Forensic Science International-Genetics最新文献

{"title":"Forensic inference in Africa: Evaluating population structure, databases, and regional assignment accuracy","authors":"Mohaimin Kasu ,&nbsp;Jessica Caroline Anne Morrow ,&nbsp;Mpasi Lesaoana ,&nbsp;Humphrey Brydon ,&nbsp;Maria Eugenia D’Amato","doi":"10.1016/j.fsigen.2026.103441","DOIUrl":"10.1016/j.fsigen.2026.103441","url":null,"abstract":"<div><div>This study reports novel 21 aSTR (autosomal Short Tandem Repeats) allele frequencies from 538 individuals, as well as 11 triallelic profiles, representing seven Bantu-speaking groups in Southern Africa (Ndebele, Pedi, Phuthi, Tsonga, Sotho, Swati, and Xhosa). These data contributed to a comprehensive representation of the Southern Bantu (SB). The defined SB reference database was evaluated for various forensic uses and applications: extant diversity, population structure, adequacy of alternative reference databases, and continental biogeographical ancestry prediction.</div><div>Different analytical methods—including summary statistics, multivariate analyses (Multidimensional Scaling, MDS; Discriminant Analysis of Principal Components, DAPC), and Bayesian clustering—detected continental structure, identifying four major clusters: Southern, Eastern, Western, and Horn of Africa.</div><div>This observation motivated the evaluation of two practical applications of this information: one methodological (alternative reference frequency database) and one predictive (biogeographic assignment). The adequacy of alternative reference databases for representing SB populations—STRidER South Africa, STRidER Africa, African American, and global datasets—was assessed by comparing reciprocal allelic coverage and shifts in random match probabilities (RMPs). Of the databases tested, the STRidER Africa database provided the closest representation of the SB. Population-level analyses evidenced the need for a stratification correction (θ = 0.005 or 0.01) for SB populations.</div><div>Intracontinental biogeographic prediction was assessed using an XGBoost machine learning classification model across four major African regions. The model’s predictive balanced accuracy ranged from 80 % to 94 % across African regions (94 % for the Horn of Africa, 87 % for Southern Africa, 84 % for Western Africa, and 80 % for Eastern Africa).</div><div>The accuracy and limitations of this practice are discussed, along with its ethical implications. The assessment of reference databases can be extended to more general applications across Africa.</div></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":"84 ","pages":"Article 103441"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146215276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Salience, legitimacy and credibility of single-cell sperm data and their evaluation","authors":"Catherine M. Grgicak ,&nbsp;Qhawe Bhembe ,&nbsp;Klaas Slooten ,&nbsp;Anu Khandelwal ,&nbsp;Ken R. Duffy ,&nbsp;Desmond S. Lun","doi":"10.1016/j.fsigen.2026.103476","DOIUrl":"10.1016/j.fsigen.2026.103476","url":null,"abstract":"<div><div>It is difficult to determine <em>how many</em> sperm donors contributed to a sample, <em>who</em> they were, or <em>what number</em> of sperm they donated. A model analyzing single-cell electropherograms (scEPG) born of diploid cells was previously introduced in [1]. In this paper we extend the model to handle haploid scEPGs, specifically sperm cells, and consider if single-cell forensics is ready to traverse the research-operations boundary. To do this we use the Salience-Legitimacy-Credibility (SLC) framework. In Salience we identify relevant forensic questions. Legitimacy then has us first describe the logic underpinning our haploid-aware model, and verify it was correctly implemented into EESCIt™. Using 121 individuated scEPGs – i.e., singlets – we show that the mean square error between the prediction and the empirical outcomes given that prediction is no more than 3.1 × 10⁻⁵ at any locus, demonstrating excellent model fidelity. LR-discrepancy analysis confirms the relative (empirical) frequency and magnitude of H₁- and H₂- LRs align with declared values, with the largest discrepancy being 0.3 in WoE – i.e., logLR – interval [1, 2]. Credibility analysis showed that no singlet produced a misdirected WoE and we receive, on average, one WoE for every ∼1200 RFU carried by a haploid. We then evaluate EESCIt™ on 33 sperm mixtures comprising 2–5 donors and find that estimates about the number of donors, the number of scEPGs by a donor, and the WoE remain legitimate. The system continues to credibly discriminate source hypotheses, often producing WoE of 10 when the PoI-true contributes as few as two haploids and values near 25 when 10 haploids are present. WoEs remain stable across mixture complexity with no dependence on the true number of contributors, <em>n,</em> showing a robustness unique to single cell data. Additionally, computational time is only mildly impacted by <em>n</em>, with the main determiner being the number of cells isolated. With a legitimate, credible haploid model able to address multifarious salient questions, we integrate it into EESCIt™, establishing a single-cell assessment system that is now capable of evaluating haploid single-cell data.