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PUBLICATIONS

A collaborative study on the precision of the Markov chain Monte Carlo algorithms used for DNA profile interpretation

Published

Author(s)

Sarah Riman, Jo-Anne Bright, Kaitlin Huffman, Lilliana Moreno, Sicen Liu, Asmitha Sathya, Peter Vallone

Abstract

Several fully continuous probabilistic genotyping software (PGS) use Markov chain Monte Carlo algorithms (MCMC) to assign weights to different proposed genotype combinations at a locus. Replicate interpretations of the same profile in these software are expected not to produce identical weights and likelihood ratio (LR) values due to the Monte Carlo aspect. This paper reports a detailed precision study under reproducibility conditions conducted as a collaborative exercise across the National Institute of Standards and Technology (NIST), Federal Bureau of Investigation (FBI), and Institute of Environmental Science and Research (ESR). Replicate interpretations generated across the three laboratories used the same input files, software version, and settings but different random number seed and different computers. This work demonstrates that using different computers to analyze replicate interpretations does not contribute to any variations in LR values. The study quantifies the magnitude of differences in the assigned LRs that is only due to run-to-run MCMC variability and addresses the potential explanations for the observed differences.
Citation
Forensic Science International
Volume
72
Pub Type
Journals

Download Paper

https://doi.org/10.1016/j.fsigen.2024.103088
Local Download

Keywords

MCMC stochasticity, Precision, Reproducibility, Mixtures, Likelihood Ratios
Forensic Science

Citation

Riman, S. , Bright, J. , Huffman, K. , Moreno, L. , Liu, S. , Sathya, A. and Vallone, P. (2024), A collaborative study on the precision of the Markov chain Monte Carlo algorithms used for DNA profile interpretation, Forensic Science International, [online], https://doi.org/10.1016/j.fsigen.2024.103088, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957074 (Accessed March 9, 2025)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created June 19, 2024, Updated March 4, 2025