE2

6.5

MAN2

23.0

MC3B

9.0

MC1B

3.5

HUM2

22.0

Table 39- # Loci by trabecular tissue samples. # of loci is out of 23 total.

Figure 8- # STR Loci by trabecular tissue samples.

123

Sample

# Loci

LH4

0.0

FEM4

23.0

HUM4

11.5

TIB4

22.0

Table 40- # STR loci by osteonal tissue sample.

Figure 9- # STR loci by osteonal tissue sample.

124

Figure 10- Graph showing how starting molecular weight from qPCR quantitation compares to samples that produced

100% STR profiles.

Nineteen of the 51 samples (37%) generated 23 STR loci which constitutes a full

profile. Conversely, sixteen of the samples (31%) generated 0 STR loci. The breakdown

for the remainder of the samples is as follows: three (3) samples generated 10% - 20%

STR profile, nine (9) generated 21% - 49% profile, seven (7) generated from 50% - 75%

profile, and eight (8) generated between 76% - 96% of a full profile. Because the STR kit

used in the study indicates the presence of both/either the X and Y chromosomes, if only

one appeared, it was counted as ½ of a locus.

In forensics, those samples which generate at least 21 loci (91% profile) are

considered usable and in this study, 24 samples (47%) met that criterion. For

bioarchaeologists and paleoanthropologists, however, even partial profiles may be

instructive depending on the genetic locus or loci under study.

125

CONCLUSIONS & DISCUSSION

Examining the raw data, and especially cross-referencing said data to the actual

skeletal material under investigation, can be instructive. Frequently, in forensic

anthropology, investigators are forced to observe morphological features and make

assessments that are difficult, if not impossible, to reliably quantify statistically. Some of

the data from this project illustrates the underlying biology and biochemistry that directly

pertains to those un-quantifiable features and indicates that statistical analyses are not

absolutely essential for scientific understanding.

For example, the degree to which taphonomic factors may be influencing low

starting DNA molecular weight are almost impossible to quantify, yet when comparing

the left os coxa of the individual to the right os coxa, or some of the vertebral elements, it

is easy to see that some elements are more heavily processed than others. And the raw

data from qPCR analysis bears this out, even though statistical analysis does not.

Statistical analytical methods are often times too large a brush for the detailed work that

is needed. In this project specifically, where maceration of the body has rendered the

skeletal tissue extremely pale and brittle, starting molecular weight DNA is not as good,

yet moving from the iliac crest to the auricular surface yielded noticeably better DNA

yield. Drawing this reasoning out, if DNA analysis is to be performed, obviously the first

choice would be to obtain a sample from a fresh skeleton. However, since the underlying

assumption of this study is that, in the real world, the nature and extent of taphonomic

126

influence is often unknown. Thus, relying on the traditional forensic anthropological

features for sample procurement is wise.

The reason for this is that the features which forensic anthropologists examine are

the results of lifelong cellular and biochemical activity on, or within, the bone tissue.

Even if the individual is relatively young at the time of death, thorough understanding of

the ontogeny, development, and decline of bone tissue allows a sound basis for sample

selection. In some situations, for example when only fragments of bone are available,

advanced knowledge of the processes of bone growth and remodeling may be necessary.

Since not all who are engaged in the pursuit of DNA from human skeletal tissue may be

so trained, attempting to address as many potential scenarios as possible, and thus

provide a clear portrayal of the optimal sampling sites across the post-crania is the main

objective of the project.

Furthermore, the project was successful in creating an effective sampling method

that is far less destructive to human or hominin bone tissue than had been attempted in

the past. By employing a deep understanding of skeletal cellular biology and

biochemistry, this study shows that it is indeed possible to reduce the amount of tissue

destruction to only that which is absolutely essential for scientific accuracy. In this case,

that success comes in the form of a decrease of 30-50% of the requisite tissue from what

has been used in past studies (3–5,134) and even from what was published in the

protocols from the manufacturer of the DNA extraction kits (163). In fact, any deviation

from the published protocols came in the form of less. Less bone tissue was used, and

less time was spent incubating and agitating the samples. This speaks to effective

127

products used on samples that were strategically obtained from a human source that was

ethically treated.

CORRELATING CELL TYPE POPULATIONS WITH DNA TYPE AND QUANTITY

In both hands, trabecular samples from the distal ends of the 3rd metacarpals were

the highest scoring sites with 1.13E-05 from the left and 2.21E-05 from the right,

respectively. Examining the skeletal material, there were five sesamoid bones present in

the hands and seven in the feet. These small accessory ossicles develop over the course of

an individual’s life due primarily to heavy use of the appendages and function as support

for them (164–167). Development of bone tissue like this is conclusive evidence of

osteoblastic activity. And, if present for a long enough period of time, the

metatarsal/metacarpals and phalanges adjacent to the sesamoids will often develop

articular facets; a process which requires resorption of the existing bone tissue by

osteoclast activity.

The pubic symphysis and the auricular surface of the os coxa are two regions that

forensic anthropologists routinely examine during age-at-death estimations. These two

regions undergo significant homeostatic alterations over the course of life. While the

interpretations of these changes are debated in their accuracy of age-at-death estimations,

the cellular populations involved with these changes are well-documented. In this study,

raw qPCR data from cortical tissue of the pubic symphyses were some of the highest

across the entire sample population and across all DNA types. Given that osteoclasts are

multi-nucleated and highly active (requiring mitochondria for energy production), the

high scores from both nuclear (autosomal and Y-chromosome) and mtDNA scores

128

indicate that there almost certainly was some in vivo osteoclast activity present at the time

of death. The auricular surface, very similar in its utility to forensic anthropologists,

differed in autosomal raw qPCR scores, but were still quite high. The auricular surface is

where the os coxa articulates with the sacrum at the sacro-iliac joint. In this individual,

the trabecular sample from sacro-iliac joint on the right side performed quite well in

mtDNA quantification (1.07E-03), even though the surface cortical did not. While

seemingly contradictory, remember that osteoclasts derive from hematopoietic stem cells

that are found in trabeculae and bone marrow. Furthermore, bone creation cannot be

carried out while bone destruction is occurring at the same place. From a functional

morphological standpoint, if the bone on one side of the joint is being resorbed, the other

side will most likely not also be undergoing resorption so as to limit the degree of

destabilization.

Similarly, the sample taken from trabecular tissue at the distal end of the sternum

was one of the highest overall performing sites for mtDNA at 3.17E-03. Examination of

the element shows a significant degree of remodeling occurring both on the sternal body

itself, as well as at costal articulation sites. Trabecular tissue samples taken from C7 and

T3 also performed well in mtDNA analysis, as did nearly all of the samples from the ribs

and the mid-diaphyseal sites of the long bones from the right side.

It was not only trabecular tissue that performed well in mtDNA quantification.

Cortical samples from the 1st proximal phalanx of the foot, the talus, calcaneus, and

patella on the right side and the medial clavicle and the olecranon process of the ulna

from the left side all scored well. Osteonal tissue samples, generally, did not perform well

in mtDNA analysis, with the exception of the right femur.

129

Osteonal samples across the entirety of the right side performed better than did

those of the left side. On the right femur, both the mid-diaphyseal cortical and trabecular

samples failed to produce any mtDNA while the osteonal sample performed quite well.

The opposite was true on the left side, with the osteonal sample failing to produce any

mtDNA at all. Samples from the other long bones follow the pattern of being higher on

the right side than the left. The left humerus performed well in nuclear DNA quantitation

and poorly in mtDNA levels. If osteocyte populations predominate in this tissue, then this

is the pattern that would be expected. Because this is the case with most sampling sites, it

is difficult to say with any confidence whether osteonal tissue is a reliable source of

mtDNA. Due to the very low starting molecular weight of mtDNA that was recovered,

however, it can be suggested with some confidence, that there are more reliable sites

throughout the post-cranial skeleton, if those elements are available for sampling. And if

the femur is all that is available, this data does suggest that either proximal or distal sites

are preferable.

Given that the mid-diaphyseal sites were located at the nutrient foramina, the

major site of vascularization and innervation to and from the long bones, the low mtDNA

scores may be easily explained in light of the processes of autolysis, decomposition,

and/or maceration. These factors are important considerations in and of themselves in

deciding where to sample for DNA. The enzymes involved with autolysis, the chemicals

used in maceration, and bacterial agents in the case of natural decomposition, may have

more access to the bone tissue through this point. The relative success of the right femur

in mtDNA extraction from the osteonal tissue may be the result of increased osteoclastic

activity in general on that side and may actually support the hypothesis of right-side

130

dominance in the individual. Whether this is the case or not is difficult to say, but mid-

diaphyseal sampling on or around the nutrient foramina of long bones does not appear to

be optimal if any other choices are available.

Another important consideration in attempting to correlate DNA levels with

cellular populations is the activity of osteoblasts in proximity to the proposed sampling

site. While not directly addressed in the hypotheses of this study, results of DNA analysis

are consistent with the biochemical communication between, and activation/deactivation

of, osteoclasts and osteoblasts (37,41,43,44). The lower thoracic and all of the lumbar

vertebrae failed to produce any mtDNA whatsoever. Examination of these elements

shows noticeable osteophytic development. Samples from the ribs bilaterally are

consistent with this, as well. Where there is pronounced osteophytic growth, there is

higher autosomal and Y-chromosome data, and lower mtDNA.

The data from this study also indicates that bone tissue quiescence may be an

integral factor in DNA extractability. The seemingly poor performance of the petrous

portions of the temporal bones (5.85E-06 from the left and 0.00 from the right), may

indicate the predominance of terminally differentiated osteocytes or bone-lining cells

over the more active osteoblasts or osteoclasts, at least on the right side. Post-

developmentally, there may not be active cell populations sufficient to obtain adequate

DNA in these tissues, barring injury or pathology. This may be the reason for poor

mtDNA data in studies which have attempted extraction from other bones of the cranium,

as well (85). While outside the scope of this project, the individual in this study did

exhibit an anomaly attributed to a surgical procedure to the left frontal bone immediately

inferior to the sagittal suture. It would be interesting to see how the bone tissue, both

131

cortical and diploic, surrounding the injury to the frontal bone would perform in DNA

analysis. The complete lack of mtDNA data from samples of the dental tissue from the

right 1st molar (the only one available for sampling in this individual) is most likely an

artifact of extremely poor dental health overall in the individual. Data from other studies

have routinely reported high mtDNA extraction and amplification from dental tissue.

This individual, however, had several serious dental caries, an abscess, and exhibited a

high degree of alveolar resorption from other missing teeth. As stated, the tooth selected

for analysis was the only remaining molar. Usually, DNA extractions from teeth highly

prioritize the molars due to their large size and robusticity in comparison to the other

tooth types.

ATR/FTIR SPECTROSCOPIC ANALYSIS AND DNA EXTRACTION

Results of the correlation testing between amino acid, phosphate, and carbonate

absorbance and the three DNA types in the different bone tissues was inconclusive. In the

autosomal data, it appears that as phosphate and carbonate levels increase, DNA levels

decrease. However, it is not clear from the data whether these mineral groups are actually

excluding or entombing the DNA or if there are hidden variables influencing the data.

More samples from a wider variety of taphonomic conditions and ages would need to be

performed. The data certainly indicates that it is worth studying because if we can discern

what the actual process is that drives the negative correlation between the minerals

(especially phosphate) and DNA, then we can adapt DNA extraction protocols. For

example, if the research indicates that the phosphate is really “entombing” the DNA, then

we can increase the amount of EDTA and/or Proteinase K to account for their entrapment

within the mineral matrix. Conversely, if the negatively charged carbonate increase is

132

shown to repel the also negatively charged DNA, then we can either alter the

amplification protocols by adapting the primers and/or thermal cycling parameters

accordingly.

The negative monotonic correlation between amino acid absorbance and DNA

levels in the osteonal bone tissue was, at first, confusing. As analyses proceeded,

however, it seems that this is most likely an artifact of taphonomy, especially in the case

of Y-chromosome levels. The Y-chromosome is known to be smaller than the other

chromosomes and more prone to molecular taphonomic damage (106,168,169).

Regardless of the DNA type, the negative correlation can be easily explained in terms of

the chemical bonds. DNA has a sugar phosphate “backbone” to which the nucleotides are

attached. If the DNA strand is in small, un-amplifiable fragments, then ATR/FTIR

spectroscopy will still “see” them, even if they are too small to be amplified either during

qPCR or the amplification phase of fragment analysis. This is supported by much of the

results of Qubit fluoroscopic analysis at the earliest stage of quantification immediately

following the DNA extraction/purification phase.

The hypothesis that ATR/FTIR spectroscopy will aid in determining suitability

for DNA extraction can neither be supported nor wholly rejected. It seems that

ATR/FTIR analysis is valuable in identifying which samples are absolutely NOT suitable

for extraction, and thus, maybe more useful in paleoanthropological studies than in

forensic cases where there is the assumption of DNA survival. And it certainly aids with

visualizing taphonomic and diagenetic processes that influence DNA extraction and

amplification. With more research, this type of spectroscopy may prove useful in

developing better extraction and purification methods and understanding the relationship

133

between phosphate/carbonate and DNA from bone tissue is certainly worth continued

study. Utilizing ATR/FTIR spectroscopy on bone tissue from a wide range of taphonomic

conditions-such as prolonged exposure to different soil pH levels and chemical types-

may help elucidate the diagenetic process that seem to inhibit or prevent DNA

extraction/amplification. It may also be useful in studies of grave soil to determine if

there may be amplifiable DNA prior to attempting more expensive methods. Other

studies of biochemical aspects of bone functional morphology in paleoanthropology,

bioarchaeology, and zooarchaeology may also benefit from the use of ATR/FTIR

spectroscopy.

OPTIMAL SAMPLING SITES

THE APPENDICULAR SKELETON

Overall results of DNA testing and comparisons between and within elements

indicate that the optimal sampling sites are those associated with the heaviest life-long

use and, thus, cellular remodeling. These regions almost ubiquitously involve proximity

to articulation sites. While the specific bone tissues involved tend to indicate that the

dense cortical bone is better, trabecular tissue did perform well at some sites, such as the

left acromion process of the scapula, the ulnar notch of the radius, the olecranon process

of the ulna, and others. Therefore, at least in the appendicular skeleton, tissue type seems

to be less important than location.

Upper Body Elements

134

Results of DNA testing of the skeletal elements of the upper body (shoulder, arm, and

hand) indicate that the indicate that the 5 most optimal sampling sites for autosomal DNA

were:

1. The medial epicondyle of the distal humerus

2. The bicipital groove of the humerus

3. The ulnar notch or head of the radius

4. The extensor carpi of the distal ulna

5. The olecranon process of the proximal ulna

In the upper appendicular skeleton, the limbs of the right side of the upper body

outperformed their left side counterparts. Forensic anthropological analysis also

estimated the individual to most likely be right-handed. The DNA results are consistent

with that assessment. Estimation of handedness in forensic anthropological analysis may

inform sampling decisions for forensic DNA analysis as to which of the sides should be

prioritized in cases where there is an option.

Figure 11- Averages of all qPCR results from the right side.

135

Figure 12- Averages of all qPCR results from the left side.

Lower Body Elements

As with the elements of the upper body, the right side performed better than the left

side. Cortical tissue generally outperformed trabecular and osteonal tissue, but the top

two sites from the lower body were the trabecular and cortical samples from the femoral

head. The top five sampling sites for autosomal DNA from the lower limbs of the body

were:

1. The fovea capitis of the femur (both trabecular and cortical performed well)

2. The intercondylar fossa of the distal femur

3. Cortical tissue from the tibial plateau

4. The nutrient foramen of the femur

5. The sustentaculum tali or calcaneal tuberosity of the calcaneus

136

While it is usually more difficult, if impossible, to empirically glean lower limb

dominance than handedness, attention should still be paid to evidence of cellular activity.

In this individual, due to the observation of increasing porosity in the femoral neck, that

area was avoided for DNA sampling purposes. To some extent, the loss of bone mineral

density (BMD) occurs as a natural process of age in humans. As age advances, osteocyte

lacunae are no longer re-filled and new secondary osteons are not constructed once the

cell has succumbed to apoptosis or programmed cell death. The transition from natural to

pathological can be difficult to ascertain, but often takes the form of transition from

microporosity to macroporosity as the minerals from bone tissue in the empty lacunae is

“looted” to maintain homeostatic levels elsewhere. This is seen especially in or near the

hips, knees, shoulders, and elbows. This process of homeostatic mineral reallocation and

ensuing macroporosity can be accelerated and/or exacerbated by numerous factors

(19,56,83,170–174). While the effect this may have on DNA extraction is not well

characterized, since optimal DNA extractability was the main goal of the study, areas of

visible macroporosity associated with BMD loss were avoided.

THE AXIAL SKELETON

Many of the elements of the axial skeleton in this individual were undergoing

considerable remodeling. There was extensive visible evidence of cellular activity on the

sternal rib ends, the manubrium, the sternum, and several vertebral elements.

Additionally, the right 6th, 7th, and 8th ribs exhibited a bony callous consistent with a

poorly healed fracture. The halves of the ribs had separated at the callous during

maceration.

137

Image 7- Three ribs with photographic scale showing breakage at the site of ante- mortem callous growth.

This provided a perfect sampling site for this experiment. Additionally, upon

attempting to obtain a trabecular tissue sample from the manubrium, it was discovered

that the inside of the element was completely dominated by thick, brown, cartilaginous

material. A sample of the material was taken for DNA analysis, but upon examination of

the sample at extraction time, a significant bloom of mold had developed. The sample

was retained, but no attempt was made at extraction due to the high probability of

contamination. Full sequencing of the human and mold DNA would be an interesting

project.

In the ribs, multiple elements from both sides performed well in DNA analysis. From

the left side, the top sites for autosomal DNA were:

1. Cortical sample of the 4th rib

138

2. Cortical tissue from the 8th rib

Interestingly, the cortical tissue from the 4th rib yielded 0.00 mtDNA, despite a very

high score in autosomal and Y-chromosome DNA. The 8th rib cortical sample, on the

other hand, yielded high scores across all DNA types. On the right side, the top sampling

sites for autosomal DNA were:

1. Cortical tissue of the 8th rib

2. Trabecular tissue of the 8th rib

In an interesting juxtaposition to its left-side counterparts, the right 8th rib yielded no

mtDNA at all. The 4th rib, though it was third highest in autosomal and Y-chromosome

DNA, was the top site for mtDNA. Again, sampling from the ribs was aimed directly at

the visible areas of cellular activity. On the ribs that did not exhibit trauma, such as the

first ribs, there was still visible evidence of cellular activity on the inferior surface near or

on the sub-clavian groove and sampling was aimed there for the cortical tissue.

Trabecular tissue was taken from the sternal ends, many of which exhibited the “crab

claw” feature, which is known to be evidence of ossification of the costal cartilage (175–

177), and thus, obvious sites of osteoblastic activity.

The sternum and the manubrium, despite having significant evidence of cellular

activity, did not perform especially well in DNA extraction. The cortical sample taken

from the clavicular notch of the manubrium was the highest performing site in both

nuclear DNA, while the distal end of the sternal body yielded the highest amount of

mtDNA. Because of these results, it recommended that these elements be avoided, if

there are other options for sampling.

139

The vertebrae of this individual provided some very interesting results; although

because of those results, it does not seem practical to attempt to effectively rank the best

sampling sites. While the lumbar vertebrae failed to yield any mtDNA at all, they scored

significantly higher than the other regions in nuclear DNA. Examination of the elements

shows osteophytic activity on both the pedicles and at the anterior margins of the

vertebral bodies. This development is not due to trauma, but more likely to osteoarthritis

(OA), and thus, is not indicative of osteoclastic resorptive activity(19,56,174,178–180).

All of the highest nuclear DNA scores came from cortical samples obtained from

locations on the vertebral bodies or articular facets immediately adjacent to the areas of

visible osteophytic growth. Trabecular samples taken from the 4th thoracic and the 7th

cervical vertebrae scored highest in mtDNA. Both of these elements also show some

signs of cellular activity and these results may indicate that the surfaces of the bones were

undergoing osteoclastic resorption in preparation for osteoblastic matrix deposition. Or

they be indicative of the early stages of osteoblastic activity when the cells are still

actively depositing new tissue.

In either case, the results of this study indicate that vertebral elements may be useful

targets of DNA extraction if there is visible evidence of cellular activity. In the spine, this

usually presents as osteophytic development of some kind but will be highly dependent

on the individual person. Since most spinal conditions associated with osteophytic

development (OA, diffuse idiopathic skeletal hyperostosis, ankylosing spondylitis, etc.)

begin in the lumbar region, if mtDNA is sought, it may be wise to target elements in the

thoracic or even cervical spines at the earliest stages of whichever condition is diagnosed

or suspected. Further investigation involving definite diagnosis of degenerative spinal

140

conditions such as those mentioned above, as well as cases of spinal stenosis, scoliosis

and kyphosis, is definitely warranted and may provide valuable insight into the effects of

interactions between osteoblasts and osteoclasts on a macroscopic scale.

The os coxa of the individual was the only group that yielded generally higher DNA

results on the left side than on the right. Cortical tissue also generally outperformed

trabecular, although the sample taken from the ischio-pubic ramus did quite well. This

study received the highest nuclear DNA from:

1. Cortical tissue from the left auricular surface

2. Cortical tissue from the left posterior inferior spine

3. Cortical tissue from the right pubic symphysis

4. Trabecular tissue from the ischio-pubic ramus

5. Cortical tissue from the left pubic symphysis

Bilaterally, the iliac crest yielded the highest levels of mtDNA. Given that the iliac

crest is a site of a major muscle attachment, it is not surprising that there might be higher

levels of osteoclast activity than at other regions. Cortical tissue from the pubic

symphysis on both sides was the second highest site of mtDNA.

Since the auricular surface scored highest in nuclear DNA and yielded 0.00 in

mtDNA (bilaterally), it may be surmised that osteoblastic populations, very possibly in

the form of bone-lining cells, are the predominant cell type, at least, in this individual at

the time of death. Correlating this data with known in vivo remodeling that contributes to

forensic age-at-death estimations, it may be assumed that the auricular surface is a

reliable source of DNA, regardless of whether the predominant cell type is osteoblastic or

141

osteoclastic in nature. Bone tissue from the adjacent sacro-iliac joint of the sacrum

yielded significantly lower nuclear DNA and higher mtDNA. In fact, the right sacro-iliac

joint surface yielded the highest mtDNA across the entire sacrum. In this individual, the

transverse line between S1 and S2 was clearly visible and so was targeted as another

sampling site. Interestingly, that sample produced 0.00 mtDNA but performed adequately

in nuclear DNA extraction. With the proximity to the auricular surface, and the data it

provided, it is possible that the trabecular tissue which performed well was the site of

osteoclastogenesis at the time of death.

Sesamoid Bones

The largest of the sesamoid bones-the patellas-were mirror images of each other

in terms of DNA results, although in both cases, the trabecular samples outperformed the

cortical tissue. The right patella yielded decent nuclear DNA and very little mtDNA. The

left side was the opposite. While it is not uncommon to recover at least one patella in a

forensic archaeological recovery, if there are other sampling options, they should be

given priority.

The smaller sesamoids are classified as accessory ossicles of the hands and feet

and are not found in all individuals. Additionally, for a variety of reasons, they are rarely

recovered in archaeological contexts. While they actually performed reasonably well in

DNA extraction, one of the three that was used failed to yield any mtDNA. Another

yielded levels that was among the higher levels found the post-crania. The cell types

involved with sesamoid bones are predominantly osteoblastic since the bones themselves

derive from the ligamentous tissue of the hands and feet in order to increase stability. For

142

this reason, they are usually found in people of advancing age and those who engage in

strenuous physical activity. Osteoclasts may be present if the bone has been present long

enough to necessitate an articulation point with an adjoining element; and this is almost

certainly the reason for the higher mtDNA results. However, whether osteoclasts will be

present in sesamoids is difficult to determine. Therefore, if mtDNA is sought, it would be

advisable to sample elsewhere. Another consideration for the exclusion of sesamoids as a

source of DNA is that, due to their very small size, the entire bone usually has to be

destroyed in order to procure enough sample for extraction. They are also too small to

hold while attempting to drill the way the other elements were. Sampling for the present

study required placing each sesamoid in a UV-sterilized plastic bag, placing a thick layer

of paper towel around the bag and smashing the bone with a hammer. For these reasons,

sesamoids should be a last resort.

DNA SURVIVABILITY

While it is difficult to conclusively say which tissue type preserves nuclear

(autosomal and Y chromosomal) DNA or mtDNA more effectively, the results of this

study do indicate concurrence with other studies which have suggested that cementum in

teeth and denser cortical layer of bone protect DNA molecules more effectively than

softer tissues (CITE Antinick & Foran, Higgins, Edson, et al). Additionally, the overall

results of the study indicate strong support for the hypothesis that knowledge of cellular

morphology and activity do inform choices of where to sample for the different types of

DNA that may be needed or desired in forensic investigations.

143

In their 2015 study, Higgins, et al. state that higher mtDNA rates were found from

softer dentine material in the roots of teeth. But they fail to adequately address the reason.

Is it truly because of the tissue itself? Or is it because there were more mitochondria in

the nerve-rich dentine, than in the cementum, to begin with? Similarly, in 2014, Antinick

and Foran published a study of differential mtDNA and nuclear DNA from an inter-and

intra-element study of bovine and porcine bone. The main issues with this are that 1-rates

of skeletal maturation and degeneration in quadrupeds are known to vary from obligate

bipeds due to differential rates of hormonal changes, overall lifetime homeostatic levels

of hormones involved with various developmental and reproductive strategies, and 2-

obvious differences in functional morphology. Indeed, the construction of lamellae, as

well as primary and secondary osteons, are not the same between humans and non-human

mammals. Furthermore, their methods of maceration and environmental exposure do not

serve as adequate proxies for true forensic, or even archaeological, conditions that surely

affect DNA of all types in human skeletal tissue.

A 2009 study by Edson, et al. compared mtDNA extraction success rates between

the various commonly attempted elements of the cranium. Unsurprisingly, they found

statistically significantly higher success rates from the petrous portion of the temporal

bone than any other. This is consistent with the hypothesis that the hard cortical layers of,

in that case, the tympanic region and internal auditory canal, will often protect the

internal trabecular tissue and the endogenous DNA therein. In the current study, this can

be seen most clearly in the results from the sampling site at the proximal end of the

femur, for in the trabecular tissue of the femoral head (accessed via the fovea capitis), as

well as in the petrous, vascularization occurs primarily through limited number of

144

canaliculi only. Whereas, in trabecular tissue taken from the mid-diaphyseal regions of

the long bones, vascularization occurs through the much larger nutrient foramen,

haversian canals, and canaliculi, the endogenous DNA is therefore more susceptible to

autolytic destruction by nucleases or other enzymes or bacteria. Therefore, it is not

merely a question of which tissue type is best for DNA sampling? Establishing the

optimal sites for DNA extractive sampling requires a thorough understanding that DNA

survivability depends on a myriad of intrinsic factors, such as starting cellular

populations, functional morphology, and homeostatic processes.

Extrinsic, or taphonomic, factors certainly play a role in DNA survivability.

While typical environmental factors (such as soil type and pH, exposure to sunlight, etc.)

played a less prominent role in the current study, the effects of maceration techniques

were prevalent. Maceration was carried out using mechanical removal of the flesh,

followed by submersion in a sub-boiling solution of water and enzyme-based bleach. One

benefit of using Y-chromosomal analysis in this study is its small size and relative

fragility, and thus, it is somewhat illustrative of damage patterns, since the qPCR kit used

in this project only amplifies one fragment size from the Y- chromosome. Unsurprisingly,

where there was low autosomal data, there was also low Y-chromosome data and vice

versa. Though it is not possible to assert with confidence that maceration was the true

cause of low DNA scores in some elements (the lack of mtDNA in the lumbar vertebrae,

for example), there is little doubt that it had some effect. Several elements of the

vertebrae (C3-5, T1, 5, 7, 8, 10, and 12 and sacral vertebrae S2) all scored very low or

0.00 in Y-chromosomal data. Visual inspection of some of these elements, though not all,

shows extreme bleaching and tissue damage.

145

Image 8- The thoracic vertebrae. Note the damage to T7, T9, and T10.

