Forensic Science International: Genetics

In forensic genetics, the evidential value of a match between the Y-chromosomal short tandem repeat (Y-STR) profiles of a trace and a suspect is typically quantified by the frequency of the profile in a population database, particularly the Y-chromosomal Haplotype Reference Database (YHRD). However, for this approach of obtaining a match probability to be valid, the database population must be representative of all plausible alternative trace donors in a given case. Since appropriately defining such a suspect population can be difficult, YHRD highlights so-called metapopulations that comprise profiles from different, geographically dispersed populations with presumed shared ancestry. We investigated whether such metapopulations are self-evident in the current version of YHRD. To this end, we performed classical cluster analysis using allele dissimilarity as a measure of pairwise distance between Y-STR profiles. Our analyses revealed only a weak genetic structure in YHRD the extent of which was inversely proportional to the respective marker mutation rate. This suggests that YHRD cannot be divided into clearly distinguishable subgroups based solely on the genetic information it contains, at least not into subgroups that would correspond closely to the metapopulations highlighted in the database itself. If profile frequencies in metapopulations are to continue to be equated with match probabilities, then a clearer definition of metapopulations and a better justification of their use in forensics are needed.

The geographical and familial origins of Christopher Columbus have remained a subject of intense historiographical debate for over five centuries. Despite numerous hypotheses, empirical genetic evidence capable of resolving his ancestral history or place of birth has been absent from the literature until now. This study presents the third stage of the first forensic genetic analysis performed on skeletal remains belonging to several direct descendants of Columbus, spanning the 16th to 18th centuries. By applying Massively Parallel Sequencing (MPS) to analyse autosomal, X- and Y- chromosome DNA markers, and integrating the results with multidisciplinary evidence from historical, genealogical, archaeological, and anthropological research implicated in this project, the identification of several individuals founded in the Crypt of Santa Maria de Gracia located in Gelves (Sevilla, Spain) has been achieved. The analysis of their biological relatedness enabled the reconstruction of kinship networks among the individuals interred in the crypt, which, when interpreted in the context of documented genealogical lineages, provides indirect but consistent evidence pointing toward the debated origin of the discoverer.

The interpretation of findings of low-template DNA given activity-level propositions requires robust statistical models capable of accommodating substantial inter-laboratory and case-specific variability. This paper presents the practical implementation of HaloGen, an open-source hierarchical Bayesian framework for calculating activity-level likelihood ratios (LRs) from DNA quantity data. We compare three modelling approaches derived from the framework: a Group model, which combines data across laboratories, a hierarchically informed Lab-Bayes model, and a standalone, laboratory specific Lab-Vague model. Through a series of simulation studies, we demonstrate that evidential strength is highly sensitive not only to DNA quantity but also to case context, particularly the assumed number of offenders (NS). We further show that inter-laboratory differences in DNA recovery and dropout can lead to materially different LRs, making unvalidated use of pooled or external data potentially misleading. To address practical implementation, we propose a minimum-effort validation pathway for laboratories wanting to report findings given activity level propositions. Our results indicate that a small number of direct/secondary transfer experiments (n {approx} 6- 12) are sufficient to obtain conservative LRs compared with a generic population model. Finally, these results clarify how contextual assumptions enter mathematically into activity-level inference, demonstrating that confirmation bias can arise naturally from unexamined modelling choices and underscoring the importance of transparent, explicit specification of propositions and parameters.

Due to the limited availability of reliable and well-validated molecular markers, the determination of postmortem interval (PMI) is still a major obstacle for forensic investigators to resolve a case. The largest human protein, known as titin, has never undergone at domain level examination of postmortem degradation patterns. This study focused on the In-silico analysis of the Immunoglobulin-like, fibronectin-type III, and Protein kinase domains of human titin to assess their potential utility in PMI estimation. Sequence data for the studied domains were retrieved from UniProt, 2D & 3D models were generated by PSIPRED and SWISS-MODEL, respectively, followed by physicochemical properties, solubility assessment, and structural comparison. This study revealed that the Ig-like domain is the most stable, followed by the Fn-III and Protein kinase domains. These findings indicate that Titin domains may degrade at different rates in the postmortem period. This study introduces the first computational basis for considering Titin as a multi-domain candidate biomarker for PMI estimation, laying the groundwork for upcoming laboratory validation.

