Dataset Information

DNA identification by pedigree likelihood ratio accommodating population substructure and mutations.

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.

SUBMITTER: Ge J

PROVIDER: S-EPMC2990736 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

DNA identification by pedigree likelihood ratio accommodating population substructure and mutations.

Ge Jianye J Budowle Bruce B Chakraborty Ranajit R

Investigative genetics 20101004 1

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 p ...[more]

PMID: 21092343

Similar Datasets

Project description:BackgroundThe study of ancestral alleles provides insights into the evolutionary history, selection, and genetic structures of a population. In cattle, ancestral alleles are widely used in genetic analyses, including the detection of signatures of selection, determination of breed ancestry, and identification of admixture. Having a comprehensive list of ancestral alleles is expected to improve the accuracy of these genetic analyses. However, the list of ancestral alleles in cattle, especially at the whole genome sequence level, is far from complete. In fact, the current largest list of ancestral alleles (~ 42 million) represents less than 28% of the total number of detected variants in cattle. To address this issue and develop a genomic resource for evolutionary studies, we determined ancestral alleles in cattle by comparing prior derived whole-genome sequence variants to an out-species group using a population-based likelihood ratio test.ResultsOur study determined and makes available the largest list of ancestral alleles in cattle to date (70.1 million) and includes 2.3 million on the X chromosome. There was high concordance (97.6%) of the determined ancestral alleles with those from previous studies when only high-probability ancestral alleles were considered (29.8 million positions) and another 23.5 million high-confidence ancestral alleles were novel, expanding the available reference list to improve the accuracies of genetic analyses involving ancestral alleles. The high concordance of the results with previous studies implies that our approach using genomic sequence variants and a likelihood ratio test to determine ancestral alleles is appropriate.ConclusionsConsidering the high concordance of ancestral alleles across studies, the ancestral alleles determined in this study including those not previously listed, particularly those with high-probability estimates, may be used for further genetic analyses with reasonable accuracy. Our approach that used predetermined variants in species and the likelihood ratio test to determine ancestral alleles is applicable to other species for which sequence level genotypes are available.

Project description:A likelihood ratio (LR) system is defined as the entire pipeline of the measurement and interpretation processes where probabilistic genotyping software (PGS) is a piece of the whole LR system. To gain understanding on how two LR systems perform, a total of 154 two-person, 147 three-person, and 127 four-person mixture profiles of varying DNA quality, DNA quantity, and mixture ratios were obtained from the filtered (.CSV) files of the GlobalFiler 29 cycles 15s PROVEDIt dataset and deconvolved in two independently developed fully continuous programs, STRmix v2.6 and EuroForMix v2.1.0. Various parameters were set in each software and LR computations obtained from the two software were based on same/fixed EPG features, same pair of propositions, number of contributors, theta, and population allele frequencies. The ability of each LR system to discriminate between contributor (H1-true) and non-contributor (H2-true) scenarios was evaluated qualitatively and quantitatively. Differences in the numeric LR values and their corresponding verbal classifications between the two LR systems were compared. The magnitude of the differences in the assigned LRs and the potential explanations for the observed differences greater than or equal to 3 on the log10 scale were described. Cases of LR < 1 for H1-true tests and LR > 1 for H2-true tests were also discussed. Our intent is to demonstrate the value of using a publicly available ground truth known mixture dataset to assess discrimination performance of any LR system and show the steps used to understand similarities and differences between different LR systems. We share our observations with the forensic community and describe how examining more than one PGS with similar discrimination power can be beneficial, help analysts compare interpretation especially with low-template profiles or minor contributor cases, and be a potential additional diagnostic check even if software in use does contain certain diagnostic statistics as part of the output.

Dataset Information

DNA identification by pedigree likelihood ratio accommodating population substructure and mutations.

Publications

DNA identification by pedigree likelihood ratio accommodating population substructure and mutations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets