Using Methylation Patterns to Determine Origin of Biological Material

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QIAGEN is pleased to present a webinar titled
“Using Methylation Patterns to Determine Origin of Biological
Material and Age”

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QIAGEN would like to thank our first speaker, Dr. Bruce McCord, Professor
of Analytical/Forensic Chemistry, Department of Chemistry, Florida
International University, for his presentation.
Disclaimer:
This is a QIAGEN sponsored webinar. QIAGEN is not affiliated with the Florida
International University. The views expressed herein are those of the speaker, and
do not necessarily express the views of QIAGEN.

Forensic Epigenetics, Methods to discriminate
body fluids as well as age and phenotype
Bruce McCord
Florida International University
Miami, FL
mccordb@fiu.edu

Scenario
 In the early 90s, A woman is found murdered.
Trace DNA is found under her fingernails.
 Ex husband (custody dispute) cant be excluded
from intimate mixture. (presumptive for blood)
 Suspect argues, not blood - DNA match is from
secondary skin cells transferred during hand off of
child.
 Critical to determination is the question? Rust or
blood? Human or animal, etc. Could this sample be
tested years later?

 current procedures for the body
fluid id date from the 1940s.
 Chemical and enzymatic tests are
not specific and lack sensitivity
when compared to the PCR.
 Forensic DNA detect subnanogram
levels of DNA, but cannot tell you
the source of the material..
 Many labs no longer do sperm
searches, yet qPCR Y based
methods cannot identify the
presence of sperm
The Problem:

New methods for body fluid analysis
exploit the process of transcription
 Proteins form the functions of
the cell
 RNA templates are translated to
form the scaffolding for proteins.
 DNA templates are transcribed to
produce RNA
 Epigentic loci control gene
transcription
Genome
Transcriptome
Proteome
Epigenome

Epigenetics
 It is obvious to anyone that the human body has
many different kinds of cells; skin, hair, teeth,
blood, etc
 Yet our DNA is all the same, How then does our
body differentiate cells? Why do twins have
different fingerprints?
DNA methylation
patterns in young and
older twins.
Why do identical twins
begin to appear different
with age?

Epigenetics
 The answer is that there are
heritable differences in our DNA that
are not related to base pairing.
 Instead these differences are
controlled by patterns of
methylation in cytosine and in post
translational modifications of
histones.
 Epigenetics is the study of heritable
changes in gene expression
unrelated to DNA base pairing.

Methylation
 Methyl residues are covalently bound to the 5’ carbon
position of cytosine pyrimidine ring via DNA
methyltransferases (Dnmt) forming 5-methylcytosines
 Observed at CpG dinucleotides (70% of CpGs are
methylated in vertebrates but distinct patterns are seen
 “CpG islands” – areas of high CpG density usually
mapping to promoter regions
 Methylation  gene silencing
http://www.hgu.mrc.ac.uk/people/r.meehan_researchb.html

How to detect CpG sites?
 Primers are designed to encompass
regions of interest
 Candidate CpGs are then assessed
to detect differential levels of
methylation

Global differences exist between
methylation levels of different tissues
Note differences occurring between sperm, keratinocytes (skin cells),
and lymphocytes(white blood cells) (Eckhardt et al. 2006).

How to exploit
this forensically?
 Find locations near genes that target
expression of cellular proteins or examine
whole genome array studies
 Look in the genome for tissue specific
methylated CpG sites (methylation based
differences occur in what are called CpG
islands.)
 Measure differences in methylation that are
dependent on cell type!

But PCR erases methylation differences-
So use Bisulfite Modified PCR to lock in place

Result following Bisulfite modified PCR
http://en.wikipedia.org/wiki/File:Wiki_Bisulfite_sequencing

Objective
 Locate sites where tissue specific gene
expression occurs. Design primers to
encompass CpG islands. Extract DNA
 Use Pyrosequencing and/or Real time PCR
with (HRM) to detect methylation
differences
 Differentiate biofluids commonly found at
crime scenes (Blood, Saliva, Sperm,
Vaginal Epithelial Cells) by examining
methylation patterns at specific loci

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QIAGEN would like to thank our second speaker, Dr. Ruth Kläver, Senior
Scientist, Product Development, Pyrosequencing, QIAGEN, for her
presentation.

