Forensic likelihood ratios for shotgun sequencing data from DNA mixtures
- 1Kobenhavns Universitet Globe Institute
Abstract
Shotgun sequencing of DNA mixtures has the potential to become an important tool in forensic genetics, particularly for degraded samples or complex mixtures. However, the analysis of shotgun-sequenced mixtures is challenged by several confounding factors, including sequencing errors, population structure, the potential presence of relatives, linkage, and linkage disequilibrium. Additionally, the statistical methods currently available for such data are limited. Here, we develop likelihood ratios that summarize the evidence for the presence of a suspects DNA in a mixture, assuming genotypes of the suspect and the victim are available, while accounting for DNA from unknown contributors modeled either as general population contamination or as a single contaminating individual. We show that these likelihood ratios are not robust to model assumptions and can falsely support the presence of the suspects DNA when the true contributor is even a distant relative. We therefore propose a combined evidence score, {Lambda}: the likelihood ratio minimized over contamination models, reported only when the evidence favors the suspect over a hypothesized sibling by at least a factor of C (default C = 10) under at least one contamination model, and otherwise set to 1 (log {Lambda} = 0). In simulations based on 1000 Genomes data, {Lambda} retains nearly all power to detect true contributors at mixture proportions as low as 5% while eliminating the false positives produced by model mis-specification, population structure, and by untested relatives. The methods are implemented in the open-source program DNAMIXTURE, providing the forensic and population genetics community a resource for robustly identifying small individual contributions to a DNA mixture.
Related
- Comparative characterization of the mucosal eukaryotic virome across independent whole-metagenome shotgun sequencing cohorts
- Research Progress on the Application of Nanopore Long-Read Sequencing Combined with DNA Methylation Marking in Complex Forensic Evidence.
- Effects of trait stacking on the maize phyllosphere microbiome: a shotgun metagenomic comparison
- Long-read shotgun metagenomics identifies distinct urogenital microbial community states associated with functional potential and urinary tract infection during pregnancy
- Comparative Shotgun Metagenomics of Microbial Communities Associated with Two Geothermal Systems in Kazakhstan.
BibTeX
@misc{nielsen2026forensic,
title = {{Forensic likelihood ratios for shotgun sequencing data from DNA mixtures}},
author = {R. Nielsen and A. D. Ramsoe and M. Sikora and E. Willerslev and T. S. Korneliussen},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.28.755155},
url = {https://www.biorxiv.org/content/10.64898/2026.09.28.755155v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Forensic likelihood ratios for shotgun sequencing data from DNA mixtures
AU - R. Nielsen
AU - A. D. Ramsoe
AU - M. Sikora
AU - E. Willerslev
AU - T. S. Korneliussen
PY - 2026
DA - 2026/09/28
PB - bioRxiv
DO - 10.64898/2026.09.28.755155
UR - https://www.biorxiv.org/content/10.64898/2026.09.28.755155v1
AB - Shotgun sequencing of DNA mixtures has the potential to become an important tool in forensic genetics, particularly for degraded samples or complex mixtures. However, the analysis of shotgun-sequenced mixtures is challenged by several confounding factors, including sequencing errors, population structure, the potential presence of relatives, linkage, and linkage disequilibrium. Additionally, the statistical methods currently available for such data are limited. Here, we develop likelihood ratios that summarize the evidence for the presence of a suspects DNA in a mixture, assuming genotypes of the suspect and the victim are available, while accounting for DNA from unknown contributors modeled either as general population contamination or as a single contaminating individual. We show that these likelihood ratios are not robust to model assumptions and can falsely support the presence of the suspects DNA when the true contributor is even a distant relative. We therefore propose a combined evidence score, {Lambda}: the likelihood ratio minimized over contamination models, reported only when the evidence favors the suspect over a hypothesized sibling by at least a factor of C (default C = 10) under at least one contamination model, and otherwise set to 1 (log {Lambda} = 0). In simulations based on 1000 Genomes data, {Lambda} retains nearly all power to detect true contributors at mixture proportions as low as 5% while eliminating the false positives produced by model mis-specification, population structure, and by untested relatives. The methods are implemented in the open-source program DNAMIXTURE, providing the forensic and population genetics community a resource for robustly identifying small individual contributions to a DNA mixture.
ER -