A diagnosis of the primary difference between EuroForMix and STRmix™.

Publisher: Blackwell Pub Country of Publication: United States NLM ID: 0375370 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1556-4029 (Electronic) Linking ISSN: 00221198 NLM ISO Abbreviation: J Forensic Sci Subsets: PubMed not MEDLINE; MEDLINE

Publication: 2006- : Malden, MA : Blackwell Pub.
Original Publication: [Chicago, Ill.] : Callaghan and Co., 1956-

There is interest in comparing the output, principally the likelihood ratio, from the two probabilistic genotyping software EuroForMix (EFM) and STRmix™. Many of these comparison studies are descriptive and make little or no effort to diagnose the cause of difference. There are fundamental differences between EFM and STRmix™ that are causative of the largest set of likelihood ratio differences. This set of differences is for false donors where there are many instances of LRs just above or below 1 for EFM that give much lower LRs in STRmix™. This is caused by the separate estimation of parameters such as allele height variance and mixture proportion using MLE under H p and H a for EFM. This can result in very different estimations of these parameters under H p and H a . It results in a departure from calibration for EFM in the region of LRs just above and below 1.
(© 2023 American Academy of Forensic Sciences.)

Ramos D, Gonzalez-Rodriguez J. Reliable support: measuring calibration of likelihood ratios. Forensic Sci Int. 2013;230(1-3):156-169. https://doi.org/10.1016/j.forsciint.2013.04.014.
Butler J, Iyer H, Press R, Taylor MK, Vallone PM, Willis S. DNA mixture interpretation: a NIST scientific foundation review. 2021. https://nvlpubs.nist.gov/nistpubs/ir/2021/NIST.IR.8351-draft.pdf. Accessed 12 Sep 2023.
Hon. Carolina Stark. State of Wisconsin vs Troy darnel Williams. 2022 [updated 2022]. https://johnbuckleton.files.wordpress.com/2022/03/williams-troy-2-4-22.pdf. Accessed 12 Sep 2022.
Alladio E, Omedei M, Cisana S, D'Amico G, Caneparo D, Vincenti M, et al. DNA mixtures interpretation-a proof-of-concept multi-software comparison highlighting different probabilistic methods performances on challenging samples. Forensic Sci Int Genet. 2018;37:143-150. https://doi.org/10.1016/j.fsigen.2018.08.002.
Taylor DA, Buckleton JS, Bright J-A. Comment on “DNA mixtures interpretation-a proof-of-concept multi-software comparison highlighting different probabilistic methods' performances on challenging samples”; by Alladio et al. Forensic Sci Int Genet. 2019;40:e248-e251. https://doi.org/10.1016/j.fsigen.2019.02.
Hahn M, Anslinger K, Eckert M, Fimmers R, Grethe S, Hohoff C, et al. Gemeinsame empfehlungen der Projektgruppe “Biostatistische DNA-Berechnungen” und der Spurenkommission zur Biostatistischen bewertung forensischer DNA-analytischer befunde mit vollkontinuierlichen modellen (VKM) [joint recommendations of the project group “biostatical DNA calculations” and the trace commission on the biostatical evaluation of forensic DNA-analytical findings using fully continuous models (VKM)]. Dent Rec. 2023;33(1):3-12. https://doi.org/10.1007/s00194-022-00599-5. (English translation: https://johnbuckleton.files.wordpress.com/2023/07/s00194-022-00599-5-1-english-british.pdf).
Benschop CCG, Hoogenboom J, Hovers P, Slagter M, Kruise D, Parag R, et al. DNAxs/DNAStatistX: development and validation of a software suite for the data management and probabilistic interpretation of DNA profiles. Forensic Sci Int Genet. 2019;42:81-89. https://doi.org/10.1016/j.fsigen.2019.06.015.
