Abstract
DNA typing is an important tool in missing-person identification, especially in mass-fatality disasters. Identification methods comparing a DNA profile from unidentified human remains with that of a direct (from the person) or indirect (for example, from a biological relative) reference sample and ranking the pairwise likelihood ratios (LR) is straightforward and well defined. However, for indirect comparison cases in which several members from a family can serve as reference samples, the full power of kinship analysis is not entirely exploited. Because biologically related family members are not genetically independent, more information and thus greater power can be attained by simultaneous use of all pedigree members in most cases, although distant relationships may reduce the power. In this study, an improvement was made on the method for missing-person identification for autosomal and lineage-based markers, by considering jointly the DNA profile data of all available family reference samples. The missing person is evaluated by a pedigree LR of the probability of DNA evidence under alternative hypotheses (for example, the missing person is unrelated or if they belong to this pedigree with a specified biological relationship) and can be ranked for all pedigrees within a database. Pedigree LRs are adjusted for population substructure according to the recommendations of the second National Research Council (NRCII) Report. A realistic mutation model was also incorporated to accommodate the possibility of false exclusion. The results show that the effect of mutation on the pedigree LR is moderate, but LRs can be significantly decreased by the effect of population substructure. Finally, Y chromosome and mitochondrial DNA were integrated into the analysis to increase the power of identification. A program titled MPKin was developed, combining the aforementioned features to facilitate genetic analysis for identifying missing persons. The computational complexity of the algorithms is explained, and several ways to reduce the complexity are introduced.
Highlights
Over the past two decades, forensic DNA typing has become widely accepted as a powerful tool in criminal and civil investigations
Statistical inference was based on pairwise comparison of the DNA profiles of the unknown sample and a single family reference sample, and ranking the likelihood ratios (LRs) for specified biological relationships
Computational complexity analysis The computational complexity of a pedigree LR calculation generally depends on the number of markers (NM), the number of untyped connector (UC) (NUC), the number of possible genotypes of each UC (NGUC) and the number of offspring with single typed parent’ (OSTP) (NO)
Summary
Over the past two decades, forensic DNA typing has become widely accepted as a powerful tool in criminal and civil investigations. This technology has become invaluable in many missing-person identifications. Substantial progress in the past few years has been made in the determination of missing-person identity by pedigree kinship analysis [4,5,6,7,8]. Dawid et al [11] used a Bayesian network for identification using pedigree information, which incorporated the possibility of mutation, but with no adjustment for population substructure. Familias [15] does address both population substructure and mutation, but the mutation models are not appropriate for human STR loci. The ‘equal probability model’ and ‘proportional model’ used in Familias are not necessarily the best for STR loci [16,17], and the ‘decreasing model’ includes a parameter (number of ‘possible’ alleles) that cannot be determined, because mutation probability is not related to allele frequency and the number of possible alleles [16,17]
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