John Buckleton

1st North Eastern STRmix

User Group Meeting

18th September 2018

Connecticut

https://johnbuckleton.wordpress.com/

2

• V2.6 Fly past

• Generic parameters - Hillary

• Recent court experiences

• Nathan Adams Code documentation

• Alan Jamieson

• Lund & Iyer

• NIST Foundational review

• PCAST

• Spanish and Portuguese Interlab

4

Generalised stutter

Heterozygote at SE33

• 21,26

Back stutter (-1,0 repeat unit [v2.3] )

• 20,21,25,26

Forward stutter (+1,0 rpt unit [v2.4] )

• 20,21,22,25,26,27

SE33 for v2.5

• 20,21,22,25,26,27

v2.6 Double back stutter (-2,0 rpt units)

• 19,20,21,22,24,25,26,27

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

17 18 19 20 21 22 23 24 25 26 27 28 29 30

rfu

v2.6 2bp back stutter (-1,2)

• 19,20,20.2,21,22,24,25,25.2,26,27

Missing data problem

Double back

12

0

10

20

30

40

50

60

0 2000 4000 6000 8000

Do

ub

le b

ack

stu

tter

h

eigh

t (r

fu)

Parent peak height (rfu)

Read low (10rfu)

Good even

highish template

We need to model

the “missing data

Variable Number of Contributors

13

VarNOC

• Deconvolutions can now be set up with a

range of NOC (Developmentally validated

for N and N+1 contributors only.)

• Independent deconvolutions are carried

out under each scenario. (NOC is kept

constant under Hp and Hd for each

deconvolution.)

14

2 or 3 contributors

Prosecution: “I think it’s 3 people”

Defence: “I think it’s 2 people”

Initial thoughts may be that this is biased in some way.

It is actually perfectly allowable

As long as 2p and 3p scenarios can be properly weighed

against each other

That is in essence what STRmix VarNOC does – weighs the

likelihood of N = 2 against N = 3

16

Generic Parameters - Hillary

17

Generic Parameters - Hillary

log(LR)s for true

contributors for

cognate vs non-

cognate

(PROVEDIt) data.

18

Some court challenges

19

Nathan Adams

• Inspected V1.08, V2.3.07, V2.4.05

• V2.4.06 pending

• Critical of code quality and documentation around

coding

• Does not accept you can validate by testing

• We are seeking accreditation

• Also considering “back documentation”

Internal validation compilation

21

LR for Hp Support

and 1/LR for Hd

Support

Verbal Qualifier

Fraction of false donor LRs in

this range (N = 28,250,000)

[1-2) Uninformative 0.003197 1 in 312

[2-99) Limited Support 0.003143 1 in 318

[99-9999) Moderate Support 5.53 x 10-5 1 in 18,000

[9999-999,999) Strong Support 7.08 x 10-7 1 in 1,400,000

≥999,999 Very Strong Support 0

2,825 mixtures 28,250,000 false donors

Likelihood ratio

27

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.

Weights and ranks

• Q. My client is not the top genotype in the

list (i.e. the one with the highest weight)

28

Weights and ranks

Contributor TPOX Weight

C1 7,11 100.00%

C2

7,11 37.80%

11,11 25.73%

7,7 23.42%

7,Q 5.85%

11,Q 5.67%

Q,Q 1.53%

29

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

16

29

7

13 1

7 19

14

8

10 1

5

37

9

15

27

22

36 45

36 45

28

55

171

120

66

136

36

153

45

36

36 45

946

45

120 171

91

0.1

1

10

100

1000

D3S

1358

vW

A

D16S

539

CS

F1P

O

TP

OX

Yin

del

D8S

1179

D21S

11

D18S

51

DY

S391

D2S

441

D19S

433

TH

01

FG

A

D22S

1045

D5S

818

D13S

317

D7S

820

SE

33

D10S

1248

D1S

1656

D12S

391

D2S

1338

Ran

k

Rank C1

#C1

genotypes

(STRmix)

#genotypes

(population)

200pg 1:1:1:1 - comparison to C1

(totals: Rank 59,719,680, GTs

1.25E+24, pop-GTs 8.55E+38)

30

200pg 1:1:1:1 - comparison to C1

(totals: Rank 59,719,680, GTs

1.25E+24, pop-GTs 8.55E+38)

