Statistical basis for positive identification in forensic anthropology

Forensic scientists are often expected to present the likelihood of DNA identifications in US courts based on comparative population data, yet forensic anthropologists tend not to quantify the strength of an osteological identification. Because forensic anthropologists are trained first and foremost as physical anthropologists, they emphasize estimation problems at the expense of evidentiary problems, but this approach must be reexamined. In this paper, the statistical bases for presenting osteological and dental evidence are outlined, using a forensic case as a motivating example. A brief overview of Bayesian statistics is provided, and methods to calculate likelihood ratios for five aspects of the biological profile are demonstrated. This paper emphasizes the definition of appropriate reference samples and of the "population at large," and points out the conceptual differences between them. Several databases are introduced for both reference information and to characterize the "population at large," and new data are compiled to calculate the frequency of specific characters, such as age or fractures, within the "population at large." Despite small individual likelihood ratios for age, sex, and stature in the case example, the power of this approach is that, assuming each likelihood ratio is independent, the product rule can be applied. In this particular example, it is over three million times more likely to obtain the observed osteological and dental data if the identification is correct than if the identification is incorrect. This likelihood ratio is a convincing statistic that can support the forensic anthropologist's opinion on personal identity in court.

An informativity index for multilocus DNA fingerprints

An informativity index for multilocus DNA fingerprints, r, was developed. It is based upon the Shannon information that a paternal DNA fingerprint conveys about the offspring phenotype pattern. Both simulation and empirical data reveal that the index r is strongly correlated with the mean log likelihood ratio (paternity vs. nonpaternity) expected in trio cases of true paternity. Since r can be estimated from DNA fingerprints of unrelated individuals in advance, it will provide an easy means to assess the potential utility of a given probe/enzyme combination in kinship testing.

Multilocus DNA fingerprinting: the independence problem in quantitative paternity testing

A simulation study was performed in order to determine whether or not the assumption of independence, made in the quantitative analysis of multilocus DNA fingerprints, represents an inadmissible over-simplification. A total of 10,000 cases of true and false paternity, respectively, were simulated in twenty replicas of various genetic models. Log-likelihood ratios (paternity vs. non-paternity; LR) were calculated using published likelihood formulae and assuming position-wise independence. The resulting LR distributions were compared to (i) the results of a classical analysis of the underlying genotype data, and (ii) the distributions expected from the likelihood model employed in the LR calculations. Although considerable discrepancies were observed between these distributions, decision making about paternity appeared to be only marginally affected, especially when only a fraction of each multilocus DNA fingerprint was analyzed.

The formal analysis of multilocus DNA fingerprints

A description is given of a novel method for the formal analysis of multilocus DNA fingerprints, the so-called 'genetic factor model'. Using this model, multilocus DNA fingerprints can be shown to be a robust means for both paternity testing and pedigree reconstruction.