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Statistical weights of single source DNA profiles

Statistical weights of single source DNA profiles. Dan E. Krane, Wright State University, Dayton, OH Forensic DNA Profiling Video Series. Forensic Bioinformatics (www.bioforensics.com). DNA statistics. Coincidental 13 locus DNA profile matches are exceedingly rare

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Statistical weights of single source DNA profiles

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  1. Statistical weights of single source DNA profiles Dan E. Krane, Wright State University, Dayton, OH Forensic DNA Profiling Video Series Forensic Bioinformatics (www.bioforensics.com)

  2. DNA statistics • Coincidental 13 locus DNA profile matches are exceedingly rare • Several factors can make statistics less impressive • Mixtures • Incomplete information • Relatives

  3. DNA profile

  4. Comparing electropherograms EXCLUDE Evidence sample Suspect #1’s reference

  5. Comparing electropherograms CANNOT EXCLUDE Evidence sample Suspect #2’s reference

  6. What weight should be given to DNA evidence? Statistics do not lie. But, you have to pay close attention to the questions they are addressing.

  7. What weight should be given to DNA evidence? Statistics do not lie. But, you have to pay close attention to the questions they are addressing. RMP: The chance that a randomly chosen, unrelated individual from a given population would have the same DNA profile observed in a sample.

  8. 2pq x 2pq x 2pq x 2pq x 2pq x p2 x 2pq x 2pq x 2pq x 2pq x p2 x p2 x 2pq x 2pq x 2pq Statistical estimates: the product rule Single source samples Formulae for RMP: At a locus: Heterozygotes: Homozygotes: Multiply across all loci 2pq p2

  9. x 0.1097 x 2 Statistical estimates: the product rule 0.1454

  10. Statistical estimates: the product rule 3.2% X X X X 6.0% 4.6% 1.2% = 0.032 0.1454 x 0.1097 x 2 X X X X X 9.8% 9.5% 6.3% 2.2% 1.0% 1 in 609 quintillion X X X X 2.9% 5.1% 29.9% 4.0% 1 in 609,000,000,000,000,000,000 X 1.1% 6.6%

  11. Two underlying assumptions of the product rule: • The events being evaluated are independent • In this context, the events are the observation of specific alleles • The frequencies of the events are known • In this context, at what frequency does each allele occur?

  12. Population genetics: testing for independence • Hardy-Weinberg equilibrium (HWE) • A test of the independence of alleles within a locus • Linkage equilibrium • A test of the independence of alleles between loci

  13. DNA profile

  14. DNA statistics • Coincidental 13 locus DNA profile matches are exceedingly rare • Corrections can be made for population substructure • RMP statistics described in terms of quintillions are common

  15. Two underlying assumptions of the product rule: • The events being evaluated are independent • In this context, the events are the observation of specific alleles • The frequencies of the events are known • In this context, at what frequency does each allele occur?

  16. What weight should be given to DNA evidence? Statistics do not lie. But, you have to pay close attention to the questions they are addressing. RMP: The chance that a randomly chosen, unrelated individual from a given population would have the same DNA profile observed in a sample.

  17. What is the relevant population? 1 in 609 quintillion

  18. How would you determine the frequency of Obama supporters in North Carolina? Obama N.C. 50.2% Popular vote in 2008 by county. McCain won red counties, Obama won blue counties.

  19. How would you determine the frequency of Obama supporters in North Carolina? Obama N.C. 50.2% Region 59.6% Popular vote in 2008 by county. McCain won red counties, Obama won blue counties.

  20. How would you determine the frequency of Obama supporters in North Carolina? Obama N.C. 50.2% Region 59.6% U.S. 52.9% Popular vote in 2008 by county. McCain won red counties, Obama won blue counties.

  21. How would you determine the frequency of Obama supporters in North Carolina? Obama N.C. 50.2% Region 59.6% U.S. 52.9% Utah? 35.5% ? Popular vote in 2008 by county. McCain won red counties, Obama won blue counties.

  22. DNA statistics • Coincidental 13 locus DNA profile matches are exceedingly rare • Several factors can make statistics less impressive • Mixtures • Incomplete information • Relatives

  23. Statistical weights of single source DNA profiles Dan E. Krane, Wright State University, Dayton, OH Forensic DNA Profiling Video Series Forensic Bioinformatics (www.bioforensics.com)

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