1. DNA Mixture Statistics Cybergenetics © 2003-2013 2013 Spring Institute Commonwealth's Attorney's Services Council Richmond, Virginia March, 2013 Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA

2. Child molestation case June, 2011: Northern Virginia daughter's birthday slumber party 10 year old girls sleeping in basement object sexual penetration aggravated sexual battery (2 counts) Prosecutor: CDCA Nicole Wittmann

3. CHILD & CHILD & CHILD VICTIM Television Cabinet Table L-Shaped Couch Bathroom Bedroom Laundry Bedroom Stairs Bookcase VICTIM Storage Door to Outside Closet underpants pajama pants

4. DNA mixture statistics Human review (using thresholds) underpants original = 10 million modified = 1 million pajama pants original = 2 million modified = 4 Computer interpretation requested

5. Prosecutor question What is the true match information of the evidence to the suspect?

6. Biology 1 trillion cells

7. Nucleus cell nucleus

8. DNA cell nucleus chromosomes

9. Locus cell nucleus chromosomes locus

10. Allele cell nucleus chromosomes locus Short Tandem Repeat (STR) alleles

11. cell nucleus chromosomes locus Short Tandem Repeat (STR) genotype 7, 8 alleles Genotype

12. Identification Evidence item

13. Identification 10 12 Lab Evidence item Evidence data

14. Identification Evidence item Evidence data Evidence genotype 10 12 10, 12 LabInfer

15. Identification Evidence genotype Known genotype 10 12 10, 12 LabInfer Compare Evidence item Evidence data

16. Identification Known genotype 10 12 10, 12 LabInfer Compare Probability(identification) = Prob(suspect matches evidence) = 100% Evidence data Evidence item Evidence genotype

17. Coincidence Biological population

18. Coincidence Biological population Allele frequency data 10 Lab 11 12 1314 15 16 17

19. Coincidence Biological population Allele frequency data Population genotype 10 10, 12 @ 5% LabInfer 11 12 1314 15 16 17 Genotype product rule, combines alleles Prob(10, 12) = 2 x p 10 x p 12 Prob(10, 10) = p 10 x p 10

20. Coincidence Biological population Allele frequency data Population genotype Known genotype 10 10, 12 @ 5% 10, 12 LabInfer Compare 11 12 1314 15 16 17

21. Coincidence Biological population Allele frequency data Population genotype Known genotype 10 10, 12 @ 5% 10, 12 LabInfer Compare 11 12 1314 15 16 17 Probability(coincidence) = Prob(coincidental match) = 5%

22. Identification information before data (population) after (evidence) Evidence changes our belief Prob(identification) Prob(coincidence) At the suspect's genotype, identification vs. coincidence?

23. Match statistic before data (population) after (evidence) At the suspect's genotype, identification vs. coincidence? Prob(evidence matches suspect) Prob(coincidental match) Perlin MW. Explaining the likelihood ratio in DNA mixture interpretation. Promega's Twenty First International Symposium on Human Identification, 2010; San Antonio, TX.

24. Match statistic Prob(evidence matches suspect) Prob(coincidental match) before data (population) after (evidence) 20 = 100% 5% = At the suspect's genotype, identification vs. coincidence?

25. Bayes theorem Calculate probability Belief in hypothesis after having seen data is proportional to how well hypothesis explains the data times our initial belief. All hypotheses must be considered. Need computers to do this properly. Hypothesis: Defendant contributed to DNA evidence Rev Bayes, 1763 Computers, 1985

26. Mixture interpretation varies National Institute of Standards and Technology Two Contributor Mixture Data, Known Victim 31 thousand (4) 213 trillion (14)

27. DNA mixture + Evidence item

28. Uncertainty 10 11 12 + Lab Evidence item Evidence data

29. Uncertainty 10 11 12 10, 12 @ 50% 11, 12 @ 30% 12, 12 @ 20% + Evidence genotype LabInfer Evidence item Evidence data

30. Uncertainty Known genotype 10 11 12 10, 12 @ 50% 11, 12 @ 30% 12, 12 @ 20% 10, 12 + Compare Evidence genotype LabInfer Evidence item Evidence data

31. Uncertainty 10 11 12 10, 12 @ 50% 11, 12 @ 30% 12, 12 @ 20% 10, 12 + Compare Probability(identification) = Prob(suspect matches evidence) = 50% Evidence genotype LabInfer Evidence item Evidence data Known genotype

