The interpretation of Y-STR evidence remains challenging, particularly for mixed or low template samples in which the observed haplotype(s) may be incomplete or ambiguous. While probabilistic genotyping (PG) has become the standard approach for interpreting autosomal STR profiles, the interpretation of Y STR profiles continues to rely largely on manual assessment, with analysts accepting or rejecting possible haplotype combinations without a formal framework for weighting uncertainty. 

In 2018, Taylor et al., described the Haplotype Centred Likelihood Ratio (HC-LR), a method that allows locus-specific haplotype weights to be incorporated into an LR. In this research, we evaluate the performance of the HC-LR on a set of ground-truth samples (single source and male only mixtures).  We also manipulate the haplotype weights to investigate the behaviour of the method. We extend the model used in Taylor et al., by stratifying the Yfiler™ Plus Eurasian metapopulation into seven clusters, allowing population structure to be represented. We apply the recommended propositions in Bright et al., (2024) 

• Proposition 1 (Hp): The source of the male DNA is the person of interest (POI). 
• Proposition 2 (Hd): The source of the male DNA is a random man (related or unrelated to the POI) from the population. 

Under the defence proposition, male relatives of the POI are represented as proportions of the population, with the proportions calibrated to the haplotype database. 

We compare the HC-LR against the inverse match probability of the POI’s haplotype and evaluate its behaviour under varying levels of haplotype ambiguity. Our results demonstrate that the HC-LR decreases appropriately as uncertainty in the contributing haplotype(s) increases. As the information in the sample decreases, the likelihood ratios trend towards one.  This highlights the suitability of the method for integration with a probabilistic Y-STR interpretation framework. 

Authors

• Hannah Kelly (New Zealand Institute for Public Health and Forensic Science, New Zealand) 
• Maarten Kruijver (New Zealand Institute for Public Health and Forensic Science, Netherlands) 
• Jo-Anne Bright (New Zealand Institute for Public Health and Forensic Science, New Zealand) 
• Kevin Cheng (New Zealand Institute for Public Health and Forensic Science, New Zealand) 
• John Buckleton (New Zealand Institute for Public Health and Forensic Science, New Zealand)