1
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Taylor D, Volgin L, Kokshoorn B. Accounting for site-to-site DNA transfer on a packaged exhibit in an evaluation given activity level propositions. Forensic Sci Int Genet 2024; 73:103122. [PMID: 39159582 DOI: 10.1016/j.fsigen.2024.103122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 08/01/2024] [Accepted: 08/02/2024] [Indexed: 08/21/2024]
Abstract
Considering activity level propositions in the evaluation of forensic biology findings is becoming more common place. There are increasing numbers of publications demonstrating different transfer mechanisms that can occur under a variety of circumstances. Some of these publications have shown the possibility of DNA transfer from site to site on an exhibit, for instance as a result of packaging and transport. If such a possibility exists, and the case circumstances are such that the area on an exhibit where DNA is present or absent is an observation that is an important diagnostic characteristic given the propositions, then site to site transfer should be taken into account during the evaluation of observations. In this work we demonstrate the ways in which site to site transfer can be built into Bayesian networks when carrying out activity level evaluations of forensic biology findings. We explore the effects of considering qualitative vs quantitative categorisation of DNA results. We also show the importance of taking into account multiple individual's DNA being transferred (such as unknown or wearer DNA), even if the main focus of the evaluation is the activity of one individual.
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Affiliation(s)
- Duncan Taylor
- Forensic Science SA, GPO Box 2790, Adelaide, SA 5001, Australia; School of Biological Sciences, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia.
| | - Luke Volgin
- Forensic Science SA, GPO Box 2790, Adelaide, SA 5001, Australia
| | - Bas Kokshoorn
- Forensic Trace Dynamics, Faculty of Technology, Amsterdam University of Applied Sciences, Amsterdam, the Netherlands; Netherlands Forensic Institute, The Hague, the Netherlands
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2
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McCarthy-Allen M, Bleka Ø, Ypma R, Gill P, Benschop C. 'Low' LRs obtained from DNA mixtures: On calibration and discrimination performance of probabilistic genotyping software. Forensic Sci Int Genet 2024; 73:103099. [PMID: 39089059 DOI: 10.1016/j.fsigen.2024.103099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 07/23/2024] [Accepted: 07/24/2024] [Indexed: 08/03/2024]
Abstract
The validity of a probabilistic genotyping (PG) system is typically demonstrated by following international guidelines for the developmental and internal validation of PG software. These guidelines mainly focus on discriminatory power. Very few studies have reported with metrics that depend on calibration of likelihood ratio (LR) systems. In this study, discriminatory power as well as various calibration metrics, such as Empirical Cross-Entropy (ECE) plots, pool adjacent violator (PAV) plots, log likelihood ratio cost (Cllr and Cllrcal), fiducial calibration discrepancy plots, and Turing' expectation were examined using the publicly-available PROVEDIt dataset. The aim was to gain deeper insight into the performance of a variety of PG software in the 'lower' LR ranges (∼LR 1-10,000), with focus on DNAStatistX and EuroForMix which use maximum likelihood estimation (MLE). This may be a driving force for the end users to reconsider current LR thresholds for reporting. In previous studies, overstated 'low' LRs were observed for these PG software. However, applying (arbitrarily) high LR thresholds for reporting wastes relevant evidential value. This study demonstrates, based on calibration performance, that previously reported LR thresholds can be lowered or even discarded. Considering LRs >1, there was no evidence for miscalibration performance above LR ∼1000 when using Fst 0.01. Below this LR value, miscalibration was observed. Calibration performance generally improved with the use of Fst 0.03, but the extent of this was dependent on the dataset: results ranged from miscalibration up to LR ∼100 to no evidence of miscalibration alike PG software using different methods to model peak height, HMC and STRmix. This study demonstrates that practitioners using MLE-based models should be careful when low LR ranges are reported, though applying arbitrarily high LR thresholds is discouraged. This study also highlights various calibration metrics that are useful in understanding the performance of a PG system.