And some elements, like the os coxae, that were quite fragile to the touch and

appeared to be heavily damaged, performed surprisingly well. The bottom line, from the

results of this study, are that 1-the maceration techniques employed on the remains from

the individual in this study most likely did have some effect 2-that DNA extraction

sampling should either occur prior to maceration or 3-avoid sites that are obviously over-

processed. Further study using the sampling method detailed in this study on remains

with different taphonomic histories is needed.

Extraction Method as an Influence on Low Copy Number

The DNA extraction and purification kits that were used in this project were

selected for their specificity in extracting DNA from bone. Bone is known as a very

challenging substance from which to get DNA due to a number of factors. These include

146

the presence of PCR inhibitors such as fat, as well as factors that are difficult to identify,

much less quantify, such as mineralization of the tissue itself. The latter can complicate

extraction if the mineralization of the bone is too high, thus a main reason for ATR/FTIR

testing in this study. But it can also complicate matters if the mineralization of the bone is

abnormally low by allowing the demineralization chemicals, like EDTA or even water,

too much direct access to the DNA molecule itself. Any of these factors can have a

negative impact of the starting copy number of the DNA and can thus impact the starting

molecular weight data in qPCR analyses and the ability of the primers used in fragment

analysis to anneal to the DNA. Usually, damage patterns in DNA can be seen in which of

the fragment sizes amplify preferentially. Lack of amplification of small fragments (i.e.,

those with small base pair length like the Y- chromosome fragment targeted in this study)

in the data is typically a good indication of damage to the endogenous DNA.

Additionally, there is significant debate regarding the exact process and extent of small

fragment size loss during extraction (162,181). Researchers do agree, however, that some

DNA will inevitably be lost during extraction regardless of the method used.

FRAGMENT ANALYSIS/ STR PROFILE DEVELOPMENT

Of the top performing samples (those that generated at least 21 out of 23 STR

loci), 67% were cortical tissue samples, 25% were trabecular tissue samples, and the

remaining were osteonal tissue samples. They represent articulation areas including the

ankle (the sustentaculum tali and the calcaneal tuberosity of the calcaneus and the sulcus

tali of the talus), the knee, the elbow, and the hip as well as sites of major muscle

attachment (the bicipital groove and the iliac crest), known homeostatic processes such as

147

the pubic symphysis, and sites of direct vascularization (the nutrient foramen of the

femur). Midline samples that performed well were the sternum which was undergoing

visible changes and the ribs where ante- mortem trauma had occurred. It is interesting to

note that the left petrous portion of the temporal bone produced a full STR profile while

the right side produced 0 loci. Also of note, is that the cortical sample taken from the

superior articular facets of the atlas (C1) produced a full STR profile. This element is

often recovered and may prove useful in cases when the cranium is too fragmented to use

for sampling from the petrous portion of the temporal bone(s). As the name implies, the

samples were taken from the sites where the element articulates with the occipital bone of

the cranium.

Analysis of the results shows that the three highest- scoring sites in starting

molecular weight of autosomal DNA in qPCR quantitation were among those that failed

to generate any STR loci. This is representative of the stochasticity of DNA analysis in

general and can be especially confounding when attempting to generate a usable profile

from bone tissue with an unknown degree of taphonomic damage. Equitable examination,

however, will also reveal that some of the lower qPCR scores still yielded full or nearly

full DTR profiles. Despite this, the general trend remains that higher starting molecular

weight will generally indicate better chances of full STR profile production. And most of

the top performing samples in STR profile production were among the top performing

sites in qPCR quantitation, as well and vice versa.

THE “HEAT MAP”

148

The concept of designing a “heat map” that can be used by anyone interested in

building a genetic profile from bone tissue has changed somewhat over the course of the

study. The data that has been produced has helped solidify how this may be done and

how it might be best approached. This study has demonstrated that there are optimal and

sub- optimal sampling sites on the human post- cranial skeleton and it has given some

ideas on how to go about finding these sites. Developing a way to make it easier for DNA

analysts to locate the sites and how best to source the bone tissue once the sites have been

located is a very real possibility.

Using the both the qPCR data for autosomal, Y chromosome, and mtDNA as well

as the STR profile construction data, a website may be built that allows analysts to see

what sampling and DNA analysis methods have been the most successful for the

elements they have to work with. Color coding the sampling site names (using red for the

highest data, and sliding through orange, yellow, green and blue for the lowest data) and

then embedding specific information within each site that can be accessed with the click

of a mouse will allow investigators to quickly and reliably determine where and how they

should sample. This will lead to a marked increase in efficiency since analysts will no

longer have to guess or do extensive research prior to sampling and it will also decrease

costs associated with excessive sampling due to uncertainty or even failures arising from

sub- optimal sampling site decisions.

This information will be useful from an ethical standpoint too, as it will help

decrease the amount of destruction required for DNA analysis. Whether the remains are

forensic, historic, or archaeological, no one wants to damage them any more than is

absolutely essential. The sampling method used in this study, plus the results that indicate

149

the locations of cellular populations in bone tissue, will allow investigators from across

biological anthropology and criminalistics to target and utilize minimal amounts of

irreplaceable bone tissue to maximum effect.

Since science must maintain replicability, and since this study could only begin

the process, the ‘heat map” webpage can be open- source, or partially open- source. This

will allow other investigators (with a subscription and login information) to add their

methods, data, and even notes. This will also lend a real aspect of transparency to the

endeavor. Certain security checks will have to be in place, but those issues will be

addressed in their time. For now, this study has shown that a deep understanding and

appreciation for the cellular contributions to bone growth and homeostatic maintenance

can and does offer a much more precise and accurate way to begin sampling for DNA

from the human post- cranial skeleton.

150

WORKS CITED

1. Antinick TC, Foran DR. Intra- and Inter-Element Variability in Mitochondrial and

Nuclear DNA from Fresh and Environmentally Exposed Skeletal Remains,.

Journal of Forensic Sciences 2019;64(1):88–97. https://doi.org/10.1111/1556-

4029.13843.

2. Misner LM, Halvorson AC, Dreier JL, Ubelaker DH, Foran DR. The Correlation

Between Skeletal Weathering and DNA Quality and Quantity*. Journal of

Forensic Sciences 2009;54(4):822–8. https://doi.org/10.1111/j.1556-

4029.2009.01043.x.

3. Emmons AL, Davoren J, DeBruyn JM, Mundorff AZ. Inter and intra-individual

variation in skeletal DNA preservation in buried remains. Forensic Science

International: Genetics 2019;:102193.

https://doi.org/10.1016/j.fsigen.2019.102193.

4. Mundorff A, Davoren JM. Examination of DNA yield rates for different skeletal

elements at increasing post mortem intervals. Forensic Science International:

Genetics 2014;8(1):55–63. https://doi.org/10.1016/j.fsigen.2013.08.001.

5. Mundorff AZ, Bartelink EJ, Mar‐Cash E. DNA Preservation in Skeletal Elements

from the World Trade Center Disaster: Recommendations for Mass Fatality

Management*,†. Journal of Forensic Sciences 2009;54(4):739–45.

https://doi.org/10.1111/j.1556-4029.2009.01045.x.

6. Geršak ŽM, Zupanič Pajnič I, Črešnar M, Zupanc T. Determination of DNA yield

rates in six different skeletal elements in ancient bones. Forensic Science

International: Genetics Supplement Series 2019.

https://doi.org/10.1016/j.fsigss.2019.09.047.

7. Goodwin WH. The use of forensic DNA analysis in humanitarian forensic action:

The development of a set of international standards. Forensic Science International

2017;278:221–7. https://doi.org/10.1016/j.forsciint.2017.07.002.

8. Obal M, Zupanič Pajnič I, Gornjak Pogorelc B, Zupanc T. Different skeletal

elements as a source of DNA for genetic identification of Second World War

victims. Forensic Science International: Genetics Supplement Series 2019.

https://doi.org/10.1016/j.fsigss.2019.09.013.

9. Higgins D, Rohrlach AB, Kaidonis J, Townsend G, Austin JJ. Differential Nuclear

and Mitochondrial DNA Preservation in Post-Mortem Teeth with Implications for

Forensic and Ancient DNA Studies. PLOS ONE 2015;10(5):e0126935.

https://doi.org/10.1371/journal.pone.0126935.

151

10. Andronowski JM, Mundorff AZ, Pratt IV, Davoren JM, Cooper DML. Evaluating

differential nuclear DNA yield rates and osteocyte numbers among human bone

tissue types: A synchrotron radiation micro-CT approach. Forensic Science

International: Genetics 2017;28:211–8.

https://doi.org/10.1016/j.fsigen.2017.03.002.

11. Akchurin T, Aissiou T, Kemeny N, Prosk E, Nigam N, Komarova SV. Complex

Dynamics of Osteoclast Formation and Death in Long-Term Cultures. PLoS One

2008;3(5). https://doi.org/10.1371/journal.pone.0002104.

12. Andronowski JM, Crowder CM, Martinez MS. Recent advancements in the

analysis of bone microstructure: new dimensions in forensic anthropology.

Forensic Sciences Research 2018;3(4):294–309.

https://doi.org/10.1080/20961790.2018.1483294.

13. Baron R. Molecular mechanisms of bone resorption An update. Acta

Orthopaedica Scandinavica 1995;66(sup266):66–70.

https://doi.org/10.3109/17453679509157650.

14. Bilezikian JP, Raisz LG, Martin TJ. Principles of Bone Biology. Academic Press,

2008.

15. Buckwalter JA, Glimcher MJ, Cooper RR, Recker R. Bone Biology. jbjs

1995;77:1256–75.

