Investigative genetic genealogy (IGG) has emerged as a new, rapidly growing field of forensic science. We describe the process whereby dense SNP data, commonly comprising more than half a million markers, are employed to infer distant relationships. By distant we refer to degrees of relatedness exceeding that of first cousins. We review how methods of relationship matching and SNP analysis on an enlarged scale are used in a forensic setting to identify a suspect in a criminal investigation or a missing person. There is currently a strong need in forensic genetics not only to understand the underlying models to infer relatedness but also to fully explore the DNA technologies and data used in IGG. This review brings together many of the topics and examines their effectiveness and operational limits, while suggesting future directions for their forensic validation. We further investigated the methods used by the major direct-to-consumer (DTC) genetic ancestry testing companies as well as submitting a questionnaire where providers of forensic genetic genealogy summarized their operation/services. Although most of the DTC market, and genetic genealogy in general, has undisclosed, proprietary algorithms we review the current knowledge where information has been discussed and published more openly.

The concept forensic genealogy was discussed already in 2005 but has recently emerged in relation to the use of public genealogy databases to find relatives of the donor of a crime stain. In this study we explored the results and evaluation of searches conducted in such databases. In particular, we focused on the statistical classification that entails from the search and study the variation observed for different relationship classes. The forensic guidelines advocate the use of the likelihood ratio (LR) as a mean to measure the weight of evidence, which requires exact formulation of competing hypotheses. We contrast the LR approach with alternative approaches relying on identical by state (IBS) measures to estimate the total length of shared genomic segments as well as identical by descent (IBD) coefficients for a pair of individuals. We used freely accessible data from the 1000 Genome project to perform extensive simulations, generating data for a number of distinct relationships. Specifically we studied some overarching relationship classes and the performance of the above-mentioned evaluative approaches to classify a known pair of relatives into each class. The results indicate that the traditional LR approach as a single source of classification is as good as, and in some cases even better than, the alternative approaches. In particular the true classification rate is higher for some distant relationship. However, the LR approach is both computer-intensive and sensitive to population frequencies as well as genetic maps (positions of the markers). We further showed that when combining different classification approaches, a lower false classification rate was achieved while still maintaining a high true classification rate.

Background/Objectives: Inferring genetic relationships based on genetic data has gained an increasing focus in the last years, in particular explained by the rise of forensic investigative genetic genealogy (FIGG) but also the introduction of expanded SNP panels in forensic genetics. A plethora of statistical methods are used throughout publications; in direct-to-consumer (DTC) testing, the shared segment approach is used, in screenings of relationships in medical genetic research, for instance, methods-of-moment estimators, e.g., estimation of the kinship coefficient, are used, and in forensic genetics, the likelihood and the likelihood ratio are commonly used to evaluate the genetic data under competing hypotheses. This current study aims to compare and contrast examples of the aforementioned statistical methods to infer relationships from genetic data. Methods/Results: This study includes some historical and some recently published panels of SNP markers to illustrate the strength and caveats of the statistical methods on different marker sets and a selection of pre-defined pairwise relationships, 1st through 7th degree. Extensive simulations are performed and subsequently subsetted based on the marker panels alluded to above. As has been shown in previous research, the likelihood ratio is most powerful, i.e., high correct classifications, when SNP data are sparse, say below 20,000 markers, whereas the windowed kinships and segment approaches are equally powerful when very dense SNP data are available, say >20,000 markers. In between lay approaches using method-of-moments estimators which perform well when the degree of relationship is below four but less so beyond, say, 4th degree relationships. The likelihood ratio is the only method that is easily adapted for non-pairwise tests and therefore has an additional depth not addressed in the current study. We furthermore perform a study of genotyping error rates and their impact on the different statistical methods employed to infer relationships, where the results show that error rates below 1% seem to have low impact across all methods, in particular for errors yielding false heterozygote genotypes.

Background/Objectives: Emerging forensic genetic applications, such as forensic investigative genetic genealogy (FIGG), advanced DNA phenotyping, and distant kinship inference, increasingly require dense SNP genotype datasets. However, forensic-grade DNA often contains missing genotypes due to its quality and quantity limitations, potentially hindering these applications. Genotype imputation, a method that predicts missing genotypes, is widely used in population and medical genetics, but its utility in forensic genetics has not been thoroughly explored. This study aims to assess the performance of genotype imputation in forensic contexts and determine the conditions under which it can be effectively applied. Methods: We employed a simulation-based approach to generate realistic forensic SNP genotype datasets with varying numbers, densities, and qualities of observed genotypes. Genotype imputation was performed using Beagle software, and the performance was evaluated based on the call rate and imputation accuracy across different datasets and imputation settings. Results: The results demonstrate that genotype imputation can significantly increase the number of SNP genotypes. However, imputation accuracy was dependent on factors such as the quality of the original genotype data and the characteristics of the reference population. Higher SNP density and fewer genotype errors generally resulted in improved imputation accuracy. Conclusions: This study highlights the potential of genotype imputation to enhance forensic SNP datasets but underscores the importance of optimizing imputation parameters and understanding the limitations of the original data. These findings will inform the future application of imputation in forensic genetics, supporting its integration into forensic workflows.

Forensic molecular autopsies have emerged as a tool for medical examiners to establish the cause of death. It is particularly useful in sudden unexplained deaths where the cause of death cannot be determined with a regular medical autopsy. We provide the first study of exome data from formalin-fixed paraffin-embedded samples (FFPE) paired with data from high-quality blood samples in forensic applications. The approach allows exploration of the potential to use FFPE samples for molecular autopsies and identify variants in extensive exome data. We leverage the high uniformity of the hybridization capture approach provided by Twist Bioscience to target the complete exome and sequence the libraries on a NextSeq 550. Our findings suggest that exome sequencing is feasible for 24 out of a total of 35 included FFPE samples. When successful, the coverage across the exome is comparatively high (> 90% covered to 20X) and uniform (fold80 below 1.5). Detailed variant comparisons for matched FFPE and blood samples show high concordance with few false variants (positive predictive value of 0.98 and a sensitivity of 0.97) with no distinct FFPE artefacts. Ultimately, we apply carefully constructed forensic gene panels in a stepwise manner to find genetic variants associated with the clinical phenotype and with relevance to the sudden unexplained death.