32. Identification information before data (population) after (evidence) Prob(identification) Prob(coincidence) At the suspect's genotype, identification vs. coincidence? Less weight of evidence, less change in our belief Numerator decreases Denominator unchanged

33. Match statistic before data (population) after (evidence) 10 = 50% 5% = At the suspect's genotype, identification vs. coincidence? Prob(evidence matches suspect) Prob(coincidental match)

34. TrueAllele operator Replicate computer runs for each item 2 or 3 unknown mixture contributors Victim genotype was considered STR evidence data.fsa genetic analyzer files Evidence genotypes probability distributions

35. DNA mixture data Quantitative peak heights at a locus peak size peak height

36. TrueAllele ® Casework ViewStation User Client Database Server Interpret/Match Expansion Visual User Interface VUIer™ Software Parallel Processing Computers

37. Mixture weight Separate mixture data into two contributor components 25%75%

38. Genotype inference Thorough: consider every possible genotype solution Objective: does not know the comparison genotype Explain the peak pattern Better explanation has a higher likelihood Victim's allele pair Another person's allele pair

39. Genotype inference Explain the peak pattern Worse explanation has a lower likelihood Victim's allele pair Another person's allele pair

40. Genotype separation major contributor

41. Genotype concordance

42. TrueAllele report Genotype probability distributions Evidence genotypeSuspect genotype Population genotype Likelihood ratio (LR) DNA match statistic

43. Probability(evidence match) Probability(coincidental match) 30x 3% 98% DNA match statistic

44. Match statistic at 15 loci

45. TrueAllele DNA match Black36.6 quintillion Caucasian20.7 quadrillion Hispanic212 quadrillion Black319 thousand Caucasian3.86 thousand Hispanic32.9 thousand LR match to Defendant UnderpantsPajama pants

46. Powers of Ten -21 -18 -15 -12 -9 -6 -3 0 +3 +6 +9 +12 +15 +18 +21 logarithmic scale thousand million billion trillionquadrillion quintillion 1 000 … 000 number of zeros

47. Trial preparation discuss case report direct examination curriculum vitae PowerPoint slides background reading answer questions

48. Computer Interpretation of Quantitative DNA Evidence Commonwealth v Defendant April, 2012 Arlington, Virginia Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA Cybergenetics © 2003-2012

49. DNA genotype 8, 9 12345678 ACGT 12345678 A genetic locus has two DNA sentences, one from each parent. 9 locus Many alleles allow for many many allele pairs. A person's genotype is relatively unique. mother allele father allele repeated word An allele is the number of repeated words. A genotype at a locus is a pair of alleles.

50. DNA evidence interpretation Evidence item Evidence data LabInfer 10 11 12 Evidence genotype Known genotype 10, 12 @ 50% 11, 12 @ 30% 12, 12 @ 20% 10, 12 Compare

51. Computers can use all the data Quantitative peak heights at locus Penta E peak size peak height

52. How the computer thinks Consider every possible genotype solution Explain the peak pattern Better explanation has a higher likelihood Victim's allele pair Another person's allele pair

53. Evidence genotype Objective genotype determined solely from the DNA data. Never sees a suspect. 1% 98%

54. DNA match information Probability(evidence match) Probability(coincidental match) How much more does the suspect match the evidence than a random person? 30x 3% 98%

55. Match information at 15 loci

56. Is the suspect in the evidence? A match between the underpants and Defendant is: 36.6 quintillion times more probable than a coincidental match to an unrelated Black person 20.7 quadrillion times more probable than a coincidental match to an unrelated Caucasian person 212 quadrillion times more probable than a coincidental match to an unrelated Hispanic person

57. Is the suspect in the evidence? A match between the pajama pants and Defendant is: 319 thousand times more probable than a coincidental match to an unrelated Black person 3.86 thousand times more probable than a coincidental match to an unrelated Caucasian person 32.9 thousand times more probable than a coincidental match to an unrelated Hispanic person

58. Outcome Guilty object sexual penetration two counts of aggravated sexual battery Sentence 22 years imprisonment Court of Appeals DNA chain of custody appeal denied

