Forensic Science International: Genetics Supplement Series最新文献

Individuals from Slovenia, Greece, Albania, and Eritrea were typed with the Precision ID Ancestry Panel and included among GenoGeographer’s nine reference populations (Sub-Saharan Africa, Horn of Africa, North Africa, Middle East, Europe, South/Central Asia, East Asia, and East and West Greenland). We tested the performance of GenoGeographer with the Admixture Module on AIM profiles of 3548 individuals assumed to belong to one of the reference populations. A total of 3387 (95.5 %) profiles were assigned to one or more of the reference populations, either a single population or an admixture of two or more populations, while 161 (4.5 %) profiles were not assigned to any reference population or admixtures thereof. For 1486 AIM profiles with no reference population of origin in GenoGeographer, the rejection rate was more than 70 % for AIM profiles from North and South America and less than 20 % for those from Central, North, and Northeast Asia.

In order to prioritize the exhumation of the most informative reference relatives to increase the statistical power of a reference group, a conditional simulation approach for missing person identification that combines both exclusion and inclusion power in reference families has been previously developed. The aim of this study is to empirically validate this approach by comparing its predicted theoretical prioritization model with the observed changes in statistical power in real cases of our laboratory, in which new relatives had already been added. We conclude that this approach is a reliable tool to choose the most appropriate reference relatives to complete a family group and improve the identification power of a Missing Person (MP).

The presence of more than one DNA contributor in an evidentiary sample may preclude attempts to use forensic genetic genealogy to develop an investigative lead. To address this issue, we developed a workflow for deconvolution of SNP mixtures into single source profiles that are suitable for matching against a genealogical database. Using the method, two-contributor DNA mixtures assayed using a commercial SNP typing kit can produce informative match results for both major and minor contributors.

Late in 2021, the Y Chromosome Haplotype Reference Database (YHRD) added the capability to perform discrete Laplace statistical calculations on searches performed against their SWGDAM-compliant U.S. subpopulations. Because discrete Laplace is not a commonly used or reported statistic in the United States, the SWGDAM Lineage Marker Committee, responsible for maintaining the SWGDAM Interpretation Guidelines for Y-Chromosome STR Testing, evaluated the feature to assess its ease of use and applicability to U.S. casework. Discrete Laplace calculates profile probabilities based on their genetic distance from sets of ancestral alleles and can yield much more informative probability estimates than the commonly used Clopper-Pearson 95% upper confidence interval (UCI). This is especially true for rare profiles with no database observations because, unlike the 95% UCI, the discrete Laplace calculation is not based upon how many times a profile is observed in the database. However, the statistic as applied by YHRD also has some limitations, such as a requirement that the query profile is complete for the ‘minimal’ kit and that expanded loci beyond those included in the Y17 kit cannot be included in the calculation. Here, we explain how discrete Laplace works and demonstrate how the results compare to those generated using the 95% UCI.

Standard Reference Material (SRM) 2391d: PCR-Based DNA Profiling Standard was released to the forensic community in 2019. Next Generation Sequencing (NGS) was used as the primary method of certification, where certified values were assigned when a high coverage sequence string was available for a marker. Using NGS to assign values has allowed for additional marker sets beyond short tandem repeat (STR) loci, including single nucleotide polymorphisms (SNPs) and mitochondrial DNA (mtDNA) whole genome sequences, to be included in the Certificate of Analysis (COA). Since the 2019 release, several commercial NGS panels have become available including the Verogen ForenSeq mtDNA Control Region, mtDNA Whole Genome, MainstAY, and Kintelligence Kits. In addition, three community Ion AmpliSeq panels from Thermo Fisher (MH-74 Plex, VISAGE, and Y-SNP) are now available. While the mtDNA whole genome sequence for the components are already included and no new STR markers are introduced by MainstAY, the other recently released panels allow for the inclusion of > 11,000 additional SNPs (e.g., identity, ancestry, phenotype, kinship, and X- and Y-SNPs) and 74 microhaplotypes to the COA for SRM 2391d in an update completed by fall of 2022.

Whole genome sequencing has opened the doors to Investigative genetic genealogy (IGG) analysis of challenging forensic samples that are not suitable for microarray genotyping. These samples still do not typically achieve high enough coverage for direct genotype calling, therefore a pipeline for imputation from low coverage sequencing data was evaluated using data from the 1000 Genomes Project. This pipeline generated results suitable for IGG down to 0.25X coverage. Additionally, forensic samples from a variety of tissue types and input amounts were sequenced and successfully uploaded to genetic genealogy databases after imputation.

GENis is a recently published open-source multi-tier information system developed to run forensic DNA databases. It relies on a Bayesian Networks framework and it is particularly well suited to efficiently perform large-size queries against databases of missing individuals. In this contribution we present a validation of the missing person identification capabilities of GENis. To that end we introduce fbnet, a free-software package written in the R statistical language that implements the complete GENis functionality to perform kinship analysis based on DNA profiles. With the aid of fbnet, we could validate likelihood ratios against estimations draw with Familias and forrel (two well-recognized R packages for kinship quantification) for complex pedigrees provided by the Argentinian reference databank (Banco Nacional de Datos Geneticos, BNDG). We found that our methodological approach presented an excellent performance in terms of accuracy and computation times.

This study describes the use of the 27 loci Yfiler® Plus kit and TaqMan™ SNP genotyping to characterise and predict the haplogroups of Y chromosomes within the four major ethnic populations of Ghana. Haplogroups were assigned using the desktop NevGen software (https://www.nevgen.org/). The E1b1a and E1b1b haplogroups are the most common in the Ghanaian population and form 95% of the dataset. The Mole-Dagomba sub-population had 4. 8% assigned to the haplogroups G, H, R1b, R2 and T. The Ewe had two samples assigned to haplogroups C and D whilst the Akan had one sample each assigned to haplogroups B, J1 and J2. The NevGen predicted haplogroups were further screened with TaqMan™ genotyping for confirmation. In conclusion, ≈ 95% of the dataset was classified as M-E1b1a using NevGen combined with TaqMan™ SNP Genotyping for confirmation. The TaqMan™ also revealed 5% as J1 and other haplogroups, using an in-house control from the J1 haplogroup.

Assigning NoC in a mixed STR profile is an important preliminary step in computing a likelihood ratio (LR). A common metric is maximum allele count (MAC) whereby the locus exhibiting the largest number of alleles is used to set the NOC. This metric can be supplemented by considering total allele count (TAC) and locus allele count (LAC). TAC is the total number of alleles across all loci and is compared with probability distributions generated in silico. LAC works similarly, save that the probability distributions are generated at the locus level. Herein, we present a comparative analysis of these three metrics using a dataset of 10,000 of each of 2–7 person simulated ground truth mixtures. These datasets were used to generate parameter distributions for each NoC. This analysis showed LAC to be the most accurate single metric in all circumstances tested. We have developmentally validated an excel-based tool to automate calculations for use by operational caseworkers.