Forensic Bioinformatics (bioforensics) - PowerPoint PPT Presentation

parry
forensic bioinformatics www bioforensics com n.
Skip this Video
Loading SlideShow in 5 Seconds..
Forensic Bioinformatics (bioforensics) PowerPoint Presentation
Download Presentation
Forensic Bioinformatics (bioforensics)

play fullscreen
1 / 26
Download Presentation
Forensic Bioinformatics (bioforensics)
313 Views
Download Presentation

Forensic Bioinformatics (bioforensics)

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Suspect-Centric Combined Probability of InclusionA means of attaching objective statistical weights to mixed DNA profiles where dropout may have occurred Forensic Bioinformatics (www.bioforensics.com) Dan E. Krane, Ph.D., Wright State University, Dayton, OH

  2. The problems with mixtures where dropout may have occurred • Determining the rate of allelic dropout is problematic • Interpreting evidence in a way that is least favorable to a defendant is taboo

  3. CPI statistics without dropout

  4. Combined Probability of Inclusion CPI statistics without dropout • Probability that a random, unrelated person could be included as a possible contributor to a mixed profile • For a mixed profile with the alleles 14, 16, 17, 18; contributors could have any of 10 genotypes: 14, 1414, 16 14, 17 14, 18 16, 1616, 17 16, 18 17, 1717, 18 18, 18 Probability works out as: CPI = (p[14] + p[16] + p[17] + p[18])2 (0.102 + 0.202 + 0.263 + 0.222)2 = 0.621

  5. The testing lab’s conclusions

  6. Ignoring loci with “missing” alleles • Some laboratories assert that this is a “conservative” approach • Ignores potentially exculpatory information • “It fails to acknowledge that choosing the omitted loci is suspect-centric and therefore prejudicial against the suspect.” • Gill, et al. “DNA commission of the International Society of Forensic Genetics: Recommendations on the interpretation of mixtures.” FSI. 2006.

  7. Mixtures where allelic drop out may have occurred • Determining the rate of allelic drop out is problematic • Interpreting evidence in a way that is least favorable to a defendant is taboo

  8. Mixtures where allelic drop out may have occurred • Determining the rate of allelic drop out is problematic • Interpreting evidence in a way that is least favorable to a defendant is taboo • What if instead of trying to assess drop out we invoke it liberally? • What if instead of trying to eliminate knowledge of a reference profile we embrace it?

  9. Mixtures where allelic drop out may have occurred • Determining the rate of allelic drop out is problematic • Interpreting evidence in a way that is least favorable to a defendant is taboo • What if instead of trying to assess drop out we invoke it liberally? • What if instead of trying to eliminate knowledge of a reference profile we embrace it? • How does a given suspect compare to 1,000,000 random individuals who are treated the same way?

  10. 1,000 1 10 100 10,000 100,000 1/Suspect-centric CPI

  11. 1,000 1 10 100 10,000 100,000 1/Suspect-centric CPI

  12. B: 10,482 A: 902 1,000 1 10 100 10,000 100,000 1/Suspect-centric CPI

  13. 1,000 1 10 100 10,000 100,000 1/Suspect-centric CPI

  14. 1,000 1 10 100 10,000 100,000 1/Suspect-centric CPI

  15. 48,564 1,000 1 10 100 10,000 100,000 1/Suspect-centric CPI

  16. 1,000 1 10 100 10,000 100,000 1/Suspect-centric CPI

  17. Mixtures where allelic drop out may have occurred • Determining the rate of allelic drop out is problematic • Interpreting evidence in a way that is least favorable to a defendant is taboo • What if instead of trying to assess drop out we invoke it liberally? • What if instead of trying to eliminate knowledge of a reference profile we embrace it? • How does a given suspect compare to 1,000,000 random individuals who are treated the same way?

  18. Suspect-Centric Combined Probability of Inclusion Dan E. Krane, Ph.D., Wright State University, Dayton, OH Forensic Bioinformatics (www.bioforensics.com)