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Pyrosequencing – principle and key features
Using Methylation Patterns to Determine Origin of Biological Material and Age
.Based on SEQUENCING-by-SYNTHESIS Principle
 4 enzymes present in the system at all time
 DNA-Polymerase
 ATP-Sulfurylase
 Luciferase
 Apyrase
 Enzyme cascade generates a light signal
upon incorporation of nucleotides
 Only one nucleotide (dNTP) is added
at a time
⇒ Detected peaks demonstrate sequence

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Pyrosequencing – principle and key features
.Based on SEQUENCING-by-SYNTHESIS Principle
 Stepwise synthesis of DNA by addition of nucleotides
Template preparation
Sequencing primer

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Pyrosequencing – assay modes
Sequencing through unknown regions
Single Nucleotide Polymorhism (SNP)

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A: 44% C: 0%
G: 56% T: 0%
Di-, tri- and tetra allelic mutations
Insertions / Deletions
- - - - - - - : 56%
ATCTGCC: 44%
C: 57%
T: 43%

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DNA methylation of multiple CpG sites

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Measuring DNA methylation after bisulfite conversion
Example: DNA methylation analysis
.A G T T A C G A C A.Sequence to be analyzed:
.After bisulfite conversion:
.A G T T A C G A C A .A G T T A C
m
G A C A.and
.A G T T A T G A T A .A G T T A C
m
G A T A.and
.Analyzed sequence:
X
.A .G .T .A .A.T/C .T.T G .A
Ratio
T:C
.A .G .T.A .A.T .C .C .T.G
.27%
.Nucleotides added: .A

Sample to Insight
Measuring DNA methylation after bisulfite conversion
Example: DNA methylation analysis
Ratio
T:C
.A G T T A C G A C A
.A G T T A C G A C A .A G T T A C
m
G A C A.and
.A G T T A T G A T A .A G T T A C
m
G A T A.and
.A .G .T.A .A.T .C .C .T.G
.27%
X
.A .G .T .A .A.T/C .T.T G .A
.A.Nucleotides added:
.After bisulfite conversion:
.Analyzed sequence:
.Sequence to be analyzed:
.Built-in quality control: successful bisulfite conversion
C = 0%
T = 100%


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QIAGEN’s Pyrosequencing solutions for HID
Pyrosequencing workflow for genetic and epigenetic marker analysis
• PAXgene Blood
DNA Tube
• QIAamp Kits
• AllPrep RNA/
DNA Kits
• EpiTect Fast
DNA Kits
• EpiTect Fast
LyseAll Kits
• EpiTect Fast
FFPE Kits
• PyroMark Assay
Design SW
• PyroMark PCR
Kit
• PyroMark Q24
Advanced
• PyroMark Q48
Autoprep
• PyroMark Q24
Advanced
Reagents
• PyroMark Q48
Advanced
Reagents
Sample
collection
&/
stabilization
DNA
purification
Assay
design
Bisulfite
conversion
Pre-
amplification
Pyro-
sequencing
For epigentics only

Sample to Insight
Pyrosequencing – workflow
Load
reagents,
nucleotides,
buffers
Load
PCR product
& beads
Manual
Template
Preparation
with VPWS
Anneal
Seq-
primer
Pyro-
sequencing
Wash
Cartridge &
VPWS
Load
reagents,
nucleotides,
buffers
Load
PCR product
& magnetic
beads
Automatic
Template
Preparation
Anneal
Seq-
primer
Pyro-
sequencing
PyroMark Q24/Q24 Advanced
PyroMark Q48 Autoprep
manual automated manual/automated
Wash
Cartridge
PyroMark Assays
Design SW 2.0
PyroMark CpG Assays
1) Assay Design 2) PCR~ 5 min 120 min
3) Pyrosequencing
PyroMark PCR Kit

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PyroMark Q48 Autoprep – Protocol
PyroMark Q48 Autoprep workflow – fully integrated automatic template preparation
manual automated manual/automated
Load &
run files via
USB
or ethernet
Load PCR
product &
magnetic
beads
Load
reagents,
nucleotides
and buffers
Automatic
template
preparation
Anneal
sequencing
primer
Pyro-
sequencing
Wash
cartridge

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PyroMark Q48 Autoprep Performance
Methylation analysis (CpG mode)
unmethylated50%methylatedHistogram

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Once again, QIAGEN would like to thank Dr. Bruce McCord, for his
presentation.

