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

Do these profiles match? But ambiguities can arise… Evidence

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

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.

The four stochastic effects 1 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

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

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

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.)

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

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

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

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.

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)

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

Support for PROSECUTION explanation Defense explanation of the DNA 1 10 0.1 100 0.01 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 Prosecution explanation of the DNA

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

Who stole my biscuit? PROSECUTION DEFENSE 1 10 0.1 100 0.01 1,000 0.001 10,000 0.0001 100,000 0.00001 <0.000001 1,000,000+ Evidence Genotype Population Genotype Who stole my biscuit?

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

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

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

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)

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).”

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).

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): 361-384.

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

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?