Searching DNA databases with complex DNA profiles: the SmartRank project

24 April 2012

Searching DNA databaseswith complex DNA profiles: the SmartRank project

Hinda [email protected]

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

Searching DNA databases with complex profiles — November 2013 1

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.


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.


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


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


Two person-mixture

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


Complex mixtures

I At least three people involved

I One major (victim), possibly two minors

I Drop-out is deemed possible


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


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


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


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.


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


LRs and database search








LR distribution for ranked genotypes


LR distribution for ranked genotypes

Person of interest outlier with

LR >> 109


Feasibility study: experimental set-up


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


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


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


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


Science

Searching DNA databases with complex DNA profiles: the SmartRank project