Likelihood Ratios for physical traits in forensicinvestigations

Abstract

Recent years have seen significant advances in DNA phenotyping, which predicts the physical traits of an unknown person, such as hair, eyes, and skin color, using DNA data. This technique is increasingly used in forensic investigations to identify missing persons, disaster victims, and suspects of crimes. A key contribution of DNA phenotyping is that it allows researchers to search through lists of individuals with similar characteristics, often gathered from testimonies, photographs, and social media data. However, despite their growing relevance, current methods lack comprehensive mathematical models to calculate likelihood ratios that accurately assess the statistical weight of evidence. Our work bridges this gap by developing new likelihood ratio models, validated through computational simulations. In addition, we demonstrate the ability of these models to improve forensic investigations in real-world scenarios. Furthermore, we introduce the R package forensicolors, freely available on CRAN, to facilitate the application of the methodologies developed.