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EXAMINATION OF STRUCTURAL POLYMORPHISM AT D21S11
LOCUS; IMPLICATIONS IN HUMAN IDENTIFICATION
APPLICATIONS

presented by

Brian Aloysius Higgins

has been accepted towards fulfillment
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EXAMINATION OF STRUCTURAL POLYMORPHISM AT D21 Sll LOC U S;
IMPLICATIONS IN HUMAN IDENTIFICATION APPLICATIONS

By

Brian Aloysius Higgins

A THESIS

Submitted to
Michigan State University
In partial fulfillment of the requirements
For the degree of

MASTER OF SCIENCE

Department of Criminal Justice

2007

ABSTRAC.r

EXAMINATION OF STRUCTURAL POLYMORPHISM AT D21 Sll LOCLIS;
IMPLICATIONS IN HUMAN IDENTIFICATION APPLICATIONS

By

Brian Aloysius Higgins

Length polymorphism analysis of DNA fragments is the conventional method
used in the human identification fields. Structural polymorphisms of STR alleles have
the same base pair length, but differ in the composition of the repeats in the variable
regions. and their examination can provide a second level of discrimination in human
identity applications. This study revealed seven structural polymorphisms in allele 3‘) at
STR locus D2181 1 from African American and Caucasian individuals in North America,
including three which have never been observed before. Statistical analysis of the power
of discrimination, heterozygosity, and the power of exclusion, useful in forensic and
paternity analysis, all increased when the seven variants characterized in this study w ere
taken into account. The study underscored the advantages of characterizing STR
structural polymorphisms over length polymorphism, and explores the potential

applications of this added discriminatory power.

Copyright by
BRIAN ALOYSIUS HIGGINS
2007

I dedicate this thesis to my loving wife Misi, who understands that quitting is not an
option and to all individuals in the world who do not realize that, when there is a will
there is a way.

iv

ACKNOWLEDGMENTS

I would like to thank Margaret Kline from National Institute of Standards and
Technology, for providing the D2181 1 forward and reverse primer sequences and
subsequent PCR conditions, Marco Scarpetta Ph.D. for providing facts pertaining to
paternity testing, Marco Scarpetts Ph. D. and David Foran Ph. D. for assistance in
formulating this thesis, and finally David Foran Ph. D. and Orchid Cellmark for

providing reagent, materials, and instrumentations for this research.

TABLE OF CONTENTS

LIST OF TABLES ........................................................................................................... viii
LIST OF FIGURES ........................................................................................................... ix
INTRODUCTION .............................................................................................................. 1
Human Identity Testing .............................................................................................. 1
Early Methods of Human Identification ................................................................... 1
Genetic Testing for Human Identification ................................................................. 2
Early Genetic Testing Methods .................................................................................. 3
Early DNA Genetic Testing ....................................................................................... 4
Introduction of the Polymerase Chain Reaction ........................................................ 7
PolyMarker and DQa — Sequence Specific Oligonucleotide Probes ........................ 7
Short Tandem Repeats ............................................................................................... 9
Statistical Calculations Used to Evaluate S TRs ...................................................... 10
DNA Databases ........................................................................................................ 1 1
Statistical Calculations Used in Human Identity Applications ............................... 12
Genetic Mutations .................................................................................................... 14
Structural Polymorphisms of STR Loci ................................................................... 15
Structural Polymorphism Researchfor Human Identity Testing ............................. 19
MATERIALS AND METHODS ...................................................................................... 20
D21 S 11 Sample Identification ................................................................................. 20
Allele Isolation ......................................................................................................... 20
DNA Sequencing ...................................................................................................... 21
DNA Sequence Analysis ........................................................................................... 22
Statistical Analysis ................................................................................................... 23
RESULTS ......................................................................................................................... 25
21511 Allele 30 Structural Polymorphism Distribution ....................................... 25
Distribution Analysis of Variance ........................................................................... 26
Power of Discrimination Analysis Incorporating Structural Variant Frequencies 27
Heterozygosity Analysis Incorporating Structural Variant Frequencies ................ 27
Power of Exclusion Analysis Incorporating Structural Variant Frequencies ......... 27
Hypothetical Statistical Analysis of Power of Discrimination, Power of Exclusion,
and Heterozygosity .................................................................................................. 28
DISCUSSION ................................................................................................................... 30
The Origins of Structural Polymorphism ................................................................. 30
Statistical Analysis of Power of Discrimination, Power of Exclusion, and
Heterozygosity ......................................................................................................... 35
Assisting Human Identification STR Testing Applications with Structural
Polymorphisms ......................................................................................................... 36
Proposed Technique for Analysis of Structural Polymorphisms ............................. 38
Structural Polymorphism in the Courtroom ............................................................ 41

Vi

APPENDICES .................................................................................................................. 44

BIBLIOGRAPHY ............................................................................................................. 66

vii

LIST OF TABLES

Table l -Allele frequencies of different populations for 12 alleles at D881 I79 locus ....... 3
Table 2 - Identification of ABO Blood Types by Agglutination ....................................... 4
Table 3 - Example of simple. compound, and complex repeats of STRs ......................... 10
Table 4 - Paternity index formulae ............................................ . ...................................... 13
Table 5 — Known structural polymorphisms at the VW A locus ........................................ 17
Table 6 - Known structural polymorphisms at the D381 358 locus .................................. 17
Table 7 - D2181 1 primer sequences used for PCR and sequence reactions ..................... 21

Table 8 - Equations used in the statistical analysis of D2181 1 allele 30 structural

polymorphisms .................................................................................................................. 24
Table 9 - Structural polymorphisms observed at allele 30 of the D2181 l locus... ........... 25
Table 10 - Frequency of structural polymorphism for allele 30 at D218] 1 ..... . ............... 26

Table l l - Comparison of descriptive statistics with and without structural

polymorphisms ......................................................................................... . ................. . ...... 28
Table 12 - Comparison of statistical analysis with and without structural variants ......... 29
Table 13 - Mutational pathways from ancestral allele 32
Table 14 - Single base substitution mutation of A—rG creating structural variants ......... 33
Table 15 - Single base substitution mutation of G—rA creating structural variants ......... 33
Table 16 - Insertion mutation in structural variants of allele 29. ...................................... 34
Table 17 - Deletion mutation in structural variants of allele 3! ........ . ......................... 34

Table 18 — Probe sequences that can distinguish all seven structural polymorphisms of
D2181] Allele 3O .............................. . ............................... . ............................................... 39

Table 10 - Pattern of hybridization for D2181 l Allele 30 Probes .................................... 40

viii

LIST OF FIGURES

Figure 1 - RFLP Analysis ................................................................................................... 6
Figure 2 - Example of DQa Typing and the reverse dot blot system ................................. 8
Figure 3 - Slippage mutations .............. I ............................................................................. 1 5
Figure 4 - Proposed view of microchip array ................................................................... 42

ix

INTRODUCTION

Human Identity Testing

Identification of individuals is an important segment of forensic science. A
number of techniques, notably fingerprints and genetic testing, are utilized to distinguish
victims and/or criminal suspects. Genetic testing. in particular short tandem repeat
analysis, is used to identify casualties in mass disasters, in patemity testing, and in
criminal investigations. A great deal of research is being performed to increase the speed
(Paegel et al. 2003 Bienvenue et al. 2006) and success rate (Coble and Butler 2005) of

genetic typing on marginal evidentiary samples.

Early Methods of Human Identification

The first system used for personal identification, termed anthropometry and
introduced by Alphonse Bertillon, was based on precise body measurements. This
technique described by Gloor (1980) and used in the late 18003, incorporated eleven
measurements of an individual; including height, reach, width of the head, and length of
the left foot. The idea behind the system was that skeletal dimensions remain fixed from
the age of twenty until death, and that bone lengths were thought to be extremely diverse.
It was believed that no two individuals could have exactly the same measurements.

Fingerprinting, which replaced Bertillon’s anthropometry system. is of course still
widely used for identification. The classification of finger ridge patterns, first described
by Francis Galton in the book Finger Prints, was developed in the late 1800’s (Crow
1993). Fingerprint identification is accomplished by first classifying ridge patterns into

three categories: arches, loops. and whorls. Ridge characteristics, or minutia. are then

examined and compared (Stigler 1995). Fingerprints are remarkably stable from early
childhood to death and although their size may increase with age, the ridge characteristics
do not change. Even with the introduction and advances of new methods to distinguish

individuals, fingerprints are still a standard form of identification throughout the world.

Genetic Testing for Human Identification

Genetic testing technologies are based on the determination of which alleles, or
genetic variants, are located at a particular site (locus) of deoxyribonucleic acid (DNA).
Every individual has two alleles per locus—one inherited from each parent. The
prevalence of a specific allele in a population is termed the allele frequency, and many
laboratories have tabulated frequencies for the loci they test (Table 1). The frequency of
an allele often varies among populations or ethnic groups and is used in statistical
calculations to determine the probability of identity or paternity. Loci are chosen such
that alleles are inherited independently, therefore, the likelihood that an individual’s
alleles would occur together is the product of the frequency with which each allele occurs
by itself. This product rule (Evett et a1. 1996) also states that the allele frequencies from
all loci used in a genetic test can be multiplied to find the probability of occurrence of the
genetic profile in a given population. As more loci are added, the probability of an
individual’s specific profile coincidentally appearing within the population decreases.
The genetic tests described below determine which alleles are found at different DNA

loci.

little I - Allele frequencies of different populations for 12 alleles at D88l17g locus.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Allele . African American I Caucasian : Hispanic
8 i 0.00237 ; 0.0169 0 00663
9 0.00487 i 0.0127 I 000957
‘0 0.0232 I 0.0915 I 0.0862
I I 0.0445 I 0.0724 0.0611
'3 0.119 0147 I 0.122 ,
‘3 0.203 0.326 i 0.295
'4 0.335 0.l98 i 0.253 ,
‘5 0.194 0.l06 ' 0.132
16 0.0606 . 0.0267 0.0309
17 0.01 19 I 000362 0.0037
'8 0.00122 0.000323 ; 0.003682
‘9 0.000321 i j

 

 

 

 

D881179 is a short tandem repeat (described below) and is located on chromosome eight. Allele
frequencies differ among ethnic groups. For example, the most common allele in African Americans
is l4, while in Caucasians and Hispanics it is 13. Note also that allele [9 has only been observed in
African Americans. Allele frequencies from Einum and Scarpetta 2004.

