Evaluation of a SNP-STR haplotype panel for forensic genotype imputation

Development and validation of a new multiplex panel using SNaPshot-based DIP-TriSNP markers for forensic DNA mixtures

Short tandem repeat analysis is challenging when dealing with unbalanced mixtures in forensic cases due to the presence of stutter peaks and large amplicons. In this research, we propose a novel genetic marker called DIP-TriSNP, which combines deletion/insertion polymorphism (DIP) with tri-allelic single nucleotide polymorphism in less than 230 bp length of human genome. Based on multiplex PCR and SNaPShot, a panel, including 14 autosomal DIP-TriSNPs and one Y chromosomal DIP-SNP, had been developed and applied to genotyping 102 unrelated Han Chinese individuals in Sichuan of China and simulated a mixture study. The panel sensitivity can reach as low as 0.1 ng DNA template, and the minor contributor of DNA can be detected with the highest ratio of 19:1, as indicated by the obtained results. In the Sichuan Han population, the cumulative probability of informative genotypes reached 0.997092, with a combined power of discrimination of 0.999999998801. The panel was estimated to detect more than two alleles in at least one locus in 99.69% of mixtures of the Sichuan Han population. In conclusion, DIP-TriSNPs have shown promising as an innovative DNA marker for identifying the minor contributor in unbalanced DNA mixtures, offering advantages such as short amplifications, increased polymorphism, and heightened sensitivity.

Machine learning applications in forensic DNA profiling: A critical review

Machine learning (ML) is a range of powerful computational algorithms capable of generating predictive models via intelligent autonomous analysis of relatively large and often unstructured data. ML has become an integral part of our daily lives with a plethora of applications, including web, business, automotive industry, clinical diagnostics, scientific research, and more recently, forensic science. In the field of forensic DNA, the manual analysis of complex data can be challenging, time-consuming, and error-prone. The integration of novel ML-based methods may aid in streamlining this process while maintaining the high accuracy and reproducibility required for forensic tools. Due to the relative novelty of such applications, the forensic community is largely unaware of ML capabilities and limitations. Furthermore, computer science and ML professionals are often unfamiliar with the forensic science field and its specific requirements. This manuscript offers a brief introduction to the capabilities of machine learning methods and their applications in the context of forensic DNA analysis and offers a critical review of the current literature in this rapidly developing field.

Solving the Arizona search problem by imputation

An "Arizona search" is an evaluation of the numbers of pairs of profiles in a forensic-genetic database that possess partial or complete genotypic matches; such a search assists in establishing the extent to which a set of loci provides unique identifications. In forensic genetics, however, the potential for performing Arizona searches is constrained by the limited availability of actual forensic profiles for research purposes. Here, we use genotype imputation to circumvent this problem. From a database of genomes, we impute genotypes of forensic short-tandem-repeat (STR) loci from neighboring single-nucleotide polymorphisms (SNPs), searching for partial STR matches using the imputed profiles. We compare the distributions of the numbers of partial matches in imputed and actual profiles, finding close agreement. Despite limited potential for performing Arizona searches with actual forensic STR profiles, the questions that such searches seek to answer can be posed with imputation-based Arizona searches in increasingly large SNP databases.