A guide to results and diagnostics within a STRmix™ report

Russell, Laura; Cooper, Stuart L.; Wivell, Richard; Kerr, Zane; Taylor, Duncan; Buckleton, John; Bright, Jo‐Anne

doi:10.1002/wfs2.1354

Cited by 20 publications

(14 citation statements)

References 35 publications

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“…STRmix interpretations were undertaken using the recommended MCMC parameters (shown in Table 1) [46]. In follow up analyses two interpretations were repeated with an increase in the number of accepts (1,000,000 burn-in and 500,000 post burn-in accepts per chain) to allow each of the chains to explore more possibilities in the probability space [59]. The reported sub-source LRs within the STRmix reports were considered for the analysis in this study.…”

Section: Lr Calculations and Data Analysismentioning

confidence: 99%

“…discussed in detail in Russell et al [59]. In actual casework, every analysis should be subjected to diagnostic checks.…”

Section: Plos Onementioning

confidence: 99%

“…In such cases and if samples are sufficient, either replicate analysis or sample reamplification is used. Otherwise, options are to either ignore that locus during deconvolution, or repeat the deconvolution in STRmix with: a random starting seed for the MCMC different than the one that gave LR = 0, or an increase in number of MCMC accepts, or a larger Random Walk Standard Deviation (RWSD) [2,7,36,59,71]. Here, we repeated the runs in STRmix with more MCMC accepts (as discussed in Section 2.7) and the repeated interpretations generated non-zero LRs for the affected loci, and profile log 10 (LRs) of 24.8 and 19.6 (S8 Table ).…”

Section: Plos Onementioning

confidence: 99%

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Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset

2021

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A likelihood ratio (LR) system is defined as the entire pipeline of the measurement and interpretation processes where probabilistic genotyping software (PGS) is a piece of the whole LR system. To gain understanding on how two LR systems perform, a total of 154 two-person, 147 three-person, and 127 four-person mixture profiles of varying DNA quality, DNA quantity, and mixture ratios were obtained from the filtered (.CSV) files of the GlobalFiler 29 cycles 15s PROVEDIt dataset and deconvolved in two independently developed fully continuous programs, STRmix v2.6 and EuroForMix v2.1.0. Various parameters were set in each software and LR computations obtained from the two software were based on same/fixed EPG features, same pair of propositions, number of contributors, theta, and population allele frequencies. The ability of each LR system to discriminate between contributor (H1-true) and non-contributor (H2-true) scenarios was evaluated qualitatively and quantitatively. Differences in the numeric LR values and their corresponding verbal classifications between the two LR systems were compared. The magnitude of the differences in the assigned LRs and the potential explanations for the observed differences greater than or equal to 3 on the log10 scale were described. Cases of LR < 1 for H1-true tests and LR > 1 for H2-true tests were also discussed. Our intent is to demonstrate the value of using a publicly available ground truth known mixture dataset to assess discrimination performance of any LR system and show the steps used to understand similarities and differences between different LR systems. We share our observations with the forensic community and describe how examining more than one PGS with similar discrimination power can be beneficial, help analysts compare interpretation especially with low-template profiles or minor contributor cases, and be a potential additional diagnostic check even if software in use does contain certain diagnostic statistics as part of the output.

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Section: Lr Calculations and Data Analysismentioning

confidence: 99%

“…discussed in detail in Russell et al [59]. In actual casework, every analysis should be subjected to diagnostic checks.…”

Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

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Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset

2021

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“…if we were interested in the potential DNA contribution of five different people to a complex sample, then the MCMC would need to be run five times, once for each person. There is also a difficultly facilitating human review of the MCMC, as one of the main intuitive diagnostic outputs are the weights [15].…”

Section: Introductionmentioning

confidence: 99%

Testing methods for quantifying Monte Carlo variation for categorical variables in Probabilistic Genotyping

Bright

Taylor

Curran

et al. 2021

Preprint

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Two methods for applying a lower bound to the variation induced by the Monte Carlo effect are trialled. One of these is implemented in the widely used probabilistic genotyping system, STRmix Neither approach is giving the desired 99% coverage. In some cases the coverage is much lower than the desired 99%. The discrepancy (i.e. the distance between the LR corresponding to the desired coverage and the LR observed coverage at 99%) is not large. For example, the discrepancy of 0.23 for approach 1 suggests the lower bounds should be moved downwards by a factor of 1.7 to achieve the desired 99% coverage. Although less effective than desired these methods provide a layer of conservatism that is additional to the other layers. These other layers are from factors such as the conservatism within the sub-population model, the choice of conservative measures of co-ancestry, the consideration of relatives within the population and the resampling method used for allele probabilities, all of which tend to understate the strength of the findings.

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“…A single unambiguous genotype combination at any locus would be assigned a weight of 1, w j = 1. A description of the results and diagnostics within a STRmix™ output is given in Russell et al (2019).…”

Section: Introductionmentioning

confidence: 99%

A description of the likelihood ratios in the probabilistic genotyping software STRmix™

Kelly

Bright

Coble

et al. 2020

WIREs Forensic Science

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Probabilistic methods of DNA profile interpretation are being adopted by forensic laboratories worldwide. One commonality to all probabilistic genotyping software is an assignment of the strength of evidence using the likelihood ratio (LR). The probabilistic genotyping software STRmix™ reports a number of LRs that differ based on the propositions considered and the level within the hierarchy of propositions considered. Within this paper, we describe the different LRs assigned in a STRmix™ software report. This article is categorized under: Forensic Biology > Interpretation of Biological Evidence Forensic Biology > Forensic DNA Technologies K E Y W O R D S forensic DNA analysis, likelihood ratio, probabilistic genotyping 1 | INTRODUCTION STRmix™ is software used for the interpretation of forensic DNA profiles (Bright et al., 2016; Taylor, Bright, & Buckleton, 2013). It is a method of probabilistic genotyping that uses "biological modeling, statistical theory, computer algorithms, and probability distributions to calculate likelihood ratios (LRs) and/or infer genotypes for the DNA typing results of forensic samples" (Scientific Working Group on DNA Analysis Methods (SWGDAM), 2015). The LR approach involves the comparison of the likelihood of obtaining the observed DNA profile given alternate, competing hypotheses; one typically aligning with the prosecution proposition (H p) and one with the defense proposition (H d) if available. These may also be referred to as H 1 and H 2. Within STRmix™, there are a range of different LRs that are calculated. In general, the LR differs depending on the propositions and the level within the hierarchy of propositions (

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A guide to results and diagnostics within a STRmix™ report

Cited by 20 publications

References 35 publications

Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset

Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset

Testing methods for quantifying Monte Carlo variation for categorical variables in Probabilistic Genotyping

A description of the likelihood ratios in the probabilistic genotyping software STRmix™

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