Earl} Genetic Testing Methods

Antigens on red blood cells can be used to help identify an individual. Antigens
themselves are not alleles, but are the result of what allele is encoded by a person’s DNA.
The six different major blood group-systems that have been used for human identification
are ABO, Rh, MNSs, Kell, Duffy, and Kidd (Singh et al. 1982). An individual’s blood
type is determined by adding anti-serum (antibodies) to a sample of blood, which may
undergo agglutination (clumping of the cells), in the presence of the correct antigen. An
individual with the blood type AB would show agglutination when testing with anti-A or
anti-B antiserum. Alternatively. a type 0 blood sample would not show a reaction
against either of these antisera (Table 2’). Discovery of the ABO system and that each
blood group is derived from a genetic locus was the beginning of genetic testing for

human identification.

Table 2 - Identification of ABO Blood Types by Ag Iutination

 

 

 

 

 

 

Blood Tested With
Blood Type Antigens Present in Anti A Anti B
Blood
A A + -
B B - +
AB Both A and B + +
0 Neither A nor B - -

 

 

 

 

 

 

The four ABO blood types are shown in the first column. The respective antigens for each type and
their agglutination reactions are given. (+ shows agglutination; - shows absence of agglutination)
Adapted from Saferstein 1998

Enzymes are also found in blood cells, and their variability can be used to further
identify an individual. These proteins are isolated by simple lysis of the red blood cells
into aqueous solution (Smithies 1955). By using the process of electrophoresis, the
enzymes can be separated and identified through various staining or specific enzymatic
reactions. Unfortunately, obtaining results from dried blood stains can be problematic in
blood group and enzyme testing. Red cell antigens and enzymes are not particularly
stable molecules and can denature when blood dries and ages. Furthermore, relatively
few alleles exist in the majority of these test systems, providing little discriminatory

power.

Early DNA Genetic Testing

Advances in DNA technology in the 19703 paved the way for the detection of
variation in specific DNA sequences and shifted the study of human polymorphisms from
the protein products of DNA to DNA itself (National Research Council 1992). The goal
of these studies was to find regions of DNA that vary among individuals. The research
showed that repeated DNA sequences—small segments of DNA that repeat multiple

times—exist and vary in length from person to person. These sequences, termed variable

number of tandem repeats (VN TRs). are highly polymorphic, and are dispersed
throughout the human genome. The first characterization of VNTRs described them as
short tandem repeated DNA sequences 10 to 15 base pairs in length (leffreys et al.
1985a). Later research characterized other VNTRs with repeats as long as 100 base pairs
(Chambers and MacAvoy 2000). Their relatively high mutation rates create the
variability in the number of repeats observed from person to person.

A method for detection of VNTRs, called restriction fragment length
polymorphism (RFLP) analysis, is the origin of modern DNA-based human identity
testing (Jeffreys et al. 1985b). RF LP testing (Figure 1) uses a restriction enzyme that
excises regions from the DNA strand, including those containing the VNTR. The
fragments of DNA are separated by size by gel electrophoresis and visualized for analysis
with radioactive or chemiluminescent probes. By analyzing a sufficient number of
VNTRs, one can reduce the probability of a chance match between two persons to an
extremely low level (National Research Council 1992), providing sufficient
discriminatory power for human identification in the forensic and paternity fields.
However, RFLP requires a large quantity of intact DNA, which is not always found in
evidentiary samples from crime scene investigations or mass disasters. Samples that do
not have substantial amounts of intact DNA may not provide results through RF LP

analysis.

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minisatellite patterns
Figure l - RF LP Analysis — A restriction enzyme is used to cut the DNA at specific sites. The
resulting DNA fragments are then separated by size through electrophoresis. The DNA fragments
(alleles) are then visualized through fluorescent probes.
The sample results are the number of repeats at a VNTR:
Lane 1 = 8 (repeats), 9 (repeats)
Lane 2 = 4, 11
Lane 3 = 8, 8
Figure from Fairbanks and Anderson 1999

Introduction ofrhe Polymerase. Chain Reaction

Enhancements have been made to human identity testing since the introduction of
VNTRs and RF LP. The polymerase chain reaction (PCR) (Mullis et al. 1986) advanced
the analysis of DNA evidence found at crime scenes. PCR can produce many copies of
the targeted VNTR through DNA amplification. This procedure uses DNA primers—
small sequences of DNA, chosen to span both ends of the VNTR of interest, and DNA
polymerase—an enzyme that assembles a new strand of DNA. During a cycle of
amplification, the primers attach to their complementary target sequence and the DNA
polymerase replicates the original DNA strand. This process is similar to DNA
replication during cell division, but is limited to the VNTR region. Each amplification
cycle doubles the amount of the targeted DNA strand. so 30 cycles amplifies the DNA ,
roughly one billion fold, producing a sufficient amount of material to determine the
allele(s) present. An advantage of PC R is that foreign DNA, for instance from bacteria or

fungi, is not amplified, thereby providing detection of only the target DNA.

PolyMarker and DQa-—Sequem'e Specific Oligonucleotide Probes

The AmpliType® HLA DQa Forensic Amplification and Typing Kit was the first
commercially available forensic kit based on PCR (Fildes and Reynolds 1995). The test
determines the HLA DQa genotype of DNA samples through the use of reverse dot blot
technology, by detecting single base polymorphisms (Conner et al. 1983) and was
implemented in hundreds of laboratories worldwide (Fildes and Reynolds 1995). The
technology allows a simultaneous screening of many known variants at the DQu locus

and consists of hybridization of amplified products to oligonucleotide probes. which are

bound to a nylon membrane. Visualization of the hybridized alleles is accomplished by a

simple colorimetric reaction, allowing the analyst to score the DNA sample for the alleles

present at the DQa locus (Figure 2) (Saiki et al. 1989).

 

 

Figure 2 - Example of DQu Typing and the reverse dot blot system
The DQa types are determined by the patterns of PCR product binding to the probes, which is
indicated by dots. Formation of a dot at 1 indicates that one or two of alleles 1.1, 1.2, and 1.3 are
present, and dots at 1.3, 2, 3, and 4 indicates that respective allele is present. A dot at ‘1.2, 1.3, 4’
indicates one or two of these alleles are present and ‘all but 1.3’ will indicate any allele is present
except 1.3. C is a control indicating that the amplification was successful.

The DQa alleles present for each row are as follows:

Row1—1.3,4 Row2—2,4 Row3—I.l,4 Row4—l.l,4 Rows-1.3.4
Row 6 - 1.1, 1.2 Row 7 — 1.3, 4 Figure from Saferstein 1993

Subsequent advances led to the development of the first DNA test in which
multiple loci were analyzed at one time, termed multiplexing. This test, performed using
the AmpliType PolyMarker (PM) Amplification and Typing Kit, simultaneously
amplifies six genetic loci; HLA DQa, Low Density Lipoprotein Receptor (LDLR),
Glycophorin A (GYPA), Hemoglobin G-gamma Globin (HBGG), D788, and Group
Specific Component (GC) (Fildes and Reynolds 1995). PCR products are hybridized to
the provided strips and visualized with the same colorimetric reaction as for DQa. The
resulting dots are used to determine the genotypes of the DNA sample at each locus.
Amplifying, visualizing, and analyzing multiple loci simultaneously greatly reduces the

testing time for a DNA sample.

Short Tandem Repeats

Short tandem repeats (STRs) are a subset of VNTRs and are comparable in all
characteristics except in the length of the repeated sequence (Craig et al. 1988). Analysis
of STRs represents a significant advancement in human identity testing over the VNTRs.
Most STRs are comprised of repeated sequences of two to five base pairs in length, with
alleles differing in the number of repeat units (Barber et a1. 1996). Three classes of STR’s
exist—simple, compound, and complex repeats (Table 3). Simple repeats contain units
of identical length, while compound repeats are composed of two or more repetitive
elements. Complex repeats may contain several repeat blocks of variable length, along
with variable intervening sequences (Urquhart 1994).

There are many advantages of analyzing STRs over VNTRs. One example is the

ability to obtain results from degraded samples, often found in forensic casework, which

is very difficult given the large length of VNTR alleles. STR alleles are shorter in repeat
length (Momhinweg et. al 1998), with those most commonly used for human
identification applications having four or five base pair repeats. Therefore, STR loci are

better candidates for analysis of degraded samples.

Table 3 - Example of simple, compound, and complex repeats of STRs

 

 

 

 

Simple Repeat (AGAT)(AGAT)(AGAT)(AGAT)(AGAT)
compound Repeat TCTA(TCTG)2 (TCTA)5
Complex Repeat (TTTC)3(TTTT)(TTCT)(CI l l)7(TTCC)2

 

 

 

Simple repeats contain units of identical length. Compound repeats are composed of two or more
repetitive elements. Complex repeats contain several repeat blocks of variable length, along with
variable intervening sequences.

Today, human identification applications use several STRs amplified
simultaneously, a technique known as multiplexing. Each locus is chosen to have a
narrow allele size range, to allow for multiple STRs to be analyzed all together without
size overlap. This procedure reduces the processing time of STR analysis. As with
VNTRs, the probability of a chance match between two persons decreases as more loci
are tested. Numerous commercial STR multiplex kits have been used by the human

identification field, which range from 3 to 16 STR loci per kit.

Statistical Calculations Used to Evaluate S TRs

The STRs chosen for human identification are used to distinguish one individual
from another. Various statistical calculations are employed to determine the
discriminatory power of each STR locus, including the power of discrimination (PD) and
heterozygosity (He). The power of discrimination is a computation of the odds that any

two people chosen at random will have different genotypes and is equal to one minus the

10

probability ofidentity (P1). P1. the sum of squares eftlie genotype frequencies. is the
probability of two randomly selected individuals having an identical genotype at a
particular locus (Fisher 1951). P1 is also known as the matching probability (pM) and
examples are shown in Appendices A and B. The greater the power of discrimination,
the lower the chance of a person‘s genotype coincidentally matching another individual's
at a particular locus.

