Charlo'e	J.	Word,	Ph.D.	

Consultant	

NIST	DNA	Analyst	Webinar	Series:		Probabilis6c	

Genotyping	and	So<ware	Programs	(Part	1)	

May	28,	2014

Why	Do	We	Need	to	Consider	

Probabilis9c	Modeling?	
Why	Now?	What’s	Changed?	

• 20+	years	of	DNA	tes9ng	

• Generally	accepted	

– Admi'ed	in	courts	world-wide	

• The	“Gold	Standard”	of	forensic	sciences	

• Changes	in:		Cases	Accepted,	Samples	Tested,	

Test	Kits	and	Instrumenta9on	leading	to	

changes	in	Profile	Results	and	Interpreta9on	

Charlo'e	J.	Word	–	2014	
Cases	Accepted	in	the	Laboratory	

• Homicides,	rapes	

• High	Profile	

• Strict	laboratory	

acceptance	policies	

• Property	crimes	also	

• Any	crime	with	possible	

biological	sample	

• Few	(or	no)	restric9ons	

on	samples	accepted	

and	tested	

BEFORE 	 						NOW	

Charlo'e	J.	Word	–	2014	
Samples	Tested	

• Blood,	Semen,	Saliva	

• Single	Source	or	Two	

Person	Mixtures	

• Large	Visible	Stains	

– Duplicate	samples	

– Reproducibility	of	

result	

– Do	addi9onal	tests	

• Handled	items	

• Complex	Mixtures	–	

many	with	Low	

Template	(LT)	DNA	

• Small	or	unknown	size	

– “Duplicate”	swab	may	

not	be	the	same	

– Limited	size	oben;	

unable	to	reproduce	the	

results	or	do	addi9onal	

tests	

BEFORE 	 						NOW	

Charlo'e	J.	Word	–	2014	
Profiles	Generated/Interpreta9on	

• Ar9facts	(stu'er,	

pull-up)	easily	

recognized	in	most	

profiles	

• Degrada9on	not	a	

significant	issue	

usually	

• Difficult	to	dis9nguish	

ar9facts	vs.	true	alleles	

in	complex	mixtures	

(especially	with	LT	

DNA)	

• Degrada9on	more	

common;	more	difficult	

to	detect	&	deal	with	

when	have		mixtures	

BEFORE 	 						NOW	

Charlo'e	J.	Word	–	2014	
Profiles	Generated/Interpreta9on	

• Analy9cal	threshold	

(AT)	can	be	high	due	

to	high	peaks		

• Generally	one	AT	is	

sufficient		

• Instrument	

differences	generally	

not	an	issue	

• Analy9cal	threshold	

needs	to	be	carefully	

determined	

• May	need	2	ATs	–	for	

Low	Template	(LT)	DNA	

vs.	high	peaks	

• Instrument	differences	

may	affect	

interpreta9on	of	the	

data	

BEFORE 	 						NOW	

Charlo'e	J.	Word	–	2014	
Mixture	Interpreta9on	

• Major	and	minor	

contributor	profiles	

oben	can	be	readily	

determined	in	2	

person	mixtures		

• Can	deduce	using	a	

known	with		

indis9nguishable	

mixtures		

• Difficult	to	determine	if	

there	is	a	major	

contributor;	mul9ple	

minor	contributors	a	

problem	

• Generally	cannot	

deduce	any	profile	even	

if	a	contributor	is	

known	

BEFORE 	 						NOW	

Charlo'e	J.	Word	–	2014	
Sta9s9cal	Calcula9ons	

• Required	in	the	US	for	all	inclusions	

– Case	law	in	many	states	

– SWGDAM	Guidelines	

• If	unable	to	provide	a	sta9s9cal	frequency	for	

a	poten9al	inclusion	statement,	may	need	to	

report	“inconclusive”	for	the	sample	

– Evidence	is	oben	deemed	inadmissible	in	court	

when	a	“weight”	determina9on	is	not	provided		

Charlo'e	J.	Word	–	2014	
Sta9s9cal	Calcula9ons	

• Random	match	

probability	(RMP)	for	

single	source	&	major	

or	minor	contributor	

• Combined	probability	

of	inclusion	(CPI)	for	

many	two	person	

mixtures	

• Likelihood	ra9o	rarely	

used	(in	US)	

