Applied 

Genetics

Mixture 

Interpretation

Michael Coble

Research Biologist, Applied Genetics Group

Forensics@NIST 2012 Meeting

Gaithersburg, MD

November 28, 2012
Applied 

Genetics Outline

• What are mixtures?

• Why are mixtures difficult to interpret?

• NIST’s role in mixture interpretation

• Workshops and training

• Software evaluation

• Conclusions
Applied 

Genetics DNA Mixtures

• Mixtures arise when two or more individuals 

contribute to the sample being tested. 

• Mixtures can be challenging to detect and 

interpret without extensive experience and 

careful training. 

• Even more challenging with poor quality data 

when degraded DNA is present...

J.M. Butler (2005) Forensic DNA Typing, 2nd Edition, p. 154
Applied 

Genetics Mixture Case Summaries

minimum # of contributors

Crime Class 1 2 3 4 >4 N

Sexual Assault 884 787 145 11 0 1827

Major Crime 1261 519 182 32 0 1994

High Volume 344 220 140 11 5 720

Total 2489 1526 467 54 5 4541

54.8% 33.6% 10.3% 1.2% 0.1%

http://www.cstl.nist.gov/biotech/strbase/pub_pres/Promega2008poster.pdf

Single source mixtures

Data Set from 14 Different Labs
Applied 

Genetics Single

-source Profile
Applied 

Genetics Mixture Profile
Applied 

Genetics Mixture Case Summaries

minimum # of contributors

Crime Class 1 2 3 4 >4 N

Sexual Assault 884 787 145 11 0 1827

Major Crime 1261 519 182 32 0 1994

High Volume 344 220 140 11 5 720

Total 2489 1526 467 54 5 4541

54.8% 33.6% 10.3% 1.2% 0.1%

http://www.cstl.nist.gov/biotech/strbase/pub_pres/Promega2008poster.pdf

Single source mixtures

Data Set from 14 Different Labs
Applied 

Genetics
Applied 

Genetics 2010 SWGDAM Guidelines

• In 2010, the Scientific Working Group on DNA 

Analysis Methods (SWGDAM) released new 

guidelines for STR interpretation. 

• The guidelines stressed the need for applying 

thresholds for data interpretation and including 

statistical support for any inclusion.
Applied 

Genetics

Why are DNA mixtures 

difficult to interpret?
Applied 

Genetics

1. We don’t know a priori the

quantity of each component 

in the mixture (mixture ratio).

2. This can lead to uncertainty in

 determining if all the alleles 

 are present. 

1.0 ng input

1:1 ratio

1.0 ng input

4:1 ratio

1.0 ng input

9:1 ratio

Data from Boston University Training Grant NIJ # 2008-DN-BX-K158
Applied 

Genetics The laboratory established 

“Stochastic” threshold is set at

200 RFU. Since all peaks are 

above this value, we can be 

confident that all of the alleles

are present at this marker.

1.0 ng input

1:1 ratio

Suspect – 16,18

Would you include him in this mixture?

Data from Boston University Training Grant NIJ # 2008-DN-BX-K158
Applied 

Genetics

1.0 ng input

9:1 ratio

The laboratory established 

“Stochastic” threshold is set at

200 RFU. Since all peaks are 

above this value, we can be 

confident that all of the alleles

are present at this marker.

1.0 ng input

1:1 ratio

Now the 16 allele is below the

200 RFU threshold. We are not

as confident that all of the alleles

are present in the mixture.

The “14” and “18” peaks are 

typical PCR artifacts called 

“Stutter.” However, the 18 allele 

is roughly the same height as the 

16 allele. 

Suspect – 16,18

Would you include him in this mixture?

Data from Boston University Training Grant NIJ # 2008-DN-BX-K158
Applied 

Genetics

1. We don’t know a priori the

quantity of each component 

in the mixture (mixture ratio).

2. This can lead to uncertainty in

 determining if all the alleles 

 are present. 