A person carrying the same two alleles at a locus is called a homozygote or a
heterozygote if two different alleles are present. A high level of heterozygosity is
desirable in a STR locus because it indicates that more allelic diversity exists, lessening
the chance of a random match. Homozygosity is tabulated by summing the squares of the
allele frequencies. while heterozygosity equals one minus the homozygosity (Edwards et
al. 1992). However. the utility of high heterozygosity must be balanced against the need
to have a tight allele range—the size difference between the smallest and largest alleles
must be relatively small and similar to the ranges of the other loci. This allows the STRs

to be successfully multiplexed.

DNA Databases

DNA databases are a tool for solving crimes, by combining forensic science and
computer technology. The Federal Bureau of Investigation laboratory‘s Combined DNA
index System (CODIS) and the United Kingdom‘s national DNA database (NDNAD)
contain DNA profiles collected from convicted offenders and casework samples. These
are compared electronically by federal, state, and local crime laboratories in an effort to

link crimes to each other or to link unsolved crimes to convicted offenders.

ll

CODIS was established in 1998. As of March 2007 it contained over 4.5 million
entries and has assisted in over 30,000 investigations (FBI 2007). Two indices and three
levels of hierarchy are utilized in CODIS. The offender index contains the profiles of
convicted felons, while the forensic index has the profiles of crime scene evidence and
missing persons. Genetic profiles are uploaded at the local level as they are obtained
from convicted offenders or crime scene evidence. The profiles navigate from the state
to the federal level, where they can be utilized for comparison. Identification of a suspect
is possible when matches are found between the offender and forensic indexes. Crimes
scenes can be linked together within or across multiple jurisdictions when matches are
found in the forensic database, allowing investigators to share information pertaining to

crime scene(s), suspect(s), lead(s), and evidence.

Statistical Calculations Used in Human Identity Applications

When a DNA profile (a collection of STR loci) is found to match between an
evidentiary sample and a suspect, the strength of the association must be stated in the
report. Different types of statistical calculations are used to describe the results of these
genetic test batteries. Random match probability, utilized in forensic analysis, is the
likelihood of any two people chosen at random having the same alleles by coincidence
for a given STR. Two formulae are used to calculate this probability, in which p and q
are the allele frequencies: p2 is used when an individual is homozygous at a locus and 2pq
is appropriate for heterozygotes. For example, if an African American has the alleles l3
and 14 at locus D8Sl 179, using the frequencies from Table 1, the random match

probability is 2(0.203)(0.335), or 0.136. Thus, 13.6% of African Americans have these

12

specific alleles at the locus. As more loci are added to a test battery, it becomes less
likely that two unrelated people will have identical profiles.

The power of exclusion (PE) is a statistic used in paternity analysis, and is
analogous to the forensic random match probability. The PE is the proportion of time
that a falsely accused man will be excluded from paternity (Fisher 1951). Typically, if all
thirteen CODIS loci are examined, on average the falsely accused alleged father will be
excluded as the father at seven to eight loci and will be included by coincidence at five to

six loci (Einum and Scarpetta 2004).

Table 4 - Paternity index formulae

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Arrangement Mother Alleles Child Alleles Alleged Father Paternity Index

Alleles Formula

I BD AB AC 1/2a

2 BC AB AC l/Za

3 BC AB AB l/Za

4 BC AB A Va

5 B AB AC l/2a

6 B AB AB l/2a

7 B AB A I Va

8 AB AB AC 1 l’[2(a+b)]

9 AB AB AB I lt'(a+b)

10 AB AB A l/(a+b)

l 1 AB A AC l/2a

12 AB A AB i/2a

13 AB A A ha

14 A A AB l/2a J

15 A A A Na 3

 

 

 

 

 

The allele arrangement of the mother, child, and alleged father that is present at a particular locus
will determine which paternity index formula is used. A, B, C, and D represent alleles; a and b are
allele frequencies. Table from AABB 2006.

F inally, the paternity index calculates the odds in favor of fatherhood. It uses
one of four formulae, dependent on the arrangement of the alleles seen in the tested
parties at each locus (Table 4) (AABB 2006). Most allelic arrangements use the formula
1/2a. In all instances, the maternal obligate allele (MOA)——the allele passed from mother

to child, is defined. while the alleged father is heterozygous at that particular locus. In

13

cases where the alleged father is homozygous and the MOA is defined, the formula l/a is
used. The formula 1/[2/(a+b)] is selected when the paternal obligate allele (POA)—the
allele passed from father to child, is defined, but the MOA is ambiguous. The formulae

1/(a+b) or '/2(a+b) are utilized in the remaining ambiguous situations.

Genetic Mutations

As with any region of DNA, genetic mutations occur at STR loci and genotypes
can change between generations (Wiegand et. al 2004). Several types of mutations can
modify the nucleotide sequence of DNA. Changes at a single base pair or a few adjacent
nucleotides are referred to as point mutations and are classified as substitutions,
insertions, or deletions. Substitutions occur when a single base pair in the DNA is
changed. An insertion or deletion occurs when one or more nucleotides are introduced
into or removed from the DNA strand.

Slippage mutations or slipped-strand mispairings during DNA replication (Figure
3a) have been proposed as a molecular mechanism that creates the variability seen in
STRs (Levinson and Gutman 1987, Klintschar et al. 2004). The newly synthesized strand
may slip or mispair causing unpaired nucleotide(s) to loop out of the strand while
nucleotides beyond the loop continue to replicate (Figure 3b). This results in the addition
of nucleotides to the newly synthesized strand, or an insertion (Figure 3d). In contrast,
the template strand may slip or mispair during replication (Figure 3c) causing unpaired
nucleotide(s) to loop out of the strand while nucleotides beyond the loop continue to

replicate, causing a deletion (Figure 3e).

14

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igtllJILlllL-[tllll ‘ g.
d New}; S‘yttll‘tiisilec: SI’H'VJ can 08 an insertion mutation. e New-'33.. .iyt';-.i'tt-'.‘SI/.9‘.I sitar-.9: natures -..; rtetetrgg: Ir”u,riai:!:r‘-

Figure 3 e Slippage mutations ,

(3) During DNA replication (b) the new synthesized strand slips creating (d) an insertion mutation,
elongating the DNA strand by one base pair or (c) the template strand slips causing (e) a deletion
mutation, shortening the DNA strand by one base pair. Figure from Fairbanks and Anderson 1999

Structural Polymorphisms ofSTR Loci

The examination of length polymorphisms is widely used in the human
identification field to resolve forensic and paternity cases. However. size analysis alone
does not differentiate alleles of equal length with different DNA sequences. Structural
polymorphisms of STR alleles are defined as having the same base pair length. while
differing in the composition of the repeats in the variable regions. Even though they are

known to exist. the human identity field does not currently examine STR alleles to this

level of detail. In theory, their examination could provide a second level of
discrimination in human identity applications. The allele frequencies would be sub-
categorized, increasing the power of discrimination or paternity index.

Structural polymorphisms have been identified at the highly characterized C ODIS
loci of F GA (Barber et al.1996, Griffiths et al. 1998), vWA (Brinkmann et al. 1996a,
Griffiths et al. 1998), D381358 (Szibor et al. 1998, Momhinweg et al. 1998), and D2181 l
(Schwartz et al. 1996, Griffiths et al. 1998, Zhou et al. 1997, Brinkmann et al. 1996b).
FGA has a complex repeat structure of (TTTC)3 TTTT TTCT (CTTT)n CTCC (TTCC)2.
The only two known structural variants are at allele 27, in which 27a has the sequence
(TTTC)3 TTTT TTCT (CTTT)19 CTCC (TTCC); This was observed in a study that
detailed sequences of 22 F GA alleles to establish their repeat unit structure and assign
allele designations (Barber et al.1996). Griffiths et al. (1998) characterized the second
structural polymorphism, 27b, as (TTTC)3 TTTT TTCT (CTTT)13 CCTT (CTTT)5 CTCC
(TTCC)2. These differ in that 27a has a tetranucleotide of CTTT that repeats 19 times
while 27b has a single base substitution from T —> C at the nineteenth repeat to produce a
C CTT structure.

Eight structural polymorphisms have been observed at the VWA locus, two each
for alleles 13, 15, 16, and 18 (Table 5). The locus has a compound repeat structure of
TCTA (TCTG)3-4 (TCTA)n. Structural polymorphism 13a and 13b differ in two respects,
the first of which is found at the beginning of the sequence, in which 133 has an insertion
of TCTA. The second is a T —+ C substitution in 138 that occurs at the fourth

tetranucleotide repeat of TCTA, thus converting it to TCCA. Structural polymorphisms

16

15a and 15b differ by the proportion of TCTG and TC TA repeats, which is also the case

for 16a, 16‘, 188, and 18“.

Table 5 - Known structural ptgymorphisms at the VWA locus

 

 

 

 

 

 

 

 

 

 

Structural Repeat Structure Reference
Polymorphism [TCTA]|TCTG|3-4[TCTA]..
13a (TCTA); (TCTG)4 (TCTA)3 TCCA (TCTA)3 Griffiths et al. 1998
13b TCTA (TCTG)4 (TCTA)3 TCCA TCTA Brinkmann et al. I996a
15a TCTA (TCTG)4 (TCTAIIO TCCA TCTA Brinkmann et al. I996a
15b TCTA (TCTG)3 (TCTG) .1 TCCA TCTA Brinkmann et al. 1996a
163 TCTA (TCTG)4 (TCTA)H TCCA TCTA Brinkmann et al. 1996a
16b TCTA (TCTG)3 (TCTA‘m TCCA TCTA Brinkmann et al. 1996a
1821 TCTA (TCTG)4 (TCTA)13 TCCA TCTA Brinkmann et al. l996a
18b TCTA (TCTG)5 (TCTA)|2 TCCA TCTA Brinkmann et al. I996a

 

 

 

 

Alleles 13, 15, 16, and 18 each have two structural polymorphisms and their respective sequences are
shown.

Table 6 - Known structural polymorphisms at the D3Sl358 locus.