Methodology
29
DNA
Extraction
Bisulfite
Modification
PCR Pyrosequencing
Real-time PCR/
HRM
STR
Analysis
Easily fits into the general flow of the forensic laboratory

Selection of Loci - ZC3H12D
"ZC3H12D, also known as MCPIP4, is a member of a family
of novel CCCH-zinc finger proteins.
Hypomethylated in sperm cells.
It is a known fact that levels of Zn are high in human sperm
“The total zinc content in semen from mammals is high, and
zinc has been found to be critical to spermatogenesis.”
Mol. Hum. Reprod. (1999) 5 (4):331-337.
Tania Madi

In ZC3H12D Blood is hypermethylated while
Semen is hypomethylated
Dr. Balamurgen

Discrimination: blood, semen, saliva, vaginal epi
*
PFN 3A Vaginal Epi
methylated methylated
unmethylated
intermediate

Our Current Multiplex for Body Fluid ID
simultaneous amplification of loci to preserve DNA extracts
Sohee Cho
Quentin
Gaither

Validation Studies
based on SWGDAM guidelines
 Sensitivity
 Age
 Species Specificity
 Mixture ratios
 Degradation

20-year old samples – blood
and semen
George Duncan
D. Silva, J. Antunes, K. Balamurugan; G.
Duncan, C. S. Alho, B. McCord. Forensic Sci
Int Genet. 2016 Jul;23:55-63.

Non-human samples fail to show peaks
for C20orf117 and three other loci

0
10
20
30
40
50
60
70
80
Mean%Methylation
Vag.Epi.(n=11)
VE
75%VE:25%S
50%VE:50%S
25%VE:75%S
S
Semen(n=12)
Mixtures of Vaginal
Epithelia and Semen.

In a real time PCR detection system fluorescence will
be a function of the amount of dsDNA. If the
temperature is increases the two strands melt and
the fluorescence is altered.
Melt Curves (HRM)

Target loci are labeled with
fluorescent intercalatating dyes. The
temperature of strand separation is
measured by a change in
fluorescence as dye is released.
Melt Curves
(the temperature @ which the 2 strands separate)
AT Rich
GC Rich
Dye
DNA
DNA
methylated
unmethylated

Realtime PCR/HRM
works well for identification of semen when a
large difference exists between methylation
levels..
Due to primer design, DNA not bisulfite modified
does not amplify
sperm
Blood_1,2
saliva_1,2
Positive control
Antunes; Silva; Balamurugan; Duncan; Alho; McCord; Analytical Biochemistry, 2016, 494: 40-45
Semen
Unmethylated
Blood
(methylated)
Saliva
methylated
Joana Antunes

Epigenetic phenotyping
(its not just body fluid typing
 Age
 Environment
 Behavior
 Diet
 Smoker/ non-smoker
 Body Mass index
 Hair color
 Drug abuse
 Because certain epigenetic effects are a response
to environment there may be advantages over
genetic phenotyping

The importance of age determination in
forensics
DNA based facial reconstruction
must be artificially aged
http://www.nytimes.com/2015/02/24/science/b
uilding-face-and-a-case-on-dna.html
Melanie McCord

Several genetic loci, including GRIA2, NPTX2, KLF14,
and SCGN, previously identified in a whole methylome
studies were examined and primers were designed to
explore the regions.
We analyzed saliva and blood samples from volunteers
with ages ranging from 5 to 72 years
So how to determine age with
epigenetics and pyrosequencing?
Deborah Silva
D. Silva, J. Antunes, K. Balamurugan;
G. Duncan, C. S. Alho, B. McCord.,
Electrophoresis, 2015, 36, 1775-1780.
Alghanim H, Antunes J, Silva DSBS,
Alho CS, Balamurugan K, McCord B.
Forensic Sci Int Genet. 2017
Nov;31:81-88

Results from individual loci at GRIA2, KLF14
and SCGN provided good correlations with age
GRIA NPTX2
Difference between predicted and
observed:
6.9 years
years
7.1
years
KLF14+SCGN
Our results show good correlation with age using CpG sites
from 1-2 amplicons – quick and simple.

