Evaluation of the MHSeqTyper47 kit for forensically challenging DNA samples

MHBase: A comprehensive database of short microhaplotypes for advancing forensic genetic analysis

Microhaplotypes (MHs) were first recommended by Prof. Kidd for use in forensics because they can improve human identification, kinship analysis, mixture deconvolution, and ancestry prediction. Since their introduction, extensive research has demonstrated the advantages of MHs in forensic applications and provided useful data for different populations. Currently, two databases, ALFRED (ALlele FREquency Database) and MicroHapDB (MicroHaplotype DataBase), house the published MH information and population data. We previously constructed a single nucleotide polymorphism SNP-SNP MH database (D-SNPsDB) of MHs within 50 bp on the whole human genome for 26 populations integrating basic data such as physical genome positions, mapping of variant identifiers (rsIDs), allele frequencies, and basic variant information. Building upon the previous research, we further selected MHs containing at least two variants (SNPs and/or insertions/deletions [InDels]) within a short DNA fragment (≤ 50 bp) in 26 populations based on the 1000 Genomes Project dataset (Phase 3) to construct a more comprehensive database. Subsequently, we established a user-friendly website that allows users to search the MH database (MHBase) based on their research objectives and study population to find suitable loci and provides other functions such as querying reported loci, performing online calculations using the PHASE software, and calculating ancestral-related parameters. The loci in the database are classified as SNP-based MHs, which include only SNPs, and InDel-including MHs, which contain at least one InDel. Here, we provide a detailed overview of the MHBase and an analysis of shared loci at the global and continental levels, ancestral markers, the genetic distance within loci, and mapping with the genome annotation file. The website is an accessible and useful tool for researchers engaged in marker discovery, population studies, assay development, and panel design.

Using simulated microhaplotype genotyping data to evaluate the value of machine learning algorithms for inferring DNA mixture contributor numbers

Inferring the number of contributors (NoC) is a crucial step in interpreting DNA mixtures, as it directly affects the accuracy of the likelihood ratio calculation and the assessment of evidence strength. However, obtaining the correct NoC in complex DNA mixtures remains challenging due to the high degree of allele sharing and dropout. This study aimed to analyze the impact of allele sharing and dropout on NoC inference in complex DNA mixtures when using microhaplotypes (MH). The effectiveness and value of highly polymorphic MH for NoC inference in complex DNA mixtures were evaluated through comparing the performance of three NoC inference methods, including maximum allele count (MAC) method, maximum likelihood estimation (MLE) method, and random forest classification (RFC) algorithm. In this study, we selected the top 100 most polymorphic MH from the Southern Han Chinese (CHS) population, and simulated over 40 million complex DNA mixture profiles with the NoC ranging from 2 to 8. These profiles involve unrelated individuals (RM type) and related pairs of individuals, including parent-offspring pairs (PO type), full-sibling pairs (FS type), and second-degree kinship pairs (SE type). Our results indicated that how the number of detected alleles in DNA mixture profiles varied with the markers' polymorphism, kinship's involvement, NoC, and dropout settings. Across different types of DNA mixtures, the MAC and MLE methods performed best in the RM type, followed by SE, FS, and PO types, while RFC models showed the best performance in the PO type, followed by RM, SE, and FS types. The recall of all three methods for NoC inference were decreased as the NoC and dropout levels increased. Furthermore, the MLE method performed better at low NoC, whereas RFC models excelled at high NoC and/or high dropout levels, regardless of the availability of a priori information about related pairs of individuals in DNA mixtures. However, the RFC models which considered the aforementioned priori information and were trained specifically on each type of DNA mixture profiles, outperformed RFC_ALL model that did not consider such information. Finally, we provided recommendations for model building when applying machine learning algorithms to NoC inference.

DNA and protein analyses of hair in forensic genetics

Hair is one of the most common pieces of biological evidence found at a crime scene and plays an essential role in forensic investigation. Hairs, especially non-follicular hairs, are usually found at various crime scenes, either by natural shedding or by forcible shedding. However, the genetic material in hairs is usually highly degraded, which makes forensic analysis difficult. As a result, the value of hair has not been fully exploited in forensic investigations and trials. In recent years, with advances in molecular biology, forensic analysis of hair has achieved remarkable strides and provided crucial clues in numerous cases. This article reviews recent developments in DNA and protein analysis of hair and attempts to provide a comprehensive solution to improve forensic hair analysis.