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

16

29

7

13 1

7 19

14

8

10 1

5

37

9

15

27

22

36 45

36 45

28

55

171

120

66

136

36

153

45

36

36 45

946

45

120 171

91

0.1

1

10

100

1000

D3S

1358

vW

A

D16S

539

CS

F1P

O

TP

OX

Yin

del

D8S

1179

D21S

11

D18S

51

DY

S391

D2S

441

D19S

433

TH

01

FG

A

D22S

1045

D5S

818

D13S

317

D7S

820

SE

33

D10S

1248

D1S

1656

D12S

391

D2S

1338

Ran

k

Rank C1

#C1

genotypes

(STRmix)

#genotypes

(population)

Rank of C1

Number of

genotypes STRmix

considered

Number of possible

genotypes at this locus

given #alleles

31

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

16

29

7

13 1

7 19

14

8

10

15

37

9

15

27

22

36 45

36 45

28

55

171

120

66

136

36

153

45

36

36 45

946

45

120 171

91

0.1

1

10

100

1000

D3S

1358

vW

A

D16S

539

CS

F1P

O

TP

OX

Yin

del

D8S

1179

D21S

11

D18S

51

DY

S391

D2S

441

D19S

433

TH

01

FG

A

D22S

1045

D5S

818

D13S

317

D7S

820

SE

33

D10S

1248

D1S

1656

D12S

391

D2S

1338

Ran

k

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

profile32

Most genotypes do not exist

Prof Bruce Weir

• In our example there are 8.55 x 1038

genotypes

• 7.5 x 109 worldwide population

• 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

33

Lund and Iyer

34

• US v Gissantaner

• California v Littleton

35

36

37

38

Interlab Spanish and

Portuguese

39

US v Gissantaner

John Butler sends paper to Defense (not prosecution)

Introduced via testimony of Steven Lund (Lund & Iyer)

Emphasis on variability

40

LRmix

1.E+00

1.E+02

1.E+04

1.E+06

1.E+08

1.E+10

1.E+12

1.E+14

1.E+16

1.E+18

LR

Euroforgen-NoE Case 2 proposition set 1

GHEP-ISFGEuroforgen-NoE Case 1

41

42

STRmix™ collaborative exercise on DNA mixture interpretation

Jo-Anne Bright[1], Kevin Cheng[1], Zane Kerr[2], Catherine McGovern[1], Hannah

Kelly[1], Tamyra R. Moretti[3], Michael A. Smith[3], Frederick R. Bieber[4], Bruce

Budowle[5] , Michael D. Coble[5], Rashed Alghafri[6], Paul Stafford Allen[7], Amy

Barber[8], Vickie Beamer[9], Christina Buettner[10], Melanie Russell[11], Christian

Gehrig[12], Tacha Hicks[13], Jessica Charak[14], Kate Cheong-Wing[15], Anne

Ciecko[16], Christie T. Davis[18], Michael Donley[19], Natalie Pedersen[20], Bill

Gartside[21], Dominic Granger[22], MaryMargaret Greer-Ritzheimer[23], Erick

Reisinger[24], Jarrah Kennedy[25], Erin Grammer[26], Marla Kaplan[27], David

Hansen[28], Hans J. Larsen[29], Alanna Laureano[30], Christina Li[31], Eugene

Lien[32], Emilia Lindberg[33], Ciara Kelly[34], Ben Mallinder[35], Simon

Malsom[36], Alyse Yacovone-Margetts[37],Andrew McWhorter[38], Sapana M.

Prajapati[39], Tamar Powell[40], Gary Shutler[41], Kate Stevenson[1], April R.

Stonehouse[42], Lindsey Smith[43], Julie Murakami[44], Eric Halsing[45], Darren

Wright[46], Leigh Clark[47], Duncan A. Taylor[48,49], John Buckleton[1,50]

43

Sample 1 Experimental NoC = 4

44

Sample 2 experimental NoC = 3

Dropped a locus

Laboratory

artifact policy

45

Conclusion

We continue to make incremental scientific

developments

Usability continues to improve

Continuing (escalating) defense challenge

Budowle, Coble

Strong community

Fred Bieber Moot court

47

Q: Would you not agree that there is a possibility

of making a mistake in estimating the true number

of contributors?

A1: Yes

A2: The NoC is unknown and should be treated as

a distribution. STRmix™ V2.6 can test two NoC.

Uncertainty in NoC does not translate into

equivalent uncertainty in the LR

48

49

Q: How do you "guestimate" the number of

contributors?

