18th annual forensic dna conference bode 2019 april 23-26 ... · 18th annual forensic dna...
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18th Annual Forensic DNA Conference – Bode
2019
April 23-26, 2019
Phoenix, AZ
John Buckleton, Redding, Ca
I shall attempt to cover/introduce
Presenting LRs
Verbal scale
Number of contributors
2
Theory of communication
3
Theory of communication
4
• Q&A
• No feedback with jury
• Deliberate noise
5
Saying it better does not guarantee
understanding better
I REALLY MEAN
6
Ma’am I really need this at grade
5 level
Can we meet half way, can you
do some study?
7
8
995/1,000
Actually drunk
show red
1/1,000
Actually sober
show red
9
0.995995
0.001
Posterior odds = LR x prior odds
Posterior odds = 995 x prior odds
Random stop at 11:00 AM low prior odds
Low prior odds say 1 to 995 against
995 x 1/995 = 1
1:1 or 50%
10
0.995995
0.001
Posterior odds = LR x prior odds
Posterior odds = 995 x prior odds
Weaving driver at 11:00 PM higher prior odds
Higher prior odds 1:1
995 x 1 = 995
995:1 or 99.9%
Conclusion
• Two bits of evidence
• The light
• The prior odds
11
Would it work?• I have doubts
– Too long for court
– I had pictures
– I will offend at least one of
prosecution/defense/Judge
– I was stopped by the Judge once in the UK
after I said Bayes Theorem
• Can we improve
communication/preparation out of court
1
2
A statement about the probability that Mr Smith
left the stain can only be made from all the
evidence, not from the DNA alone.
The DNA evidence by itself increases the
odds that Mr Smith is the donor LR times
Over what they would be from the other evidence
This represents extremely strong support
that he is the donor
Objection! Arbitrary, subjective
This represents extremely strong support
that he is the donor
1. Adventitious matches can happen…
15
2. and always could
Verbal scales
3. You cannot summarise a distribution with one
number
5. There are different view of conservative
4. Apologies to those who, rightly, integrate
16
False inclusions (Adventitious
matches)• 28+ million Hd true tests – comparisons to non-contributors
• Average LR of all non-contributors = 0.12
• 20 twenty highest false contributors’ shared at least 61% and up
to 98% of the alleles with the mixture
Number Kit Apparent N Known N LR% overlapping
alleles
1 GlobalFiler™ 3 3 505,924 81%
2 Identifiler Plus™ 3 3 379,716 90%
3 GlobalFiler™ 4 4 197,907 98%
4 GlobalFiler™ 3 4 134,486 83%
5 GlobalFiler™ 4 4 88,022 98%
18
Highest adventitious match 730,000
Internal validation compilation
19
2,825 mixtures 28,250,000 false donors
LR for Hp
Support and 1/LR for Hd
Support
Verbal QualifierExpected less than
[1-2) Uninformative 1 in 2[2-99) Limited Support 1 in 99
[99-9999)Moderate Support
1 in 9,999
[9999-999,999) Strong Support 1 in 999,999
≥999,999 Very Strong Support
Internal validation compilation
20
2,825 mixtures 28,250,000 false donors
LR for Hp
Support and 1/LR for Hd
Support
Verbal QualifierExpected less than
Fraction of false donor LRs in this range (N = 28,250,000)
[1-2) Uninformative 1 in 2 0.003197 1 in 312[2-99) Limited Support 1 in 99 0.003143 1 in 318
[99-9999)Moderate Support
1 in 9,999 5.53 x 10-5 1 in 18,000
[9999-999,999) Strong Support 1 in 999,999 7.08 x 10-7 1 in 1,400,000
≥999,999 Very Strong Support
0
Please step outside your
history/ policy/position (defense
or government) etc.
21
Ask what is the best way to
convey these next three
examples?
I find it good to pretend this is
not an LR, maybe number of
oranges
22
-10
-5
0
5
10
15
logLR
This slide is no challenge
if you report the “best
estimate”
Best estimate
Lower bound
Lowest or
Nearest 1
Supports Ha or Hp or
?
No one understand the LR (so you
can’t use PG)
This illustrates that if the LRs of all the millions of
potential genotypes from a mixture were
calculated and then arranged in order of size, the
suspect is unlikely to be the highest LR.
In other words, the LR provides only the weight
of evidence against the specific defendant
without reference to other people who would
also have a LR greater that 1 …
In effect, the LR is a sophisticated version of the
disparaged ‘consistent with’ statement.