</div></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":"84 ","pages":"Article 103476"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Internal validation of the fully continuous model in EuroForMix for its implementation in routine forensic DNA profiling","authors":"J. González-Bao ,&nbsp;A. Mosquera-Miguel ,&nbsp;L. Casanova-Adán ,&nbsp;A. Ambroa-Conde ,&nbsp;A. Freire-Aradas ,&nbsp;A. Cabrejas-Olalla ,&nbsp;M. Boullón-Cassau ,&nbsp;M. Palhares ,&nbsp;J. Ruiz-Ramírez ,&nbsp;A. Rodríguez-López ,&nbsp;C. Phillips ,&nbsp;M.V. Lareu ,&nbsp;M. de la Puente","doi":"10.1016/j.fsigen.2026.103460","DOIUrl":"10.1016/j.fsigen.2026.103460","url":null,"abstract":"<div><div>Despite being increasingly more common in the forensic routine, DNA mixtures still present an important roadblock in the analysis of genetic evidence. The International Society for Forensic Genetics (ISFG) recommends the use of the Likelihood Ratio (LR) in the statistical evaluation of the evidence for the contribution of a certain Person of Interest (POI). Numerous mathematical models have been developed for this purpose, which can be classified according to the information included: i) binary models only use the allelic information, ii) qualitative or semi-continuous models also accommodate drop-out and drop-in probabilities and iii) quantitative or fully continuous models include additionally allelic signal intensity (peak heights or read coverage). Before being implemented in the forensic routine, any software that supports a form of these models must be tested in different scenarios to perform an internal validation.</div><div>In this work we present the results of our internal validation of the fully continuous model present in EuroForMix (EFM) and DNAStatistX (used as control of EFM in this study), comparing its performance to the semi-continuous model in LRmix Studio. To do so, 59 artificial samples were prepared from 8 unrelated contributors in mixtures of 2, 3 and 4 contributors; besides, 19 tests from 7 real cases were run in both software. The following analyses were conducted: i) scrutiny of contributors in single-source serially diluted samples; ii) estimation of the percentage of contribution to the mixture; iii) comparison of results obtained in artificial mixtures for H1-True and H1-False hypotheses; iv) effect of sensitivity analysis in the final result; v) discrimination power of each model; vi) the behaviour of the Automatic Model Search option in EuroForMix and vii) comparison of results obtained in real casework analyses.</div><div>Our results show that EFM returns higher values than LRmix Studio both in single-source and mixed scenarios. The fully continuous model has demonstrated a neutral behaviour, returning inconclusive results when genetic evidence is scarce. If a reporting threshold is applied EFM is especially affected, with a higher proportion of negative log(LR) results turning inconclusive in comparison to LRmix Studio. Overall, EuroForMix has shown a correct performance, returning reliable results and reducing the subjectivity of the analysis.</div></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":"84 ","pages":"Article 103460"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large-scale analysis of DNA quantification metrics and SNP sequencing performance in unidentified human remains","authors":"Steven A. Bates ,&nbsp;Bruce Budowle ,&nbsp;Morgan Johnson ,&nbsp;Jianye Ge ,&nbsp;Kristen Mittelman ,&nbsp;David Mittelman","doi":"10.1016/j.fsigen.2026.103470","DOIUrl":"10.1016/j.fsigen.2026.103470","url":null,"abstract":"<div><div>Massively parallel sequencing (MPS)–based single nucleotide polymorphism (SNP) profiling is being increasingly used in forensic DNA testing, yet the informative value of common pre-sequencing DNA metrics to predict SNP profile completeness remains unclear. In this study, 500 anonymized skeletal samples submitted for forensic genome sequencing were analyzed to assess the relationships between bone type, DNA quantitation metrics, and SNP call rate (the latter used as a measure of profile completeness). Human-specific DNA quantity was measured using short and long autosomal quantitative PCR targets, and total DNA content was assessed fluorometrically. Of the 500 samples, 399 met a minimum human DNA threshold (short autosomal target ≥0.005 ng/µl) and were sequenced. Among these samples, SNP call rates ranged from 8% to 91%, with 95.7% achieving call rates greater than 50%. Call rate was most strongly associated with features reflecting the ratio between endogenous human DNA and total DNA, which includes exogenous background DNA. The total:short DNA ratio (log₁₀ r = −0.48) and estimated human DNA input into library preparation (log₁₀ r = 0.41) showed the strongest correlations, while degradation index showed only a modest association. Bone type influenced the likelihood that samples progressed to sequencing, but among sequenced samples, call rate distributions were similar across major bone type categories. Machine-learning models achieved moderate predictive performance (best validation R² = 0.47), indicating that current quantification metrics are correlated with, but not sufficient to reliably predict, SNP profile completeness. Overall, these results demonstrate that MPS-based SNP profiling of UHRs is highly effective and support forensic genetic genealogy as the preferred approach for generating actionable genetic data for identification of human remains. However, due to varying degrees of DNA quality and quantity in bones samples, the common pre-sequencing metrics, while providing some actionable workflow decisions in this study, are insufficient predictors for the range of samples encountered in unidentified human remains.