Meester R, Slooten K. Probability and forensic evidence: theory, philosophy, and applications. Cambridge, UK: Cambridge University Press; 2021.
Susik M, Sbalzarini IF. Analysis of the Hamiltonian Monte Carlo genotyping algorithm on PROVEDIt mixtures including a novel precision benchmark. Forensic Sci Int Genet. 2023;64:102840. https://doi.org/10.1016/j.fsigen.2023.102840.
Susik M, Schönborn H, Sbalzarini IF. Hamiltonian Monte Carlo with strict convergence criteria reduces run-to-run variability in forensic DNA mixture deconvolution. Forensic Sci Int Genet. 2022;60:102744. https://doi.org/10.1016/j.fsigen.2022.102744.
Riman S, Iyer H, Vallone PM. Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset. PLoS One. 2021;16(9):e0256714. https://doi.org/10.1371/journal.pone.0256714.
Buckleton J, Bright J-A, Taylor D, Wivell R, Bleka Ø, Gill P, et al. Re: Riman et al. examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset. Forensic Sci Int Genet. 2022;59:102709. https://doi.org/10.1016/j.fsigen.2022.102709.
Grgicak CM, Karkar S, Yearwood-Garcia X, Alfonse LE, Duffy KR, Lun DS. A large-scale validation of NOCIt's a posteriori probability of the number of contributors and its integration into forensic interpretation pipelines. Forensic Sci Int Genet. 2020;47:102296.
Costa C, Figueiredo C, Amorim A, Costa S, Ferreira PM, Pinto N. Quantification of forensic genetic evidence: comparison of results obtained by qualitative and quantitative software for real casework samples. Forensic Sci Int Genet. 2022;59:102715.
Cheng K, Bleka O, Gill P, Curran J, Bright J-A, Taylor D, et al. A comparison of likelihood ratios obtained from EuroForMix and STRmix™. J Forensic Sci. 2021;66(6):2138-2155. https://doi.org/10.1111/1556-4029.14886.
Taylor D, Buckleton J, Bright J-A. Factors affecting peak height variability for short tandem repeat data. Forensic Sci Int Genet. 2016;21:126-133. https://doi.org/10.1016/j.fsigen.2015.12.009.
Buckleton JS, Bright J-A, Cheng K, Budowle B, Coble MD. NIST interlaboratory studies involving DNA mixtures (MIX13): a modern analysis. Forensic Sci Int Genet. 2018;37:172-179. https://doi.org/10.1016/j.fsigen.2018.08.014.
Taylor D, Bright J-A, Buckleton J. Interpreting forensic DNA profiling evidence without specifying the number of contributors. Forensic Sci Int Genet. 2014;13:269-280. https://doi.org/10.1016/j.fsigen.2014.08.014.
Slooten K, Caliebe A. Contributors are a nuisance (parameter) for DNA mixture evidence evaluation. Forensic Sci Int Genet. 2018;37:116-125. https://doi.org/10.1016/j.fsigen.2018.05.004.
Buckleton JS, Kruijver M, Curran JM, Bright J-A. Calibration of STRmix LRs following the method of Hannig et al. 2020 [updated 2020]. https://research.esr.cri.nz/articles/report/Calibration_of_STRmix_LRs_following_the_method_of_Hannig_et_al_/12324011. Accessed 15 Sep 2022.
Taylor D, Curran JM, Buckleton J. Importance sampling allows Hd true tests of highly discriminating DNA profiles. Forensic Sci Int Genet. 2017;27:74-81. https://doi.org/10.1016/j.fsigen.2016.12.004.
Bleka Ø, Storvik G, Gill P. EuroForMix: An open-source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artifacts. Forensic Sci Int Genet. 2016;21:35-44. https://doi.org/10.1016/j.fsigen.2015.11.008.

2020-DQ-BX-0022 US National Institute of Justice

Keywords: EuroForMix; STRmix™; forensic DNA; probabilistic genotyping; reliability; validation

Date Created: 20230927 Latest Revision: 20240103

20240103

10.1111/1556-4029.15387

37753814