Analysis of the Hamiltonian Monte Carlo genotyping algorithm on PROVEDIt mixtures including a novel precision benchmark

Mateusz Susik; Ivo F Sbalzarini

doi:10.1016/j.fsigen.2023.102840

Analysis of the Hamiltonian Monte Carlo genotyping algorithm on PROVEDIt mixtures including a novel precision benchmark

Forensic Sci Int Genet. 2023 May:64:102840. doi: 10.1016/j.fsigen.2023.102840. Epub 2023 Feb 1.

Authors

Mateusz Susik¹, Ivo F Sbalzarini²

Affiliations

¹ Biotype GmbH, Dresden, 01109, Germany; Technische Universität Dresden, Faculty of Computer Science, Dresden, 01187, Germany. Electronic address: m.susik@biotype.de.
² Technische Universität Dresden, Faculty of Computer Science, Dresden, 01187, Germany; Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, 01307, Germany; Center for Systems Biology Dresden, Dresden, 01307, Germany.

PMID: 36764220
DOI: 10.1016/j.fsigen.2023.102840

Abstract

We provide an internal validation study of a recently published precise DNA mixture algorithm based on Hamiltonian Monte Carlo sampling (Susik et al., 2022). We provide results for all 428 mixtures analysed by Riman et al. (2021) and compare the results with two state-of-the-art software products: STRmix™ v2.6 and Euroformix v3.4.0. The comparison shows that the Hamiltonian Monte Carlo method provides reliable values of likelihood ratios (LRs) close to the other methods. We further propose a novel large-scale precision benchmark and quantify the precision of the Hamiltonian Monte Carlo method, indicating its improvements over existing solutions. Finally, we analyse the influence of the factors discussed by Buckleton et al. (2022).

Keywords: DNA mixtures; HMC; Probabilistic genotyping; Validation.

MeSH terms

Algorithms*
Benchmarking*
Genotype
Humans
Monte Carlo Method
Software