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Affiliation(s)
- M McCarthy-Allen
- Netherlands Forensic Institute, Division of Biological Traces, the Netherlands
| | - Ø Bleka
- Oslo University Hospital, Department of Forensic Sciences, Norway
| | - R Ypma
- Netherlands Forensic Institute, Division of Digital and Biometric Traces, the Netherlands
| | - P Gill
- Oslo University Hospital, Department of Forensic Sciences, Norway; University of Oslo, Institute of Clinical Medicine, Department of Forensic Medicine, Norway
| | - C Benschop
- Netherlands Forensic Institute, Division of Biological Traces, the Netherlands.
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3
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Agudo MM, Fantinato C, Roseth A, Aanes H, Gill P, Fonneløp AE, Bleka Ø. A comparison of likelihood ratios calculated from surface DNA mixtures using MPS and CE Technologies. Forensic Sci Int Genet 2024; 73:103111. [PMID: 39128429 DOI: 10.1016/j.fsigen.2024.103111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 06/14/2024] [Accepted: 07/30/2024] [Indexed: 08/13/2024]
Abstract
This study evaluates the performance of analysing surface DNA samples using massively parallel sequencing (MPS) compared to traditional capillary electrophoresis (CE). A total of 30 samples were collected from various surfaces in an office environment and were analysed with CE and MPS. These were compared against 60 reference samples (office inhabitants). To identify contributors, likelihood ratios (LRs) were calculated for MPS and CE data using the probabilistic genotyping software MPSproto and EuroForMix respectively. Although a higher number of sequences/peaks were observed per DNA profile in MPS compared to CE, LR values were found to be lower for MPS data formats. This might be the result of the increased complexity of MPS data, along with a possible elevation of unknown alleles and/or artefacts. The study highlights avenues for improving MPS data quality and analysis to facilitate more robust interpretation of challenging casework-like samples.
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Affiliation(s)
- Maria Martin Agudo
- Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway; Department of Forensic Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Chiara Fantinato
- Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway; Department of Forensic Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Arne Roseth
- Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway
| | - Håvard Aanes
- Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway
| | - Peter Gill
- Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway; Department of Forensic Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Øyvind Bleka
- Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway.
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4
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Liu Z, Wu E, Li R, Liu J, Zang Y, Cong B, Wu R, Xie B, Sun H. Improved individual identification in DNA mixtures of unrelated or related contributors through massively parallel sequencing. Forensic Sci Int Genet 2024; 72:103078. [PMID: 38889491 DOI: 10.1016/j.fsigen.2024.103078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 06/07/2024] [Accepted: 06/11/2024] [Indexed: 06/20/2024]
Abstract
DNA mixtures are a common sample type in forensic genetics, and we typically assume that contributors to the mixture are unrelated when calculating the likelihood ratio (LR). However, scenarios involving mixtures with related contributors, such as in family murder or incest cases, can also be encountered. Compared to the mixtures with unrelated contributors, the kinship within the mixture would bring additional challenges for the inference of the number of contributors (NOC) and the construction of probabilistic genotyping models. To evaluate the influence of potential kinship on the individual identification of the person of interest (POI), we conducted simulations of two-person (2 P) and three-person (3 P) DNA mixtures containing unrelated or related contributors (parent-child, full-sibling, and uncle-nephew) at different mixing ratios (for 2 P: 1:1, 4:1, 9:1, and 19:1; for 3 P: 1:1:1, 2:1:1, 5:4:1, and 10:5:1), and performed massively parallel sequencing (MPS) using MGIEasy Signature Identification Library Prep Kit on MGI platform. In addition, in silico simulations of mixtures with unrelated and related contributors were also performed. In this study, we evaluated 1): the MPS performance; 2) the influence of multiple genetic markers on determining the presence of related contributors and inferring the NOC within the mixture; 3) the probability distribution of MAC (maximum allele count) and TAC (total allele count) based on in silico mixture profiles; 4) trends in LR values with and without considering kinship in mixtures with related and unrelated contributors; 5) trends in LR values with length- and sequence-based STR genotypes. Results indicated that multiple numbers and types of genetic markers positively influenced kinship and NOC inference in a mixture. The LR values of POI were strongly dependent on the mixing ratio. Non- and correct-kinship hypotheses essentially did not affect the individual identification of the major POI; the correct kinship hypothesis yielded more conservative LR values; the incorrect kinship hypothesis did not necessarily lead to the failure of POI individual identification. However, it is noteworthy that these considerations could lead to uncertain outcomes in the identification of minor contributors. Compared to length-based STR genotyping, using sequence-based STR genotype increases the individual identification power of the POI, concurrently improving the accuracy of mixing ratio inference using EuroForMix. In conclusion, the MGIEasy Signature Identification Library Prep kit demonstrated robust individual identification power, which is a viable MPS panel for forensic DNA mixture interpretations, whether involving unrelated or related contributors.