Subtyping of ketoacidosis, a metabolic state characterized by blood acidification due to various causes, remains challenging in forensic casework. Postmortem omics samples paired with machine learning offers an independent tool to address this challenge. However, such data, especially related to real forensic cases, are rare. In Sweden, high-resolution mass spectrometry data routinely collected in forensic toxicology, can be leveraged for metabolomic analysis. Here, we integrate postmortem metabolomics and machine learning models to detect and subtype ketoacidosis-related deaths using real forensic cases in Sweden. From femoral blood samples of 109 alcoholic ketoacidosis cases, 220 diabetic ketoacidosis cases, 140 hypothermia cases, and 1,229 controls (hanging cases), we developed and tested three machine learning models, which achieved over 90% accuracy in ketoacidosis detection and over 80% in subtyping. Validation with independent cohorts (21 starvation cases, 29 alcoholic controls, and 40 diabetic controls) confirmed robustness with over 80% of starvation cases classified as ketoacidosis-related. Feature clustering highlighted metabolites such as cortisol to be important for subtyping. In summary, our findings demonstrate that combining machine learning with postmortem metabolomics enables accurate detection and subtyping of ketoacidosis-related deaths, which is useful for forensic casework.

Kinship reconstruction in ancient populations provides key insights into past social organization and evolutionary history. Sedimentary ancient DNA (sedaDNA) enables access to deep-time human populations in the absence of skeletal remains. However, it is characterized by severe degradation and the potential mixture of genetic material from multiple individuals, raising questions about its suitability for kinship inference. Here, we use extensive simulations to evaluate the feasibility and limitations of kinship inference in sparse and damaged sedaDNA data, with a focus on Neandertals. We find that the main obstacle to accurate kinship inference in sedaDNA is the presence of multiple contributors to a given sample. To address this, we introduce a simple heterozygosity-based test to identify samples containing DNA from multiple individuals. Guided by these results, we analyze published Neandertal sedaDNA from the Galeria de las Estatuas site to assess the practical limits of kinship inference in real sedimentary ancient DNA data. Together, our results define methodological considerations and practical limits for kinship inference in sedimentary ancient DNA.

Herbarium collections are a vast but underutilized resource for ancient DNA research, containing over 400 million specimens with detailed metadata and spanning centuries of global biodiversity. Understanding patterns of DNA preservation in natural collections is crucial for optimizing ancient DNA studies and informing future curation practices. We analysed genomic data for 573 herbarium specimens from six plant species from the genera Hordeum and Oryza collected from the Americas and Eurasia over 220 years. Using standardized laboratory protocols and shotgun sequencing, we quantified DNA degradation and elucidated factors that accelerate it. We find significant age-dependent DNA fragmentation rates, indicating temporal degradation processes not detected in prehistoric samples. In our analysis, DNA decay rates in herbarium specimens were almost eight times faster than in moa bones, reflecting fundamental differences in tissue composition and preservation environments. Environmental conditions at the time of specimen collection emerged as the major determinants of post-mortem damage rates, with the interaction term between temperature and genus being the dominant driver of cytosine deamination. We find no effect of sample storage on DNA damage and degradation. These findings provide insights into how climatic origin, preservation environment, taxonomic identity and age influence DNA preservation while highlighting opportunities for improving institutional preservation practices. Due to standardised preservation conditions, museum collections can provide better insights into DNA damage and degradation over time than archaeological and paleontological samples.

