Mixture Deconvolution with Massively Parallel Sequencing Data: Microhaplotypes Versus Short Tandem Repeats

Background/Objectives: Interpretation of mixture profiles generated from crime scene samples is an important element in forensic genetics. Here, a workflow for mixture deconvolution of sequenced microhaplotypes (MHs) and STRs using the probabilistic genotyping software MPSproto v0.9.7 was developed, and the performance of the two types of loci was compared. Methods: Sequencing data from a custom panel of 74 MHs (the MH-74 plex) and a commercial kit with 26 autosomal STRs (the ForenSeq™ DNA Signature Prep Kit) were used. Single-source profiles were computationally combined to create 360 two-person and 336 three-person mixtures using the Python script MixtureSimulator v1.0. Additionally, 72 real mixtures typed with the MH-74 plex and 18 real mixtures typed with the ForenSeq Kit from a previous study were deconvoluted using MPSproto. Results: The deconvoluted MH profiles were more complete and had fewer wrong genotype calls than the deconvoluted STR profiles. The contributor proportion estimates were more accurate for MH profiles than for STR profiles. Wrong genotype calls were mostly caused by locus and heterozygous imbalances, noise reads, or an inaccurate contributor proportion estimation. The latter was especially problematic in STR sequencing data, when two contributors contributed equally to the mixture. A total of 34,800 deconvolutions of the simulated mixtures were performed with two defined hypotheses: H_p, "The sample consists of DNA from one/two unknown contributor(s) and the suspect" and H_d, "The sample consists of DNA from two/three unknown individuals". All true contributors were identified (LR > 10¹⁵ for MHs and LR > 10⁹ for STRs) and all non-contributors excluded (LR < 10^-6 for MHs and LR < 0.2 for STRs). Conclusions: In simulated and real mixtures, the MHs performed better than STRs.