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Affiliation(s)
- Zhiyong Liu
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou 510080, China
| | - Enlin Wu
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou 510080, China
| | - Ran Li
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou 510080, China; School of Medicine, Jiaying University, Meizhou 514015, China
| | - Jiajun Liu
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou 510080, China
| | - Yu Zang
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou 510080, China
| | - Bin Cong
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Shijiazhuang 050017, China
| | - Riga Wu
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou 510080, China
| | - Bo Xie
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou 510080, China
| | - Hongyu Sun
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou 510080, China.
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5
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Riman S, Bright JA, Huffman K, Moreno LI, Liu S, Sathya A, Vallone PM. A collaborative study on the precision of the Markov chain Monte Carlo algorithms used for DNA profile interpretation. Forensic Sci Int Genet 2024; 72:103088. [PMID: 38908322 DOI: 10.1016/j.fsigen.2024.103088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 06/24/2024]
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.
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Affiliation(s)
- Sarah Riman
- National Institute of Standards and Technology, Applied Genetics Group, 100 Bureau Drive, Gaithersburg, MD 20899, USA.
| | - Jo-Anne Bright
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142 New Zealand
| | - Kaitlin Huffman
- Federal Bureau of Investigation Laboratory, DNA Support Unit, 2501 Investigation Parkway, Quantico, VA 22135, USA
| | - Lilliana I Moreno
- Federal Bureau of Investigation Laboratory, DNA Support Unit, 2501 Investigation Parkway, Quantico, VA 22135, USA
| | - Sicen Liu
- National Institute of Standards and Technology, Applied Genetics Group, 100 Bureau Drive, Gaithersburg, MD 20899, USA; Johns Hopkins University Whiting School of Engineering, 3400 N Charles St, Baltimore, MD 21218, USA
| | - Asmitha Sathya
- National Institute of Standards and Technology, Applied Genetics Group, 100 Bureau Drive, Gaithersburg, MD 20899, USA; Johns Hopkins University Whiting School of Engineering, 3400 N Charles St, Baltimore, MD 21218, USA
| | - Peter M Vallone
- National Institute of Standards and Technology, Applied Genetics Group, 100 Bureau Drive, Gaithersburg, MD 20899, USA
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6
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Greenspoon SA, Schiermeier-Wood L, Jenkins BC. A tale of two PG systems: A comparison of the two most widely used continuous probabilistic genotyping systems in the United States. J Forensic Sci 2024; 69:1840-1860. [PMID: 38899548 DOI: 10.1111/1556-4029.15571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/23/2024] [Accepted: 06/06/2024] [Indexed: 06/21/2024]
Abstract
The development of probabilistic genotyping (PG) systems to quantitatively analyze DNA mixture samples has been transformative in forensic science. TrueAllele® Casework (TA) and STRmix™ (STRmix) are the two most widely used PG systems in the United States. The two systems were challenged with 48 two-, three-, and four-person mock casework samples, for a total of 152 likelihood ratio (LR) comparisons. TA and STRmix converged on the same result (supportive, non-supportive, or inconclusive) for ~91% of contributor-specific comparisons. Where moderate or substantial differences in log(LR) values were observed, 9% affected the conclusion of the reference association to the mixture. The PG systems exhibited high correlations for estimated contributor-specific template quantities (~92%) and log(LR)s produced (>88%). When the log(LR)s for only low-template contributors (<100 pg) were compared, the R2 value dropped to ~68% and the difference became statistically significant. Of the 14 contributor comparisons where the conclusion differed, two were contradictory (supportive vs. non-supportive) and 12 were either inconclusive versus non-supportive or inconclusive versus supportive. The differing results were likely due to dissimilarities in the mixture input file as STRmix uses a lab-defined analytical threshold (AT) and TA models to 10 RFUs for each electropherogram. When 7 of the 14 mixtures were reanalyzed by STRmix using a 10 RFU AT, the log(LR)s for the low-template contributors became more similar to TAs. This study shows that while both systems may produce accurate and calibrated LRs, their results can deviate, especially for low-template, degraded contributors, and the deviation is generally predictable.
