Search Results (3)

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. Full article

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. Full article

Background: Forensic investigative genetic genealogy (FIGG) has developed rapidly in recent years and is considered a novel tool for crime investigation. However, crime scene samples are often of low quality and quantity and are challenging to analyze. Deciding which approach should be used for kinship inference in forensic practice remains a troubling problem for investigators. Methods: In this study, we selected four popular approaches—KING, IBS, TRUFFLE, and GERMLINE—comprising one method of moment (MoM) estimator and three identical by descent (IBD) segment-based tools and compared their performance at varying numbers of SNPs and levels of genotyping errors using both simulated and real family data. We also explored the possibility of making robust kinship inferences for samples with ultra-high genotyping errors by integrating MoM and the IBD segment-based methods. Results: The results showed that decreasing the number of SNPs had little effect on kinship inference when no fewer than 164 K SNPs were used for all four approaches. However, as the number decreased further, decreased efficiency was observed for the three IBD segment-based methods. Genotyping errors also had a significant effect on kinship inference, especially when they exceeded 1%. In contrast, MoM was much more robust to genotyping errors. Furthermore, the combination of the MoM and the IBD segment-based methods showed a higher overall accuracy, indicating its potential to improve the tolerance to genotyping errors. Conclusions: In conclusion, this study shows that different approaches have unique characteristics and should be selected for different scenarios. More importantly, the integration of the MoM and the IBD segment-based methods can improve the robustness of kinship inference and has great potential for applications in forensic practice. Full article