3. This is exacerbated by very 

 low level DNA evidence.

1.0 ng input

1:1 ratio

1.0 ng input

4:1 ratio

1.0 ng input

9:1 ratio

0.062 ng input

4:1 ratio

Data from Boston University Training Grant NIJ # 2008-DN-BX-K158
Applied 

Genetics This difficulty is enhanced 

with 3+ person mixtures 

Six alleles at this marker 

suggests a 3-person mixture.

Determining the number of 

contributors to a mixture is one

of the first steps in interpretation.

Data from Boston University Training Grant NIJ # 2008-DN-BX-K158
Applied 

Genetics NIST’s Role in Mixture Interpretation

Study Dates Labs References

Mixed Stain Studies #1 and #2 (Apr.–Nov 1997 and Jan–May 1999) 4 5 Duewer et al. (2001)

Kline et al. (2003) 

Mixed Stain Study #3 (Oct 2000-May 2001) 7 4 Duewer et al. (2004) 

Mixture Interpretation Study (Jan-June 2005) 6 9

“Some of the primary benefits we hope to gain from this study include

recommendations for a more uniform approach to mixture

interpretation and training tools to help educate the community.”

http://www.cstl.nist.gov/biotech/strbase/interlab/MIX05/MIX05poster.pdf
Applied 

Genetics Recent NIJ Training Support of BU

• NIJ Forensic Science Training Development and 

Delivery Program Grant # 2008-DN-BX-K158, 

awarded to Biomedical Forensic Science Program at 

Boston University School of Medicine

Applied Genetics
Mixture Training Workshops

• Collaborators from Boston 

University (formerly Cellmark)

• ISHI 2012 workshop covered 

issues with thresholds, 

statistics, probabilistic 

genotyping, complex 

mixtures, court testimony, 

and assumptions made 

– Audience response systems 

(clickers) used to gather 

data from participants

• Slides are available on 

STRBase

http://www.cstl.nist.gov/strbase/mixture.htm

Mike 

Coble

John 

Butler

Robin 

Cotton

Catherine 

Grgicak

Charlotte 

Word
Alaska

Hawaii

DNA Mixture Workshop Attendees

49 states and 25 other countries, so far:

Algeria

Argentina

Bahamas

Belgium

Brazil

Canada

Croatia

Finland

France

Israel

Italy

Jamaica

Japan

Korea

Mexico

Netherlands

New Zealand

Panama

Peru

Russia

Saudi Arabia

Singapore

Spain

Switzerland

UK

Puerto Rico Green = participants

Gray = no attendees

Federal Labs

FBI

ATF

AFDIL

USACIL

*

*

*

*

4 regional 

workshops

ISHI 

2010

ISHI 

2011

AAFS 

2011

ISHI 

2012

With collaborators from
Training Workshops (Past 2 Years; selected)

March 2011

Training for Capital Litigators 

(organized by National Clearinghouse on 

Science, Technology, and Law)

Aug 2012

Training for DNA Analysts 

(organized by National Forensic 

Science Technology Center)

April 2011 April 2011 May 2011 June 2011

Regional DNA Mixture Workshops Taught in Crime Laboratories 

with Boston University Collaborators (NIJ-Funded Training)

Full listing available at http://www.cstl.nist.gov/strbase/training.htm
Applied 

Genetics Use of Audience Response Systems 

(the TurningPoint Clickers)

• Kept the audience engaged with the 

opportunity to participate and offer their 

opinions with anonymity

• Provided real-time results so the 

audience could enjoy learning how 

everyone responded to the question

• Enabled us to gather information

from audience members

– answers can be tracked across the 

questions to the specific clicker used

Used in ISHI 2011-12 

workshops and FL, 

TX, MI, and AZ 

regional workshops
Applied 

Genetics Has your lab implemented changes to your SOPs 

based on the [SWGDAM 2010] guidelines?

