Interpretation of Complex DNA 

Mixtures 

Michael D Coble, Ph.D.

November 9, 2016
Official Disclaimer

The opinions and assertions contained herein are solely 

those of the author and are not to be construed as official 

or as views of the U.S. Department of Commerce.

Commercial equipment, instruments, software, or 

materials are identified in order to specify experimental 

procedures as completely as possible. In no case does 

such identification imply a recommendation or 

endorsement by the U.S. Department of Commerce, nor 

does it imply that any of the materials, instruments, 

software or equipment identified are necessarily the best 

available for the purpose.
Current DNA Interpretation Methods

Threshold-based Interpretation

A B C

1. Random Man Not Excluded (CPI or CPE)

 - Considers all possible genotype combinations

(f

a + fb + fc

)

2

AA and BC - possible
Current DNA Interpretation Methods

Threshold-based Interpretation

A B C

1. Random Man Not Excluded (CPI or CPE)

 - Considers all possible genotype combinations

2. Modified Random Match Probability (mRMP)

3. Likelihood Ratio (LR)

 - assumptions to the number of contributors

 and other parameters (PHR, Mixture Ratios) 

 can be used to restrict combinations

 If AB is the complainant, 

2f then POI = AC or BC or CC

a fc + 2fb fc + fc

2
Why Change?!?

(1) Drop-out – when alleles may be missing 

from the profile (‘dropped’ below the AT).

(2) Alleles that are between the Analytical 

Threshold and the Stochastic Threshold. 
Current DNA Interpretation Methods

Threshold-based Interpretation

A B C

1. Random Man Not Excluded (CPI or CPE)

 - Considers all possible genotype combinations

(f

a + fb + fc+ fq

)

2

Any genotype is possible

Q
Current DNA Interpretation Methods

Threshold-based Interpretation

A B C

1. Random Man Not Excluded (CPI or CPE)

 - Considers all possible genotype combinations

(f

a + fb + fc+ fq

)

2

Any genotype is possible

Q
Current DNA Interpretation Methods

Threshold-based Interpretation

A B C

1. Random Man Not Excluded (CPI or CPE)

 - Considers all possible genotype combinations

(f

a + fb + fc+ fq

)

2

Any genotype is possible

Q

Your mixture belongs 

to Negan now
Current DNA Interpretation Methods

Threshold-based Interpretation

1. Random Man Not Excluded (CPI or CPE)

 - Considers all possible genotype combinations

(f

a + fb + fc+ fq

)

2

Any genotype is possible

This locus is no longer

available for statistics
Current DNA Interpretation Methods

Threshold-based Interpretation

1. Random Man Not Excluded (CPI or CPE)

 - Considers all possible genotype combinations

(f

a + fb + fc+ fq

)

2

Any genotype is possible

This locus is no longer

available for statistics
CPI

1 in 24,600
Current DNA Interpretation Methods

Threshold-based Interpretation

A B C

1. Random Man Not Excluded (CPI or CPE)

 - Considers all possible genotype combinations

2. Modified Random Match Probability (mRMP)

3. Likelihood Ratio (LR)

 - assumptions to the number of contributors

 and other parameters (PHR, Mixture Ratios) 

 can be used to restrict combinations

 If AB is the complainant, 

2p = 2f then POI = CQ

c

Q
mRMP/LR

1 in 70,000
The “2p” Rule

Stain = CC

POI = CC

ST

LR = 5 LR = 100

Let

f

c = 0.10 

1/p2 = 100 

1/2p = 5 
The “2p” Rule

Stain = CC

POI = AC

Let

f

c = 0.10 

1/p2 = 100 

1/2p = 5 

ST

LR = 5 Exclusion
The Motivation for Change

• STR kits and CE instruments have become 

more sensitive than in the past. 

• Evidence submitted to the lab has moved 

from predominately high quality/quantity 

sources to more trace profiles.

• More mixtures and increased stochastic 

effects
Probabilistic Genotyping

• “The use of biological modeling, statistical 

theory, computer algorithms, and probability 

distributions to calculate likelihood ratios (LRs) 

and/or infer genotypes for the DNA typing 

results of forensic samples.” 

SWGDAM Guidelines for the Validation 

of Probabilistic Genotyping Systems
Current DNA Interpretation Methods

Non Threshold-based Interpretation

A B C

1. Discrete Models of Interpretation

 - Considers only the alleles present

 - Peaks heights are ignored

 - Uses a Probability of dropout Pr(Dout) to account for missing alleles

 and a Pr (Din) for any spurious alleles

 - All possible genotypes are considered 

A = Yes

B = Yes

C = Yes

Pr(Dout) = 0.15

Pr (Din) = 0.01

Q
Current DNA Interpretation Methods

Non Threshold-based Interpretation

A B C

2. Continuous Models of Interpretation

 - Mathematical modeling of the profile to determine optimal

 genotypes

 - Peaks heights, mixture ratios, stutter, etc... are considered.

 - Drop out is modeled (not ‘physically entered’)

 - May use simulations via MCMC (not all programs!) 

AC BC 

CC CQ

Q
Benefits of Probabilistic Genotyping

2 person mixtures at 5 different

quantities and 5 different mixture ratios

CPI RMP (2P) Discrete Continuous
1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E+07

1.E+08

1.E+09

1.E+10

1.E+11

1.E+12

1.E+13

1.E+14

1.E+15

0 100 200 300 400 500

Log of match statistic

Template

CPI

RMP

Lab Retriever

STRmix

1:1 Mixture Ratio

Discrete

Continuous

mRMP

CPI
3:1 Mixture Ratio

Discrete

Continuous

mRMP

CPI
11 PG Software

Profiled

https://www.forensiccoe.org/Our-Impact/Advancing-Technology/Reports/Demystifying-MIST-Landscape-Report-for-DNA-Mixture-Interpretation-Software-Tools
And...
Validation and Guidance

Guidance for Developmental and Internal Validation

29 numbered recommendations 
Validation and Guidance

9 recommendations for software developers

7 recommendations for end-users
Validation and Guidance

• Other resources – references in the Mixture 

Software Landscape study

• Recent publications

• NIST validation page on STRbase? 
Validation Resources (NIST)
Validation Consensus

Plan Ahead!

Plan A

Slide courtesy of Robin Cotton
Validation Consensus

Plan Ahead!

Slide courtesy of Robin Cotton

Plan B
Summary

• Probabilistic Methods make better use of the data than 

RMNE or the binary LR with 2p. 

• The goal of the software programs should not be to simply 

“get bigger numbers” but to understand the details of these 

approaches and not treat the software as a “black box.” 

• Know your models!!

• Understanding and properly using these software programs 

will become evident from a well planned and executed 

validation study. 
Thank you!

michael.coble@nist.gov

Funding

– NIST Special Programs Office 

(Forensic DNA)

Collaborators

Applied Genetics Group

Todd Bille & Steven Weitz (ATF)

Jo Bright (ESR)

John Buckleton (ESR and NIST)

Robin Cotton (BU) 

Charlotte Word (Consultant)

John Butler (NIST SPO)