Kinship assignment with the ForenSeq™ DNA Signature Prep Kit: Sources of error in simulated and real cases

Development and validation of YARN: A novel SE-400 MPS kit for East Asian paternal lineage analysis

Y-chromosomal short tandem repeat polymorphisms (Y-STRs) and Y-chromosomal single nucleotide polymorphisms (Y-SNPs) are valuable genetic markers used in paternal lineage identification and population genetics. Currently, there is a lack of an effective panel that integrates Y-STRs and Y-SNPs for studying paternal lineages, particularly in East Asian populations. Hence, we developed a novel Y-chromosomal targeted panel called YARN (Y-chromosome Ancestry and Region Network) based on multiplex PCR and a single-end 400 massive parallel sequencing (MPS) strategy, consisting of 44 patrilineage Y-STRs and 260 evolutionary Y-SNPs. A total of 386 reactions were validated for the effectiveness and applicability of YARN according to SWGDAM validation guidelines, including sensitivity (with a minimum input gDNA of 0.125 ng), mixture identification (ranging from 1:1-1:10), PCR inhibitor testing (using substances such as 50 μM hematin, 100 μM hemoglobin, 100 μM humic acid, and 2.5 mM indigo dye), species specificity (successfully distinguishing humans from other animals), repeatability study (achieved 100% accuracy), and concordance study (with 99.91% accuracy for 1121 Y-STR alleles). Furthermore, we conducted a pilot study using YARN in a cohort of 484 Han Chinese males from Huaiji County, Zhaoqing City, Guangdong, China (GDZQHJ cohort). In this cohort, we identified 52 different Y-haplogroups and 73 different surnames. We found weak to moderate correlations between the Y-haplogroups, Chinese surnames, and geographical locations of the GDZQHJ cohort (with λ values ranging from 0.050 to 0.340). However, when we combined two different categories into a new independent variable, we observed stronger correlations (with λ values ranging from 0.617 to 0.754). Overall, the YARN panel, which combines Y-STR and Y-SNP genetic markers, meets forensic DNA quality assurance guidelines and holds potential for East Asian geographical origin inference and paternal lineage analysis.

An approach to unified formulae for likelihood ratio calculation in pairwise kinship analysis

Introduction: The likelihood ratio (LR) can be an efficient means of distinguishing various relationships in forensic fields. However, traditional list-based methods for derivation and presentation of LRs in distant or complex relationships hinder code editing and software programming. This paper proposes an approach for a unified formula for LRs, in which differences in participants' genotype combinations can be ignored for specific identification. This formula could reduce the difficulty of by-hand coding, as well as running time of large-sample-size simulation. Methods: The approach is first applied to a problem of kinship identification in which at least one of the participants is alleged to be inbred. This can be divided into two parts: i) the probability of different identical by descent (IBD) states according to the alleged kinship; and ii) the ratio of the probability that specific genotype combination can be detected assuming the alleged kinship exists between the two participants to the similar probability assuming that they are unrelated, for each state. For the probability, there are usually recognized results for common identification purposes. For the ratio, subscript letters representing IBD alleles of individual A's alleles are used to eliminate differences in genotype combinations between the two individuals and to obtain a unified formula for the ratio in each state. The unification is further simplified for identification cases in which it is alleged that both of the participants are outbred. Verification is performed to show that the results obtained with the unified and list-form formulae are equivalent. Results: A series of unified formulae are derived for different identification purposes, based on which an R package named KINSIMU has been developed and evaluated for use in large-size simulations for kinship analysis. Comparison between the package with two existing tools indicated that the unified approach presented here is more convenient and time-saving with respect to the coding process for computer applications compared with the list-based approach, despite appearing more complicated. Moreover, the method of derivation could be extended to other identification problems, such as those with different hypothesis sets or those involving multiple individuals. Conclusion: The unified approach of LR calculation can be beneficial in kinship identification field.

DNA as in-formation

Traces are fundamental vectors of information. This is the first of seven forensic principles formulated by the 2022 Sydney declaration. To better understand the trace as information, this article proposes the notion of in-formation. DNA is matter in becoming. DNA changes as it travels across forensic sites and domains. New formations occur as humans, technologies and DNA interact. Understanding DNA as in-formation is of particular relevance vis-à-vis the increase of algorithmic technologies in the forensic sciences and the rendering of DNA into (big) data. The concept can help identifying, acknowledging and communicating those moments of techno-scientific interaction that require discretion and methodical decisions. It can assist in tracing what form DNA will take and what consequences this may have. This article is categorized under:Crime Scene Investigation > From Traces to Intelligence and EvidenceForensic Biology > Ethical and Social ImplicationsForensic Biology > Forensic DNA Technologies.

Open-Access Worldwide Population STR Database Constructed Using High-Coverage Massively Parallel Sequencing Data Obtained from the 1000 Genomes Project

Achieving accurate STR genotyping by using next-generation sequencing data has been challenging. To provide the forensic genetics community with a reliable open-access STR database, we conducted a comprehensive genotyping analysis of a set of STRs of broad forensic interest obtained from 1000 Genome populations. We analyzed 22 STR markers using files of the high-coverage dataset of Phase 3 of the 1000 Genomes Project. We used HipSTR to call genotypes from 2504 samples obtained from 26 populations. We were not able to detect the D21S11 marker. The Hardy-Weinberg equilibrium analysis coupled with a comprehensive analysis of allele frequencies revealed that HipSTR was not able to identify longer alleles, which resulted in heterozygote deficiency. Nevertheless, AMOVA, a clustering analysis that uses STRUCTURE, and a Principal Coordinates Analysis showed a clear-cut separation between the four major ancestries sampled by the 1000 Genomes Consortium. Except for larger Penta D and Penta E alleles, and two very small Penta D alleles (2.2 and 3.2) usually observed in African populations, our analyses revealed that allele frequencies and genotypes offered as an open-access database are consistent and reliable.