1. Yes

2. No

3. Reviewed SOPs but 

no changes needed

4. Working on it

5. Not applicable (I do not 

work in a forensic lab)

1 2 3 4 5

61%

5% 4%

23%

7%

Data from 150 responses 

ISHI Mixture Workshop (Oct 2011)

84% have undergone 

recent changes or are in the 

midst of changing SOPs for 

mixture interpretation
Applied 

Genetics Our Own Evolution...

ISHI 2010

San Antonio, TX

ISHI 2011

Washington, DC

ISHI 2012

Nashville, TN

Back to the Basics,

SWGDAM Guidelines

Focus on Examples, 

Validation, Stats

Challenging examples,

Limitations of current

strategies 
Applied 

Genetics

Which of the topics below would be your first 

choice for additional training?

1 2 3 4 5

4%

9%

48%

24%

15%

1. Relevant literature

2. How to validate 

thresholds in more 

detail 

3. Reporting and the 

use of assumptions

4. Interpretation of low 

level mixtures

5. Likelihood ratios and 

other statistical 

approaches

Data from 111 responses 

ISHI Mixture Workshop 

(Oct 2012)
Applied 

Genetics Two Parts to Mixture Interpretation

• Determination of alleles present in the 

evidence and deconvolution of mixture 

components where possible 

– Many times through comparison to victim and 

suspect profiles

• Providing some kind of statistical answer

regarding the weight of the evidence

– There are multiple approaches and philosophies

Software tools can help with one or both of these...
Applied 

Genetics Expert Software for Mixture Analysis
Applied 

Genetics Software Limitations...

Software programs often use fixed values for mixture 

interpretation (e.g. “maximum” values for stutter peaks).

Thresholds are “all or none” propositions – using a threshold of

150 RFUs in the example above will change the analytical and 

statistical interpretation of the mixture. 

1.0 ng input

9:1 ratio
Applied 

Genetics

“Probabilistic” approaches 

to mixture interpretation
Applied 

Genetics Probabilistic Approaches

A probability that a sister allele has been lost (called “drop-out”) 

is determined from validation data. 

For example, at 176 RFUs, there is a 3% chance that drop-out 

has occurred. This value can now be incorporated in the 

statistical interpretation of the data. 

1.0 ng input

9:1 ratio
Applied 

Genetics “Simulation” Approach

50,000 - 100,000

Markov Chain

Monte Carlo

Simulations

Probable Genotypes

to explain the mixture

Peak Height Ratio, 

Mix Ratio, Stutter etc...

Mathematical Modeling

of the Data

http://www.cstl.nist.gov/strbase/mixture.htm
Applied 

Genetics Summary of the Issues

• Strategies to improve mixture interpretation protocols 

are progressing, but there is still a need for training –

especially for statistical interpretation. 

• The challenges of DNA mixture interpretation will only 

become worse as we move to higher sensitivity STR 

kits and instruments along with a focus on solving 

complex high volume crimes.

• Probabilistic methods will be necessary for resolving 

highly complex, low-level mixtures. We have 

generated data sets at NIST that can be useful for 

evaluating these new software programs.

http://www.cstl.nist.gov/strbase/mixture.htm
Applied 

Genetics Acknowledgments

Contact Info: 

michael.coble@nist.gov

301-975-4330

NIST

Funding from the National 

Institute of Justice (NIJ)

through NIST Office of Law 

Enforcement Standards

NIST Disclaimer: Certain commercial equipment, instruments and materials are identified in order to specify experimental procedures as 

completely as possible. In no case does such identification imply a recommendation or it imply that any of the materials, instruments or 

equipment identified are necessarily the best available for the purpose.

Points of view are those of the presenters and do not necessarily represent the official position of the National Institute of Standards and 

Technology or the U.S. Department of Justice.

John Butler

Collaborators

Robin Cotton

Boston University

Catherine Grgicak

Boston University

Charlotte Word

Consultant
Applied 

Genetics Thank you for your attention!