In wildlife forensic practice, species identification and estimation of the Minimum Number of Individuals (MNI) for highly processed specimens have long relied on weight-based conversion methods, which may result in underestimation of the number of individuals involved in a case. Focusing on confiscated casque products of the helmeted hornbill (Rhinoplax vigil), this study combines macroscopic morphological examination with mitochondrial DNA barcoding (16S rRNA, COI, and Cytb) to explore a more robust approach for individual quantification. The results demonstrate that the conventional "weight-based" approach overlooks critical biological information contained in anatomical structures and cannot accurately reflect the actual number of individuals involved. Based on this, we propose an anatomy-based criterion centered on the principle of structural uniqueness: specimens retaining biologically unique beak or casque structures should be directly assigned to a single individual, whereas weight-based estimation should only be applied when original anatomical features are entirely absent. In addition, considering material loss during processing, we propose approximately 85 g as a reference threshold for estimating the number of individuals in heavily processed solid casque products. This approach improves the scientific rigor and accuracy of forensic identification and provides reliable technical support for the conviction, sentencing, and law enforcement of wildlife trafficking cases involving helmeted hornbill and other endangered species.

The interpretation of trace DNA evidence at activity level requires explicit modelling of transfer, persistence, and failure to detect a person of interest. We present the theoretical foundations of HaloGen, an open-source hierarchical Bayesian framework for evaluating biological results under competing activity-level propositions, such as direct versus secondary transfer. HaloGen accounts for dropout, multiple contributors, and multiple stains. Evidence is evaluated using an exhaustive-propositions likelihood ratio frame-work that combines information across contributors and stains, while fully accounting for uncertainty in transfer and detection. Observed DNA quantities and non-detects are handled consistently within a single probabilistic model, avoiding reliance on fixed parameter estimates. The framework yields intuitive and robust behaviour: strong support for direct transfer when DNA quantities are informative, and appropriately neutral or defence-leaning likelihood ratios in low-information or non-detect scenarios. An empirically constrained fail-rate parameter prevents spurious inflation of likelihood ratios when offender detection is unlikely, providing stability across laboratories and experimental conditions. This paper establishes the theoretical basis of HaloGen; a companion paper addresses validation and applied casework examples.

Quantitative polymerase chain reaction (qPCR) is limited in measuring absolute nucleic acid copy numbers due to the inherent variability of calibrators. Here, we introduce the Quantal PCR (quPCR), a novel method that eliminates the need for calibrators by defining an intrinsic quantal unit derived from the thermodynamic and kinetic properties of the replication system. This approach first determines amplification efficiency at high template concentrations, which is then used as the replication probability to construct quantification cycle (Cq) distribution profiles. These profiles are compared with those from limiting dilution PCR to derive the Cq value for the minimal quantal-replication unit ("quCq"), enabling calculation of the sample copy number. Validation using a dual-target DNA template showed near-identical copy numbers using two distinct target-specific replication systems. Thus, quPCR represents a new method for absolute nucleic acid quantification at the single-molecule level, offering a calibrator-free alternative for absolute quantification.

The early evolutionary history of modern domestic horses (Equus caballus/E. ferus caballus), known as the DOM2 lineage, is well documented due to numerous archaeological and ancient DNA (aDNA) studies. Although many uncertainties remain in the domestication timeline, current evidence suggests that the domestication of modern horses began in the Pontic-Caspian steppe at least [~]2700 BCE (before common era), or even earlier. However, it is not known how long remnant wild horse populations survived or when domestic horses were introduced into Northern Europe. In this study, we review the current knowledge of horse domestication, focusing on Northern Europe. We analysed prehistoric horses from western Russia to assess the body sizes of wild horses from the Ivanovskaya site (5900-3800 BCE) in the Pontic-Caspian steppe, and the body weight of one Lithuanian wild horse (4000-3800 BCE). Additionally, we analysed body sizes of Late Bronze Age-Early Roman Age horses (1100 BCE-300 CE; common era) and re-analysed body sizes and estimated rider weights of historic domestic horses from Lithuania (100-1400 CE). We searched for pathological changes and signs of bit wear indicative of bridling. Furthermore, we investigated maternal genetic diversity by sequencing ancient mitochondrial DNA. We found that wild horses from Ivanovskaya were intermediate in body size between earlier and more recent horses of the Eurasian Steppe, and that the Lithuanian wild horse weighed only [~]270 kg and Late Bronze Age-Early Roman Age horses 200-300 kg. Lithuanian domestic horses were pony-sized (< 130 cm on average). Bit wear was confirmed on one tooth, the oldest domestic horse in Lithuania (799-570 cal BCE). Another tooth showed signs of the Equine Odontoclastic Tooth Resorption and Hypercementosis (EOTRH) condition. Mitochondrial DNA was successfully amplified from one Ivanovskaya wild horse along with 25 other ancient samples, including Lithuanias oldest domestic horse. mtDNA diversity was high, revealing several maternal lineages.

