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Using probabilistic genotyping to distinguish family members

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Presentation on theme: "Using probabilistic genotyping to distinguish family members"— Presentation transcript:

1 Using probabilistic genotyping to distinguish family members
May, 2019 Morgantown, WV Beatriz Pujols, MS Cybergenetics, Pittsburgh, PA Cybergenetics ©

2 DNA evidence interpretation
Lab Infer Evidence item Evidence data Evidence genotype 10, 12 DNA from one person Compare Known genotype 10, 12

3 DNA interpretation challenges
Low-level DNA Degradation Mixtures

4 Mixtures How many contributors?

5 Relatives share alleles
10,12 11,14 10,13 11,12 12,14 12,12

6 Science in the name of justice
Use tools Gain knowledge Get answers Impact society

7 Computer DNA interpretation
7 Computer DNA interpretation

8 State of West Virginia v. Defendant

9 Case context April 2016 House party Drugs and alcohol Alleged assault

10 Case context Taken to hospital Aware of assault
Sexual assault kit collected

11 The evidence

12 Crime lab findings

13 Computers can use all the data
Quantitative peak heights at locus vWA peak size peak height

14 How the computer thinks
Consider every possible genotype solution Better explanation has a higher likelihood Explain the peak pattern

15 How the computer thinks
Consider every possible genotype solution Better explanation has a higher likelihood Explain the peak pattern

16 How the computer thinks
Consider every possible genotype solution Better explanation has a higher likelihood Explain the peak pattern

17 How the computer thinks
Consider every possible genotype solution Worse explanation has a lower likelihood

18 Evidence genotype Objective genotype determined solely from the DNA data. Never sees a comparison reference. 100% Prob(evidence match) Prob(coincidental match) 8x 12%

19 Match information at 21 loci

20 Is the victim in the evidence?
A match between the vaginal swab and victim is: 55.2 nonillion times more probable than a coincidental match to an unrelated African-American person 4.07 octillion times more probable than a coincidental match to an unrelated Caucasian person 3.99 octillion times more probable than a coincidental match to an unrelated Southeast Hispanic person 11.5 octillion times more probable than a coincidental match to an unrelated Southwest Hispanic person

21 Match statistics 21 27.60 Item Victim Suspect Brother Defendant
Vaginal swabs 27.60

22 How the computer thinks
Peeling process assuming victim as known Better explanation has a higher likelihood Explain the peak pattern

23 Evidence genotype Objective genotype determined solely from the DNA data. Never sees a comparison reference. 96% Prob(evidence match) Prob(coincidental match) 24x 4%

24 Match statistics 24 27.60 29.94 Item Victim Suspect Brother Defendant
Vaginal swabs 27.60 29.94

25 How the computer thinks
Peeling process assuming victim and suspect as known Better explanation has a higher likelihood Explain the peak pattern

26 Match statistics 26 27.60 29.94 11.84 Item Victim Suspect Brother
Defendant Vaginal swabs 27.60 29.94 11.84

27 Vaginal swabs vs. defendant
27 Vaginal swabs vs. defendant Number of contributors Assumed references Average match statistic 3 none -9.94 victim -11.40 victim, suspect -12.74 victim, brother -41.39 4 victim, suspect, brother -3.44

28 Anal swabs vs. defendant
28 Anal swabs vs. defendant Number of contributors Assumed references Average match statistic 3 none -23.77 suspect -28.13 suspect, victim -33.56 suspect, brother -39.67 4 suspect, victim, brother -1.99

29 TrueAllele Results Vaginal swabs

30 TrueAllele Results Anal swabs

31 DNA evidence in the courtroom
Trial in April 2018 Teaching about probabilistic genotyping Cross exam Outcome: Not guilty of sexual assault

32 Validation studies 36 studies, 7 peer-reviewed publications
Cybergenetics. Australia TrueAllele® Validation Report Caponera, J. New York State Police Crime Laboratory System TrueAllele® Casework Validation Addendum Guest, K., Ludvico, L., Ferrara, L., Perlin, M. Development of Kinship Mixtures and Subsequent Analysis Using TrueAllele® Casework Greenspoon, S.A., Schiermeier-Wood, L., and Jenkins, B.C. Establishing the limits of TrueAllele® Casework: a validation study. Journal of Forensic Sciences, 60(5): , 2015.

33 Thank you! For more information www.cybgen.com Beatriz Pujols, M.S.
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