Download Low copy number (LCN) DNA testing

Solving the problem of
mixed DNA profiles
Dan E. Krane, Wright State University
Courtroom Knowledge of Forensic Technology and
the Impact on Frye and Daubert Standards
Wednesday, August 10, 2016
Forensic Bioinformatics
(www.bioforensics.com)
DNA profile
Comparing electropherograms
EXCLUDE
Evidence sample
Suspect #1’s reference
Comparing electropherograms
CANNOT EXCLUDE
Evidence sample
Suspect #2’s reference
What weight should be given to
DNA evidence?
Statistics do not lie.
But, you have to pay close attention to the
questions they are addressing.
What is the chance that a randomly
chosen, unrelated individual from a given
population would have the same DNA
profile observed in a sample?
Single source statistics:
Random Match Probability (RMP)
Statistical estimates: the product rule
2pq x 2pq x 2pq x 2pq x
Single source
samples
Formulae for RMNE:
At a locus:
Heterozygotes: 2pq
Homozygotes: p2
Multiply across all
loci
2pq x p2 x 2pq x 2pq x 2pq x
2pq x p2 x p2 x 2pq x
2pq
x
2pq
Statistical estimate: Single source sample
0.1454
x 0.1097
x 2
Statistical estimate: Single source sample
X
3.2%
0.1454
x 0.1097
9.8%
X
6.0%
x 2
4.6%
X
1.2%
6.3%
X
2.2%
X
1.0%
1 in 608 quintillion
(“less than one inX oneX billion”)
X
X
2.9%
5.1%
1.1%
X
X
= 0.032
X
9.5%
X
29.9%
4.0%
6.6%
1 in 608,961,665,956,361,000,000
X
What weight should be given to
DNA evidence?
Statistics do not lie.
But, you have to pay close attention to the
questions they are addressing.
What is the chance that a randomly
chosen, unrelated individual from a given
population would have the same DNA
profile observed in a sample?
Mixture statistics:
Combined Probability of
Inclusion (CPI)
Mixed DNA samples
Put two people’s names into a
mixture.
How many names can you take
out of this two-person mixture?
How many names can you take
out of this two-person mixture?
CPI statistics
CPI statistics
Combined Probability of
Inclusion
• Probability that a random, unrelated person
could be included as a possible contributor to
a mixed profile
• For a mixed profile with the alleles 14, 16,
17, 18; contributors could have any of 10
genotypes:
14, 14
14, 16
16, 16
14, 17
16, 17
17, 17
14, 18
16, 18
17, 18
18, 18
Probability works out as:
CPI = (p[14] + p[16] + p[17] + p[18])2
(0.102 + 0.202 + 0.263 + 0.222)2 = 0.621
Mixed DNA samples
Mixtures with drop out
CPI statistics
without
dropout
Combined Probability of
Inclusion
• Probability that a random, unrelated person
could be included as a possible contributor to
a mixed profile
• For a mixed profile with the alleles 14, 16,
17, 18; contributors could have any of 10
genotypes:
14, 14
14, 16
16, 16
14, 17
16, 17
17, 17
14, 18
16, 18
17, 18
18, 18
Probability works out as:
CPI = (p[14] + p[16] + p[17] + p[18])2
(0.102 + 0.202 + 0.263 + 0.222)2 = 0.621
The testing lab’s conclusions
Ignoring loci with “missing” alleles
• Some laboratories assert that this is a
“conservative” approach
• Ignores potentially exculpatory information
• “It fails to acknowledge that choosing the
omitted loci is suspect-centric and therefore
prejudicial against the suspect.”
– Gill, et al. “DNA commission of the International Society
of Forensic Genetics: Recommendations on the
interpretation of mixtures.” FSI. 2006.
LCN statistics
•No generally accepted method for
attaching weight to mixed samples with
an unknown number of contributors
where dropout may have occurred.
•No stats = not admissible.
Why has this become an issue?
– More challenging evidence samples
• Touch DNA
• Guns, steering wheels, doorknobs, etc.
