1. Seventh Annual Prescriptions for Criminal Justice Forensics Program Fordham University School of Law June 3, 2016 DNA Panel

2. Why are the reported statistics so different from laboratory to laboratory? The actual statistic reported 1,000 1,000,000 1,000,000,000 The wording of the reported statistic The probability of randomly selecting an unrelated individual with a DNA profile matching… The probability of randomly selecting an unrelated individual who could be included as a contributor to the mixture… The evidence is 1,000,000 times more likely if…

3. Why are the reported statistics so different from laboratory to laboratory? It depends on the statistical approach used to determine the significance of a DNA match Combined Probability of Inclusion/Exclusion (CPI/CPE) Random Match Probability Likelihood ratio (LR) Restricted Unrestricted Probabilistic Genotyping Semi-continuous Fully continuous

4. How long will the drive from my lab to my favorite burger place take?

5. Why are the reported statistics so different from laboratory to laboratory? 22 miles 32 miles

6. Why are the reported statistics so different from laboratory to laboratory?

7. It comes down to how much of the available information is used for the statistical calculation CPI RMP LR Restricted LR Unrestricted Probabilistic Genotyping Semi- continuous Probabilistic Genotyping Continuous Amount of information used Alleles only Peak Height Modeling of drop-out Modeling of drop-out, stutter, peak balance, etc.

8. Approaches answer different questions CPI: What portion of the population would be possible contributors to the mixture without any assumptions as to the number of contributors RMP: What portion of the population would be possible contributors to the mixture given X number of contributors LR: Ratio of probabilities for two hypotheses

9. Approaches answer different questions There can even be variation with LRs depending on the hypotheses being compared H1: Suspect 1 + Unknown Individual / H2: Two Unknown Individuals H1: Suspect 1 + Suspect 2 / H2: Two Unknown Individuals

10. The approach used can affect the loci used

11. Binary vs Continuous Models Tvedebrink et al., 2009 Prob of Drop-out Mean pk ht

12. Probability of Drop-out Pr(D) The probability that drop-out occurred associated with peaks below the stochastic threshold is not equal across the range of peak heights. A 198 rfu vs A 51 rfu Using the binary approach, allele drop-out would be equally likely in both cases

13. EXAMPLE - 1 : 1 mixture  Bille, TW, Weitz, SM, Coble MD, Buckleton, J, Bright, JA. “Comparison of the performance of different models for the interpretation of low level mixed DNA profiles.” Electrophoresis, 2014 Nov, 35(21-22): 3125-33. Bille, TW, Weitz, SM, Coble, MD, Buckleton, J, Bright, JA. “Comparison of the performance of different models for the interpretation of low level mixed DNA profiles.” Electrophoresis, 2014 Nov, 35(21-22): 3125-33.

14. EXAMPLE – 3:1 mixture Bille, TW, Weitz, SM, Coble, MD, Buckleton, J, Bright, JA. “Comparison of the performance of different models for the interpretation of low level mixed DNA profiles.” Electrophoresis, 2014 Nov, 35(21-22): 3125-33.

15. Challenges Which method to use? Validation of the method Courtroom challenges As the level of sophistication of the statistical analysis increases, the difficulty for the laboratory and attorneys increases Most forensic DNA scientists are not statisticians Increased training for the analysts to understand the output Increased difficulty conveying the meaning of the results to the attorneys involved and the jury

16. Bottom Line If implemented correctly, each of the approaches provides a valid estimation of the significance of the DNA match Each statistical approach answers a different question Likelihood ratios may be new to many jurisdictions Most laboratories are in the process of transitioning