• RMP	generally	not	an	

op9on	unless	a	clear	

major	contributor	is	

present	

• CPI	generally	not	

suitable	due	to	possible	

loss	of	alleles	

• Likelihood	ra9os	rarely	

used,	but	may	be	the	

next	step	

BEFORE 	 						NOW	

Charlo'e	J.	Word	–	2014	
Improved	Sensi9vity	of	Amplifica9on	Kits	

• PCR	reac9on	buffer	

good;	inhibi9on	a	

possible	problem	

– More	DNA	needed	

– 1-2	ng	minimum	

• Limited	flexibility	of	

use	of	kits		

• Few	Stochas9c	Effects	

• Improved	reac9on	

buffer;	inhibi9on	less	of	

a	problem

– Less	DNA	needed	

– 0.2-0.75	ng	min/max	

• Variable/Increased	

cycle	number	(LCN	

tes9ng)	

• Increased	Stochas9c	

Effects	present	

BEFORE 	 						NOW	

Charlo'e	J.	Word	–	2014	
Quan9ty	of	DNA	and	Cell	Count		

Approximate	Amount	of	DNA	per	Person	in	ng

Approximate	Number	of	Cells		

Amount	of	

DNA	in	PCR

1	Person	 2	Person	Mixture	

1:1	

2	Person	Mixture	

4:1	

1	ng 1		 0.5	+	0.5	 0.8	+	0.2	

150	 75	+	75	 120	+	30	

0.5	ng 0.5		 0.25	+	0.25	 0.4	+	0.1	

75	 38	+	38	 60	+	15	

0.25	ng 0.25		 0.125	+	0.125	 0.2	+	0.05	

38	 19	+	19	 30	+	7	

0.1	ng 0.1		 0.05	+	0.05	 0.075	+	0.025	

15	 7	+	7	 11	+	4	

0.05	ng 0.05		 0.025	+	0.025	 0.04	+	0.01	

7	 4	+	4	 6	+	1	

Charlo'e	J.	Word	–	2014	
Quan9ty	of	DNA	and	Cell	Count		

Approximate	Amount	of	DNA	per	Person	in	ng

Approximate	Number	of	Cells		

Amount	of	

DNA	in	PCR

1	Person	 2	Person	Mixture	

1:1	

2	Person	Mixture	

4:1	

1	ng 1		 0.5	+	0.5	 0.8	+	0.2	

150	 75	+	75	 120	+	30	

0.5	ng 0.5		 0.25	+	0.25	 0.4	+	0.1	

75	 38	+	38	 60	+	15	

0.25	ng 0.25		 0.125	+	0.125	 0.2	+	0.05	

38	 19	+	19	 30	+	7	

0.1	ng 0.1		 0.05	+	0.05	 0.075	+	0.025	

15	 7	+	7	 11	+	4	

0.05	ng 0.05		 0.025	+	0.025	 0.04	+	0.01	

7	 4	+	4	 6	+	1	

Charlo'e	J.	Word	–	2014	
Quan9ty	of	DNA	and	Cell	Count	

Approximate	Amount	of	DNA	per	Person	in	ng

Approximate	Number	of	Cells

Amount	of

DNA	in	PCR

3	Person	

1:1:1	

3	Person	

5:2:1	

4	Person	

1:1:1:1	

4	Person	

5:2:2:1	

1	ng 0.33	x	3	 0.6+0.25+.125	 0.25	x	4	 0.5+0.2+0.2+0.1	

50	x	3	 94	+	38	+	19	 38	x	4	 75	+	30	+	30	+	15	

0.5	ng 0.16	x	3	 0.31+0.12+0.06	 0.125	x	4	 0.25+0.1+0.1+0.05	

24	x	3	 47	+	18	+	9	 19	x	4	 38	+	15	+	15	+	7	

0.25	ng 0.08	x3	 0.15+0.06+0.03	 0.062	x	4	 0.12+0.05+0.05+0.02	

12	x	3	 23	+	9	+	4	 9	x	4	 18	+	7	+	7	+	3	

0.1	ng 0.03	x3	 0.062+0.02+0.01	 0.025	x	4	 0.05+0.02+0.02+0.01	

5	x	3	 10	+	3	+	1	 4	x	4	 7	+	3	+	3	+	1	

0.05	ng 0.016	x3	 0.03+0.012+0.006	 0.0125	x	4	 0.025+0.01+0.01+0.005	