– Resulting DNA profiles often:
•
•
•
•
Small amounts of DNA
Complex mixtures (3 or more persons)
Degradation (differential degradation)
Minor components in major/minor
mixtures
– Stochastic effects!
– Existing test kits were not designed to test these
kinds of samples
– Existing statistical methods used in the US are
poorly suited to reporting these kinds of
samples
Applied Biosystems AmpFlSTR®
Identifiler® Plus User Guide pg 17
30
The stochastic threshold
• The amount of template DNA where
random factors influence test results as
much as the actual template.
– Exaggerated peak height imbalance
– Exaggerated stutter
– Allelic drop-in
– Allelic drop-out
• Sampling error is at the heart of it all
STR Kit Amplification with conventional
SOP and with LCN protocol
Input DNA
Data from Debbie Hobson (FBI) – LCN Workshop AAFS 200
SOP
1ng
PHR = 87%
50 µL PCR
PHR = 50%
5 µL PCR
Allele Drop Out
LCN
8pg
Allele Drop In
Peak Height
Imbalance
Amplify same sample 4 times
with insufficient DNA
Amplification 1
Amplification 2
Equal Mixture of DNA from
two persons:
Person A: 9, 13
Person B: 21, 24
Amplification 3
Amplification 4
But
ambiguities
can
arise…
Do these profiles match?
Evidence
Likelihood ratios (LRs)
– Compares two alternative hypothesis
• “Prosecution” explanation Hp (or H1)
• “Defense” explanation Hd (or H2)
– The likelihood ratio is better able to deal
with to continuous data
• Enables scientist model stochastic effects and
complex mixtures
• Complicated – need computer program
– Track record:
• Widely used in UK, Europe, Australia & New
Zealand
• Not much in US (other than Paternity Index)
Prosecution
explanation
of the DNA
DNA evidence is:
A mixture of two
persons
consisting of
victim and
defendant
Pr(E|Hp)
Likelihood ratio =
Pr(E|Hd)
Defense
explanation
of the DNA
DNA evidence is:
A mixture of two
persons
consisting of
victim and an
unknown person
100
10
1,000
10,000
1
0.1
0.01
0.001
0.0001
100,000
0.00001
<0.000001
1,000,000+
“VERY
STRONG”
Support for
PROSECUTION
explanation
Defense
explanation
of the DNA
Prosecution
explanation
of the DNA
Likelihood Ratio: Drawbacks
• Choice of hypotheses can be challenging:
– Prosecution Hypothesis (Hp) is usually easy
(based on specific allegation)
– Defense Hypothesis (Hd) may be more
difficult to anticipate
• Can do multiple pairs of hypotheses
• In mixtures need to specify number of
contributors
– Can have different numbers of contributors in
Hp and Hd
• Always look at the hypotheses carefully to check
they accurately represent the facts of the case
Why do we need probabilistic genotyping?
– More challenging evidence samples
• Touch DNA
• Guns, steering wheels, doorknobs, etc.
– Resulting DNA profiles often:
•
•
•
•
Small amounts of DNA
Complex mixtures (3 or more persons)
Degradation (differential degradation)
Minor components in major/minor
mixtures
– Stochastic effects!
Existing statistical methods used in the US
are poorly suited to reporting these kinds of
samples
Software Models
Lab Retriever (Rudin et.al.)
LRmix Studio (Haned et.al.)
Forensic Statistical Tool
(OCME NY)
LikeLTD (Balding)
ArmedXpert (Niche
Vision)
DNA View (Brenner)
STRMix (Buckleton et.al.)
TrueAllele (Perlin)
SEMICONTINUOUS
MODELS
Do NOT take peak
height into
account
CONTINUOUS
MODELS
Take peak height
into account
So, what do most of these programs do (… in plain
language)? Part I
•
•
•
•
Run DNA test (as usual) – resulting in e-data
Analyze electronic data with GeneMapper ID (as usual)
Review electropherograms (as usual)
Interpret (as usual)
– Decide on MATCHES, EXCLUSIONS and INCONCLUSIVES
USUALLY AT THIS STAGE ANALYST WOULD USE POPSTATS TO
CALCULATE STATS AND THEN WRITE REPORT
• Consider the LR hypotheses you may want to use
– Victim present?