 

 

 

 

 

 

 

 

Structural Repeat Structure Reference
Polymorphism TCTA (TCTG)2-3 (TCTA)n

15a TCTA (TCTG)3 (TCTA)11 Szibor et al. 1998

ISb TCTA (TCTG)2 (TCTA)|2 Szibor et al. 1998

16“ TCTA (TCTG)3(TCTA)12 Szibor et al. 1998

16” TCTA (TCTG)2 (TCTA)13 Momhinweg et al. 1998

1.73 TCTA (TCTG)3 (TCTA)13 Szibor et al. 1998

17" TCTA (TCTG)2(TCTA)14 Momhinweg et al. 1993

 

 

 

 

Alleles 15, 16, and 17 each have two structural polymorphisms and their respective sequences are
shown.

17

Six structural polymorphisms have been characterized at the D38 1 358 locus
(Table 6), two each in alleles 15, 16, and 17. The locus has a compound repeat sequence
with a basic motif of TCTA (TCTG)2-3 (TCTA)... The sequences of 15“, 15b, 16‘, and 17a
were identified in a human population study from the northern and southern regions of
Germany (Szibor et a1. 1998). In a separate study, structural polymorphisms 16b and 17b
were found to differ in the number of TCTG and TCTA repeats (Momhinweg et al.
1998).

D21S11 has a complex tetranucleotide repeat sequence of three variable regions;
I (TCTA)n, II (TCTG),,, and III (TCTA),,, along with a constant region of 43 base pairs
inserted between regions II and III, and its repeat structure is (TCTA)n (TC TG)n
{(TCTA)3 TA (TCTA)3 TCA (TCTA); TCCATA} (TCTA)... Structural polymorphisms
have been observed at 15 alleles of D2181 1 (Appendix A). These were identified in
studies of allelic ladders (Griffiths et al. 1998), while investigating length and structural
variations (Mollier et a1. 1994, Brinkmann et al. 1996b, Walsh et al. 2003), and in
population studies (Schwartz et al. 1996, Bagdonavieius et al. 2002, Zhou et al. 1997).

Known structural polymorphisms for most D21S11 alleles differ in the number of
repeats in each variable region, the sole exception being 322°, which has only two
tetranucleotide repeats of TCTA in the first repeated sequence of the constant region.
Alleles 37.2, 38.2, and 39.2 show a 14bp deletion from the constant region producing a
repeat motif of (TCTA)n (TCTG)n (TCTA)3 TCA (TCTA)2 TCCATA} (TCTA)n (Walsh

et al. 2003).

18

Structural Polymorphism Research for Human Identity Testing

Despite the numerous structural variants identified across many loci utilized for
forensic identification, no research has characterized all of the structural polymorphisms
at a single STR locus. Therefore, population frequency data do not exist for any of the
STR structural variants. The goals of this study were to examine structural
polymorphisms for allele 30 of D2181] to determine what variants exist, to establish
their relative frequencies, and to investigate whether the structural alleles have a
distribution that is not dominated by one allele. The statistical calculations used in
human identification applications were re-examined after the frequencies of the structural
variants were established. D21 S11 was chosen because of its extremely polymorphic
nature, and the 30 allele was examined because four known structural variants have
already been reported (Schwartz et al. 1996, Griffiths et a1. 1998, Zhou et al. 1997,
Brinkmann et al. 1996b), suggesting the potential existence of still more variants.

This study consisted of sequence analysis of one hundred and eighty six samples
collected from African American and Caucasian individuals in North America.
Structural variants were identified, their relative frequencies established, and statistical
analyses of the power of discrimination, heterozygosity, and the power of exclusion were

recalculated incorporating the frequencies of the structural variants.

l9

MATERIAIS AND METHODS

D2151] Sample Identification

Upon prior review and approval from the University Committee on Research
Involving Human Subjects (UCRIHS) at Michigan State University. a list of DNA
profiles from unrelated individuals was generated through a database query of Orchid
C ellmark’s paternity testing laboratory. Each candidate was from a Caucasian or African
American individual in North America and was required to be a heterozygote at the

D2181 1 locus with the 30 allele being the smaller of the two alleles.

Allele Isolation

DNA samples were PC R amplified with primers synthesized by Integrated DNA
Technologies, specific to the D2181 l locus. The primers sequences and cycling
conditions were provided by the National Institute of Standards and Technology through
Margaret Kline (personal communication) and are displayed in Table 7. Primer stocks
were diluted to a concentration of 9.8pM/uL. The PCR amplification reaction contained
18.75ng of genomic DNA, l4pM of forward and reverse primer, 1.25 units of Taq
polymerase (Invitrogen), and 5.011L of a reaction buffer, provided by Orchid C ellmark,
containing Tris-HCI, KCI, BSA, MgClg, and dNTPs, for a total reaction volume of 28uL.
PCR amplification was carried out in a MJ Research PT-225 Tetrad thermal cycling
block with an initial step at 95° C for ten minutes, then 35 cycles 0f 94° C for 30 seconds.
59° C for one minute, and 72° C for one minute. followed by a final extension step at 60°

C for 45 minutes. The amplified products were resolved by 4% polyacrylamide gel

electrophoresis, with a Life Technologies 82 sequencing gel electrophoresis apparatus.
The D2181] alleles were visualized with Cambrex SYBR-Green 1 Nucleic Acid Gel
Stain and a UV transilluminator and the 30 allele was identified as the smaller band.
D2181] 30 alleles were excised using a disposable scalpel and placed in 2mL
microcentrifuge tubes containing 150uL of deO. The gel slices were allowed to
incubate for at least 1 hour at room temperature, followed by centrifugation at 14,000
rpm for 30 seconds. Serial dilutions of 1/10‘1 to 1/10'5 of the supernatant were prepared
and amplified to determine which dilution produced adequate amounts of the target
amplicon without nonspecific product formation. Serial dilutions of 1/104 and 1/10'5
typically produced the least nonspecific product and were used for sequencing, after

being purified with a QiaQuick column (Qiagen) and concentrated into 30uL of H20.

Table 7 - D2181] primer sequences used for PCR and seflence reactions.

 

Forward 5’ — CCA GCT TCC CTG ATT CTTA CA -— 3’

 

 

Reverse 5’ — CAC TGA GAA GGG AGA AAC ACT G — 3’

 

 

 

The primers sequences were provided by the National Institute of Standards and Technology
through Margaret Kline.

DNA Sequencing

Seventy one of the PCR products were sequenced on a Beckman Coulter C EQ
8000 Genetic Analyzer. Forward and reverse sequence reactions were prepared for each
PC R product, with the same primers used for PCR. A dye terminator cycle sequence
reaction was performed following the Beckman Coulter Quick Start Kit standard
operating procedure. The reactions were stopped by addition of 2.511L of Stop
Solution/Glycogen mixture, containing 211L of 3M Sodium Acetate, 2pL of lOOmM Naz-

EDTA pH 8.0, and 1 11L of 20mg/mL of glycogen, followed by 6011L of 95% cold ethanol

21

in a sterile 1.5m1 centrifuge tube. liach sample was mixed tl‘ioroughly and centrifuged at
14000 rpm for 15. minutes. The supernatant was removed and the pellet was rinsed twice
with 100p]- of 70% cold ethanol, followed by immediate centrifugation at 14,000 rpm for
three minutes after which the pellet was vacuum dried for30 minutes. DNAs were re-
suspended in 40p]. of Sample Loading Solution, provided in the Quick Start kit, and
sequencing was conducted on a CEQ 8000, with the following conditions: injection
voltage of 2.0 kV. injection duration of 120 seconds. separation voltage of 4.2 kV, and
separation duration of 60 minutes.

I Quantification of the remaining 1 15 PC R products was performed with a
spectrophotometer. From these results, lOng of PCR product was added to a 12111-
sequence reaction volume containing ngO and 30pM of primer. The reactions were
submitted to Michigan State University Research Technology Support Facility Genomic
Core, which utilized an Applied Biosystems Prism 3730xl capillary sequencer and a

BigDye® Terminator v3.1 Cycle Sequencing Kit.

DNA Sequence Analysis

The raw sequences were analyzed with either CEQ 8000 or ABI Prism 3700
analysis software and compiled in BioEdit Biological Sequence Alignment software (Hall
1999). Forward and reverse sequences for each sampleiwere aligned and binned

according to the structural polymorphism observed.

Statistical Analysis

A chi-square test was performed to investigate whether the distribution of the
structural polymorphisms in African Americans differed significantly from the
distribution of the structural polymorphisms in Caucasians. The published D2181] 30
allele frequencies for African Americans and Caucasians are 0.187 and 0.258
respectively (Einum and Scarpetta 2004). The structural variants’ allele frequencies were
calculated by multiplying the number of times each variant was observed by their
respective D2181] 30 allele frequency. For example, if the allele frequency for an
African American structural variant was observed to be 17.43%, that structural
polymorphism allele frequency would be (0.187)*(O.1743) or 0.0326. Power of
discrimination, power of exclusion, and heterozygosity calculations were performed with
published D2181] allele frequencies (Einum and Scarpetta 2004), then recalculated using
the structural polymorphisms allele frequencies for comparison against the original
published calculations. Formulae for statistical calculations can be found in Table 8.

A hypothetical statistical analysis of discrimination, power of exclusion, and
heterozygosity at the D2181] locus was also performed to evaluate the statistical change
in these variables if structural polymorphism frequencies for all alleles at the locus were
known. No structural variant allele frequencies are known for the other D2181] alleles;
therefore hypothetical African American structural polymorphism allele frequencies were
used, with the assumption that the structural polymorphism allele frequencies would have
a diverse distribution. In cases where only two structural polymorphisms have been
identified, the frequencies of 75% and 25% were used. Frequencies of 60%, 30%, and

10% were used where three structural variants were known. Structural allele frequencies

23

found in this study for D218] 1 30 allele of 57%, 1796, 14%. and 12% were used where

four have been recognized.

