The presentation explains the principles of DNA database searching via a likelihood ratio model.

1. 24 April 2012
Searching DNA databases
with complex DNA
profiles: the SmartRank
project
Hinda Haned
h.haned@nfi.minvenj.nl

2. DNA mixtures
A large number of traces recovered from crime stains are
mixtures
Up to two distinct alleles per contributor
As few as a single allele: allele sharing
Searching DNA databases with complex profiles — November 2013 1

3. Mixture analysis: a difficult task
What genotypes created the
mixture?
• Individual 1 : 13/15
• Individual 2 : 14/17
Or
• Individual 1 : 13/17
• Individual 2 : 14/15
Or
• Individual 1 : 13/15
• Individual 2 : 14/17
• Individual 3 : 15/15
Large number of potential genotypes consistent with the mixture.
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4. ENFSI recommendation
If possible, mixed DNA-profiles should be interpreted and designated
into their contributing DNA-profiles. Mixed profiles from (known)
victims and (unknown) donors sometimes can be resolved because
the alleles of the DNA-profile of the victim can be subtracted from
the mixed profile. The remaining alleles must belong to the unknown
donor.
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5. Basic deconvolution method: Binary model
Manual method for the resolution of two-person mixtures. It
relies upon the experience of the expert along with the
application of a number of numerical guidelines
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6. Two person-mixture
Significant differences in contribution
Major and Minor profiles can be deduced
Drop-out from the minor is deemed possible
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7. Two person-mixture
Major 13,13 14,17 11,11 16,18
Minor 14,15 16,16 9,9 19,20
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8. Complex mixtures
At least three people involved
One major (victim), possibly two minors
Drop-out is deemed possible
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9. Complex mixtures
Binary model cannot be applied to high order mixtures
Consequences for data base search:
• Mixed profiles cannot be fully exploited
• A person of interest could be in the database, but the
genotype of the minors cannot be deduced
Perform search with all alleles or with ‘required alleles’ that
may have come from the person of interest. However:
• Searching with mixtures: increased risk of spurious associations
• Risk of reporting a ‘numerical match’: inconsistency with ratio
of contribution in the questioned sample
Solution: Likelihood ratio framework
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10. The likelihood ratio framework
Development of a likelihood-ratio model enabling the
interpretation of complex DNA samples:
• DNA mixtures: multiple donors
• Low template DNA samples: allele drop-out (missing allele)
and allele drop-in (spurious allele)
Haned et al, Forensic Sci. Int. Genet. 2012
Gill & Haned, Forensic Sci. Int. Genet. 2013
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11. The likelihood ratio framework
LR =
Probability of the evidence under prosecution hypothesis
Probability of the evidence under defense hypothesis
LR = 1: evidence is neutral
LR > 1: the evidence suppports the prosecution hypothesis
LR < 1: the evidence suppports the defense hypothesis
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12. Likelihood ratios and mixtures
Two alternative hypotheses:
• Prosecution hypothesis: the victim, the suspect and one
unknown are the donors
• Defense hypothesis: the victim and two unknowns are the
donors
The probability of the evidence is 125,000 more likely if the
prosecution hypothesis is true than if the defense hypothesis is true.
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13. Requirements for LR calculations
Formulate hypotheses:
• Hp: Victim, the individual in the database and one unknown
are the donors
• Hd: Victim, and two unrelated unknowns are the donors
Evaluation of drop-in and drop-out levels
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14. LRs and database search
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15. LRs and database search
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16. LRs and database search
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17. LRs and database search
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18. LR distribution for ranked genotypes
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19. LR distribution for ranked genotypes
Person of interest outlier with
LR >> 109
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20. Feasibility study: experimental set-up
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21. Successful extractions rates
Two-person mixtures: % of profiles with a given rank
Bins for ranks High drop-out Moderate drop-out Low drop-out
1 5 58 94
1-50 11 94 100
1-100 11 95 100
Three-person mixtures: % of profiles with a given rank
1 0 19 30
1-50 0 62 94
1-100 0 72 99
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22. False positives
False positives for two-person mixtures
High drop-out Moderate drop-out Low drop-out
Min. 0 11 6
Max. 234 1103 473
False positives for three-person mixtures
Min. 0 4 124
Max. 16 1722 2406
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23. Limitations
Although complex mixtures can be searched in the database, bear
in mind that the true perpetrator may not be in the database
Hp: Victim, the individual in the database and one unknown
are the donors
Hd: Victim, and two unrelated unknowns are the donors
Probabilistic model ≡ data intelligence
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24. Future work
Implementation guidelines:
• Extraction efficiency: to be increased by improving LR-model
parameters
• Robustness of the model vs. partiality of the profiles
User-friendly free software:
• SmartRank software project
• Supported by ENFSI Monopoly 2013 project (starting in 2015)
• To be distributed for free on-line
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