1. Probabilistic genotyping
Dan E. Krane, Wright State University, Dayton, OH
Forensic DNA Profiling Video Series
Forensic Bioinformatics (

2. Do these profiles match? But ambiguities can arise…
Evidence

3. Why has this become an issue?
More challenging evidence samples
Touch DNA
Guns, steering wheels, doorknobs, etc.
Resulting DNA profiles often:
Small amounts of DNA
Complex mixtures (3 or more persons)
Degradation (differential degradation)
Minor components in major/minor mixtures
Stochastic effects!
Existing test kits were not designed to test these kinds of samples
Existing statistical methods used in the US cannot simultaneously handle drop-out and an unknown number of contributors

4. Stochastic: From greek στόχος (stokhos) “aim” or “guess”
Having a random probability distribution or pattern that may be analyzed statistically but may not be predicted precisely
When a system's outcome is determined not just by the predictable performance characteristics of the system, but by random elements as well.

5. The four stochastic effects1 2 3 4
Peak height
imbalance
Increased
stutter
Drop-out
Drop-in
No stochastic
effects
The danger of dropout
How do you know when you’ve got something missing?
How do you know what it is that you’ve not got?
Answer = threshold

6. The stochastic threshold
The amount of template DNA where random factors influence test results as much as the actual template.
Exaggerated peak height imbalance
Exaggerated stutter
Allelic drop-in
Allelic drop-out
• Sampling error is at the heart of it all

7. STR Kit Amplification with conventional SOP and with LCN protocol
Input DNA
Data from Debbie Hobson (FBI) – LCN Workshop AAFS 2003
SOP
1ng
PHR = 87%
PHR = 50%
50 µL PCR
Allele Drop Out
LCN
Allele Drop In
Peak Height Imbalance
8pg
5 µL PCR

8. How have labs dealt with low levels of DNA?
RULES and THRESHOLDS
Based on VALIDATION STUDIES (experiments)
Developmental validation (Manufacturer)
Internal validation (Crime Lab)
Documented in INTERPRETATION GUIDELINES
Specific to Crime Lab
Specific to test platform (test kit, instrumentation, etc.)

9. Analytical and Stochastic Thresholds
Drop-out possible?
Set at 200 RFU
Detection threshold
Real or noise?
Set at 50 RFU

10. Peak Height Ratios and Stutter
Height of lower peak divided by higher peak as percentage = PHR
Peak Height Ratio
Height of -4 peak compared to height of parent peak
If -4 peak exceeds a certain value then it is considered a real allele
-4 peak

11. Importance of Interpretation Guidelines
Labs are required to establish thresholds and rules based on validation research in order to be accredited
The values for these thresholds may differ significantly from one lab to another
Even for the same test kit and instrument platform
Labs are expected to follow their Interpretation Guidelines to the letter
Departures from a lab’s Interpretative Guidelines is typically a fruitful area of cross-examination

12. LCN statistics
No generally accepted method for attaching weight to mixed samples with an unknown number of contributors where dropout may have occurred.
No stats = not admissible.

13. Likelihood ratios (LRs)
Compares two alternative hypothesis
“Prosecution” explanation Hp (or H1)
“Defense” explanation Hd (or H2)
LRs are better able to deal with continuous data
Enables scientist to model stochastic effects and complex mixtures
Complicated – need computer assistance
Track record:
Widely used in UK, Europe, Australia & New Zealand
Not much in US (other than Paternity Index)

14. Prosecution explanation of the DNA Defense explanation of the DNA
DNA evidence is:
A mixture of two persons consisting of victim and defendant
Pr(E|Hp)
Likelihood ratio =
Pr(E|Hd)
DNA evidence is:
A mixture of two persons consisting of victim and an unknown person
Defense explanation of the DNA

15. Support for PROSECUTION explanation Defense explanation of the DNA1 10
0.1
100
0.01
1,000
0.001
10,000
0.0001
100,000
<
1,000,000+
“VERY STRONG”
Support for PROSECUTION explanation
Defense explanation of the DNA
Prosecution explanation of the DNA

16. INCONCLUSIVE PROSECUTION DEFENSE 1 10 0.1 100 0.01 1,000 0.001 10,000
0.0001
100,000
<
1,000,000+
Evidence
Genotype
Population
Genotype
INCONCLUSIVE

17. Who stole my biscuit? PROSECUTION DEFENSE 1 10 0.1 100 0.01 1,000
0.001
10,000
0.0001
100,000
<
1,000,000+
Evidence
Genotype
Population
Genotype
Who stole my biscuit?

