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