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7
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Durán JM, van der Vloed D, Ruifrok A, Ypma RJF. From understanding to justifying: Computational reliabilism for AI-based forensic evidence evaluation. Forensic Sci Int Synerg 2024; 9:100554. [PMID: 39285895 PMCID: PMC11402526 DOI: 10.1016/j.fsisyn.2024.100554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 08/22/2024] [Accepted: 08/27/2024] [Indexed: 09/19/2024]
Abstract
Techniques from artificial intelligence (AI) can be used in forensic evidence evaluation and are currently applied in biometric fields. However, it is generally not possible to fully understand how and why these algorithms reach their conclusions. Whether and how we should include such 'black box' algorithms in this crucial part of the criminal law system is an open question that has not only scientific but also ethical, legal, and philosophical angles. Ideally, the question should be debated by people with diverse backgrounds. Here, we present a view on the question from the philosophy of science angle: computational reliabilism (CR). CR posits that we are justified in believing the output of an AI system, if we have grounds for believing its reliability. Under CR, these grounds are classified into 'reliability indicators' of three types: technical, scientific, and societal. This framework enables debates on the suitability of AI methods for forensic evidence evaluation that take a wider view than explainability and validation. We argue that we are justified in believing the AI's output for forensic comparison of voices and forensic comparison of faces. Technical indicators include the validation of the AI algorithm in itself, validation of its application in the forensic setting, and case-based validation. Scientific indicators include the simple notion that we know faces and voices contain identifying information along with operationalizing well-established metrics and forensic practices. Societal indicators are the emerging scientific consensus on the use of these methods, as well as their application and interpretation by well-educated and certified practitioners. We expect expert witnesses to rely more on technical indicators to be justified in believing AIsystems, and triers-of-fact to rely more on societal indicators to believe the expert witness supported by the AIsystem.
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Affiliation(s)
- Juan M Durán
- Delft University of Technology, Jaffalaan 5, 2628 BX, Delft, Netherlands
| | - David van der Vloed
- Division of digital and biometric traces, Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB, Den Haag, Netherlands
| | - Arnout Ruifrok
- Division of digital and biometric traces, Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB, Den Haag, Netherlands
| | - Rolf J F Ypma
- Division of digital and biometric traces, Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB, Den Haag, Netherlands
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8
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Dash HR. Advancements in differentiation between sperm cells and epithelial cells for efficient forensic DNA analysis in sexual assault cases. Int J Legal Med 2024:10.1007/s00414-024-03285-1. [PMID: 38995400 DOI: 10.1007/s00414-024-03285-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 06/30/2024] [Indexed: 07/13/2024]
Abstract
Most of the sexual assault casework samples are of mixed sources. Forensic DNA laboratories are always in the requirement of a precise technique for the efficient separation of sperm and non-sperm DNA from mixed samples. Since the introduction of the differential extraction technique in 1985, it has seen significant advancements in the form of either chemicals used or modification of incubation times. Several automated and semi-automated techniques have also adopted the fundamentals of conventional differential extraction techniques. However, lengthy incubation, several manual steps, and carryover over non-sperm material in sperm fraction are some of the major limitations of this technique. Advanced cell separation techniques have shown huge promise in separating sperm cells from a mixture based on their size, shape, composition, and membrane structure and antigens present on sperm membranes. Such advanced techniques such as DEParray, ADE, FACS, LCM, HOT and their respective pros and cons have been discussed in this article. As current-day forensic techniques should be as per the line of Olympic slogan i.e., faster, higher, stronger, the advanced cell separation techniques show a huge potential to be implemented in the casework samples.