</div></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":"84 ","pages":"Article 103470"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147313636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MitoMetrics: Incorporation of mtDNA profile discrepancies in likelihood ratio calculations","authors":"Floor Claessens ,&nbsp;Mikkel Meyer Andersen ,&nbsp;Christina Amory ,&nbsp;Cristina Arévalo ,&nbsp;Stijn Desmyter ,&nbsp;Sophie Dognaux ,&nbsp;Kristiaan J. van der Gaag ,&nbsp;Tomasz Grzybowski ,&nbsp;Gabriela Huber ,&nbsp;Filomena Melchionda ,&nbsp;Hiroaki Nakanishi ,&nbsp;Tóra Olsen ,&nbsp;Cristina Pie ,&nbsp;Lourdes Prieto ,&nbsp;Katarzyna Skonieczna ,&nbsp;Chiara Turchi ,&nbsp;Walther Parson ,&nbsp;Vania Pereira","doi":"10.1016/j.fsigen.2026.103477","DOIUrl":"10.1016/j.fsigen.2026.103477","url":null,"abstract":"<div><div>Interpretation of mitochondrial DNA (mtDNA) evidence in a forensic context faces challenges, particularly when evaluating mtDNA profiles from different tissues. In hair, for example, the segregation of mtDNA shows tight bottlenecks that can result in different mtDNA profiles between and along hair shafts, and between hair and other reference tissues of the same donor. Current forensic interpretation guidelines for mtDNA are based on conventions on the number of discrepant positions observed between the two compared profiles. Most legislations consider two discrepancies between samples as an exclusion, and one discrepancy as an inclusion or as an inconclusive result. More data are needed to understand the variation and occurrence of discrepancies in samples from the same donor, to be able to incorporate this knowledge into a mathematical approach that effectively quantifies the probability of these events. This study reports the first project of the MitoMetrics collaborative initiative. In this study, data were generated from several participating laboratories, and previously published data, along with data generated from casework. mtDNA profiles from blood/buccal reference samples were compared to those from hair shafts from the same individuals. Results report the levels of heteroplasmy detected in the different tissues, the number of differences observed between the tissue comparisons, and the number of discrepant positions observed. We suggest a preliminary model for calculating the evidential value of mtDNA-based evidence using a likelihood ratio approach, that takes into consideration the occurrence of discrepancies between profiles from the same donor. This work represents the first attempt to quantify the probability of finding discrepant events between tissues and to incorporate these when reporting mtDNA in a forensic context.</div></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":"84 ","pages":"Article 103477"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147373679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shotgun DNA sequencing evidence: Sample-specific and unknown genotyping error probabilities","authors":"Mikkel Meyer Andersen","doi":"10.1016/j.fsigen.2026.103474","DOIUrl":"10.1016/j.fsigen.2026.103474","url":null,"abstract":"<div><div>Many forensic genetic trace samples are of too low quality to obtain short tandem repeat (STR) DNA profiles as the nuclear DNA they contain is highly degraded (e.g., telogen hairs). Instead, performing shotgun DNA sequencing of such samples can provide valuable information on, e.g., single nucleotide polymorphism (SNP) markers. As a result, shotgun sequencing is starting to gain more attention in forensic genetics and statistical models to correctly interpret such evidence, including properly accounting for sequencing errors, are needed. One such model is the <span>wgsLR</span> model by Andersen et. al. (2025) that enabled evaluating the evidential strength of a comparison between the genotypes in the trace sample and reference sample assuming a single-source contribution to both samples. This paper extends the <span>wgsLR</span> model to allow for different (asymmetric) genotyping error probabilities (e.g., from a low quality trace sample and a high quality reference sample). The model was also extended to handle unknown genotyping error probabilities via both maximising profile likelihood and using a prior distribution. It was found that an unknown genotyping error probability of the trace sample could be handled with these methods when having a sufficient number of independent markers and that the results were was robust to different specifications of the prior distribution. The sensitivity of the <span>wgsLR</span> model against overdispersion was also investigated and it was found robust against it. The extensions of the model are implemented in the <span>R</span> package <span>wgsLR</span>.