Table 8 - Equations used in the statistical analysis of D2181] allele 30 structural polymorphisms

 

 

Statistical Test Equation

Chi Square Test where: f 2
f“ is the observed frequency 2’ 2 = Z 370— — n

e

f? is the expected frequency
n is the number of samples

 

Power of Discrimination where:

P is frequency ofith allele in a population ofn

1
samples

 

Heterozygosity where: H = l _ 2 P12

P is fre uencv of i‘h allele in a o ulation ofn
q . P P

1
samples

 

 

Power of Exclusion where: PE : H 2 (1 _ (1 — H )H 2 )
H is the Heterozygosity

 

 

 

A chi square test was used to evaluate if a significant difference existed between proportions of
observed frequencies of structural polymorphism for African American and Caucasian. Power of
discrimination was used to calculate the odds that any two people chosen at random will have
different genotypes. Heterozygosity was used to calculate the proportion of heterozygous individuals
in the population. The power of exclusion was used to calculate the proportion of time that a falsely
accused man will be excluded from paternity. Equations from Butler 2005

RESULTS

D2151] Allele 30 Structural Polymorphism Distribution

A total of 186 samples were sequenced and analyzed at allele 30 of the D218] 1
locus. Forward and reverse sequences were obtained in all cases, for a total of 372
sequences. two hundred and eighteen of which were derived from unrelated African
Americans and 154 from unrelated Caucasian Americans. The four previously described
structural polymorphisms (303, 30", 30C, and 30“) as well as three novel polymorphisms
(30¢, 30f, and 30g) were observed (Table 9). The novel structural variants are different by
the number of repeats in the variable regions; all had the same constant region as the

previously reported structural polymorphisms.

Table 9 - Structural polymorphisms observed at allele 30 of the 021811 locus.

 

 

 

 

 

 

 

 

 

Structural Repeat Structure Reference
Polymorphism |TCTA|n|TCTG]n{|TCTA]3TA|TCTA|3
TCAITCTAlzTCCATAHTCTAIn
30a [TCTA]4[TCTG]6{43 bp}[TCTA]12 Schwartz et al. (1996)
30" [TCTA]5[TCTG]6{43 bp}[TCTA]1 1 Zhou et al. (1997)
30c [TCTA]6[TCTG]5{43 bp}[TCTA]H Griffiths et al. (I998)
30‘I [TCTA]6[TCTG]6{43 bp}[TCTA]10 Brinkmann et al. (I996b)
30e [TCTA]4[TCTG]7{43 bp}[TCTA]H Novel Allele
30T [TCTA]5[TCTG]5{43 bp}[TCTA]12 : Novel Allele
30g [TCTA]5[TCTG]7{43 bp}[TCTA]|0 Novel Allele

 

 

 

 

Seven structural polymorphisms were observed in this study. three of which were novel. All seven
have the same length, but differ by the number of repeats in each variable region. All have the same
constant sequence.

25

The observed frequency ofthe structural vanants is shown in Table 10. Six
structural polymorphisms were found in African Americans (30“. 30b. 30“. 30“. 30C and
30"). while five were observed in Caucasians (3 ". 30b. 30C. 30". and 30%). Structural
polymorphism 30b was the most frequent in African Americans, appearing 56.88% of the
time. in comparison to 12.99% in Caucasians. In contrast, structural variant 30C appeared
53.25% of the time in Caucasian, compared to 1 1.93% in African Americans. Novel
variant 308 was observed three times (2.75%) and 30f once (0.92%) in African

Americans. while not being found in Caucasians. Novel structural polymorphism 30g

was observed once (1.3%) in Caucasians and was not observed in African Americans.

Table 10 - Frequency of structural polymorphism for allele 30 at D2151 1.

 

. l

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

; | African American : Caucasian
;. '.
1 Structural n Observed l Structural i n f Observed Structural
l Polymorphism Frequency Polymorphism I 1 Frequency Polymorphism
Allele Frequency? 1 Allele Frequency 1
308 19 . 0.1743 0.0326 1 19 ' 0.2468 0.0037
.r J l 1
1 30b ; 62 T 05688 0.1064 1 10 0.1299 0.0335
.L l 1
30C i 13 0.1193 0.0223 1 41 0.5325 1 0.1374 1
l : r 2
30“ 3 11 0.1009 0.0189 1 6 ; 0.0779 1 0.0201 ‘-
' % l 4 a
308 i 3 0.0275 . 00051 1 i 3 .
r 30' 1 1 0.0092 0.0017 1 g i
i 30% .. l 1 ‘- 0.0130 ‘ 0.0033
Total 109 ' 1.0000 0.1870 1 77 1.0000 0.2580

 

 

 

 

 

 

 

 

A total of l86 structural variants were observed. 109 in African Americans and 77 in Caucasians.
The D215] 1 30 allele frequencies are 0.187 for African Americans and 0.258 for Caucasians. and the
structural polymorphisms’ allele frequencies sum to these published allele frequencies (Einum and
Scarpetta 2004). To obtain each structural polymorphism’s allele frequency the observed structural
polymorphism frequency was multiplied by its respective D2181] 30 allele frequency. n = number of
occurrences. indicates no occurrences.

Distribution A nulysis of l-"ariam'e
A chi-square test result below the critical value signifies the structural

polymorphisms distributions for African Americans and Caucasians are similar. while a

26

greater value indicates they are different. The chi square value was 50.9869 and was
greater than the critical values at a level of significance of 0.995. Thus. the distribution
of structural polymorphisms in African Americans. differed significantly from Caucasians

and is noticeable by each distribution having different variants being the most frequent.

Power of Discrimination Analysis Incorporating Structural Variant F requencies

The power of discrimination was calculated with and without the structural
polymorphism allele frequencies (Table 11). The established power of discrimination at
the D2181] locus in African Americans is 0.961 and in Caucasians is 0.955 (Einum and
Scarpetta 2004). After accounting for the structural polymorphism frequencies, the
power of discrimination increased by 0.011 (1.1%) to 0.972 in African Americans and by

0.020 (2.0%) to 0.975 in Caucasians (Appendix B).

Heterozygosity Analysis Incorporating Structural Variant Frequencies

A heterozygosity analysis was performed incorporating the structural
polymorphisms (Table 11). The expected heterozygosity at the D21S11 locus for African
Americans is 0.850 and for Caucasians is 0.839 (Einum and Scarpetta 2004). This value
increased by 0.022 (2.6%) to 0.872 in African American and by 0.042 (5.0%) to 0.881 in
Caucasian after including the structural polymorphism allele frequencies in the

calculation.

Power of Exclusion Analysis Incorporating Structural Variant Frequencies

The power of exclusion analysis was reanalyzed including the structural

27

polymorphisms data (Table 1 1). The power of exclusion at D21 S1 1 is 0.706 in African
Americans and 0.685 in Caucasians (Einum and Scarpetta 2004). The result increased by
0.045 (6.4%) to 0.751 in African Americans, and by 0.079 (1 1.5%) to 0.764 in
Caucasians. after incorporating the structural polymorphism allele frequencies (Table

11).

Table 1] - Comparison of descriptive statistics with and without structural polymorphisms

 

 

 

 

 

 

African American Caucasian
Without Structural j With Structural Without Structural With Structural
Polymorphisms Polymorphisms Polymorphisms Polymorphisms
PD 0.961 0.972 0.955 0.975
He 0.850 0.872 0.839 0.881
PE 0.706 0.751 0.685 0.764

 

 

 

 

 

 

Power of discrimination (PD), power of exclusion (PE), and heterozygosity (He) results with and
without the structural polymorphism allele frequencies. All descriptive statistics increased when
structural polymorphisms were included.

Hypothetical Statistical Analysis of Power of Discrimination. Power of Exclusion, and
Heterozygosity

Table 12 shows the comparison of the power of discrimination, heterozygosity.
and power of exclusion without structural variant frequencies, with only the structural
polymorphisms frequencies of allele 30, and with hypothetical structural variant
frequencies from all alleles at the D21S11 locus. The descriptive statistics for D2181 1
showed greater discriminatory capability with inclusion of allele 30 structural
polymorphism data. In comparison to the published power of discrimination. the
hypothetical result increased by 0.027 (2.8%) to 0.988 when incorporating the

hypothetical structural polymorphism data (Table 12. Appendix C). The heterozygosity

increased by 0.067 (7.8%) to 0.917. and the power of exclusion increased by 0.131

(18.5%) to 0.837, with the hypothetical structural polymorphism frequencies.

Table 12 - Comparison of statistical analysis with and without structural variants

 

 

 

 

 

 

African American
Without With Structural Polymorphisms With hypothetical D2181 1
Structural From Allele 30 at D2181 1 Structural Polymorphisms Alleles
Polymorphisms
PD 0.961 0.972 0.988
He 0.850 0.872 0.917
PE 0.706 0.751 0.837

 

 

 

 

 

Results for power of discrimination (PD), expected heterozygosity (He), and power of exclusion (PE)
with and without structural variant allele frequencies in African Americans. Published African
American allele frequencies were used for all D2181] alleles (Einum and Scarpetta 2004).
Hypothetical values were assigned to each structural variant, with the observed subdivision of allele
30 as a guide, and assuming that all structural polymorphisms display diverse distributions. In cases
where only two structural polymorphisms are known, the frequencies of 75% and 25% were used,
60%, 30%, and 10% where three structural variants were known, and 57%, 17%, 14%, and 12%
were used with four. All descriptive statistics increased when the hypothetical structural
polymorphism frequencies were included.

29

DISCUSSION

Structural polymorphisms have been identified at four CODIS loci (Barber et
a1. 1 996, Griffiths et al. 1998, Brinkmann et al. 1996a, Szibor et a1. 1998, Momhinweg et
a1. 1998, Schwartz et al. 1996, Zhou et a1. 1997, Brinkmann et al. 1996b). Since these
studies were not specifically designed to discover all variants, it is likely that more exist,
which is also evident in research of structural polymorphisms in non CODIS loci
(Urquhart et al. 1993, Mdllier and Brinkmann 1994, Rolf et al. 1997). Structural
polymorphisms are not used for human identification because STRs provide enough
information to resolve most cases of human identity, but could assist in cases that are not
conclusively resolved, particularly those in which only a few STRs can be obtained. The
aim of this study was to examine structural polymorphisms and assess their ability to
assist in casework that cannot be resolved by STR length analysis alone. The research
was designed to catalogue structural variants for allele 30 at locus D2181] to determine if
additional variants exist beyond the four already characterized and whether a distinct
racial distribution is present. Furthermore, the results allowed for the establishment of
relative frequencies for the structural polymorphisms and an examination of their effect

on probative statistical parameters in human identification.