– Number of contributors?
• Return to GeneMapper and prepare a special tabular export of the
allele calls (including peak heights) for the evidence sample and
refs. that you want to compare
– Remove artifacts and rare alleles
– May or may not include stutter peaks
– May drop analytical threshold to a lower level to capture more peaks
So, what do most of these programs do (… in plain
language)? Part II
• Import tabular data into Probabilistic Software
• Frame LR Hypotheses, for example:
– HP = VICTIM plus DEFENDANT plus ONE UNKNOWN PERSON
– Hd = VICTIM plus TWO UNKNOWN PERSONS
• Set drop-out estimate
– Methods differ in how this is done
– May be based on the data
– May be flat estimate
• Set drop-in estimate
– Usually use flat estimate
• Set up additional variables
– Depends of software program
• Run program!
• Review output
• Program will give a numerical value indicating the Likelihood Ratio
– If above 1, supports prosecution hypothesis
– If below 1, supports defense hypothesis
– Inconclusive range around 1
This is true for most of the programs, but TrueAllele is
different
•
•
•
•
Run DNA test (as usual) – resulting in e-data
Analyze electronic data with GeneMapper ID (as usual)
Review electropherograms (as usual)
Interpret (as usual)
TrueAllele
USUALLY AT THIS
STAGE ANALYST
WOULD
USE POPSTATS TO
DOES
THE
REST
CALCULATE STATS AND THEN WRITE REPORT
(and
most
of
the
other
page
• Consider the LR hypotheses you may want to use
as well)
– Decide on MATCHES, EXCLUSIONS and INCONCLUSIVES
– Victim present?
– Number of contributors?
• Return to GeneMapper and prepare a special tabular export of the
allele calls (including peak heights) for the evidence sample and
refs. that you want to compare
– Remove artifacts and rare alleles
– May or may not include stutter peaks
– May drop analytical threshold to a lower level to capture more peaks
TrueAllele
– Continuous approach
• Models peak heights
• Uses MCMC
– Imports raw electronic data
– Uses its own smoothing (not GeneMapper)
• Perlin says it is “equivalent” to ABI’s data in terms of
peak heights
• But peak heights are not the same
– TrueAllele performs all the analysis of the data
• Including the GeneMapper analysis usually done by
the lab analyst
– TrueAllele is intended to replace the analyst
• Interpret the data
• Make the “matches”
• Calculate the statistics
TrueAllele
– Models 100s of variables:
• Some are known, such as degradation and relative amounts of
DNA:
• The vast majority have not been described
– Uses a very low analytical threshold (10 RFU)
– Unlike STRMix and other approaches, TrueAllele does
not need a lab or test kit-specific variance factor
– The program is able to take into account such things as:
•
•
•
•
•
Stutter (plus and minus)
Biochemical and electrical artifacts
Type of test (Identifiler, Profiler etc.)
Type of instrument (3130, 3500)
What else?
TrueAllele
– Proponents say that validation studies show that it ”gets
the right answers”:
• Known mixtures rarely have LRs for known non-contributors
that are greater than those for known contributors
• Several peer-reviewed papers outline general approach
– Detractors worry about the black box and failure to
define limitations:
• At least a dozen hotly debated questions must have been
resolved to generate a reliable result
• Software engineering concerns/right to confrontation
• Validation studies do find known non-contributors with positive
LRs
• No clear features of samples for which TrueAllele is known to
not generate reliable results
Solving the problem of
mixed DNA profiles
Dan E. Krane, Wright State University
Courtroom Knowledge of Forensic Technology and
the Impact on Frye and Daubert Standards
Wednesday, August 10, 2016
Forensic Bioinformatics
(www.bioforensics.com)