16. Florencio-Silva R, Sasso GR da S, Sasso-Cerri E, Simões MJ, Cerri PS. Biology

of Bone Tissue: Structure, Function, and Factors That Influence Bone Cells.

BioMed Research International. 2015.

https://www.hindawi.com/journals/bmri/2015/421746/abs/ (accessed September

19, 2019).

17. Kalfas IH. Principles of bone healing. Neurosurgical Focus 2001;10(4):1–4.

https://doi.org/10.3171/foc.2001.10.4.2.

18. Manolagas SC, Parfitt AM. For whom the bell tolls: Distress signals from long-

lived osteocytes and the pathogenesis of metabolic bone diseases. Bone

2013;54(2):272–8. https://doi.org/10.1016/j.bone.2012.09.017.

19. Ortner DJ. Identification of Pathological Conditions in Human Skeletal Remains.

Academic Press, 2003.

20. Rodan GA. Introduction to bone biology. Bone 1992;13:S3–6.

https://doi.org/10.1016/S8756-3282(09)80003-3.

21. Roodman GD. Advances in Bone Biology: The Osteoclast. Endocr Rev

1996;17(4):308–32. https://doi.org/10.1210/edrv-17-4-308.

152

22. Skerry TM. The response of bone to mechanical loading and disuse: Fundamental

principles and influences on osteoblast/osteocyte homeostasis. Archives of

Biochemistry and Biophysics 2008;473(2):117–23.

https://doi.org/10.1016/j.abb.2008.02.028.

23. Vaananen HK, Zhao H, Mulari M, Halleen JM. The cell biology of osteoclast

function. J Cell Sci 2000;113(3):377–81.

24. Leskovar T, Zupanič Pajnič I, Geršak ŽM, Jerman I, Črešnar M. ATR-FTIR

spectroscopy combined with data manipulation as a pre-screening method to assess

DNA preservation in skeletal remains. Forensic Science International: Genetics

2020;44:102196. https://doi.org/10.1016/j.fsigen.2019.102196.

25. Leskovar T, Zupanič Pajnič I, Jerman I, Črešnar M. Separating forensic, WWII,

and archaeological human skeletal remains using ATR-FTIR spectra. Int J Legal

Med 2020;134(2):811–21. https://doi.org/10.1007/s00414-019-02079-0.

26. Leskovar T, Pajnič IZ, Jerman I, Črešnar M. Preservation state assessment and

post-mortem interval estimation of human skeletal remains using ATR-FTIR

spectra. Australian Journal of Forensic Sciences 2020;0(0):1–22.

https://doi.org/10.1080/00450618.2020.1836254.

27. Amadasi A, Cappella A, Cattaneo C, Cofrancesco P, Cucca L, Merli D, et al.

Determination of the post mortem interval in skeletal remains by the comparative

use of different physico-chemical methods: Are they reliable as an alternative to

14C? HOMO 2017;68(3):213–21. https://doi.org/10.1016/j.jchb.2017.03.006.

28. Boskey AL, Mendelsohn R. Infrared analysis of bone in health and disease. JBO

2005;10(3):031102. https://doi.org/10.1117/1.1922927.

29. Chan KLA, Kazarian SG. Attenuated total reflection Fourier-transform infrared

(ATR-FTIR) imaging of tissues and live cells. Chem Soc Rev 2016;45(7):1850–

64. https://doi.org/10.1039/C5CS00515A.

30. Fredericks JD, Bennett P, Williams A, Rogers KD. FTIR spectroscopy: A new

diagnostic tool to aid DNA analysis from heated bone. Forensic Science

International: Genetics 2012;6(3):375–80.

https://doi.org/10.1016/j.fsigen.2011.07.014.

31. The National Missing and Unidentified Persons System (NamUs). NamUs.gov.

https://www.namus.gov/ (accessed October 10, 2019).

32. 2018 NCIC Missing Person and Unidentified Person Statistics. Federal Bureau of

Investigation. https://www.fbi.gov/file-repository/2018-ncic-missing-person-and-

unidentified-person-statistics.pdf/view (accessed October 10, 2019).

153

33. Pearson OM, Lieberman DE. The aging of Wolff’s “law”: Ontogeny and

responses to mechanical loading in cortical bone. American Journal of Physical

Anthropology 2004;125(S39):63–99. https://doi.org/10.1002/ajpa.20155.

34. Gegenbaur C. About the formation of bone tissue. Jena Journal of Natural

Sciences 1864;1:343–60.

35. Kölliker A. Die normale Resorption des Knochengewebes und ihre Bedeuting für

die Entstehung der typischen Knochenformen. F.C.W. Vogel, 1873.

36. Foote JS. A Contribution to the Comparative Histology of the Femur. Smithsonian

institution, 1916.

37. Martin TJ. Historically significant events in the discovery of

RANK/RANKL/OPG. World J Orthop 2013;4(4):186–97.

https://doi.org/10.5312/wjo.v4.i4.186.

38. Bonewald LF. The amazing osteocyte. Journal of Bone and Mineral Research

2011;26(2):229–38. https://doi.org/10.1002/jbmr.320.

39. Arnold JS, Jee WSS. Bone growth and osteoclastic activity as indicated by

radioautographic distribution of plutonium. American Journal of Anatomy

1957;101(3):367–417. https://doi.org/10.1002/aja.1001010303.

40. Cartier P. Biochemistry of ossification. Role of adenosine triphosphoric acid in the

mineralisation of ossifiable cartilage. Journal de Physiologie 1951;43:677–8.

41. Takahashi N, Akatsu T, Udagawa N, Sasaki T, Yamaguchi A, Moseley JM, et al.

OSTEOBLASTIC CELLS ARE INVOLVED IN OSTEOCLAST FORMATION.

Endocrinology 1988;123(5):2600–2. https://doi.org/10.1210/endo-123-5-2600.

42. Chambers TJ. The pathobiology of the osteoclast. Journal of Clinical Pathology

1985;38(3):241–52. https://doi.org/10.1136/jcp.38.3.241.

43. Martin TJ, Sims NA. Osteoclast-derived activity in the coupling of bone formation

to resorption. Trends in Molecular Medicine 2005;11(2):76–81.

https://doi.org/10.1016/j.molmed.2004.12.004.

44. Sims NA, Gooi JH. Bone remodeling: Multiple cellular interactions required for

coupling of bone formation and resorption. Seminars in Cell & Developmental

Biology 2008;19(5):444–51. https://doi.org/10.1016/j.semcdb.2008.07.016.

45. Gould SJ, Gold SJ, Gould TAAP of ZSJ. The Mismeasure of Man. W. W. Norton

& Company, 1996.

46. Butler J. Fundamentals of Forensic DNA Typing. USA: Academic Press, 2010.

154

47. Aubin J, Triffitt J. Mesenchymal Stem Cells and Osteoblast Differentiation.

Principles of Bone Biology. Academic Press, 2002;59–81.

48. Christensen AM, Smith MA, Thomas RM. Validation of X-Ray Fluorescence

Spectrometry for Determining Osseous or Dental Origin of Unknown Material*.

Journal of Forensic Sciences 2012;57(1):47–51. https://doi.org/10.1111/j.1556-

4029.2011.01941.x.

49. Zimmerman HA, Meizel-Lambert CJ, Schultz JJ, Sigman ME. Chemical

Differentiation of Osseous, Dental, and Non-skeletal Materials in Forensic

Anthropology using Elemental Analysis. Science & Justice 2015;55(2):131–8.

https://doi.org/10.1016/j.scijus.2014.11.003.

50. Ubelaker DH. The forensic evaluation of burned skeletal remains: A synthesis.

Forensic Science International 2009;183(1):1–5.

https://doi.org/10.1016/j.forsciint.2008.09.019.

51. Tersigni-Tarrant MT, Shirley N. Forensic Anthropology. 1st ed. Boca Raton, FL,

USA: CRC Press, 2013.

52. Byers S. Introduction to Forensic Anthropology - Steven N. Byers - Google

Books. 5th ed. 2017.

53. Burns KR. Forensic Anthropology Training Manual. Routledge, 2015.

54. Komar DA, Buikstra J. Forensic Anthropology: Contemporary Theory and

Practice. 1st ed. Oxford University Press, 2008.

55. Dirkmaat DC, Cabo LL, Ousley SD, Symes SA. New perspectives in forensic

anthropology. American Journal of Physical Anthropology 2008;137(S47):33–52.

https://doi.org/10.1002/ajpa.20948.

56. Brickley M, Ives R. The Bioarchaeology of Metabolic Bone Disease. 1st ed.

Academic Press, 2008.

57. Blau S. How traumatic: a review of the role of the forensic anthropologist in the

examination and interpretation of skeletal trauma. Australian Journal of Forensic

Sciences 2017;49(3):261–80. https://doi.org/10.1080/00450618.2016.1153715.

58. Passalacqua NV, Fenton TW. Developments in Skeletal Trauma: Blunt-Force

Trauma. A Companion to Forensic Anthropology. John Wiley & Sons, Ltd,

2012;400–11.

59. Love JC. Sharp force trauma analysis in bone and cartilage: A literature review.

Forensic Science International 2019;299:119–27.

https://doi.org/10.1016/j.forsciint.2019.03.035.

155

60. Sims NA, Vrahnas C. Regulation of cortical and trabecular bone mass by

communication between osteoblasts, osteocytes and osteoclasts. Archives of

Biochemistry and Biophysics 2014;561:22–8.

https://doi.org/10.1016/j.abb.2014.05.015.

61. Galloway A, Zephro L. Skeletal Trauma Analysis of the Lower Extremity. In:

Rich J, Dean DE, Powers RH, editors. Forensic Medicine of the Lower Extremity:

Human Identification and Trauma Analysis of the Thigh, Leg, and Foot. Totowa,

NJ: Humana Press, 2005;253–77.