</div></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":"84 ","pages":"Article 103474"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147373713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved deconvolution of sequenced based mixtures by extending EuroForMix","authors":"Ø. Bleka ,&nbsp;J. González-Bao ,&nbsp;M. de la Puente ,&nbsp;P. Gill","doi":"10.1016/j.fsigen.2026.103479","DOIUrl":"10.1016/j.fsigen.2026.103479","url":null,"abstract":"<div><div>We present an extension of the EuroForMix model to improve genotype prediction for unknown contributors in Massively Parallel Sequencing (MPS) mixture data by incorporating marker amplification efficiency (MAE) parameters. The proposed two-step procedure first estimates MAE directly from the tested mixture profile and then treats these estimates as fixed inputs to the extended EuroForMix model, allowing the remaining parameters to be inferred as in the original framework. This eliminates the need for external calibration data. Fully joint inference is theoretically appealing but computationally impractical for large MPS panels; the two-step strategy offers a tractable alternative. We evaluated four approaches for estimating MAE: two empirical methods, which are susceptible to overfitting, and two Bayesian methods that apply shrinkage to mitigate this. For the Bayesian variants, variational inference was employed to obtain accurate posterior MAE estimates, enabling efficient analysis of profiles with thousands of markers. Predictive performance was assessed using five DNA mixture datasets containing either 2- or 3-person mixtures, without conditioning on any of the contributing profiles. Four were based on MPS data: two SNP datasets (one high-density with ∼10,000 markers and one with ∼100 markers), one microhaplotype (MH) dataset, and one STR dataset. The fifth dataset comprised capillary electrophoresis (CE) STR data. Performance was evaluated using Brier score, accuracy and calibration, where the latter reflects how well prediction probabilities can be trusted. For practical forensic relevance, we also applied a high-certainty prediction threshold (p ≥ 0.95) and calculated the corresponding coverage and accuracy. Across all datasets, the MAE-based methods improved genotype prediction compared to the default EuroForMix model, with largest gain observed for the MPS datasets. Empirical approaches produced more decisive predictions and higher high-certainty coverage, whereas Bayesian approaches yielded more conservative and better-calibrated probabilities. Improvement for CE-STR data was modest. The binned version of EuroForMix provided a competitive alternative, particularly for high-density SNP panels.</div></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":"84 ","pages":"Article 103479"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147380180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the impact of bone type on the accuracy of methylation-based forensic age estimation","authors":"Charlotte Sutter,&nbsp;Cordula Haas,&nbsp;Jacqueline Neubauer","doi":"10.1016/j.fsigen.2026.103459","DOIUrl":"10.1016/j.fsigen.2026.103459","url":null,"abstract":"<div><div>Methylation-based forensic donor age estimation is an emerging field in forensic genomics. It can be used not only to estimate the age of a living person, but also to determine age-at-death of an unknown corpse or skeleton. Therefore, age estimation tools suitable for bone samples have been developed in recent years. As commonly known, methylation profiles differ considerably between different sample types. However, it is unclear whether different types of bones display different methylation patterns and whether exposure to forensically relevant environmental conditions affects bone methylation.</div><div>In this study, we used the VISAGE enhanced age estimation tool for bones to estimate the age of freshly collected bone samples from 30 individuals. Five bone types (rib, femur, clavicle, iliac crest and petrous bone) were analysed for each individual. While we found high overall age estimation errors with this tool (MAE = 9.79 years, mean error = −7.25 years), only the petrous bones displayed statistically significantly different age estimation errors compared to at least one of the other four bone types. As is commonly necessary when adapting a methylation-based assay in different laboratories, a linear transformation between predicted and chronological age was applied to calibrate the estimates to our cohort, which led to an improved overall MAE of 7.03 years. In addition, this study mimicked casework samples by exposing the femur samples of the cohort to burying, submersion in water or burning. From the mock casework samples, only 12 produced a result with an MAE of approximately 10–15 years using the transformation model. Finally, two actual casework samples were analysed. Although it was technically possible to generate an age estimate, the results obtained with the transformation model deviated considerably from the chronological ages (estimation error of 10 and 29 years, respectively).