18. Some DNA profiles can be interpreted confidently
What features make you confident?
Peak heights and shapes
Number of alleles
Peak height balance
Trend in peak heights
Baseline noise levels
Stutter peaks
What else? 18

19. But ambiguities can arise…
What can be done with difficult samples?
But ambiguities can arise…
Evidence

20. Software Models Lab Retriever (Rudin et.al.)
LRmix Studio (Haned et.al.)
Forensic Statistical Tool (OCME NY)
LikeLTD (Balding)
SEMI-CONTINUOUS MODELS
Do NOT take peak height into account
ArmedXpert (Niche Vision)
DNA View (Brenner)
STRMix (Buckleton et.al.)
TrueAllele (Perlin)
CONTINUOUS MODELS
Take peak height into account

21. STRMix and TrueAllele use MCMC
Never give the same numerical answer twice
Because of MCMC
Run very same data twice – get different LRs
LR is x 1014 (215 trillion)
LR is x 1014 (204 trillion)

22. Where do things stand?
President’s Council of Advisors on Science and Technology (PCAST) 2016 report
“It is often impossible to tell with certainty which alleles are present in a mixture or how many separate individuals contributed to the mixture, let alone accurately infer the DNA profile of each individual.”
“Objective analysis of complex DNA mixtures with probabilistic genotyping software is a relatively new and promising approach.”
On September 20, 2016 the President’s Council of Advisors on Science and Technology (PCAST) released a report to the President of the United States addressing Forensic Science in Criminal Courts: Ensuring Scientific Validity of Feature-Comparison Methods. One goal of the “PCAST report” was to determine if there were additional steps that could be taken that could help ensure the validity of forensic evidence used in the Nation’s legal system. One issue that received careful consideration was the interpretation of complex-mixed DNA samples. In section 5.1 (pg 75) of the report, the PCAST notes that “DNA analysis of complex mixtures – defined as mixtures with more than two contributors – is inherently difficult and even more for small amounts of DNA (SWGDAM). Such samples result in a DNA profile that superimposes multiple individual DNA profiles. Interpreting a mixed profile is different for multiple reasons: each individual may contribute two, one or zero alleles at each locus; the alleles may overlap with one another; the peak heights may differ considerably, owing to differences in the amount and state of preservation of the DNA from each source; and the “stutter peaks” that surround alleles (common artifacts of the DNA amplification process) can obscure alleles that are present or suggest alleles that are not present (Butler, J.M., 2015). It is often impossible to tell with certainty which alleles are present in a mixture or how many separate individuals contributed to the mixture, let alone accurately to infer the DNA profile of each individual (Thompson, W.C., 2009).”

23. Where do things stand?
President’s Council of Advisors on Science and Technology (PCAST) 2016 report
“At present, published evidence supports the foundation validity of analysis, with some programs, of DNA mixtures of 3 individuals in which the minor contributor constitutes at least 20 percent of the intact DNA in the mixture and in which the DNA amount exceeded the minimum required level for the method.”
These difficulties surrounding the interpretation and significance of complex mixtures have led to the development of “probabilistic genotyping” computer programs that apply various algorithms to interpret these mixtures. OCME’s FST can be considered a probabilistic genotyping program. While several probabilistic genotyping programs appear to show promise, the PCAST report notes that “Objective analysis of complex DNA mixtures with probabilistic genotyping software is a relatively new and promising approach. Empirical evidence is required to establish the foundational validity of each such method within specified ranges. At present, published evidence supports the foundation validity of analysis, with some programs, of DNA mixtures of 3 individuals in which the minor contributor constitutes at least 20 percent of the intact DNA in the mixture and in which the DNA amount exceeded the minimum required level for the method. The range in which foundational validity has been established is likely to grow as adequate evidence for more complex mixtures is obtained and published” (pg 82).

24. Challenges about black boxes
Black box: “A device which performs intricate functions but whose internal mechanisms may not readily be inspected or understood.”
Conflict between protection of intellectual property and the constitutional right to confront an opposing witness.
Steele, Christopher D., and David J. Balding. "Statistical evaluation of forensic DNA profile evidence." Annual Review of Statistics and Its Application 1 (2014):

25. Probabilistic genotyping
Dan E. Krane, Wright State University, Dayton, OH
Forensic DNA Profiling Video Series
Forensic Bioinformatics (

26. Post-test on “Probabilistic genotyping”
Why can’t random match probability (RMP) statistics be used for samples with an unknown number of contributors?
Why can’t combined probability of inclusion statistics be used for samples where drop-out may have occurred?
How do you convert an RMP statistic to a likelihood ratio (LR)?
What features of an electropherogram do probabilistic genotyping approaches consider?
For what kinds of results have probabilistic genotyping approaches been foundationally validated for use?