62. Wedel VL, Galloway A. BROKEN BONES: Anthropological Analysis of Blunt

Force Trauma (2nd Ed.). Charles C Thomas Publisher, 2013.

63. Christensen AM, Rickman JM, Berryman HE. Forensic fractography of bone:

fracture origins from impacts, and an improved understanding of the failure

mechanism involved in beveling. 2021. https://doi.org/10.5744/fa.2020.0041.

64. Lowe JS, Anderson PG. Chapter 13 - Musculoskeletal System. In: Lowe JS,

Anderson PG, editors. Stevens & Lowe’s Human Histology (Fourth Edition)

(Fourth Edition). Philadelphia: Mosby, 2015;239–62.

65. Carter DR, Fyhrie DP, Whalen RT. Trabecular bone density and loading history:

Regulation of connective tissue biology by mechanical energy. Journal of

Biomechanics 1987;20(8):785–94. https://doi.org/10.1016/0021-9290(87)90058-3.

66. Cunningham C, Scheuer L, Black S. Developmental Juvenile Osteology.

Academic Press, 2016.

67. Franz‐Odendaal TA, Hall BK, Witten PE. Buried alive: How osteoblasts become

osteocytes. Developmental Dynamics 2006;235(1):176–90.

https://doi.org/10.1002/dvdy.20603.

68. Burr DB. Repair Mechanisms for Microdamage in Bone. Journal of Bone and

Mineral Research 2014;29(12):2534–6. https://doi.org/10.1002/jbmr.2366.

69. Parfitt AM. Bone- forming cells in clinical conditions. Bone: A Treatise.

Caldwell, NJ: Telford, 1990;351–429.

70. Bourne G. The Biochemistry and Physiology of Bone. 2nd ed. Elsevier, 2012.

71. Martin RB. Does osteocyte formation cause the nonlinear refilling of osteons?

Bone 2000;26(1):71–8. https://doi.org/10.1016/S8756-3282(99)00242-2.

72. Marotti G, Ferretti M, Remaggi F, Palumbo C. Quantitative evaluation on

osteocyte canalicular density in human secondary osteons. Bone 1995;16(1):125–

8. https://doi.org/10.1016/8756-3282(95)80022-I.

156

73. Dallas SL, Bonewald LF. Dynamics of the Transition from Osteoblast to

Osteocyte. Ann N Y Acad Sci 2010;1192:437–43. https://doi.org/10.1111/j.1749-

6632.2009.05246.x.

74. Palumbo C, Palazzini S, Mariotti G. Morphological Study of Intercellular

Junctions during Osteocyte Differentiation. Bone 1990;(11):401–6.

75. Candaliere GA, Liu F, Aubin JE. Individual osteoblasts in the developing calvaria

express different gene repertoires. Bone 2001;28:351–61.

76. Liu K-Z, Jackson M, Sowa MG, Ju H, Dixon IMC, Mantsch HH. Modification of

the extracellular matrix following myocardial infarction monitored by FTIR

spectroscopy. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease

1996;1315(2):73–7. https://doi.org/10.1016/0925-4439(95)00118-2.

77. Frost HM. Bone Remodeling Dynamics. 1963;(CR Lam ed):65–75.

78. Pinhasi R, Fernandes D, Sirak K, Novak M, Connell S, Alpaslan-Roodenberg S, et

al. Optimal Ancient DNA Yields from the Inner Ear Part of the Human Petrous

Bone. PLOS ONE 2015;10(6):e0129102.

https://doi.org/10.1371/journal.pone.0129102.

79. Pinhasi R, Fernandes DM, Sirak K, Cheronet O. Isolating the human cochlea to

generate bone powder for ancient DNA analysis. Nature Protocols

2019;14(4):1194–205. https://doi.org/10.1038/s41596-019-0137-7.

80. de Souza Faloni AP, Schoenmaker T, Azari A, Katchburian E, Cerri PS, de Vries

TJ, et al. Jaw and Long Bone Marrows Have a Different Osteoclastogenic

Potential. Calcif Tissue Int 2011;88(1):63–74. https://doi.org/10.1007/s00223-010-

9418-4.

81. Davenport CB. Responsive Bone. Proceedings of the American Philosophical

Society 1941;84(1):65–70.

82. Hancox NM. The Osteoclast. The Biochemistry and Physiology of Bone. Elsevier,

2012.

83. Katsimbri P. The biology of normal bone remodelling. European Journal of

Cancer Care 2017;26(6):e12740. https://doi.org/10.1111/ecc.12740.

84. Miloš A, Selmanović A, Smajlović L, Huel RLM, Katzmarzyk C, Rizvić A, et al.

Success Rates of Nuclear Short Tandem Repeat Typing from Different Skeletal

Elements. Croat Med J 2007;48(4):486–93.

85. Edson SM, Christensen AF, Barritt SM, Meehan A, Leney MD, Finelli LN.

Sampling of the cranium for mitochondrial DNA analysis of human skeletal

remains. Forensic Science International: Genetics Supplement Series

2009;2(1):269–70. https://doi.org/10.1016/j.fsigss.2009.09.029.

157

86. Marks SC, Odgren PR. Chapter 1 - Structure and Development of the Skeleton.

In: Bilezikian JP, Raisz LG, Rodan GA, editors. Principles of Bone Biology

(Second Edition). San Diego: Academic Press, 2002;3–15.

87. White T, Folkens P. The Human Bone Manual. 4th ed. Elsevier Academic Press,

2005.

88. Brundin M, Figdor D, Sundqvist G, Sjögren U. DNA Binding to Hydroxyapatite:

A Potential Mechanism for Preservation of Microbial DNA. Journal of

Endodontics 2013;39(2):211–6. https://doi.org/10.1016/j.joen.2012.09.013.

89. Ambrose SH, Krigbaum J. Bone chemistry and bioarchaeology. Journal of

Anthropological Archaeology 2003;22(3):193–9. https://doi.org/10.1016/S0278-

4165(03)00033-3.

90. Olszta MJ, Cheng X, Jee SS, Kumar R, Kim Y-Y, Kaufman MJ, et al. Bone

structure and formation: A new perspective. Materials Science and Engineering: R:

Reports 2007;58(3):77–116. https://doi.org/10.1016/j.mser.2007.05.001.

91. Jeffreys AJ, Wilson V, Thein SL. Individual specific “fingerprints” of human

DNA. Nature 1985;316.

92. Coble MD, Butler JM. Characterization of New MiniSTR Loci to Aid Analysis of

Degraded DNA. Journal of Forensic Sciences 2005;50(1):1–11.

https://doi.org/10.1520/JFS2004216.

93. Shewale J, Liu R, editors. Forensic DNA Analysis: Current Practices and

Emerging Technologies. 1st ed. USA: Taylor & Francis, 2014.

94. Pilli E, Berti A, editors. Forensic DNA Analysis: Technological Developments

and Innovaitve Applications. 1st ed. CRC Press, 2021.

95. Steffen CR, Kiesler KM, Borsuk LA, Vallone PM. Beyond the STRs: A

comprehensive view of current forensic DNA markers characterized in the PCR-

based DNA profiling standard SRM 2391D. Forensic Science International:

Genetics Supplement Series 2017;6:e426–7.

https://doi.org/10.1016/j.fsigss.2017.09.152.

96. Lee H-J, Lee JW, Jeong SJ, Park M. How many single nucleotide polymorphisms

(SNPs) are needed to replace short tandem repeats (STRs) in forensic applications?

Int J Legal Med 2017;131(5):1203–10. https://doi.org/10.1007/s00414-017-1564-z.

97. Hwa H-L, Lin C-P, Huang T-Y, Kuo P-H, Hsieh W-H, Lin C-Y, et al. A panel of

130 autosomal single-nucleotide polymorphisms for ancestry assignment in five

Asian populations and in Caucasians. Forensic Sci Med Pathol 2017;13(2):177–87.

https://doi.org/10.1007/s12024-017-9863-8.

158

98. Romanini C, Catelli ML, Borosky A, Pereira R, Romero M, Salado Puerto M, et

al. Typing short amplicon binary polymorphisms: Supplementary SNP and Indel

genetic information in the analysis of highly degraded skeletal remains. Forensic

Science International: Genetics 2012;6(4):469–76.

https://doi.org/10.1016/j.fsigen.2011.10.006.

99. Phillips C, Manzo L, de la Puente M, Fondevila M, Lareu MV. The MASTiFF

panel—a versatile multiple-allele SNP test for forensics. Int J Legal Med

2020;134(2):441–50. https://doi.org/10.1007/s00414-019-02233-8.

100. Alonso A, Barrio PA, Müller P, Köcher S, Berger B, Martin P, et al. Current state-

of-art of STR sequencing in forensic genetics. ELECTROPHORESIS

2018;39(21):2655–68. https://doi.org/10.1002/elps.201800030.

101. Moretti TR, Moreno LI, Smerick JB, Pignone ML, Hizon R, Buckleton JS, et al.

Population data on the expanded CODIS core STR loci for eleven populations of

significance for forensic DNA analyses in the United States. Forensic Science

International: Genetics 2016;25:175–81.

https://doi.org/10.1016/j.fsigen.2016.07.022.

102. Mulero JJ, Hennessy LK. Next generation STR Genotyping Kits for Forensic

Applications. Forensic DNA Analysis: Current Practices and Emerging

Technologies. CRC Press, 2014;163–80.