</div><div>We concluded that it is generally possible to estimate age-at-death from bone material through DNA methylation analysis when the bone material is fresh. However, under forensically relevant conditions, it might be difficult to obtain reliable age estimation results.</div></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":"84 ","pages":"Article 103459"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147380226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mutation or uniparental disomy? Evaluating abnormal inheritance patterns using Object-Oriented Bayesian Networks","authors":"Eduardo Avila ,&nbsp;Cássio Ritzel ,&nbsp;Márcio Dorn ,&nbsp;Clarice S. Alho ,&nbsp;Alessandro Kahmann","doi":"10.1016/j.fsigen.2026.103447","DOIUrl":"10.1016/j.fsigen.2026.103447","url":null,"abstract":"<div><div>Current methods of analysis in paternity testing are often based on the assumption that most incompatibilities observed between the child and the parents genetic profiles have a paternal origin. In addition, many inconsistencies are commonly treated as a product of slippage mutational events, and this premise is considered when evaluating the statistical result. Recent high-density genotype data suggest that uniparental disomy (UPD) has a higher frequency of occurrence than previously believed. Therefore, the possibility of UPD must be considered when likelihood ratios (LR) for paternity hypotheses are calculated, especially in cases where mutation events are incompatible with stepwise STR mutation models. This work proposes a statistical framework for the evaluation of paternity trio results based on Object-Oriented Bayesian Networks (OOBN) models, simultaneously considering the possibility of abnormal inheritance patterns such as mutational events and UPD. An OOBN was designed for this task using the software <em>GeNIe Academic</em>. A computational tool was then programmed using the <em>python</em> language, using <em>pmgpy</em> package as backend. Three million simulated paternity trios evidence, comprising all inheritance patterns modeled by the network, were used to test the model and evaluate its capacity to identify and distinguish mutation and UPD events. The proposed method can successfully assess LR values for paternity hypotheses, including statistical treatment of mutation and UPD events. For UPD occurrences, STR markers located on a single chromosome account for a unique Paternity Index (PI) value, avoiding the underestimation of paternity probability resulting from multiple mutation events. The model provides LRs to estimate the probability of occurrence of mutation or UPD based on population frequencies and case-related genetic data. All three types of UPD are included in the model, and the probability of each is provided, given the evidence. Finally, the developed computational tool can be used to process large datasets, allowing scalability of evidence inclusion and evaluation process. The proposed model provides a useful method to evaluate genetic parentage testing data when discrepancies in observed evidence might be a result of atypical inheritance situations. The statistical results are unbiased towards the paternal origin of incompatibilities and are particularly useful when results are not compatible with commonly adopted STR stepwise mutation models.</div></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":"84 ","pages":"Article 103447"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147313677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the resolution of extended pedigrees using hybridization capture SNP panels: Evidence from real and simulated data","authors":"Tiantian Shan ,&nbsp;Zhigang Mao ,&nbsp;Haoyu Wang ,&nbsp;Yifan Wei ,&nbsp;Jiarong Zhang ,&nbsp;Tingyun Hou ,&nbsp;Jiawen Li ,&nbsp;Chu Wang ,&nbsp;Yuntian Jin ,&nbsp;Yuhao Zhang ,&nbsp;Qiang Zhu ,&nbsp;Ming Ni ,&nbsp;Ji Zhang","doi":"10.1016/j.fsigen.2026.103478","DOIUrl":"10.1016/j.fsigen.2026.103478","url":null,"abstract":"<div><div>Accurate inference of distant biological relationships remains challenging in forensic genetics. Here, we evaluated a hybridization capture SNP panel for high-resolution pairwise kinship analysis using two extended Han Chinese pedigrees (93 individuals, first to sixth degree). A total of 6049 high-quality autosomal SNPs were obtained, and kinship inference was performed using LR-based and IBS-based methods. Simulations showed that both SNP number and allele polymorphism strongly affected discrimination power, with ≥ 16,000 SNPs enabling near-complete resolution of third- and fourth-degree relatives. In real pedigrees, a panel of 6049 SNPs achieved inference accuracies of 99.69%, 98.26%, and 92.12% for third-, fourth-, and fifth-degree relationships, respectively (log₁₀LR &gt; 0). Genotyping remained robust with 100 pg of input DNA (95% accuracy), supporting applicability to trace forensic samples. Overall, hybridization capture SNP sequencing offers an effective and scalable strategy for extended kinship analysis, with performance further enhanced by expanded high-MAF marker sets.</div></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":"84 ","pages":"Article 103478"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}