TY - JOUR T1 - Hamiltonian Monte Carlo with strict convergence criteria reduces run-to-run variability in forensic DNA mixture deconvolution JF - bioRxiv DO - 10.1101/2022.02.15.480571 SP - 2022.02.15.480571 AU - Mateusz Susik AU - Holger Schönborn AU - Ivo F. Sbalzarini Y1 - 2022/01/01 UR - http://biorxiv.org/content/early/2022/03/23/2022.02.15.480571.abstract N2 - Motivation Analysing mixed DNA profiles is a common task in forensic genetics. Due to the complexity of the data, such analysis is often performed using Markov Chain Monte Carlo (MCMC)-based genotyping algorithms. These trade off precision against execution time. When the default settings are used, as large as a 10-fold changes in inferred likelihood ratios (LR) are observed when the software is run twice on the same case. So far, this uncertainty has been attributed to the stochasticity of MCMC algorithms. Since LRs translate directly to strength of the evidence in a criminal trial, forensic laboratories desire LR with small run-to-run variability.Results We present a Hamiltonian Monte Carlo (HMC) algorithm that reduces run-to-run variability in forensic DNA mixture deconvolution by around an order of magnitude without increased runtime. We achieve this by enforcing strict convergence criteria. We show that the choice of convergence metric strongly influences precision. We validate our method by reproducing previously published results for benchmark DNA mixtures (MIX05, MIX13, and ProvedIt). We also present a complete software implementation of our algorithm that is able to leverage GPU acceleration, accelerating the inference process. In the benchmark mixtures, on consumer-grade hardware, the runtime is less than 7 minutes for 3 contributors, less than 35 minutes for 4 contributors, and less than an hour for 5 contributors with one known contributor.Competing Interest StatementGenoProof Mixture - a probabilistic genotyping software mentioned in the submitted manuscript - is developed by qualitype GmbH. Holger Schoenborn is one of the authors of this software. Mateusz Susik is employed by Biotype GmbH. Biotype GmbH and qualitype GmbH are members of the Molecular Diagnostics Group consortium. ER -