Epigenetics can also be used to detect suspect
lifestyle. Here we show a marker for smoking status
Hussain Alghanim
47%
81%
92%

Conclusions
 Epigenetics methods can be used to to discriminate blood, saliva
semen, and vaginal epithelia.
 Methods fit easily into current forensic laboratory flow and body fluid
markers can be multiplexed
 Epigenetic methods are human specific and show great stability in
samples stored for up to 20 years
 Mixtures of 2 different body fluids produce intermediate levels of
methylation
 Age, smoking status, and other phenotypic loci can also be defined
using this technique

Publications
1. Madi,T.; Balamurugan,K; Bombardi,R.;Duncan, G.; McCord,B. The determination of tissue specific DNA methylation
patterns in forensic biofluids using bisulfite modification and pyrosequencing Electrophoresis, 2012, 33(12) 1736-1745.
2. Balamurugan,K.; Bombardi,R.; Duncan,G.; McCord, B., The identification of spermatozoa by tissue specific differential
DNA methylation using bisulfite modification and pyrosequencing, Electrophoresis, 2014, 35, 3079-3086.
3. Deborah S.B.S. Silva, Joana Antunes, K. Balamurugan; G. Duncan, C. S. Alho, B. McCord. Evaluation of DNA methylation
markers and their potential to predict human aging, Electrophoresis, 2015, 36, 1775-1780.
4. Joana Antunes1, Kuppareddi Balamurugan2, George Duncan1, Bruce McCord1 Tissue specific DNA methylation patterns in
forensic samples detected by pyrosequencing, Jörg Tost and Ulrich Lehmann (eds) Methods in Microbiology, Springer, 2015.
5. Deborah S.B.S. Silva, Joana Antunes, K. Balamurugan; G. Duncan, C. S. Alho, B. McCord. Developmental validation
studies of epigenetic DNA methylation markers for the detection of blood, semen and saliva samples, Forensic Science
International: Genetics, 2016,23:55–63.
6. Joana Antunes, Deborah S.B.S. Silva, K. Balamurugan3; G. Duncan, C. S. Alho2, B. McCord. High Resolution Melt analysis
of DNA methylation to discriminate semen in biological stains, Analytical Biochemistry, 2016, 494: 40-45
7. Sang-Eun Jung; Sohee Cho; Joana Antunes; Iva Gomes; Mari L. Uchimoto; Yu Na Oh; Lisa Di Giacomo; Peter M.
Schneider; Min Sun Park; Dieudonne van der Meer; Graham Williams; Bruce McCord; Hee-Jung Ahn; Dong Ho Choi;
Yang-Han Lee; Soong Deok Lee; Hwan Young Lee. A collaborative exercise on DNA methylation based body fluid typing,
Electrophoresis, 2016, 37, 2759-2766.
8. Antunes, J. Deborah S.B.S. Silva K. Balamurugan; G. Duncan, C. S. Alho, B. McCord, Epigenetic discrimination of vaginal
epithelia using bisulfite modified PCR and pyrosequencing, Electrophoresis,2016, 37, 2751-2758.
9. Alghanim H; Antunes J;Silva D; Alho C, Balamurugan K; McCord B. Detection and evaluation of DNA methylation markers
found at SCGN and KLF14 loci to estimate human age, FSI Genetics, 2017,31 81-88.
10. Alghanim, H. , Wu, W. and McCord, B. (2018), DNA methylation assay based on pyrosequencing for determination of
smoking status. Electrophoresis 2018, in press.
.

Acknowledgements
Award 2012-DN-BX-K018
Major support for this
work was provided by:
The National Institute of
Justice
Points of view in the document
are those of the authors and do
not necessarily represent the
official view of the U.S.
Department of Justice
Tania Madi, Kuppareddi Balamurugan, Joana
Antunes, Deborah Silva, Clarice Alho, Hussain
Alghanim, Quentin Gaither, Sohee Cho
Florida International University (USA)
University of Southern Mississippi (USA)
Catholic University of Rio Grande do Sol (Brazil)
Broward Sheriff’s Office Ft Lauderdale, FL (USA)
San Francisco Police Department (USA)
CNPq - Brazil- Conselho Nacional
de Desenvolvimento Cientifico e
Tecnologico
Institute of Forensic Science,
Seoul National University”.
Qiagen

Sample to Insight
QIAGEN would like to thank our last two speakers, Ms. Amy S. Lee and Mr.
Peter St. Andre, Criminalist II, SFPD Crime Lab, for their presentation.
Disclaimer:
This is a QIAGEN sponsored webinar. QIAGEN is not affiliated with the San
Francisco Police Department (SFPD) Crime Laboratory. The views expressed herein
are those of the speakers, and do not necessarily express the views of QIAGEN.

