DNA Identification: Inclusion Genotype and LR

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DNA Identification: Mixture Weight & Inference

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Presentation transcript:

DNA Identification: Inclusion Genotype and LR Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA TrueAllele® Lectures Fall, 2010 Cybergenetics © 2003-2010 Cybergenetics © 2007-2010

Mixture Data Some amount of contributor A genotype Mixture data with genotypes of contributors A & B + PCR Other amount of contributor B genotype

Quantitative Likelihood

Qualitative Likelihood

Punnett Square 1 2 1 2

Prior Probability 0.10 probability 1, 1 1, 2 2, 2 … allele pairs

Likelihood Function included excluded likelihood 1, 1 1, 2 2, 2 … 1, 1 1, 2 2, 2 … allele pairs

Genotype Inference

Posterior Probability 0.30 0.20 probability 0.10 1, 1 1, 2 2, 2 …

Likelihood Ratio

Uncertain Genotypes Bayes theorem updates probability • prior probability • likelihood function • posterior probability Probability concentration determines likelihood ratio

Uniform population prior

Realistic population prior

Likelihood: all pairs are equal

Filter allele pair list

Unequal: quantitative data

Population prior probability

Combine with equal listing

Posterior proportional to prior

Population genotype prior

Likelihood: smaller allele pair list

Posterior focuses probability

Population genotype prior

Quantitative likelihood function

Evidence genotype posterior

Conclusions • No real inclusion vs. LR controversy: same construction • All uncertain genotypes are probability distributions • Efficacy measured by preserving identification information apply log(LR) information measure to inclusion method • Inclusion has justification for use in court, since is a LR • PI statistic understandable via inclusion likelihood function • Relevance of inclusion? Depends on the data.