ObjectiveGeneration and testing of IGF1 reference materials (RM), suitable for the harmonization of immunoassay (IA) and LC-MS/MS methods for the IGF1 determination in blood. In addition, establishment of age related reference intervals for men and women. MethodsIn a split sample study of 42 patients, and 30 healthy volunteers we tested the commutability of four RMs for IGF1, using four commercial IAs and an LC-MS/MS method. A new set of age dependent reference intervals was established using Lifelines biobank samples, based on the IGF1 LC-MS/MS method. ResultsThe four RMs were found to be commutable, except the RM with the lowest concentration measured with the Siemens Immulite method. The value assignment of the RMs was based on the IGF1 LC-MS/MS method, which was calibrated against WHO international standard 02/254. LC-MS/MS results were on average about 0 to 60% lower than those of the immunoassays. Combining the recalculated IGF1 results in patient samples from a former study with the data from healthy volunteers in this study, showed a reduction in the variation of the data points (standard error of estimate) of 42% and 62% respectively. ConclusionCommutable RMs for IGF1 can be made from serum of healthy blood donors. However, it remains necessary to test the commutability of these RMs in IAs that were not included in this study. By harmonizing methods using the four RMs, the same age-related reference intervals can be used.

BackgroundFatal insulin intoxication remains difficult to diagnose because insulin undergoes rapid degradation after death, limiting the reliability of direct biochemical measurements. This creates diagnostic uncertainty when objective molecular confirmation of insulin excess are required. We hypothesised that insulin excess induces systemic metabolic alterations that persist beyond insulin degradation and can be captured using postmortem metabolomics in a forensic setting. MethodsHigh-resolution mass spectrometry (HRMS)-based metabolomics was applied to a national cohort comprising 51 fatal insulin intoxications. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were trained on cases collected between 2017-2022 to identify insulin-associated metabolite features using a shared-and-unique-structures approach. Performance was evaluated using two temporally distinct test sets (2023-2024): a matched validation cohort and a heterogeneous forensic cohort reflecting biological variability. ResultsHere we show that an insulin-associated metabolomic fingerprint comprising 91 features demonstrated reproducible discrimination across independent cohorts. In the matched cohort (n=59, including 14 insulin cases), insulin intoxication classification achieved 100% sensitivity and 73% specificity within the applicability domain. In the heterogeneous cohort (n=154, including 14 insulin cases), 100% sensitivity was maintained with a 72% specificity despite increased biological variability. Univariate analyses demonstrated significant alterations across multiple metabolite classes, including acylcarnitines, fatty acids/lipids, and purine/nucleoside metabolites, with moderate effect sizes, consistent with systemic effects of insulin-induced hypoglycaemia. ConclusionsFatal insulin intoxication is associated with a reproducible metabolomic fingerprint detectable after death. These findings demonstrate that postmortem metabolomics may serve as a complementary decision-support tool when conventional biomarkers are unreliable.