PYROSEQUENCING
IN FORENSICS
Peter St. Andre, MFS
Amy S. Lee, MFS
SFPD Criminalistics Laboratory

Laboratory Workflow
1) Determine which DNA extracts need to be tested.
2) Perform bisulfite conversion on the remaining DNA extracts using the EpiTect
Fast Bisulfite kit (~15-20 ul) and incubate on a thermal cycler (~1 hour)
3) Clean-up converted DNA using QIAcube protocol (~1 hour)
4) Amplify the purified DNA using the PyroMark PCR kit and the proper tissue-
specific PCR primers (~4 hours)

Laboratory Workflow (cont.)
5) Create a Pyrosequencing assay setup file using the PyroMark Q48 Autoprep
software and load the file onto the instrument
6) Set up a Pyrosequencing reaction disc and place the disc onto the Pyromark
Q48 Autoprep for sequencing (~1.5 hours)
7) Analyze the data using the PyroMark Q48 Autoprep software
W x D x H: 9.8” x 11.8” x 11.8” with
chamber lid and injector cover closed;
9.8” x 22” x 15.4” with chamber lid and
injector cover open

Preliminary testing by SFPD lab
Testing performed using only the semen primer (ZC3H12D)
1) Determined semen primer is specific to sperm
2) Developed more sensitive PCR amplification protocol by adjusting ramp rates
3) Performed basic tests to confirm concepts discussed in other published papers

Confirmation of sperm as ZC3H12D target
85-95%
0-4%
32-38%

Effects of ramp rates during PCR amplification

50 ng input – Neat Semen
0.05 ng input – Neat Semen
0.01 ng input – Neat Semen
Sensitivity test for ZC3H12D

Traditional screening methods
• How does the pyrosequencing protocol compare to the traditional screening
methods, i.e. sperm searching?
1) Sensitivity limits in detecting male DNA (and observing sperm)
2) Mixture ratios that allow for deductions
Went through all casework reports from 2017 where the lab was using
traditional screening methods and compiled data for the following
categories:
1) Substrate
2) Total human quant
3) Total male quant,
4) Whether a male DNA profile was detected
5) Whether said male profile could be deduced

Finding the benchmarks
• Compiled data from 432 sperm fractions from female victims
• Quick facts:
• 216 sperm positive samples
• 181 male profiles detected
• 139 male profiles deduced
• Smallest male quant w/ deduced male profile: 0.0105 ng total input
• Smallest male quant w/ male detected in EPG: 0.0045 ng
• Smallest male:female ratio w/ deduced male profile: 1:33
• Smallest male:female ratio w/ male detected in EPG: 1:100

Sensitivity benchmarks
1) Visual detection of
sperm more sensitive
than quant
2) More than half of
samples with greater
than 0.2 ng total input
yield deducible profiles
3) Large dropoff in
deducible profiles seen
at 0.1 ng total input DNA
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
>1 ng 0.5-1.0 ng 0.2-0.5 ng 0.05-0.2 ng <0.05 ng 0 ng
Sensitivity based on male quant
Sperm Detected Male Detected Male Deduced

Sensitivity benchmarks
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
>10:1 4:1 - 10:1 2:1 - 4:1 1:1 - 2:1 1:2 - 1:1 1:4 - 1:2 1:10 - 1:4 <1:10
Deduction based on male:female ratio
<0.1 ng 0.1 ng 0.2 ng
1) Large dropoff at
ratios of less than
1:10
2) Large dropoff at
quants less than
0.1 ng total input
3) Likely to deduce
foreign male as a
minor contributor
in two person
mixtures
Note: Samples used
where sperm was
detected.

Testing of various tissues using semen primer
• Need to examine methylation values of non-sperm samples using semen primer
• Differential samples containing sperm are often accompanied with different tissue types
including blood or vaginal epithelial cells among others.
• Methylation values are the only differentiating information
• Important to know that different tissue types amp similarly using the semen
primer
• The goal is to find the thresholds where we can be confident in confirming the
presence of semen in a mixture sample

Pyrosequencing benchmarks
We used a two-tiered strategy to identify sensitivity limits based on
reproducibility
• Part A:
• Test a dilution series of non-semen samples with the semen primer*
• Identify sensitivity limits
• Part B:
• Once limit is identified, test replicates to look at reproducibility around that
sensitivity range identified in Part A
*Testing done on blood, saliva, vaginal epithelial samples with range 0.05ng – 50ng

Pyrosequencing sensitivity results
1) All biological material tested with semen primer amped comparable to semen
2) Dependable methylation results required two conditions: a) at least 0.2ng of total input
DNA, b) proper quality flags by pyrosequencing software.
With both conditions met, we were able to positively ID sperm in samples using pyrosequencing.
• At 0.1ng of total input DNA, methylation results become less reproducible
• At 0.05ng of total input DNA, methylation results are not usable
• Q48 software program begins to flag data for quality around 0.1ng
• At 0.05ng, almost all data is flagged as a fail
0.2ng serves as both sensitivity and amp fidelity thresholds