The Origins of Structural Polymorphism
The human species began in Africa and expanded throughout the world (Cann et
al. 1987). Therefore the ancestral D2181] allele—the allele from which all alleles were

derived, would be in the oldest and most diverse population (Wiengand et. a1 2000). This

30

can be seen in the allelic variability in African Americans, as the most common allele at
the D218] 1 locus is 28, with a frequency of 24%, and is most likely the progenitor allele.
Furthermore, if the most common allele is the ancestral allele, then the most common
structural variant is likely the progenitor sequence. Two structural variants are known at
allele 28 in African Americans and the most frequent has the greatest chance to mutate
and produce other sequences, particularly the most common sequence variants of other
alleles. For the 30 allele studied here, structural polymorphism 30b is most common in
African Americans and could possibly be created from the progenitor sequence variant at
allele 28. Two insertion mutations are necessary for allele 28 to transition to allele 30.
the first to create allele 29 and the second to produce allele 30. Table 13a shows an
insertion mutation of TCTA in the first variable region (VRI) of 288, which creates a
sequence variant for allele 29 that is unknown, followed by an insertion mutation of
TCTA in the third variable region VRIII to produce 30b. On the contrary, if the mutation
occurred in VRIII first, then in VRI, allele 283 would transition to 29a and then to 30b
(Table 13b). Additionally, two separate insertion mutation of TCTA in VRIII of 28b
would generate 30b (Table 13c), which creates a structural variant for allele 29 that is
unknown. The likely primitive sequence can be more conclusively determined once
sequence variant frequencies for allele 28 are known. As structural polymorphism allele
frequencies are determined for other D218] 1 alleles. other mutational pathways could be

proposed.

31

Table 13 - Mutational pathways from ancestral allele

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

a Mutation Pathway ] h Mutation Pathway 2
28‘”l (TCTA), (TCTG)..~:43bpt (TCTA).0 28“ (TCTA): ('TCTG)(.{43bp} (TC-TAM
Insertion TCTA (VRI) Insertion TCTA (VRIII)
29(7) (TCTA 1, (TCTG).,{43bp} (TCTA)1o 29a ('1‘CTA)4(TCTG)6{43bp} (TCTA)..
Insertion TCTA (VRIII) Insertion TCTA (VRI)
30" (TCTA), (TCTG)6{43bp} (TCTA)., 30b (TCTA), (TCTG).t43bp} (TCTA)..
- . . 30b is the most common structural variant and
c Mutation Pathway 3 is most likely closely associated with the
ancestral allele, which is allele 28. There are
28b (TCTA)5 (TCTG).,-{43bp} (TCTA)9 three mutational pathwaysb from structural
variants 28a and 28b to 30 . a) Insertion
- mutation of TCTA in VRI of 28' followed by an
Insemon TCTA (VRIII) insertion mutation of TCTA in VRIII would
(71 , _ . produce 30". b) Insertion mutation of TCTA in
29 (TCTAl5 (TCTG).,{43bp} (TCTAM VRIII of 28a followed by an insertion mutation
of TCTA in VRI would produce 30". c) Two
Insertion TCTA (VRIII) successive insertion mutation of TC TA in VRIII
of 28‘l would produce 30". Note mutational
30b (TCTA), (TCTG)(,{43bp} (TCTA)11 pathways ] and 3 produce a 29 allele that is

 

 

 

unknown.

The variability of alleles and their sequences started from mutational events on

the ancestral allele, from which more alleles and their sequence variants are created. The

variability seen in allele 30 was likely created from mutations on the 30 structural variant

sequences or from other structural polymorphisms of other alleles. This study revealed

seven allele 30 structural polymorphisms, of which three were novel and all were

different in the composition of repeats in the variable regions. It is feasible that some of

the 30 allele structural polymorphisms could have been derived from some other 30

structural variant. A single substitution mutation of A—+G in the last base of the last

tetranucleotide repeat of VRI can transition 30", 3 c. 30". and 30f to 30°, 30b, 30g, and 308

respectively (Table 14). Alternatively. a single substitution mutation of G——>A in the last

 

base of the first tetranucleotide repeat of VRII can transition 30“, 30". 306. and 30g to 30f.

30?. 30b, and 30d respectively (Table 15).

Table 14 - Single base substitution mutation of A—+G creating structural variants

 

 

 

 

 

 

 

 

 

 

Structural polymorphism Substitution Structural Polymorphism
30b [TCTA]5[TCTG]6{43bp} [TCTA]11 Ago 30c [TCTA]4[TCTG]7{43bp}[TCTAII1
30c [TCTA]6[TCTG]5{43bp} [TCTA]11 A—’G 30b [TCTA]5[TCTG]6{43bp}[TCTA]11
30a [TCTA]6[TCTG]6{43bp} [TCTA]10 A“’G 30g [TCTA]5[TCTG]7{43bp}[TCTA]10
30r [TCTA]5[TCTG]5{43bp} [TCTA]12 A_’G 30a [TCTA]4[TCTG]6{43bp}[TCTA]12

 

A single base substitution, of A—>G, on the last base pair of the last tetranucleotide repeat of VRI in
some 30 structural variants can produce other 30 allele structural polymorphisms. Each row shows
a 30 variant be created by a different 30 polymorphism.

Table 15 - Single base substitution mutation of G—>A creating structural variants

 

 

 

 

 

 

 

 

Structural Polymorphism Substitution Structural Polymorphism
30a [TCTA]4[TCTG]6{43bp}[TCTA]12 G—’A 30r [TCTA]5[TCTG]5{43bp}[TCTA112
30b [TCTA]5[TCTG]6{43bp}[TCTA]1 1 G—’A 30c [TCTA]6[TCTG]5{43bp}[TCTA]11
[—30e [TCTA]4[TCTG]7 {43bp} [TCTA]11 G—’A 30b [TCTA]5[TCTG](,{43bp}[TCTA]1 1
/ 3Og [TCTA]5[TCTG]7 {43 bp} [TCTA]|0 0‘4!“ 30d [TCTA](,[TCTG](.{43bp} [TCTA]10

 

 

 

A single base substitution, of G—rA, on the last base pair of the first tetranucleotide repeat of VRII in
some 30 structural variants can produce other 30 allele structural polymorphisms. Each row shows
a 30 variant be created by a different 30 polymorphism.

Following the theory that slippage mutations create the genetic variability seen

today, it would take one slippage mutation on allele 29 or 31 to derive most of the 30

variants. A mutation resulting in an insertion of a tetranucleotide of TCTA in the VRI of

293 produces 30b (Table 16). An insertion of TCTG in VRII in 29a and 29b would create

306 and 30d respectively. Insertions of TCTA in VRIII in 29a and 29h create 30a and 30c.

Slippage mutations resulting in a deletion of TCTA in VRI-of3 1“, 31b, 31C, and 31d

33

 

 

Table 16 - Insertion mutation in structuralyariants of allele 29

 

 

 

 

 

 

Structural polymorphism Insertion Structural Polymorphism

(VR)

29a [TCTA]4[TCTG]6{43bp}[TCTA]11 TCTA 30b [TCTA]5[TCTG]6{43bp}[TCTA]I1
(VRI)

29a [TCTA]4[TCTG](,{43bp}[TCTA]H TCTG 30e [TCTA]4[TCTG]7{43bp}[TCTA]11
(VRII)

29"1 [TCTA]4[TCTG]6{43bp}[TCTA]H TCTA 30a [TCTA]4[TCTG]6{43bp}[TCTA]12
(VRIII)

29 1TCTA].[TCTG],{4315p}[TCTA]In (TCTUG 30d [TCTA]6[TCTG]6{43bp}[TCTA]10
VR)

29" [TCTA]6[TCTG]5{43bp}[TCTA]10 TCTA 30C [TCTA]6[TCTG]5{43bp}[TCTA]11
(VRIII)

 

 

 

 

 

Each row shows a separate tetranucleotide insertion from a slippage mutation. The first column
indicates the starting 29 sequence variant. The second column shows the tetranucleotide sequence
that was inserted and the variable region (VR) in which it occurred. The third column shows the
resulting 30 variant.

Table ]7 - Deletion mutation in structural 1 ariants of allele 3]

 

 

 

 

 

 

 

 

 

 

Structural polymorphism Deletion Structural Polymorphism
(VR)
31a [TCTA]5[TCTG]6{43bp} [TCTA]12 ($111]; 308 [TCTA]4[TCTG]6{43bp}[TCTA]12
31a [TCTA]5[TCTG]6{43bp} [TCTA]12 (TERTHG) 30f [TCTA]5[TCTG]5{43bp}[TCTA]12
31a [TCTA]5[TCTG]¢,{43bp} [TCTA] .2 (11%;?) 30b [TCTA]5[TCTG]6{43bp} [TCTA]11
3? [TCTA161TCT015{43bp} [TCTA112 {51:3 30‘ [TCTA]5[TCTG]5{43bp}[TCTA]12
31b [TCTAloITCTGls {43bp} [TCTA112 ($313) 30C [TCTA]6[TCTG]5{43bp}[TCTA]11
31C [TCTA]6[TCTG]6{43bp} [TCTAJH {51:3 30b [TCTA]5[TCTG]6{43bp}[TCTA]1 .
31° [TCTAloITCTG]e{43bp} [TCTA111 (T613310) 30c [TCTA]6[TCTG]5{43bp}[TCTA]11
3 lc [TCTAIoITCTGlot43bp1 [TCTA11 1 ($33) 30d [TCTA]¢,[TCTG]6{43bp}[TCTA]10
31d [TCTA]7[TCTG]5 {43bp} [Term]. {513; 30c [TCTA]6[TCTG]5{43bp}[TCTA]11

 

 

 

 

 

Each row shows a separate tetranucleotide deletion from a slippage mutation. The first column
indicates the starting 29 sequence variant. The second column shows the tetranucleotide sequence
that was inserted and the variable region (VR) in which it occurred. The third column shows the
resulting 30 variant.

34

 

 

would produce 308, 30f. 30b. and 30C respectively (Table 17). Deletion of TCTG in VRII
in 31a and 31c creates 30r and 30c. A deletion in VRIII ofTCTA in 313, 31b, and 31c

produces 30b, 30°, and 30d.

Statistical Analysis o/‘Power of Discrimination, Power of Exclusion, and Heterozygosity

No previous research has been designed to investigate the racial distribution of
structural variants, calculate their frequencies, and assess their potential application
toward human identification. The observed frequencies of the structural polymorphisms
at allele 30 show that a distinct distribution. exists. This characteristic can be used with
additional STRs and/or structural variants to distinguish individuals in human
identification, which can provide additional discriminatory statistical power.