103. Parsons TJ, Huel RML, Bajunović Z, Rizvić A. Large scale DNA identification:

The ICMP experience. Forensic Science International: Genetics 2019;38:236–44.

https://doi.org/10.1016/j.fsigen.2018.11.008.

104. Barbisin M, Shewale J. Assessment of DNA Extracted from Forensic Samples

Prior to Genotyping. Forensic DNA Analysis: Current Practices and Emerging

Technologies. CRC Press, 2014;101–28.

105. Stray J, Shewale J. Extraction of DNA from Human Remains. Forensic DNA

Analysis: current practices and emerging technologies. CRC Press, 2014.

106. Ballantyne KN, Keerl V, Wollstein A, Choi Y, Zuniga SB, Ralf A, et al. A new

future of forensic Y-chromosome analysis: Rapidly mutating Y-STRs for

differentiating male relatives and paternal lineages. Forensic Science International:

Genetics 2012;6(2):208–18. https://doi.org/10.1016/j.fsigen.2011.04.017.

107. Kayser M. Forensic use of Y-chromosome DNA: a general overview. Human

Genetics 2017;136(5):621–35. https://doi.org/10.1007/s00439-017-1776-9.

108. Yang X, Zhang X, Zhu J, Chen L, Liu C, Feng X, et al. Genetic analysis of 19 X

chromosome STR loci for forensic purposes in four Chinese ethnic groups.

Scientific Reports 2017;7(1):42782. https://doi.org/10.1038/srep42782.

159

109. Susana J-M, Celia G, Ester L, Miriam B, Eladio B, de Pancorbo Marian M.

Genetic analysis of 12 X-chromosomal STRs an autochthonous population of

Southeast Spain. Forensic Science International: Genetics Supplement Series

2019;7(1):482–4. https://doi.org/10.1016/j.fsigss.2019.10.060.

110. Cann R, Stoneking M, Wilson AC. Mitochondrial DNA and human evolution.

Nature 1987;325(January):31–6.

111. Lewin R. The Unmasking of Mitochondrial Eve. Science 1987;238(4823):24–6.

112. Lassen C, Hummel S, Herrmann B. Comparison of DNA extraction and

amplification from ancient human bone and mummified soft tissue. International

Journal of Legal Medicine 1994;107(3):152–5.

https://doi.org/10.1007/BF01225603.

113. Latham KE, Madonna ME. DNA Survivability in Skeletal Remains. Manual of

Forensic Taphonomy. CRC Press, 2013;403–25.

114. Furtwängler A, Reiter E, Neumann GU, Siebke I, Steuri N, Hafner A, et al. Ratio

of mitochondrial to nuclear DNA affects contamination estimates in ancient DNA

analysis. Scientific Reports 2018;8(1):1–8. https://doi.org/10.1038/s41598-018-

32083-0.

115. Dabney J, Knapp M, Glocke I, Gansauge M-T, Weihmann A, Nickel B, et al.

Complete mitochondrial genome sequence of a Middle Pleistocene cave bear

reconstructed from ultrashort DNA fragments. Proceedings of the National

Academy of Sciences 2013;110(39):15758–63.

https://doi.org/10.1073/pnas.1314445110.

116. Knapp M. From a molecules’ perspective – contributions of ancient DNA research

to understanding cave bear biology. Historical Biology 2019;31(4):442–7.

https://doi.org/10.1080/08912963.2018.1434168.

117. Gibbons A. Who Were the Denisovans? Science 2011;333(6046):1084–7.

https://doi.org/10.1126/science.333.6046.1084.

118. Slon V, Hopfe C, Weiß CL, Mafessoni F, Rasilla M de la, Lalueza-Fox C, et al.

Neandertal and Denisovan DNA from Pleistocene sediments. Science

2017;356(6338):605–8. https://doi.org/10.1126/science.aam9695.

119. Rohland N, Hofreiter M. Ancient DNA extraction from bones and teeth. Nature

Protocols 2007;2(7):1756–62. https://doi.org/10.1038/nprot.2007.247.

120. Leney MD. Sampling Skeletal remains for ancient DNA: A measure of success.

Historical Archaeology 2006;40(3):31–49.

160

121. Götherström A, Collins MJ, Angerbjörn A, Lidén K. Bone preservation and DNA

amplification. Archaeometry 2002;44(3):395–404. https://doi.org/10.1111/1475-

4754.00072.

122. Gaytmenn R, Sweet D. Quantification of forensic DNA from various regions of

human teeth. J Forensic Sci 2003;48(3):622–5.

123. Pinchi V, Torricelli F, Nutini AL, Conti M, Iozzi S, Norelli G-A. Techniques of

dental DNA extraction: Some operative experiences. Forensic Science

International 2011;204(1):111–4. https://doi.org/10.1016/j.forsciint.2010.05.010.

124. Kostyukevich Y, Bugrova A, Chagovets V, Brzhozovskiy A, Indeykina M,

Vanyushkina A, et al. Proteomic and lipidomic analysis of mammoth bone by

high-resolution tandem mass spectrometry coupled with liquid chromatography.

European Journal of Mass Spectrometry 2018;24(6):411–9.

https://doi.org/10.1177/1469066718813728.

125. Cleland TP, Schroeter ER. A Comparison of Common Mass Spectrometry

Approaches for Paleoproteomics. J Proteome Res 2018;17(3):936–45.

https://doi.org/10.1021/acs.jproteome.7b00703.

126. Latham KE, Miller JJ. DNA recovery and analysis from skeletal material in

modern forensic contexts. Forensic Sciences Research 2019;4(1):51–9.

https://doi.org/10.1080/20961790.2018.1515594.

127. Steadman DW, DiAntonio LL, Wilson JJ, Sheridan KE, Tammariello SP. The

Effects of Chemical and Heat Maceration Techniques on the Recovery of Nuclear

and Mitochondrial DNA from Bone*. Journal of Forensic Sciences 2006;51(1):11–

7. https://doi.org/10.1111/j.1556-4029.2005.00001.x.

128. Lee EJ, Luedtke JG, Allison JL, Arber CE, Merriwether DA, Steadman DW. The

Effects of Different Maceration Techniques on Nuclear DNA Amplification Using

Human Bone. Journal of Forensic Sciences 2010;55(4):1032–8.

https://doi.org/10.1111/j.1556-4029.2010.01387.x.

129. Blain H, Chavassieux P, Portero-Muzy N, Bonnel F, Canovas F, Chammas M, et

al. Cortical and trabecular bone distribution in the femoral neck in osteoporosis

and osteoarthritis. Bone 2008;43(5):862–8.

https://doi.org/10.1016/j.bone.2008.07.236.

130. Gilbert MTP, Willerslev E, Hansen AJ, Barnes I, Rudbeck L, Lynnerup N, et al.

Distribution Patterns of Postmortem Damage in Human Mitochondrial DNA. The

American Journal of Human Genetics 2003;72(1):32–47.

https://doi.org/10.1086/345378.

131. Gates KS. An Overview of Chemical Processes That Damage Cellular DNA:

Spontaneous Hydrolysis, Alkylation, and Reactions with Radicals. Chem Res

Toxicol 2009;22(11):1747–60. https://doi.org/10.1021/tx900242k.

161

132. M. Fleming A, J. Burrows C. On the irrelevancy of hydroxyl radical to DNA

damage from oxidative stress and implications for epigenetics. Chemical Society

Reviews 2020;49(18):6524–8. https://doi.org/10.1039/D0CS00579G.

133. Junod CA, Pokines JT. Subaerial Weathering. Manual of Forensic Taphonomy.

CRC Press, 2013;287–314.

134. Edson SM, Root K, Kahline I, Dunn C, Trotter B, O’Rourke J. Flexibility in

Testing Skeletonized Remains for DNA Analysis Can Lead to increased Success:

Suggestions and Case Studies. In: Latham KE, Bartelink EJ, Finnegan M, editors.

New Perspectiveds in Forensic Human Skeletal Identification. United States:

Academic Press, 2018;141–56.

135. Allentoft ME, Collins M, Harker D, Haile J, Oskam CL, Hale ML, et al. The half-

life of DNA in bone: measuring decay kinetics in 158 dated fossils. Proceedings of

the Royal Society B: Biological Sciences 2012;279(1748):4724–33.

https://doi.org/10.1098/rspb.2012.1745.

136. Thomas M, Gilbert P. Postmortem Damage of Mitochondrial DNA. In: Bandelt

H-J, Macaulay V, Richards M, editors. Human Mitochondrial DNA and the

Evolution of Homo sapiens. Berlin, Heidelberg: Springer, 2006;91–115.

137. Vass AA. Beyond the grave – understanding human decomposition. Microbiology

Today 2001;28:190–3.

138. Briggs AW, Stenzel U, Johnson PLF, Green RE, Kelso J, Prufer K, et al. Patterns

of damage in genomic DNA sequences from a Neandertal. Proceedings of the

National Academy of Sciences 2007;104(37):14616–21.

https://doi.org/10.1073/pnas.0704665104.

139. Collins MJ, Nielsen–Marsh CM, Hiller J, Smith CI, Roberts JP, Prigodich RV, et

al. The survival of organic matter in bone: a review. Archaeometry

2002;44(3):383–94. https://doi.org/10.1111/1475-4754.t01-1-00071.

140. Darzynkiewicz Z, Juan G, Li X, Gorczyca W, Murakami T, Traganos F.

Cytometry in cell necrobiology: Analysis of apoptosis and accidental cell death

(necrosis). Cytometry 1997;27(1):1–20. https://doi.org/10.1002/(SICI)1097-

0320(19970101)27:1<1::AID-CYTO2>3.0.CO;2-L.