Sample dilution series w/ semen
SampleID Pos. 1 (%) Pos. 2 (%) Pos. 3 (%) Pos. 4 (%) Pos. 5 (%)
Semen B 0.5 4.73 1.88 3.2 4.79 3.12
Semen B 0.5 5.59 3.94 6.32 9.04 6.55
Semen B 0.5 4.25 6.99 2.96 6.6 4.34
Semen C 0.5 3.5 3.11 2.79 5.62 3.26
Semen C 0.5 3.61 2.34 3.62 6.13 4.66
Semen C 0.5 4.16 4.38 3.63 5.23 4.89
Semen B 0.4 3.48 2.75 3.66 6.35 3.82
Semen B 0.4 5.36 4.01 4.05 5.31 3.63
Semen B 0.4 5.67 3.78 4.17 6.93 5.96
Semen C 0.4 4.68 1.92 4.39 3.88 5.56
Semen C 0.4 3.68 4 5.06 5.44 4
Semen C 0.4 5.15 2.04 3.75 6.23 3.58
Semen B 0.3 4.66 3.05 3.44 5.23 3.52
Semen B 0.3 5.01 4.41 7.1 6.79 7.34
Semen B 0.3 5.92 5.47 4.64 6.43 5.66
Semen C 0.3 9 3.14 5.34 7.76 5.46
Semen C 0.3 4.35 2.9 4.06 8.58 6.42
Semen C 0.3 4.45 4.32 5.61 7.14 5.12
Semen B 0.2 10.94 6.72 6.05 13.39 10.14
Semen B 0.2 5.85 4.31 4.34 6.63 6.9
Semen B 0.2 5.37 2.35 4.22 5.78 3.72
Semen C 0.2 3.59 2.31 3.03 7.44 4.31
Semen C 0.2 4.78 4.05 4.75 8.61 7
Semen C 0.2 3.52 5.88 5.3 6.72 6.65
Semen B 0.1 8 6.2 5.33 8.11 6.26
Semen B 0.1 6.33 4.47 5.24 8.37 8.58
Semen B 0.1 5.21 3.99 3.37 4.38 5.36
Semen C 0.1 10.1 8.18 9.42 10.75 12.31
Semen C 0.1 5.96 6.62 7.61 9.74 7.18
Semen C 0.1 - - - - -
SampleID Pos. 1 (%) Pos. 2 (%) Pos. 3 (%) Pos. 4 (%) Pos. 5 (%)
Semen B 0.5 4.73 1.88 3.2 4.79 3.12
Semen B 0.5 5.59 3.94 6.32 9.04 6.55
Semen B 0.5 4.25 6.99 2.96 6.6 4.34
Semen C 0.5 3.5 3.11 2.79 5.62 3.26
Semen C 0.5 3.61 2.34 3.62 6.13 4.66
Semen C 0.5 4.16 4.38 3.63 5.23 4.89
Semen B 0.4 3.48 2.75 3.66 6.35 3.82
Semen B 0.4 5.36 4.01 4.05 5.31 3.63
Semen B 0.4 5.67 3.78 4.17 6.93 5.96
Semen C 0.4 4.68 1.92 4.39 3.88 5.56
Semen C 0.4 3.68 4 5.06 5.44 4
Semen C 0.4 5.15 2.04 3.75 6.23 3.58
Semen B 0.3 4.66 3.05 3.44 5.23 3.52
Semen B 0.3 5.01 4.41 7.1 6.79 7.34
Semen B 0.3 5.92 5.47 4.64 6.43 5.66
Semen C 0.3 9 3.14 5.34 7.76 5.46
Semen C 0.3 4.35 2.9 4.06 8.58 6.42
Semen C 0.3 4.45 4.32 5.61 7.14 5.12
Semen B 0.2 10.94 6.72 6.05 13.39 10.14
Semen B 0.2 5.85 4.31 4.34 6.63 6.9
Semen B 0.2 5.37 2.35 4.22 5.78 3.72
Semen C 0.2 3.59 2.31 3.03 7.44 4.31
Semen C 0.2 4.78 4.05 4.75 8.61 7
Semen C 0.2 3.52 5.88 5.3 6.72 6.65
Semen B 0.1 8 6.2 5.33 8.11 6.26
Semen B 0.1 6.33 4.47 5.24 8.37 8.58
Semen B 0.1 5.21 3.99 3.37 4.38 5.36
Semen C 0.1 10.1 8.18 9.42 10.75 12.31
Semen C 0.1 5.96 6.62 7.61 9.74 7.18
Semen C 0.1 - - - - -