The descriptive statistical parameters used in human identification were
recalculated incorporating this study’s D2181] 30 allele data. The power of
discrimination. heterozygosity, and power of exclusion values all increased when the
structural polymorphism allele frequencies were taken into account. Replacing the
established 30 allele frequency with the structural variants’ frequencies is essentially
adding more categories (alleles) to the D2181] locus, thereby strengthening these
statistical parameters. However, applying structural polymorphism allele frequencies
from only one allele at a STR locus gave only a minor improvement of 1% to 2% in
discriminatory power. This was expected as the structural variants are a subdivision of
an allele. If structural polymorphism frequencies for all alleles at a locus were known.
then the minor increments found for one allele would add up to a large improvement in

the discriminatory power for that particular locus.

35

To estimate if the minor increments add up to a large improvement, a hypothetical
statistical analysis of power of discrimination, heterozygosity, and power ofexclusion
was performed on the D2181] locus using all alleles known to have variants. As
expected, all the results increased, with the largest impact being on the power of
exclusion. Adding structural variants to the power of discrimination adds more
possibilities to discriminate individuals; in the case of the 30 allele, seven additional
ways. Meanwhile, incorporating all structural polymorphisms in the power of exclusion
adds 21 more combinations to exclude an alleged father at the 30 allele, which provides a
larger improvement than what is seen in the power of discrimination (Table 12).
Therefore, adding more categories (alleles), especially among the more common alleles.
allows for a substantial improvement in the descriptive statistics. Identifying structural
variants and determining their frequencies would clearly increase an STR locus and a

battery of STRs discriminatory power.

Assisting Human Identification S T R Testing Applications with Structural Polymorphisms
A problem facing the forensic community is obtaining viable STR information
from degraded samples (Butler et a1. 2003), often associated with cold cases. A few STR
markers may be obtained from a degraded sample, but this gives a relatively low power
of discrimination, resulting in a weak statistical match. Adding and analyzing structural
polymorphisms could strengthen these types of cases, by supplying a second level of
discrimination. If structural polymorphisms exist at an allele, sequence analysis could be
performed to determine which variant is present and resolve whether the sequences match

between the suspect and the evidentiary samples. Non-matching sequences can eliminate

36

suspects. while matching sequences would increase the power of discrimination, thereby
decreasing the random match probability.

In parentage testing, it is an accepted standard that two genetic inconsistencies
between a child and the tested parent are required to conclusively exclude an individual
from being the biological parent (AABB Standards 7‘h edition). However, the majority of
paternity laboratories are conservative and require three exclusions before issuing a
conclusive report (Scarpetta, personal communication). Paternity cases with only two
exclusions at 13 or more STR loci are frequently observed, especially in cases where the
mother is not tested (AABB Standards 7‘h edition). If a known structural polymorphism
exists at the paternal obligate allele, sequence analysis could be performed to determine
which structural variant is present, and possibly provide a third conclusive exclusion.

Occasionally paternity laboratories have to work with poor quality DNA samples.
in which only a few STR loci can be identified. Therefore, a low power of exclusion and
combined paternity index is obtained in inclusion cases, or an insufficient number of
exclusions may be seen. If structural polymorphisms exist at a paternal obligate allele,
sequence analysis could be performed. This would increase the power of exclusion and
combined paternity index, or provide a sufficient number of exclusions, which could
convert an inconclusive report to a conclusive one.

Samples from mass disasters are usually degraded from fire, water, and other
environmental conditions. To identify individuals in these unfortunate events, techniques
from both forensics and paternity are employed. A known sample of DNA can be
obtained from a toothbrush, hair brush, or razor for an individual suspected to have been

caught in the disaster. These sample DNA profiles can be compared to unknown samples

37

found at the scene. In addition, if a known sample cannot be obtained, samples of
relatives can be obtained for a paternity, grandparent, or family study test. Structural
variants can assist in identifying individuals in mass disasters, by employing the same

methods described above for degraded samples in forensics and patemity.

Proposed Technique for Analysis of Structural Polymorphisms

Structural polymorphism analysis has some shortcomings. Sequencing the
variants is more labor intensive than size analysis and is not amenable to high throughput
processing at this time. The allele subjected to analysis needs to be separated from the
other allele of the same locus, otherwise the sequence reaction would produce multiple
sequences, making the results uninterpretable. Furthermore, structural polymorphism
analysis of homozygotes would be problematic because both alleles migrate at the same
rate and thus cannot be separated by size by any electrophoretic technique. It would still
be beneficial to isolate the homozygotes and sequence them, because if they were
identical, a clean sequence would be obtained. Alternatively, if they had different
structural polymorphisms, the sequence results will be unreadable due to overlapping
sequences from each variant.

A solution for the drawbacks discussed above is to utilize a technique that can
distinguish alleles by their sequences and not by their size, as electrophoresis does.
Microchip technologies for analysis of STR alleles and single nucleotide polymorphisms
(SNPs) (Radtkey et al. 2000, Tillib and Mirzabekov 2001, Sobrino et al. 2005) have this
capability. Microchip assays are similar to the reverse dot blot technique, in which

oligonucleotides probes are developed for each STR allele. After hybridization of the

38

amplicon, visualization occurs through excitation of the fluorescent probe (Tillib and
Mirzabekov 2001). Microchip arrays lend themselves to highly multiplexed testing
(Radtkey et al. 2000), have the capability to detect 1250 or more alleles on one micro-
array slide, and are comparable in processing time to STR analysis (Jiang et. a1 2006).
Table 18 shows four probes that could be created to identify all the D2181 1 allele
30 structural polymorphisms. Probe one will bind to VRI and to the first tetranucleotide
repeat of VRII. Probe two will bind to VRI and to the first two repeats of VRII. Three
and four hybridize to VRII, with probe three binding to the last tetranucleotide repeat of
VRI. Each probe will hybridize to multiple allele 30 structural variants. The pattern of
hybridization would allow the identification of each variant (Table 19), as was the case

for PolyMarker and DQa Typing Kits.

Table 18 - Probe sequences that can distinguish all seven structural polymorphisms of D2181] Allele
30

 

 

 

 

 

 

Probe Sequence Tm
1 3’ - AGAT AGAT AGAT AGAT AGAT AGAC AGAC - 5’ 54°C
2 3’ - AGAT AGAT AGAT AGAT AGAT AGAT AGAC - 5’ 53°C
3 3’ - AGAT AGAC AGAC AGAC AGAC AGAC AGAC - 5’ 60°C
4 3’ - AGAC AGAC AGAC AGAC AGAC AGAC AGAC - 5’ 61°C

 

 

 

All of the probes will hybridize to multiple sequence polymorphisms and the resulting patterns would
allow each variant to be identified. T... - melting temperature

This proposed scheme will have limitations, for example, it will not distinguish
homozygous 30 alleles with different structural polymorphisms. The pattern of
hybridization for certain combinations of structural variants would be the same for other

pairs. For example, if a homozygous 30 allele had variants 30° and 30°, it would produce

 

the same pattern of hybridization as 30° and 30°. The 30 allele would have to be isolated

from any other D2181 1 product, to prevent the other amplicon from hybridizing to these

probes. Furthermore, since all four probes have different melting temperatures (T...), four

different hybridizations will need to be performed.

Table 19 - Pattern of hybridization for D2181] Allele 30 Probes

 

 

 

 

 

 

 

Structural Polymorphism
Probe 30a 30" 30c 30(1 30e 30f 30g
1 X X X X X
2 X X
3 X X X X X
4 X X

 

 

 

 

 

 

 

 

 

All of the probes will hybridize to multiple sequence polymorphisms and the resulting patterns would
allow each variant to be identified. For example, 30" would only hybridize to probe 3, while 30d will
hybridize to probes 1, 2, and 3. X indicates hybridization.

Despite these limitations, structural polymorphism analysis is a logical next step
in human identification. It may be possible to develop probes to identify all D218] 1
alleles and their sequence variants (Appendix D). However, as more D2181 1 structural
polymorphisms are identified the development of suitable probes may become more
difficult. Furthermore, the alleles would still need to be isolated with gel electrophoresis
and excised before hybridization is performed, because the pattern of alleles produced by
two D2181 I alleles may be the same for other pairs of alleles.

Structural polymorphism analysis will not be truly effective until all structural
variants for each allele at every locus have been identified. Once this happens, a

technology will need to be developed, or a current one adapted, to analyze both the STR

40

n l—

alleles and their structural variants, without the need to excise bands from gels. For
example, a microchip array could be developed that allows only one target hybridization
site for each structural variant. Currently there are 161 alleles throughout the thirteen

C ODIS loci (Applied Biosystems 2006a, Applied Biosystems 2006b), not including
microvariant or off ladder alleles. Twenty three of these have sixty known structural
variants (Butler 2005), including the variants observed in this study. Therefore, at least
198 sequence specific probes would need to be created. Some probes will have to be
long. perhaps the entire length of the target sequence. so specific temperature dependent
hybridization would not be possible with current technologies. It would be difficult to
develop all the probes with the same Tm. Consequently, the analysis would necessitate
serial hybridizations, where the temperature across the array is reduced and hybridization
is detected for a given probe when its specific temperature is reached. If these hurdles
can be overcome, the microchip array could hold all the necessary probes on one plate,
detect alleles and their respective structural variants at the same time, and ultimately
eliminate the need to separate the STR alleles from each other. Furthermore, it would
have the ability to distinguish homozygous alleles with two different structural

polymorphisms, due to the specificity of the probes (Figure 4).

Structural Polymorphism in the Courtroom

Currently, the vast majority of DNA evidence admitted in court proceedings is
based on STR allele size analysis. Structural polymorphisms have not been introduced in
the courtroom in forensic or paternity cases, but could potentially be used by the

prosecution or defense in an attempt to strengthen STR analysis. Structural variants have

41

the potential to be utilized in courtroom proceedings as the technology and methodology

are established in the scientific community.

—-._— ___.-_ .

 

 

 

l 8 9 10 11 12 13 14 158 15b 15.2 16a 16b 162 17a 17b

l ’ x "‘x ,""". r “a , r ‘5 ,c“ "x_ ‘~~_ , ' “ , /'“\ ,x"~\ / ‘»\_ // \\ 1'" -~.

