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Unleashing Forensic DNA through Computer Intelligence Forensics Europe Expo Forensic Innovation Conference April, 2013 London, UK Mark W Perlin, PhD, MD,

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Presentation on theme: "Unleashing Forensic DNA through Computer Intelligence Forensics Europe Expo Forensic Innovation Conference April, 2013 London, UK Mark W Perlin, PhD, MD,"— Presentation transcript:

1 Unleashing Forensic DNA through Computer Intelligence Forensics Europe Expo Forensic Innovation Conference April, 2013 London, UK Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA Cybergenetics © 2003-2013

2 cell nucleus chromosomes locus Short Tandem Repeat (STR) genotype 10, 12 alleles Genotype

3 DNA Identification Pathway Evidence genotype Known genotype 10 12 10, 12 LabInfer Compare Evidence item Evidence data

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

5 DNA Mixture Data Quantitative peak heights at a locus peak size peak height

6 DNA Pathway Broken Evidence genotype Known genotype ??? 10, 12 LabInfer Compare Evidence item Evidence data + 7 10 12 14

7 Human Interpretation Issues Evidence call good data inconclusive peaks are too low for them too many contributors to handle potential examination bias Database hit by association, not by match comparison: make false hits restrict upload: lose true hits

8 TrueAllele ® Casework Evidence preserve data information use all peaks, high or low any number of contributors entirely objective, no bias Database hit based on LR match statistic sensitive: find true hits specific: only true hits

9 DNA Pathway Restored LabInfer Evidence item Evidence data 7 10 12 14 + Known genotype 10, 10 @ 30% 10, 12 @ 50% 10, 14 @ 20% 10, 12 Compare Evidence genotype

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

11 Gang crime in Kern County Food mart gun hat Perlin MW. DNA mapping the crime scene: do computers dream of electric peaks? Promega's Twenty Third International Symposium on Human Identification, 2012; Nashville, TN.

12 Escalation Food mart gun hat Hardware safe phone Jewelry counter safe

13 Evidence from multiple scenes Food mart gun hat Hardware safe phone Jewelry counter safe Convenience keys tape Market hat 1 hat 2 overalls shirt

14 DNA evidence: genotypes 1314 1618 1720 Allele size DNA amount First contributor Second contributor Third contributor

15 Develop STR data First contributor Second contributor Third contributor

16 Laboratory processing gun hat safe phone counter safe keys tape hat 1 hat 2 overalls shirt 10 reference items 5 victims V1 V2 V3 V4 V5 5 suspects S1 S2 S3 S4 S5 12 evidence items Scene 1 Scene 2 Scene 3 Scene 4 Scene 5

17 DNA match questions log(LR)Suspect 1Suspect 2Suspect 3Suspect 4Suspect 5 1. Gun 1. Hat 2. Safe 2. Phone 3. Counter 3. Safe 4. Keys 4. Tape 5. Hat 1 5. Hat 2 5. Overalls 5. Shirt

18 Human review: no results Below threshold, data unused Above threshold, peak heights are ignored

19 How the computer thinks 1314 1618 1720 First contributor Second contributor Third contributor

20 TrueAllele: objective genotypes For each contributor, at every locus 16, 18 14, 18 13, 18 18, 20 17, 18 65% 12% 10% 8% 4% Allele pairProbability

21 TrueAllele: match answers log(LR)Suspect 1Suspect 2Suspect 3Suspect 4Suspect 5 1. Gun4 1. Hat34 2. Safe 2. Phone 3. Counter6 3. Safe 4. Keys 4. Tape 5. Hat 16 5. Hat 2 5. Overalls11 5. Shirt3

22 DNA mapping the crime scene Food mart gun hat Hardware safe phone Jewelry counter safe Convenience keys tape Market hat 1 hat 2 overalls shirt Suspects: S1, S2, S3, S4, S5

23 Report results in court M. W. Perlin, "Easy reporting of hard DNA: computer comfort in the courtroom," Forensic Magazine, vol. 9, pp. 32-37, 2012. A match between the evidence and the suspect is 553 million times more probable than a coincidental match to an unrelated Black person

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25 TrueAllele reliability 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.

26 TrueAllele validation Perlin MW, Belrose JL, Duceman BW. New York State TrueAllele ® Casework validation study. Journal of Forensic Sciences. 2013;58(6):in press.

27 TrueAllele in criminal trials Court testimony: state federal military foreign Over 100 case reports filed on DNA evidence Crimes: armed robbery child abduction child molestation murder rape terrorism weapons

28 TrueAllele in the United States Casework system Interpretation services

29 TrueAllele in the United Kingdom 2000: FSS & TrueAllele Databank first fully automated DNA expert system for reference samples only faster: eliminated 350,000 item backlog better: eliminated human error of 0.05% cheaper: eliminated 100 jobs 2010: PSNI & TrueAllele Casework match stats for complex DNA evidence 3-4 person DNA mixtures low-template & degraded DNA Massereene Barracks attack

30 Real Information, Real Time Evidence Genotype database Crime scene Reference Genotype database Criminals Infer & Upload Match & Report +

31 TrueAllele computer age Currently used to: eliminate DNA backlogs reduce forensic costs solve crimes find criminals convict the guilty free the innocent create a safer society Objective, reliable truth-seeking tool solves the DNA mixture problem handles low-copy and degraded DNA provides accurate DNA match statistics automates DNA evidence interpretation

32 More information perlin@cybgen.com http://www.cybgen.com/information Courses Newsletters Newsroom Presentations Publications http://www.cybgen.com/support/sending_data.shtml


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