Forensic DNA profiling utilizes autosomal short tandem repeat (STR) markers to establish identity of missing persons, confirm familial relations, and link persons of interest to crime scenes. It is a widely accepted notion that genetic markers used in forensic applications are not predictive of phenotype. At present, there has been no demonstration of forensic STR variants directly causing or predicting disease. Such a demonstration would have many legal and ethical implications. For example, is there a duty to inform a DNA donor if a medical condition is discovered during routine analysis of their sample? In this review, we evaluate the possibility that forensic STRs could provide information beyond mere identity. An extensive search of the literature returned 107 articles associating a forensic STR with a trait. A total of 57 of these studies met our inclusion criteria: a reported link between a STR-inclusive gene and a phenotype and a statistical analysis reporting a p-value less than 0.05. A total of 50 unique traits were associated with the 24 markers included in the 57 studies. TH01 had the greatest number of associations with 27 traits reportedly linked to 40 different genotypes. Five of the articles associated TH01 with schizophrenia. None of the associations found were independently causative or predictive of disease. Regardless, the likelihood of identifying significant associations is increasing as the function of non-coding STRs in gene expression is steadily revealed. It is recommended that regular reviews take place in order to remain aware of future studies that identify a functional role for any forensic STRs.

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.

Low levels of DNA from an unidentified human source, often referred to as trace DNA, are ubiquitous, can be transferred onto objects by either direct or indirect methods and have an unknown longevity in situ. Clothing items from crime scenes are often submitted for trace DNA analysis, usually in attempt to identify a person of interest. This study examined the transfer of DNA onto three 10 × 10 cm areas located on the front, back and shoulder of an individual’s external clothing (n = 300) during a regular day’s activity. After wearing for a day, the DNA quantity on all three areas increased approximately 8-fold, which usually corresponded with an increase in the endogenous DNA from the wearer on the front area of the shirt. However, the back area of the shirt was more likely to demonstrate mixtures of endogenous and extraneous DNA. An additional study was also carried out to examine whether domestic laundering is a possible mechanism for the transfer of foreign DNA onto freshly laundered items and revealed that 74% of UV-treated cotton swatch samples produced DNA profiles after laundry with household garments. In summary, this study highlights the ease of DNA transfer onto an individual’s external clothing during a regular day, and that extraneous DNA may be already on the clothing item prior to it being worn. The study provides empirical data to assist in the interpretation of trace DNA profiles and support a Bayesian approach to estimate statistical likelihoods for the transfer of foreign DNA.