Probabilistic genotyping

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

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. 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. Stochastic:From greek στόχος (stokhos) “aim” or “guess”

5. The four stochastic effects 1 4 Peak height imbalance Increased stutter No stochastic effects 2 3 Drop-out Drop-in

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. PHR = 87% Allele Drop Out PHR = 50% Peak Height Imbalance Allele Drop In STR Kit Amplification with conventional SOP and with LCN protocol Input DNA Data from Debbie Hobson (FBI) – LCN Workshop AAFS 2003 SOP 1ng 50 µL PCR LCN 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 Set at 200 RFU Stochastic threshold Drop-out possible? Detection threshold Real or noise? Set at 50 RFU

10. Peak Height Ratios and Stutter 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 Height of lower peak divided by higher peak as percentage = PHR -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. DNA evidence is: A mixture of two persons consisting of victim and defendant Defense explanation of the DNA Prosecution explanation of the DNA Pr(E|Hp) Likelihood ratio = Pr(E|Hd) DNA evidence is: A mixture of two persons consisting of victim and an unknown person

15. 1 10 0.1 100 0.01 Prosecution explanation of the DNA 1,000 0.001 10,000 0.0001 100,000 0.00001 <0.000001 1,000,000+ “VERY STRONG” Support for PROSECUTION explanation Defense explanation of the DNA

16. 1 10 0.1 PROSECUTION DEFENSE 100 0.01 Evidence Genotype Population Genotype 1,000 0.001 10,000 0.0001 100,000 0.00001 <0.000001 1,000,000+ INCONCLUSIVE

17. 1 10 0.1 PROSECUTION DEFENSE 100 0.01 Evidence Genotype Population Genotype 1,000 0.001 10,000 0.0001 100,000 0.00001 <0.000001 1,000,000+ 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?

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

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

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 2.1523 x 1014 (215 trillion) • LR is 2.0499 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.”

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.”

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.

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

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?