All profiles
All profiles
Possible profiles
from stain
Actual people
All profiles
Possible profiles
from stain
Actual people
All profiles
Possible profiles
from stain
Defendant’s profile
Actual people
All profiles
Possible profiles
from stain
Defendant’s profile
Weights and ranks
TPOXC1 7,11 100.00%
C2 7,7 23.42%7,Q 5.85%7,11 37.80%
11,11 25.73%11,Q 5.67%Q,Q 1.53%
5
2
1
6
1
2
1
2
3
1
4
1
8
3
1 1
9
4 4
3
2
10 11 12 14
10
10 1
6
29
7
13 1
7 19
14
8 10 1
5
37
9
15
27
223
6 45
36 45
28
55
17
1
12
0
66
13
6
36
15
3
45
36
36 45
94
6
45
12
0 17
1
91
0.1
1
10
100
1000
D3S1…
vW
A
D16S…
CSF1…
TP
OX
Yin
del
D8S1…
D21S
11
D18S
51
DY
S391
D2S
441
D19S…
TH
01
FG
A
D22S…
D5S
818
D13S…
D7S
820
SE
33
D10S…
D1S1…
D12S…
D2S1…
Ran
k
200pg 1:1:1:1 - comparison to C1 (totals: Rank 59,719,680, GTs 1.25E+24,
pop-GTs 8.55E+38)Rank C1
#C1
genotypes
(STRmix)
#genotypes
(population)
Note that the true donor is not always rank 1
He would only be rank 1 everywhere in a very clear
profile
Rank of C1
Number of genotypes
STRmix considered
Number of genotypes at
this locus
Most genotypes do not exist
Weir, BS
In our example 8.55 x 1038 genotypes
7.5 x 109 people
Only about 1 in 1029 genotypes exist
There are about 6 x 107 genotypes above our rank
Hence potentially no actual people above our rank
“The probability of observing this evidence is n
times more likely if it arose from Mr X + an
unknown person rather than two unknowns”
Likelihood ratio
• Is NOT measuring the probability of Mr Lee being a
contributor – many profiles will produce a high LR
• High LRs can be obtained for false propositions
• Depends on the number of contributors
• Does not test all of the possible explanations for the
evidence
• Q: What’s your opinion about how likely it is
that there are more than four contributors to
this mixed DNA sample
• A: I have absolutely no idea and nor does
[the prosecution witness].
34
35
A bit of over interpretation
Its our fault
Many of these had
very tiny minors
3 4 5
-30
-20
-10
0
10
20
30
-30 -20 -10 0 10 20 30
log(
LR)
as 4
log(LR) as 3
Hp true
Underestimating – Hp true
36
LRN-1
some false
exclusions of
‘true’
contributors
LRN ≈ LRN-1
Mostly no effect
4p mixtures
-30
-20
-10
0
10
20
30
-30 -20 -10 0 10 20 30
log(
LR)
as 4
log(LR) as 3
Hp true Hd true
Underestimating – Hd true
37
log(LR) previously ≈ 0
Now, LRN-1
supports
exclusion
4p mixtures
Overestimating – Hp true
39
-30
-20
-10
0
10
20
30
-30 -20 -10 0 10 20 30
log(
LR)
as 5
log(LR) as 4
Hp true
Little effect on
known
contributors
4p mixtures
Overestimating – Hd true
40
-30
-20
-10
0
10
20
30
-30 -20 -10 0 10 20 30
log(
LR)
as 5
log(LR) as 4
Hp trueHd true
log(LR)
previously
supported
exclusion… Now, LRN+1 ≈ 0
4p mixtures
41
True donors False donors
One under
Kicks out the smallest, you didn’t think it
was there
Some inc excl
42
True donors False donors
One under
Kicks out the smallest, you didn’t think it
was there
Some inc excl
One overBig ones stay the same smallest down 2-3 orders
Some excl inc
43
True donors False donors
One under
Kicks out the smallest, you didn’t think it
was there
Some inc excl
One overBig ones stay the same smallest down 2-3 orders
Some excl inc
One over and Mxprior
Stay the same Some excl inc
As long as your LR is big then you are
correct or conservative
Conclusion
• Number of Contributors
– LR stable over NoC
• Adventitious matches do happen
– Actually at less than the expected rate
• Communication – cannot be fixed at one
end
44
Acknowledgements
• Bode
• Bruce Budowle
• Dawn Boswell
45
46
What is the easiest way to explain PG to a jury?
What are any good analogies for presenting PG in court?
PG utilizes modern main stream mathematics to inform
complex DNA mixture interpretation.
Propose a solution. Propose genotypes template,
degradation and a few more. Predict the profile this would
produce. If this is a good fit to the observed take it. If it isn’t
take it sometimes.
47
What do you think about LA type version of reporting
LRS (see slide below).
Gap (0.5,1)
48
Sharks or Jets? East vs. West Coast theory of
deconvolution?
• Not East Coast v West Coast I think but…
• Triage first and review after or Shove through STRmixTM
• Focus on “The same answer.” This is fine as long as its not
at the expense of “a good answer”
• Human evaluation still needed – in my opinion
• Do you still want a pilot?
49
What do you hate on each other about (i.e. where is
the conflict reasonable and brilliant minds might differ
about?)
Very little
50
Where are you at on reporting qualitative conclusions as
“included” or “excluded” without a qualifier such as
strong, limited etc…
Excluded needs no qualifier.
DNA & Included Guilty
So if the evidence is weak it REALLY needs a qualifier