MAY 28, 2014 

SUSAN G. BERDINE 

Probabilistic Genotyping: 

A Perspective from the Denver Crime Laboratory 
Why are we changing? 

 1/14/2010 SWGDAM Interpretation Guideline 4.6.3 

 RMP, CPI, restricted CPI & 2p – p2 

 Many DNA profiles not suitable for CPI, no 

conclusions given 

 Problem: how to give statistical weight to complex 

mixtures with dropout and drop-in? 

 Other steps of DNA testing optimized for maximum 

sensitivity 

 End product is data that may appear probative, but 

current tools don’t allow statistical weight to be given 
What prompted us to change? 

 Communication with the NYC OCME laboratory 

about FST in late 2012 

 April 2013 NIST webinar about DNA mixtures 

 Examined the options available: 

¡ Lab Retriever 

¡ LRmix

¡ TrueAllele® 

¡ STRMixTM was not available for purchase last year 

 Downloaded the first two, which are both free and 

open source 
Why Lab Retriever? 

 Scientifically sound 

 Produces quality results for single source, 2 & 3 person 

mixtures 

 Availability and cost 

 Supported by published scientific literature 

 Formulas used are published & open source 

 Ease of use 

 Speed of analysis 

 Training from developers 

 Court admissibility support 
Training 

 DNA analysts (9 of us) read the following: 

¡ 2012. Lohmueller and Rudin, Calculating the weight of 

evidence in low-template forensic DNA casework 

¡ 2009. Balding and Buckleton, Interpreting low template DNA 

profiles 

¡ 2009. Tvedebrink et al., Estimating the probability of allelic 

drop-out of STR alleles in forensic genetics 

¡ 2012. Mitchell et al., Validation of a DNA mixture statistics 

tool incorporating allelic drop-out and drop-in 
Training 

 Hosted 3-day on-site training with Norah Rudin, 

Keith Inman and by Skype Kirk Lohmueller 

 Topics covered: 

¡ Analytical thresholds 

¡ Estimating probability of dropout 

¡ Construction of an LR with dropout 

¡ Preparing data and running Lab Retriever 

¡ Performing the calculations by hand 

¡ Stutter considerations 

¡ Each of the 9 analysts brought a real case to work 

¡ Internal validation 

¡ Reporting, court testimony 
Validation 

1. New color specific analytical thresholds to maximize 

sensitivity 

2012. Bregu et al., Analytical Thresholds and Sensitivity: 

Establishing RFU Thresholds for Forensic DNA Analysis 

2012. Rakay et al., Maximizing allele detection: Effects of analytical 

threshold and DNA levels on rates of allele and locus drop-out 

0 

10 

20 

30 

40 

50 

60 

0 1 2 3 4 5 

T

h

r

e

s

ho

l

d

R

F

U

Color 

Threshold by Color 

Blue 

Green 

Yellow 

Red 
Validation 

2. Create a probability of dropout curve using 

previous sensitivity validation data. Plot RFU of 

detected alleles against % dropout. Logistic 

regression in MS Excel. 

Note 1: Can do all loci together or locus-specific curves 

Note 2: Can use quantitation value or input DNA (pg) into the 

PCR instead of RFU. 

Note 3: 2012. Tvedebrink et al., Statistical model for degraded 

DNA samples and adjusted probabilities for allelic drop-out. 

 
Probability of dropout curve 

0.000 

0.100 

0.200 

0.300 

0.400 

0.500 

0.600 

0.700 

0.800 

0.900 

1.000 

0 50 100 150 200 250 300 350 400 

Fraction of alleles that dropped out 

Average Peak Heights (RFU) 

Logistic Model: 

y=L/(1+Ae^(-Bx)) 

Sensitivity samples 

Best Fit Curve 
Validation 

3. Validate drop-in rates (expected ≤1%) 

4. Test DNA profiles in Lab Retriever to known 

contributors and non-contributors 

5. Perform likelihood ratio calculations on 3-5 

samples to verify calculations 

6. Test major/minor mixtures with stutter filters on 

and off 

7. Write SOP, reporting guidelines 
Implementation 

 Lab Retriever will be used 

¡ By all DNA analysts 

¡ For 2 & 3 person mixtures where major/minor cannot be interpreted 

¡ For future cases 

 Selecting hypotheses can be challenging 

 Limitations to Lab Retriever 

¡ Not currently capable of 2 simultaneous suspects, but can do 1 

assumed, 1 unknown 

¡ How to treat possible masked alleles in stutter positions 

¡ Cannot calculate 4 & 5 person mixtures unless assumed contributor 

¡ Does not analyze peak heights as fully continuous models do 
Case example 

 Burglary/sex assault 

5/20/2012 

 Victim’s head covered 

with a sheet, bound, held 

at knifepoint 

 DNA evidence – bedding, 

scarf, stocking cap and 

nail scrapings 
Suspect: 12, 12 30, 30 8, 11 9, 10 

15, 17 6, 7 12, 14 12, 12 20, 21 
Suspect: 13, 16 16, 17 8, 9 12, 16 

X, Y 12, 13 22, 25 
Locus 6A-1 Comforter (RFU) 

D8S1179 217 

D7S820 50 

CSF1PO 87 

D3S1358 125 

TH01 88 

TH01 123 

D13S317 56 

D16S539 66 

D2S1338 36 

D19S433 49 

D19S433 60 

vWA 59 

TPOX 46 

D18S51 52 

FGA 108 

81.5 AVE 

Lab Retriever Analysis 
Inputs into Lab Retriever: 

•Probability dropout 

•Probability drop-in 

•Evidence profile 

•Assumed contributor profiles (victim + CP) 

•Suspected contributor profile (suspect) 

•H1: Vic + Consensual + Sus

•H2: Vic + Consensual + Unk
Locus 

Detected – 6A1 

Comforter 

Assumed - 

Victim + 

Consensual Unattributed 

Suspected - 

Suspect AA CAU HIS 

D8 12 13 13 12 12 9.2 5.9 8.2 

D21 30 31 30 31 30 3.4 2.5 2.6 

D7 7 8 11 11 7 8 8 11 2.9 5.1 5.6 

CSF 9 10 11 10 11 9 9 10 18.0 39.0 26.4 

D3 15 17 18 15 18 17 15 17 3.8 3.3 3.6 

TH0 6 7 8 9.3 9.3 8 6 7 6 7 8.9 10.5 7.9 

D13 12 13 14 12 13 14 12 14 1.2 1.6 2.1 

D16 11 12 11 12 12 2.1 1.8 2.1 

D2 19 21 24 25 19 24 25 21 20 21 1.0 2.9 3.2 

D19 12 13 14 15 16 12 14 15 13 16 13 16 101.0 31.8 61.3 

vWA 15 16 17 15 16 17 16 17 1.6 1.8 1.7 

TPO 8 9 11 8 9 11 8 9 1.5 1.2 1.3 

D18 12 17 17 12 12 16 12.8 8.5 9.2 

D5 11 12 11 12 12 13 0.4 0.2 0.3 

FGA 22 23 23 22 22 25 1.0 0.9 1.4 

Final Likelihood Ratios 1.42E+08 1.10E+08 6.07E+08