59. TrueAllele mixture validation: Virginia case study Mark W Perlin, PhD, MD, PhD Kiersten Dormer, MS and Jennifer Hornyak, MS Cybergenetics, Pittsburgh, PA Lisa Schiermeier-Wood, MS and Susan Greenspoon, PhD Department of Forensic Science, Richmond, VA Establish the reliability of TrueAllele mixture interpretation

60. Case composition 72 criminal cases 92 evidence items 111 genotype comparisons Criminal offense 18 homicide 12 robbery 6 sexual assault 20 weapon

61. DNA mixture distribution

62. Data summary – “alleles” Threshold Over threshold, peaks are labeled as allele events All-or-none allele peaks, each given equal status Allele Pair 7, 7 7, 10 7, 12 7, 14 10, 10 10%10, 12 10, 14 12, 12 12, 14 14, 14

63. CPI information Nothing reported 25 6.70 2.26 CPI Combined probability of inclusion

64. SWGDAM 2010 guidelines Threshold Under threshold, alleles less used Allele Pair 7, 7 7, 10 7, 12 7, 14 10, 10 0%10, 12 10, 14 12, 12 12, 14 14, 14 Higher threshold for human review

65. Modified CPI information 25 56 2.126.70 1.75 2.26 Nothing reported CPI mCPI

66. SWGDAM 2010 guidelines 3.2.2. If a stochastic threshold based on peak height is not used in the evaluation of DNA typing results, the laboratory must establish alternative criteria (e.g., quantitation values or use of a probabilistic genotype approach) for addressing potential stochastic amplification. The criteria must be supported by empirical data and internal validation and must be documented in the standard operating procedures. Use TrueAllele ® Casework for DNA mixture statistics

67. Validated genotyping method Perlin MW, Sinelnikov A. An information gap in DNA evidence interpretation. PLoS ONE. 2009;4(12):e8327. Perlin MW, Legler MM, Spencer CE, Smith JL, Allan WP, Belrose JL, Duceman BW. Validating TrueAllele ® DNA mixture interpretation. Journal of Forensic Sciences. 2011;56(6):1430-47. Perlin MW, Belrose JL, Duceman BW. New York State TrueAllele ® Casework validation study. Journal of Forensic Sciences. 2013;58(6):in press.

68. TrueAllele reinterpretation Virginia reevaluates DNA evidence in 375 cases July 16, 2011 “Mixture cases are their own little nightmare,” says William Vosburgh, director of the D.C. police’s crime lab. “It gets really tricky in a hurry.” “If you show 10 colleagues a mixture, you will probably end up with 10 different answers” Dr. Peter Gill, Human Identification E-Symposium, 2005

69. Mixture weight Separate mixture data into two contributor components 25%75%

70. Genotype inference Thorough: consider every possible genotype solution Objective: does not know the comparison genotype Explain the peak pattern Better explanation has a higher likelihood Victim's allele pair Another person's allele pair Allele Pair 7, 7 7, 10 7, 12 7, 14 10, 10 98%10, 12 10, 14 12, 12 12, 14 14, 14

71. TrueAllele sensitivity 9 25 56 2.126.7010.93 5.52 1.75 2.26 Nothing reported CPI mCPI TrueAllele

72. TrueAllele specificity True exclusions, without false inclusions – 19.69

73. TrueAllele reproducibility log(LR 1 ) log(LR 2 ) Concordance in two independent computer runs standard deviation (within-group) 0.305

74. Validation results A reliable method sensitive specific reproducible TrueAllele ® Casework DNA mixture interpretation is: TrueAllele computer genotyping is more effective than human review

75. TrueAllele Virginia outcomes 144 cases analyzed 72 case reports – 10 trials CityCourtChargeSentence RichmondFederalWeapon50 years AlexandriaFederalBank robbery90 years QuanticoMilitaryRape3 years ChesapeakeStateRobbery26 years ArlingtonStateMolestation22 years RichmondStateHomicide35 years FairfaxStateAbduction33 years NorfolkStateHomicide8 years CharlottesvilleStateHomicide15 years HamptonStateHome invasion5 years

76. TrueAllele in Virginia Department of Forensic Science has their own TrueAllele system Training, validation, approvals Services centralized in Richmond DFS will provide DNA mixture statistics and court testimony

77. TrueAllele in the United States Casework system Interpretation services

78. More information perlin@cybgen.com http://www.cybgen.com/information Courses Newsletters Newsroom Presentations Publications