Natural history and agricultural collections, which contain hundreds of millions of specimens classified in terms of time, space, and taxonomy, are valuable resources for diverse fields of research. Since the first success of ancient DNA (aDNA) isolation in the 1980s, these repositories, including herbaria for plants, have been intensively used to support studies in taxonomy, macroevolution, and genetic responses to anthropogenic activities over the past centuries. Two major challenges of aDNA research are environmental contamination and DNA degradation. For herbarium specimens, aDNA is usually extracted from leaf samples. It is highly fragmented (typically length of 50 to 100 bp) with a higher breakdown rate than that in most bone remains. To optimise the amount of data retrieved and minimise destructive sampling, we isolated DNA from an unconventional plant tissue type - seed embryos. We carried out whole-genome sequencing and compared sequenced DNA quality between embryo and leaf tissue. We evaluated endogenous DNA proportion, median fragment length, damage fraction per site ({lambda}), decay rates, nucleotide misincorporations, and library complexity for three species: cultivated rice Oryza sativa, wild rice O. rufipogon, and wild barley Hordeum spontaneum. In O. sativa, embryos exhibited significantly higher endogenous content and median fragment length than leaves, while in O. rufipogon only median fragment length was higher. The superior DNA preservation was likely due to the protective role of the seed husk, which might play an important role in DNA preservation in plants collected in the tropics. By contrast, in temperate H. spontaneum, tissue type had minimal impact on DNA quality. Despite the minuscule size of the embryos, all derived genomic libraries were highly complex, sufficient for deep whole genome sequencing. These results highlight seed embryos as a promising alternative aDNA source for millions of herbarium specimens, and enable effective genomic analyses of other historical plant collections, such as economic botany and anthropological museum collections.

MotivationLocal Ancestry Inference (LAI) allows us to study evolutionary processes in admixed populations[1], uncover ancestry-specific disease risk factors[2], and to better understand the demographic history of these populations[3]. Many methods for LAI exist, however, these methods usually focus on cases of intercontinental admixture. In this work, we evaluate both existing and novel methods in challenging scenarios, such as downsampled reference panels, intracontinental admixture, and distant admixture events. ResultsWe present four novel LAI implementations based on neural network architectures, including Bidirectional Long Short-Term Memory and Transformer networks which have not previously been used for LAI. We compare these novel implementations to existing methods for LAI across a variety of scenarios using the 1 Thousand Genomes dataset and other synthetic datasets. We find that while all networks achieve high performance for intercontinental admixture scenarios, inference power is comparatively low for scenarios of intracontinental or distant admixture. We further show how our implementations achieve the best performance of all methods through specialized preprocessing and inference smoothing steps. AvailabilityAll implementations and benchmarking code available at https://github.com/Jazpy/LAINNs.

BackgroundThe hyperpolymorphic nature and structural complexity of the human leukocyte antigen (HLA) genomic region present challenges for accurate and scalable typing across diverse sample types. While wholegenome sequencing (WGS) offers the opportunity to infer HLA genotypes without targeted enrichment, systematic benchmarks across sequencing platforms, biospecimens and coverage levels remain limited. ResultsWe assembled a multi-platform resource of WGS datasets derived from short-read (Illumina, MGI) and long-read (Oxford Nanopore Technologies R9 and R10) sequencing, spanning 29 biospecimens including cell lines, blood, buccal swab and saliva. We evaluated the performance of the HLA caller HLA*LA across 13 HLA genes, using a clinically validated assay as reference. WGSbased HLA genotyping achieved [~]95% accuracy across sequencing platforms, with Class I loci exhibiting higher accuracy than Class II. Crossplatform concordance was high, and performance remained consistent across Illumina, MGI and Oxford Nanopore chemistries. Analysis of blood, buccal swab and saliva samples showed that blood and buccal swabs supported accurate HLA inference, whereas saliva yielded reduced concordance. Downsampling experiments demonstrated that 15x coverage was sufficient to retain >95% accuracy at twofield resolution, with lower depths supporting lower-resolution typing. ConclusionsOur results demonstrate that WGS provides a robust, platformagnostic framework for accurate HLA genotyping across sample types and coverage levels. These benchmarks establish practical conditions for reliable HLA inference and underscore the utility of WGS for populationscale HLA analyses and future clinical applications.

Characterized by the earliest use of pottery, the Jomon culture was a unique Neolithic culture that spread throughout the Japanese Archipelago. Previous archaeological evidence suggests that Jomon hunter-gatherers colonized the southernmost islands, the Ryukyu Archipelago, by approximately 7,000 years before present (YBP). However, genetic characteristics of the Ryukyu Jomon population and its contribution to the modern population have not been elucidated yet. In this study, we newly sequenced 273 modern and 25 ancient (6,700-900 YBP) whole genomes collected across the Ryukyu Archipelago. Our analysis demonstrated a genetic differentiation between the Hondo (Japanese mainland) and Ryukyu Jomon, dating back to [~]6,900 YBP. After the divergence from the Hondo Jomon, the Ryukyu Jomon experienced severe bottlenecks, with an effective population size of [~]2,000. Admixture between the Ryukyu Jomon and migrants from the historic Hondo population occurred [~]1,000 YBP, which corresponds to the widespread adoption of iron tools and agriculture in the Central Ryukyus. Different demographic histories between modern Hondo and Ryukyu populations resulted in different rates of Jomon ancestry in these populations. By providing a new perspective on the peopling of the Ryukyu Archipelago, this study significantly enhances our understanding of cultural transitions in the region.

