Evolving Paradigms in Low-Template DNA Analysis: From Fixed Analytical Thresholds to Probabilistic Modeling
DOI:
https://doi.org/10.62051/mjbq7s89Keywords:
Forensic Genetics; Low-Template DNA (LT-DNA); Analytical Threshold (AT); Probabilistic Genotyping Systems (PGS); DNA Mixture Interpretation; Forensic Science.Abstract
Inferring conclusions in low-template DNA (LT-DNA) profiles is a very difficult task in forensic genetics. The analytical threshold (AT), which distinguishes allele signals from noise, is the most important parameter for the quality of these profiles. This paper recounts the development of the AT, beginning as a fixed value and progressing to a parameter in complex probabilistic models. It explains how the intrinsic difficulty of LT-DNA, especially the stochastic nature, revealed the ineffectiveness of fixed-threshold schemes, that suffer from an information recovery vs noise introduction tradeoff. This challenge led to the development of the probabilistic genotyping systems (PGS), which evaluate evidence through continuous models, taking all signal information into account. While PGS has proven to be the powerful tool for the interpretation of difficult co-mingled DNA, it has added complexity when: software validation; inter-system variability; and court room communication are considered. In this review, it aims to highlight that signal interpretation can be redefined by next-generation sequencing (NGS) and machine learning (ML), where NGS and ML are transforming a view on the cellular state to bring the closer to a threshold free analysis. The development of the AT is indicative of the maturation of the field of trace evidence towards a greater focus on accuracy and serves to demonstrate that rigorous validation and standardization protocols are necessary to guide the appropriate use of this valuable forensic technology.
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