Example: Vaginal Epithelial
Blue = Strong amp, no quality flags
Yellow = Moderate amp, check data
Red = Weak amp/control failure, fail
Green = Methylation % w/in 2 SD of
position-specific mean (per SFPD testing)
Pyrogram Quality Acceptable methylation range [55.25% - 100%]
SampleID Pos. 1 Pos. 2 Pos. 3 Pos. 4 Pos. 5
Vag 50 68.35 68.48 89.66 72.98 77.02
Vag 0.5 92.45 94.94 74.97 72.44 68.06
Vag 0.25 93.28 63.78 99.61 100 93.43
Vag 0.1 94.84 91.37 100 100 94.98
Vag 0.1 93.31 91.17 100 100 90.52
Vag 0.1 92.22 3.2 99.43 99.09 93
Vag 0.05 3.2 2.41 98.9 10.72 90.81
Vag 0.05 92.26 89.62 99.68 100 91.5
Vag 0.05 - - - - -
Vag 0.01 94.1 91.74 99.93 100 96.13
Vag 0.005 - - - - -
SampleID Pos. 1 Pos. 2 Pos. 3 Pos. 4 Pos. 5
Vag 50 68.35 68.48 89.66 72.98 77.02
Vag 0.5 92.45 94.94 74.97 72.44 68.06
Vag 0.25 93.28 63.78 99.61 100 93.43
Vag 0.1 94.84 91.37 100 100 94.98
Vag 0.1 93.31 91.17 100 100 90.52
Vag 0.1 92.22 3.2 99.43 99.09 93
Vag 0.05 3.2 2.41 98.9 10.72 90.81
Vag 0.05 92.26 89.62 99.68 100 91.5
Vag 0.05 - - - - -
Vag 0.01 94.1 91.74 99.93 100 96.13
Vag 0.005 - - - - -

Application to mixtures
Ratio Pos. 1 (%) Pos. 2 (%) Pos. 3 (%) Pos. 4 (%) Pos. 5 (%)
9:1 16.12 12.51 16.41 16.75 15.54
9:1 15.25 10.49 15.87 18.53 17.37
9:1 31.38 20.43 31.43 24.57 30.85
9:1 21.14 20.32 22.2 25.83 21.75
4:1 19.17 16.79 25.61 20.75 21.11
4:1 18.16 17.58 21.84 24.91 20.46
4:1 25.62 23.4 28.3 27.46 23.68
4:1 17.72 17.39 24.79 20.01 22.79
2:1 26.36 25.11 30.65 28.96 29.19
2:1 24.7 22.23 26.6 26.74 25.29
2:1 23.83 34.93 41.21 39.33 39.17
2:1 21.56 21.01 32.78 33.67 31.42
1:1 33.81 34.87 40.9 36.28 37.91
1:1 37.58 34.15 43.37 37.47 37.96
1:1 20.59 25.08 39.72 28.38 37.86
1:1 33.07 30.69 36.02 36.65 35.31
1:2 44.18 43.56 52.69 51.3 47.42
1:2 47.8 39.63 53.44 51.84 48.54
1:2 46.16 36.57 64.69 49.07 52.73
1:2 39.4 53.76 55.52 48.92 60.72
1:4 54.42 52.13 68.22 65.32 58.21
1:4 58.68 51.19 73.2 57.5 64.63
1:4 61.86 54.91 66.23 57.78 61.94
1:4 40.93 23.9 49.02 36.19 37.13
1:9 56.8 57.16 71.3 62 64.09
1:9 53.21 50.72 71.89 59.57 61.67
1:9 54.32 55.34 58.85 50.69 57.49
1:9 89.26 86.44 89.31 84.76 94.25
• Mixtures of extracted DNA from
vaginal epithelial cells and liquid
semen were made.
• Ratios ranged from 9:1 to 1:9,
semen to vaginal epithelial
• Includes two sets of data: 10 ng of
total input and 1 ng of total input.
No significant difference between
the two data sets.
• Acceptable range used was <50%.
No non-sperm samples with input
of greater than 0.2 ng and passing
quality flags had a methylation
percentage below 50%.

