searching dna databases with complex dna profiles: the smartrank project

24 April 2012

Searching DNA databaseswith complex DNA profiles: the SmartRank project

Hinda Hanedh.haned@nfi.minvenj.nl

DNA mixtures

I A large number of traces recovered from crime stains aremixtures

I Up to two distinct alleles per contributor

I As few as a single allele: allele sharing

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Mixture analysis: a difficult task

I What genotypes created themixture?

• 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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ENFSI recommendation

If possible, mixed DNA-profiles should be interpreted and designatedinto their contributing DNA-profiles. Mixed profiles from (known)victims and (unknown) donors sometimes can be resolved becausethe alleles of the DNA-profile of the victim can be subtracted fromthe mixed profile. The remaining alleles must belong to the unknowndonor.

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Basic deconvolution method: Binary model

I Manual method for the resolution of two-person mixtures. Itrelies upon the experience of the expert along with theapplication of a number of numerical guidelines

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Two person-mixture

I Significant differences in contribution

I Major and Minor profiles can be deduced

I Drop-out from the minor is deemed possible

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Two person-mixture

Major 13,13 14,17 11,11 16,18Minor 14,15 16,16 9,9 19,20

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Complex mixtures

I At least three people involved

I One major (victim), possibly two minors

I Drop-out is deemed possible

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Complex mixtures

I Binary model cannot be applied to high order mixturesI 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

I Perform search with all alleles or with ‘required alleles’ thatmay 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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The likelihood ratio framework

I Development of a likelihood-ratio model enabling theinterpretation of complex DNA samples:

• DNA mixtures: multiple donors• Low template DNA samples: allele drop-out (missing allele)

and allele drop-in (spurious allele)

I Haned et al, Forensic Sci. Int. Genet. 2012

I Gill & Haned, Forensic Sci. Int. Genet. 2013

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The likelihood ratio framework

LR =Probability of the evidence under prosecution hypothesis

Probability of the evidence under defense hypothesis

I LR = 1: evidence is neutral

I LR > 1: the evidence suppports the prosecution hypothesis

I LR < 1: the evidence suppports the defense hypothesis

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Likelihood ratios and mixtures

I 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 theprosecution hypothesis is true than if the defense hypothesis is true.

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Requirements for LR calculations

I Formulate hypotheses:• Hp: Victim, the individual in the database and one unknown

are the donors• Hd: Victim, and two unrelated unknowns are the donors

I Evaluation of drop-in and drop-out levels

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LRs and database search

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LRs and database search

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LRs and database search

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LRs and database search

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LR distribution for ranked genotypes

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LR distribution for ranked genotypes

Person of interest outlier with

LR >> 109

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Feasibility study: experimental set-up

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Successful extractions rates

Two-person mixtures: % of profiles with a given rankBins for ranks High drop-out Moderate drop-out Low drop-out1 5 58 941-50 11 94 1001-100 11 95 100

Three-person mixtures: % of profiles with a given rank1 0 19 301-50 0 62 941-100 0 72 99

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False positives

False positives for two-person mixturesHigh drop-out Moderate drop-out Low drop-out

Min. 0 11 6Max. 234 1103 473

False positives for three-person mixturesMin. 0 4 124Max. 16 1722 2406

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Limitations

Although complex mixtures can be searched in the database, bearin mind that the true perpetrator may not be in the database

I Hp: Victim, the individual in the database and one unknownare the donors

I Hd: Victim, and two unrelated unknowns are the donors

Probabilistic model ≡ data intelligence

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Future work

I Implementation guidelines:

• Extraction efficiency: to be increased by improving LR-modelparameters

• Robustness of the model vs. partiality of the profiles

I 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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