Accurate quantification of fungi is important for a myriad of applications but remains challenging. Previously, we demonstrated that an approach called the adenine-HPLC method can quantify bacteria, including those with aggregating properties that are difficult to quantify using conventional methods, by measuring cellular adenine derived from DNA and converting the adenine amount to genome copy number, without being influenced by cell morphology. However, in this study, when this adenine-HPLC method was applied to the quantification of budding yeast as a model fungus, accurate measurement proved impossible. This limitation was attributed to adenine release from other adenine-containing biomolecules, such as RNA and ATP, and we therefore developed a method that suppresses adenine release from these molecules. This method involves reducing the temperature of the acid treatment and prewashing the cells before acid treatment. In addition, we incorporated a process that corrects for the naturally occurring free adenine level as background during total adenine measurement. The improved adenine-HPLC method based on these modifications enables accurate quantification of budding yeast using genomic DNA content in whole cells as the quantification unit.

The expansion of short tandem repeats is a feature of over 60 different human diseases. Ongoing somatic instability throughout a patients lifetime can influence disease progression and has emerged as a therapeutic target. Understanding its mechanism is essential for the identification of both drug targets and therapeutic interventions. A major obstacle towards this translational goal has been to measure changes in repeat size distribution in a timely manner. To address this, here we present Single Clone-based Instability Assay (SCIA), a streamlined experimental design that saves weeks in assessing the effect of a gene knockout on repeat instability. The approach avoids bulk cultures and does not require a reporter cell line. It uses targeted long-read sequencing as a readout for repeat instability. We have validated the approach using FAN1, PMS1, and MLH1 knockouts in HEK293-derived cells. We provide a visualization software that generates delta plots, extracts the instability frequency, the bias towards expansion or contraction, and the average size of the changes. Using SCIA, we find that although FAN1 knockout clones showed increased frequency of expansions, the size of the expansions were smaller. This highlights the wealth of information that can be extracted and the potential for novel insights into the mechanism of repeat instability.

BackgroundMeiotic recombination creates genetic diversity through reciprocal exchange of haplotypes between homologous chromosomes. Scalable and robust methods for mapping recombination breakpoints are essential for understanding meiosis and for genetic mapping. Single cell sequencing of gametes offers a direct approach to recombination mapping, yet the effect of technical differences between single-cell sequencing methods for crossover detection remains unclear. ResultsWe benchmark single cell methods for droplet-based chromatin accessibility and RNA sequencing and plate-based whole-genome amplification for mapping meiotic recombination in Arabidopsis thaliana. For this purpose we introduce two novel open-source tools coelsch_mapping_pipeline and coelsch for haplotype-aware alignment and per-cell crossover detection, using them to recover known recombination frequencies and quantify the effects of coverage sparsity. We subsequently apply our approach to a panel of 40 recombinant F hybrids derived from crosses of 22 diverse natural accessions, successfully recovering genetic maps for 34 F1s in a single dataset. This analysis reveals substantial variation in recombination rate and identifies a [~]10 Mb pericentric inversion in the accession Zin-9, the largest natural inversion reported in A. thaliana to date. ConclusionsThese results demonstrate the applicability and scalability of single-cell gamete sequencing for high-throughput mapping of meiotic recombination, and highlight the strengths and limitations of different single-cell modalities. The accompanying open-source tools provide a framework for haplotyping and crossover detection analysis using sparse single-cell sequencing data. Our methodology enables parallel analysis of large numbers of hybrids in a single dataset, removing a major technical barrier to large-scale studies of natural variation in recombination rate.