. u ‘ ' '. ‘. , ; . _ l , _‘ f _r' ,l I 1‘ I ‘ ‘
' D381 358 - _ ' , . a ‘ 1 ‘ ~ " ' ‘. ‘ ' '
I K .I i 1‘ / °\ / i‘ . ’ \'\ .. i \s. ’ “\_ ,’ l‘ . 7 ,‘ \c 4 ‘ \ , ,1" R. .‘l ‘3 '4 .../'

10 11 12 13a 13b 14 15a 15b 152 16a 16b 16.1 17 18a 18b

11A\‘ //’_\ r. ’4 \x l " ""~ l/"‘\\ (,f'\ ./-fii\ ,/F\‘ /—-~\\ ;I'/-« .\ //—-\ “ /,—\\I\‘ - . ,_ .\ i
vWA 1: j 1' ’1 ( :j y l l j It :. 1. ; I . 1' 1‘ 6 a l'_ l I ‘1 l . I . i
\ -. .- \ 2" \\~ _ “I, \~._ ,3 \‘ ’14 \ 4", \\V/ \\~’/, \‘ - 1' , .\~~/’. \._ «._ _..-' ‘ \_-," a 7'
18.2 13.3 19 192 20 21 22 23 24 25

l "1T1 1" A) 1“ ,2,» (A) (A) TI) b1 1’ 1 1 1 .l

1 \2—1' xx \J' \_/’ ‘\_/ \/ K," ‘~\_/ \2/ \V/ 1
1 1
Figure 4 - Proposed view of a microchip array

Shown is a partial illustration of a proposed micro array for simultaneous analysis of STR alleles and
structural polymorphisms. In this display the genotype for the sample at D3Sl358 is alleles 14 and
17, with structural variant 17b at allele 17. The genotype at vWA is 16‘I and 16°, which by size

analysis it would be a homozygote 16.

d ,A
/ \ / \ A

Some lawyers already have the knowledge that structural polymorphisms exist
(Scarpetta, Personal Communication) at certain STR alleles and may use this information
in an attempt to discredit STR evidence, since the sequences of alleles found in the
evidence may be different than the alleles of their client(s). Even though structural
variants have only been observed at four CODIS loci, they may exist at all thirteen.
Therefore, omitting STR loci with known structural polymorphisms from a test report is
not an option, as forensic scientists must report all the STR results that are obtained
during testing. Otherwise, it may be perceived that evidence is being withheld that could
possible eliminate a suspect from a crime. As a result. they may need to compare and
confirm the sequences of each allele in the evidentiary sample to the suspect’s sample. to

ensure they match. Once sufficient research has been completed to identify all structural

42

variants at every allele used in human identification, it will only be necessary to
determine the sequence of alleles with known structural polymorphisms. In addition,
when all variant frequencies are calculated, the statistical gains afforded by structural

polymorphisms would strengthen the power of DNA evidence in the courtroom.

43

APPENDIX A

44

Known structural polymorphisms at D2181] locus

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Structural Repeat Structure Reference
Polymorphism [TCTAlanCTGM[TCTAI3TAITCTA|3TCA
[TCTAhTCCATAuTCTAh
27a (TCTA)4 (TCTG)6 {43bp} (TCTA)9 Mollier et a1, 1994
27" (TCTA)6 (TCTG)5 {43bp} (TCTA)3 Schwartz et al. 1996
27c (TCTA)5 (TCTG)5{43bp} (TCTA)9 Griffiths et al. 1998
28° (TCTA)4 (TCTG)6{43bp} (TCTA)10 Mollier et a1, 1994
28" (TCTA)5 (TCTG)6{43bp} (TCTA)o Zhou et al. 1997
29’ (TCTA). (TCTG)o{43bp} (TCTAm corms or al. 1998
29" (TCTA)6 (TCTG)5{43bp} (TCTA)10 Zhou et al. 1997
308 (TCTA)4 (TCTG)6{43bp} (TCTA)12 Schwartz et al. 1996
30" (TCTA)5 (TCTG)6{43bp} (TCTA)“ Zhou et al. 1997
30c (TCTA)6 (TCTG), {43bp} (TCTA)11 Griffiths et al. 1998
30d (TCTA)(, (TCTG)6{43bp} (TCTA)10 Brinkmann et al. 1996b
30.2a (TCTA)5 (TCTG)(,{43bp} (TCTA)10 TA TCTA Griffiths et al. 1998
30.2" (TCTA)5 (TCTG)5{43bp} (TCTA)11 TA TCTA Schwartz et al. 1996
31a (TCTA)5 (TCTG).,{43bp} (TCTA)12 Griffiths et al. 1998
31" (TCTA)6 (TCTG)5 {43bp} (TCTA)12 Mollier et al. 1994
31" (TCTA)6 (TCTG)6{43bp} (TCTA)11 Zhou et al. 1997
31d (TCTA)7 (TCTG); {43bp} (TCTA)H Schwartz et al. 1996
32a (TCTA)6 (TCTG)5{43bp} (TCTA)13 Griffiths et al. 1998
32" (TCTA)5 (TCTG)6{43bp} (TCTA)13 Zhou et al. 1997
32.23 (TCTA)5 (TCTG)6{43bp} (TCTA)12 TA TCTA Griffiths et al. 1998
32.2" (TCTA)4 (TCTG)6{43bp} (TCTA)13 TA TCTA Brinkmann et al. I996b
32.2L (TCTA)5(TCTG)6{[TCTAhTAlTCTAbTCA Brinkmann et al. l996b
[TCTAjoCCATM (TCTA).3 TA TCTA
33.2a (TCTA)5 (TCTG)(,{43bp} (TCTA)13 TA TCTA Griffiths et al. 1998
33.2" (TCTA)6 (TCTG)5{43bp} (TCTA)13 TA TCTA Brinkmann et al. I996!)
33.2C (TCTA)6 (TCTG)(,{43bp} (TCTA)12 TA TCTA Brinkmann et al. 1996b

 

 

 

45

 

Known structuralpolymorphisms at D2181] locus

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Structural Repeat Structure Reference
Polymorphism |TCTA|..|TCTG|..{|TCTA13TA|TCTA|3TCA
[TCTAhTCCATAuTCTAh
I 343 ('I‘CTA)5 (TCTG)(,{43bp} (TCTA115 Zhou et al. 1997
l 34" (TCTA)1Q (TCTG)5{43bp} (TCTA)H Brinkmann et al. I906b
35. (TCTAlio (TCTGlsl43hp1 (TCTAllz Griffiths et al. 1998
35" (TCTAlu (TCTG)5{43bp} (TCTA)11 Brinkmann et al 1996b
3o“ (TCTA)11 (TCTG)5{43bp} (TCTA)12 Griffiths et al. 1998
36b (TCTAllo (TCTG)5{43bp} (TCTA)13 Brinkmann et al. 1996b
36’ ('ICTAllotTCTGlol43bp} (TCTAhz Brinkmann et al. 1996b
37.2a (TCTA)-;(TCTG)14{[TCTAI3TCA[TCTA]2 Walsh et al. 2003
TCCATA} (TCTA)12
37.2" (TCTA)9(TCTG)12{[TCTA]3TCA[TCTA]2 Walsh et al. 2003
TCCATA} (TCTA)12
37.2“ (TCTA)o('I‘CTG)13{[TCTAI3TCA[TCTA]3 Walsh et al. 2003 i
TCCATA} (TCTA)11
37.2" (TCTA)10(TCTG)111[TCTA]3TCA[TCTA]2 Walsh et al. 2003
TCCATA} (TCTAMZ
37.2c (TCTA). 1(TCTG)1 . {[TCTA]3TCA[TCTA]2 Walsh et al. 2003
TCCATA} (TCTAh.
382’ (TCTA)9(TCTG )13{[TCTA]3TCA[TCTA 12 Walsh et al. 2003
TCCATA} (TCTAm
38.2" (TCTA)10(TCTG)11([TCTA];TCA[TCTA]3 Walsh et al. 2003
TCCATA} (TCTA)13
39.2"l (TCTA)10(TCTGI13{[TCTAlsTCAITCTA‘IZ Bagdonavieius et al. 2002
I J TCCATA} (TCTA),3
39.2Tr l Bagdonavieius et al. 2002

 

!
1

I (TCTA)1[(TCTG)12{[TCTA];TCA[TCTA];

TCCATA} (TCTA)17_

46

 

 

n—.—.—— .-

APPENDIX B

47

 

 

 

 

 

48

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APPENDIX D

60

Table contains the probe sequences that can distinguish all alleles and their structural
polymorphisms at D2181]. All of the probes will hybridize to multiple alleles and sequence
polymorphisms and the resulting patterns would allow each variant to be identified.

 

Probe Sequence

 

1 5’ - GCCT (TCTA)4 TCTG - 3’

 

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3 5’ - GCCT (TCTA)6 TCTG - 3’
4 5’ - GCCT (TCTA)7 TCTG - 3’
5 5’ - TCTA (TCTG)5 TCTA - 3’
6 5’ - TCTA (TCTG)6 TCTA - 3’
7 5’ - TCTA (TCTG)7 TCTA - 3’
8 5’ - CATA (TCTA)3 TCTG - 3’
9 5’ - CATA (TCTA)9 TCTG - 3’
l0 5’ - CATA (TCTA)10 TCTG - 3’

 

I l 5’ - CATA (TCTA)H TCTG - 3’

 

 

 

 

 

 

 

 

 

 

 

 

l2 5’ - CATA (TCTA)|2 TCTG - 3’
13 5’ - CATA (TCTA)13 TCTG - 3’
I4 5’ - CATA (TCTA)I4 TCTG - 3’
15 5’ - TCTG (TCTA)3 TA — 3’
l6 5’-TA TCTA TCTG-3’
l7 5’ - GCCT (TCTA)9 TCTG - 3’
l8 5’ - GCCT (TCTA)10 TCTG - 3’
l9 5’ - GCCT (TCTA)H TCTG - 3’
20 5’ - TCTA (TCTG)H TCTA - 3’
21 5’ - TCTA (TCTG)12 TCTA - 3’
22 5’ - TCTA (TCTG)13 TCTA - 3’

 

 

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