2	x	3	 5	+	<2	+	<1	 2	x	4	 4	+	1	+	1	+	<1	

Charlo'e	J.	Word	–	2014	
1	

2	

3	

4	

Low	Template	DNA	Leads	to	Stochas9c	Effects		

1	

2	

3	

4	

Peak	Height	Imbalance	

at	Heterozygous	Loci	–		

Peak	Height	Ra9o	has	

less	meaning	

Allele	Drop-Out	

Allele	Loss	

Charlo'e	J.	Word	–	2014	
Low	Template	DNA	Leads	to	Stochas9c	Effects

Allele	Drop-In	

(&	Drop-out)	

1	

2	

3	

4	

Elevated	Stu'er	

1	

2	

3	

Allele	Gain	

Charlo'e	J.	Word	–	2014	
Single	Source	or	a	Mixture?	

Single	Source	with	Peak	Height	Imbalance	and	Drop-in	

Charlo'e	J.	Word	–	2014	
Number	of	Contributors?	Major	Contributor?	

Charlo'e	J.	Word	–	2014	
Are	ALL	Alleles	Present?	Ar9facts?	

Charlo'e	J.	Word	–	2014	
How	Can	We	Assess	if	ALL	Alleles	are	

Present?	

• Stochas9c	threshold	–	not	an	ideal	method	

– Gives	a	good	es9mate	of	where	cau9on	is	needed	

– “Line	drawn	in	the	sand”	–	peaks	of	almost	same	

height	treated	differently	

– Ignores	the	increased	likelihood	of	stochas9c	effects	

as	the	amount	of	DNA	amplified	and	peak	heights	

decrease		

• Es9mate	probability	of	drop-out	

200	

RFU	

Charlo'e	J.	Word	–	2014	
Confidence	in	Data	

													THEN 			NOW	

High	Confidence											Decreased	Confidence

Minimal	Uncertainty												Higher	Uncertainty	

Real	allele	vs.	stu'er,	pull-up	ar9fact	

Real	allele	vs.	drop-in	“allele”	

Number	of	contributors	

Major/minor	contributors	

Peak	height	ra9os	

Mixture	ra9os	

Ability	to	exclude	a	non-contributor	

Charlo'e	J.	Word	–	2014	
Whatever	way	uncertainty	is	approached,	

probability	is	the	only	sound	way	to	think	

about	it.		

		

					-Dennis	Lindley	

Borrowed	from	Michael	Coble,	NIST	
Now	and	Future	for	Complex	Mixtures	and	

LT	DNA	Profile	Interpreta9on	

Paradigm	shib	in	the	field	requires	a	shib	in	the	

methods	used		

• Probabilis9c	Modeling	of	data	with	Likelihood	

Ra9o	calcula9ons	may	be	the	answer	for	some	

of	the	profiles	obtained	today	

– Some	profiles	may	s9ll	be	uninterpretable	

• Valida9ons	needed	

• TRAINING	needed	(analysts,	a'orneys,	judges,	

law	enforcement)	

Charlo'e	J.	Word	–	2014	
THANK	YOU!!	

John	Butler		

Mike	Coble		

Robin	Co'on	

Catherine	Grgicak

Bruce	Heidebrecht

For	many	hours	of	

discussions!	

Catherine	Grgicak

Robin	Co'on	

NIJ	Grant	to	Boston	

University	

For	the	profiles!

Crime	lab	analysts	for	

excellent	ques9ons	and	

comments	from	past	

presenta9ons	

Charlo'e	J.	Word	–	2014