141. Ginolhac A, Rasmussen M, Gilbert MTP, Willerslev E, Orlando L. mapDamage:

testing for damage patterns in ancient DNA sequences. Bioinformatics

2011;27(15):2153–5. https://doi.org/10.1093/bioinformatics/btr347.

142. Fulton T. Setting Up an Ancient DNA Laboratory. In: Shapiro B, Hofreiter M,

editors. Ancient DNA. Totowa, NJ: Humana Press, 2012.

162

143. Paabo S. Ancient DNA: extraction, characterization, molecular cloning, and

enzymatic amplification. Proceedings of the National Academy of Sciences

1989;86(6):1939–43. https://doi.org/10.1073/pnas.86.6.1939.

144. Arismendi JL, Baker MALE, D P, Matteson KJ, D P. Effects of processing

techniques on the forensic DNA analysis of human skeletal remains. 5. Armed

Forces DNA Identification Laboratory. Armed Forces Institute of Pathology. 2004.

145. Duijs FE, Sijen T. A rapid and efficient method for DNA extraction from bone

powder. Forensic Science International: Reports 2020;2:100099.

https://doi.org/10.1016/j.fsir.2020.100099.

146. Kim TK. Understanding one-way ANOVA using conceptual figures. Korean J

Anesthesiol 2017;70(1):22–6. https://doi.org/10.4097/kjae.2017.70.1.22.

147. Miller RG. Beyond ANOVA: Basics of Applied Statistics. CRC Press, 1997.

148. Delacre M, Leys C, Mora YL, Lakens D. Taking Parametric Assumptions

Seriously: Arguments for the Use of Welch’s F-test instead of the Classical F-test

in One-Way ANOVA. International Review of Social Psychology 2019;32(1):13.

https://doi.org/10.5334/irsp.198.

149. Obertová Z, Stewart A, Cattaneo C, editors. Statistics and Probability in Forensic

Anthropology. 1st ed. Elsevier Academic Press, 2020.

150. Stahle L, Wold S. Analysis of variance (ANOVA). Chemometrics and Intelligent

Laboratory Systems 1989;6(4):259–72. https://doi.org/10.1016/0169-

7439(89)80095-4.

151. Shaw RG, Mitchell-Olds T. Anova for Unbalanced Data: An Overview. Ecology

1993;74(6):1638–45. https://doi.org/10.2307/1939922.

152. Jan S-L, Shieh G. Sample size determinations for Welch’s test in one-way

heteroscedastic ANOVA. British Journal of Mathematical and Statistical

Psychology 2014;67(1):72–93. https://doi.org/10.1111/bmsp.12006.

153. Levy KJ. Some empirical power results associated with welch’s robust analysis of

variance technique. Journal of Statistical Computation and Simulation

1978;8(1):43–8. https://doi.org/10.1080/00949657808810246.

154. Liu H. Comparing Welch’s ANOVA, a Kruskal-Wallis test and traditional

ANOVA in case of Heterogeneity of Variance. 2015.

155. Ruxton GD, Beauchamp G. Time for some a priori thinking about post hoc

testing. Behavioral Ecology 2008;19(3):690–3.

https://doi.org/10.1093/beheco/arn020.

163

156. Akoglu H. User’s guide to correlation coefficients. Turkish Journal of Emergency

Medicine 2018;18(3):91–3. https://doi.org/10.1016/j.tjem.2018.08.001.

157. Armstrong RA. Should Pearson’s correlation coefficient be avoided? Ophthalmic

and Physiological Optics 2019;39(5):316–27. https://doi.org/10.1111/opo.12636.

158. Lee Rodgers J, Nicewander WA. Thirteen Ways to Look at the Correlation

Coefficient. The American Statistician 1988;42(1):59–66.

https://doi.org/10.1080/00031305.1988.10475524.

159. Fredricks GA, Nelsen RB. On the relationship between Spearman’s rho and

Kendall’s tau for pairs of continuous random variables. Journal of Statistical

Planning and Inference 2007;137(7):2143–50.

https://doi.org/10.1016/j.jspi.2006.06.045.

160. Caruso JC, Cliff N. Empirical Size, Coverage, and Power of Confidence Intervals

for Spearman’s Rho. Educational and Psychological Measurement

1997;57(4):637–54. https://doi.org/10.1177/0013164497057004009.

161. Davoren J, Vanek D, Konjhodzić R, Crews J, Huffine E, Parsons TJ. Highly

Effective DNA Extraction Method for Nuclear Short Tandem Repeat Testing of

Skeletal Remains from Mass Graves. Croat Med J 2007;48(4):478–85.

162. Barta JL, Monroe C, Teisberg JE, Winters M, Flanigan K, Kemp BM. One of the

key characteristics of ancient DNA, low copy number, may be a product of its

extraction. Journal of Archaeological Science 2014;46:281–9.

https://doi.org/10.1016/j.jas.2014.03.030.

163. Purification of DNA from Bone Samples Using Bone DNA Extraction Kit, Custom

and DNA IQTM Chemistry.

https://www.promega.com/resources/pubhub/applications-notes/an343-

purification-of-dnafrom-bone-samples-using-bone-dnaextraction-kit-custom/

(accessed December 2, 2022).

164. Sarin VK, Erickson GM, Giori NJ, Bergman AG, Carter DR. Coincident

development of sesamoid bones and clues to their evolution. The Anatomical

Record 1999;257(5):174–80. https://doi.org/10.1002/(SICI)1097-

0185(19991015)257:5<174::AID-AR6>3.0.CO;2-O.

165. Goldberg I, Nathan H. Anatomy and pathology of the sesamoid bones.

International Orthopaedics 1987;11(2):141–7.

https://doi.org/10.1007/BF00266700.

166. WOOD VE. The Sesamoid Bones of the Hand and their Pathology. Journal of

Hand Surgery 1984;9(3):261–4. https://doi.org/10.1016/0266-7681(84)90038-X.

164

167. Yammine K. The prevalence of the sesamoid bones of the hand: A systematic

review and meta-analysis. Clinical Anatomy 2014;27(8):1291–303.

https://doi.org/10.1002/ca.22378.

168. Jobling M, Pandya A, Tyler-Smith C. The Y chromosome in forensic analysis and

paternity testing. International Journal of Legal Medicine 1997;110(3):118–24.

169. Roewer L. Y chromosome STR typing in crime casework. Forensic Science,

Medicine, and Pathology 2009;5(2):77–84.

170. Abate M, Vanni D, Pantalone A, Salini V. Cigarette smoking and musculoskeletal

disorders. Muscles Ligaments Tendons J 2013;3(2):63–9.

https://doi.org/10.11138/mltj/2013.3.2.063.

171. Bethard JD, Berger JM, Maiers J, Ross AH. Bone Mineral Density Adult Age

Estimation in Forensic Anthropology: A Test of the DXAGE Application. Journal

of Forensic Sciences 2019;64(4):1125–8. https://doi.org/10.1111/1556-

4029.13987.

172. Crowder CM, Andronowski JM, Dominguez VM. Chapter 18 - Bone Histology as

an Integrated Tool in the Process of Human Identification. In: Latham KE,

Bartelink EJ, Finnegan M, editors. New Perspectives in Forensic Human Skeletal

Identification. Academic Press, 2018;201–13.

173. Maurel DB, Boisseau N, Benhamou CL, Jaffre C. Alcohol and bone: review of

dose effects and mechanisms. Osteoporos Int 2012;23(1):1–16.

https://doi.org/10.1007/s00198-011-1787-7.

174. Waldron T. Chapter 20 - Joint Disease. In: Buikstra JE, editor. Ortner’s

Identification of Pathological Conditions in Human Skeletal Remains (Third

Edition). San Diego: Academic Press, 2019;719–48.

175. Bayaroğulları H, Yengil E, Davran R, Ağlagül E, Karazincir S, Balcı A.

Evaluation of the postnatal development of the sternum and sternal variations

using multidetector CT. Diagn Interv Radiol 2014;20(1):82–9.

https://doi.org/10.5152/dir.2013.13121.

176. Hartnett KM. Analysis of Age-at-Death Estimation Using Data from a New,

Modern Autopsy Sample—Part II: Sternal End of the Fourth Rib*,†. Journal of

Forensic Sciences 2010;55(5):1152–6. https://doi.org/10.1111/j.1556-

4029.2010.01415.x.

177. İşcan MY, Loth SR, Wright RK. Age Estimation from the Rib by Phase Analysis:

White Males. JFS 1984;29(4):1094–104. https://doi.org/10.1520/JFS11776J.

178. Barnes E. Atlas of Developmental Field Anomalies of the Human Skeleton: A

Paleopathology Perspective. John Wiley & Sons, 2012.

165

179. Glyn-Jones S, Palmer AJR, Agricola R, Price AJ, Vincent TL, Weinans H, et al.

Osteoarthritis. The Lancet 2015;386(9991):376–87. https://doi.org/10.1016/S0140-

6736(14)60802-3.

180. Klaassen Z, Tubbs RS, Apaydin N, Hage R, Jordan R, Loukas M. Vertebral spinal

osteophytes. Anat Sci Int 2011;86(1):1–9. https://doi.org/10.1007/s12565-010-

0080-8.

181. Iyavoo S, Hadi S, Goodwin W. Evaluation of five DNA extraction systems for

recovery of DNA from bone. Forensic Science International: Genetics Supplement

Series 2013;4(1):e174–5. https://doi.org/10.1016/j.fsigss.2013.10.090.

USING BONE BIOLOGY TO ENHANCE FORENSIC AND PALEOANTHROPOLOGICAL DNA ANALYSIS.

Students also viewed