A: In casework and even in mock samples the

number of contributors to a sample is never known

for certain.

A reasonable number is assigned usually by expert

judgement based on on the number of alleles and

peak heights.

In empirical trials using mock samples an error of

overestimation or underestimation tends to result

in a lowering of large LRs either by a small amount

or in some cases a few orders of magnitude.

50

Q: How do you "guestimate" the number of

contributors?

The net effect of uncertainty in the number of

contributors for larger LRs is that either a correct

NoC was used or the result is likely to be even

more conservative

51

Q: What is effect of choosing too few contributors?

A: One too few contributors tends to cause a

“false” exclusion of the smallest donor. This is the

one that the expert did not think was even there.

This is why the word “false” has airquotes.

52

Q: What is effect of choosing too many contributors?

A: One too many contributors has little or no effect

on the LRs for the large donors (the major(s)). With

proper use of the informed priors function it can often

also have little effect on a minor. If there is an effect

on a positive LR for a minor it is downwards.

One too many contributors can produce low grade

adventitious matches with LRs near 1 from amongst

a large set of false donors.

53

Q: Identical twins...appear as a single source?

What about other close relatives?

A: Close relatives increase allelic overlap. This can

cause under assignment of NoC and can cause false

exclusions.

If one or more of the relatives can be “assumed” the

situation is much improved.

In artificial mother, father, child mixtures the

assumption of one parent and use of informed priors

gets the correct solution if the fraction of the child is

large enough.

54

Q: How to choose Hd? Is there only one possible

set of hypotheses?

A1: Too scientific: In theory It should be Hp vs

everything else. Practically all reasonable

propositions with a good posterior (LR x prior) need

to be considered.

A2: The defense are entitled to all reasonable

propositions. It is usually possible to safeguard the

interests of the defense by sensible choice of one or

a few propositions.

55

Q: What is confidence interval about LR estimates?

A: A philosophy available in STRmix™ and applied

by most laboratories worldwide is to assess all major

sources of uncertainty and make a rational

concession in the interests of the defense.

Most labs apply 3 or 4 layers of conservativism.

The population genetic model is conservativism at

the ratio of about 99:1

Most labs use a conservative value for theta

Most labs report the 99% lower bound on allele

frequency (technically probability) and MCMC

uncertainty.

Some labs also make a generous allowance for

relatives.

56

Q: What is highest LR you found in your validations

for a true non-contributor? (~500,000) ?

A: We had LR = 505,000 out of 28+ million false

donors in the Internal lab compilation. 703,000 in

Bright et al.

I cannot guarantee that these are the biggest ever

found.

In theory you could eventually get something really

big – say 1028 by chance. This is the correct result.

If you examined enough false donors, by random

simulation since this is more than there are people,

you would eventually get the right set of alleles.Searching mixed DNA profiles directly against profile databases

Jo-Anne Bright , Duncan Taylor, James Curran, John Buckleton

57

Q: Verbal predicates with LRs?

Q: If CI bridges 2 verbal predicates, which verbal

predicate do you use?

A: Lower

58

Q: Verbal predicates with LRs?

Q: If CI bridges 2 verbal predicates, which verbal

predicate do you use?

Arbitrary

Subjective

Strong for one person may be

moderate for another

If you just give the

number someone will

ask “What does this

mean?” and possibly

mention “we are lay

people.”

If you have to say

something why not get

organised in advance

Manage prosecutor’s

fallacy

59

Q: What constitutes an "intimate sample" for

purposes of assuming the presence of an individual's

DNA in a forensic mixture?

A: ASCLD have removed this statement

But you should assume someone when it increases

the Pr(E|Hd)

60

Q: Does STRMix impose binary structure on a continuous

biology???

Q: Is it correct to set a limit of 50rfu ..gives exaggerated

weight to a "homozygote" genotype, when one allele drops

below 50rfu...

A: We think some of the stability of STRmix’s

performance comes from not messing with the junk. Some

software appear to go astray with artifacts and small peaks.

We are considering automated spike and pull-up removal.

61

Assuming an incorrect number of contributors may

result in an inflated LR in favour of the prosecution

(provided the total number of contributors is the

same under both hypotheses).

62

Conservative

Non-conservative

Black edge means

realistic incorrect

63

Conservative

Non-conservative

Even the worst ones are

probably still within the

conservativism buffer