GlobalFiler sensitivity
• Dilution sets were run concurrently with Globalfiler to compare the sensitivity
ranges between the Pyrosequencing amp kit and an STR kit
• Full profiles from single source samples were obtained down to 0.1 ng total input
• Dropout was observed in nearly every sample with 0.05 ng total input
• Pyrosequencing results were concordant with Globalfiler. Results were obtained
down to 0.1 ng, but at 0.05 ng failure rates spiked.
SampleID
Meth.
%
Meth.
%
Meth.
% Meth. % Meth. % Full profile
Semen 0.5 ng 3.05 2.49 3.73 4.13 3.69 Yes
Semen 0.5 ng 3.72 2.75 5.35 4.18 5 Yes
Semen 0.1 ng 4.21 2.53 2.91 4.02 4.25 No
Semen 0.1 ng 4.22 2.49 3.88 4.48 5.13 No
Semen 0.1 ng 2.7 3.57 52.57 6.03 4.24 No
Semen 0.05 ng 6.02 4.67 5.72 10.01 10.41 No
Semen 0.05 ng 3.46 3.74 3.68 5.41 5.41 No
Semen 0.05 ng 11.66 5.28 14.5 13.26 25.03 No
Figure: Data from one
dilution set.

Pyrosequencing vs. Sperm search
• Currently our lab only sperm searches at the request of the DA.
• Only searching cases where a probative male profile has been deduced and the case is
going to court.
• In 2017, we had 216 sperm positive samples; 139 of those had a male profile deduced.
• Assuming 0.2 ng of total male input DNA and a male:female ratio greater than 1:2,
pyrosequencing should yield sperm positive results.
• How many of the 139 male deduced male profiles identified as sperm positive would
also be sperm positive with pyrosequencing?
• Pyrosequencing: 88/139 = 63% (88 of the 139 samples fall within the conditions)
• How many male profiles were deduced that were negative for sperm?
• Traditional: 12/208 = 6% (possibly from other body fluids?)

Are there stochastic effects?
From the beginning we would see outlier methylation values at low levels in samples
where data quality was acceptable. Why?
• Naue et al. ran simulations using models to predict methylation % of biological
material at varying input levels
• Methylation is a binary event. The position is either methylated or it is non-methylated.
• Consider the tissue as a total population. The methylation % is the average for thousands
of cells.
• At low levels, we are only examining a couple of cells.
• This is not a true sampling of the cell population

Main Takeaways
1) Reliable results are dependent on both the amount of DNA and the robustness
of the amp.
2) We have shown that it can be possible to confirm the presence of sperm in a
mixture sample.
Next…
• More data necessary for all body fluids, especially vaginal epithelial.
• Need to conduct population studies to show that the methylation % holds true
across different populations.
• Multiplex could provide greater specificity (but may lose sensitivity).
A probabilistic approach could greatly improve the ability for confirmation of a
specific biological material in a mixture.

References
• Silva, D. et al. Developmental validation studies of epigenetic DNA
methylation markers for the detection of blood, semen and saliva
samples. FSIG 23(2016) 55-63.
• QIAGEN. "Pyrosequencing Technology and Platform Overview.”
• Fischinger, F. Pyrosequencing Workflow for DNA Methylation Analysis.
QIAGEN.
• Naue, Jana et al. Forensic DNA Methylation profiling from minimal
traces: How low can we go?. FSIG 33(2018) 17-23.

Thanks to:
Mark Powell, SFPD
Eleanor Salmon, SFPD
Fabiola Siordia, SFPD
John Haley, QIAGEN
Sim Winitz, QIAGEN
Frank Fischinger, QIAGEN
Bruce McCord, Florida International University

Sample to Insight
All QIAGEN products mentioned here are intended for molecular biology
applications. The products are not intended for diagnosis, prevention, or treatment of
a disease. The PAXgene product is for research use only and not for use in
diagnostic procedures.
Thank you for your attention!
Trademarks: QIAGEN®, Sample to Insight®, QIAamp®, AllPrep®, EpiTect®, PyroMark®, Pyrosequencing®
(QIAGEN Group), PAXgene® (PreAnalytiX GmbH). Registered names, trademarks, etc. used in this
document, even when not specifically marked as such, are not to be considered unprotected by law.
PROM-12928-001 08/2018 © 2018, QIAGEN, all rights reserved.
For more information, contact your QIAGEN sales representative
or visit www.qiagen.com

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