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Affiliation(s)
- Hirak Ranjan Dash
- National Forensic Sciences University, Delhi Campus, Sector-3, 110085, Rohini, New Delhi, India.
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9
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Agudo MM, Aanes H, Albert M, Janssen K, Gill P, Bleka Ø. An overview of autosomal STRs and identity SNPs in a Norwegian population using massively parallel sequencing. Forensic Sci Int Genet 2024; 71:103057. [PMID: 38733649 DOI: 10.1016/j.fsigen.2024.103057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 02/27/2024] [Accepted: 04/28/2024] [Indexed: 05/13/2024]
Abstract
In recent years, probabilistic genotyping software has been adapted for the analysis of massively parallel sequencing (MPS) forensic data. Likelihood ratios (LR) are based on allele frequencies selected from populations of interest. This study provides an outline of sequence-based (SB) allele frequencies for autosomal short tandem repeats (aSTRs) and identity single nucleotide polymorphisms (iSNPs) in 371 individuals from Southern Norway. 27 aSTRs and 94 iSNPs were previously analysed with the ForenSeq™ DNA Signature Prep Kit (Verogen). The number of alleles with frequencies less than 0.05 for sequenced-based alleles was 4.6 times higher than for length-based alleles. Consistent with previous studies, it was observed that sequence-based data (both with and without flanks) exhibited higher allele diversity compared to length-based (LB) data; random match probabilities were lower for SB alleles confirming their advantage to discriminate between individuals. Two alleles in markers D22S1045 and Penta D were observed with SNPs in the 3´ flanking region, which have not been reported before. Also, a novel SNP with a minor allele frequency (MAF) of 0.001, was found in marker TH01. The impact of the sample size on minor allele frequency (MAF) values was studied in 88 iSNPs from Southern Norway (n = 371). The findings were then compared to a larger Norwegian population dataset (n = 15,769). The results showed that the smaller Southern Norway dataset provided similar results, and it was a representative sample. Population structure was analyzed for regions within Southern Norway; FST estimates for aSTR and iSNPs did not indicate any genetic structure. Finally, we investigated the genetic differences between Southern Norway and two other populations: Northern Norway and Denmark. Allele frequencies between these populations were compared, and we found no significant frequency differences (p-values > 0.0001). We also calculated the pairwise FST values per marker and comparisons between Southern and Northern Norway showed small differences. In contrast, the comparisons between Southern Norway and Denmark showed higher FST values for some markers, possibly driven by distinct alleles that were present in only one of the populations. In summary, we propose that allele frequencies from each population considered in this study could be used interchangeably to calculate genotype probabilities.
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Affiliation(s)
- Maria Martin Agudo
- Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway; Department of Forensic Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Håvard Aanes
- Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway
| | - Michel Albert
- Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway
| | - Kirstin Janssen
- Centre for Forensic Genetics, UiT The Arctic University of Norway, Norway
| | - Peter Gill
- Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway; Department of Forensic Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Øyvind Bleka
- Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway.
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van Lierop S, Ramos D, Sjerps M, Ypma R. An overview of log likelihood ratio cost in forensic science - Where is it used and what values can we expect? Forensic Sci Int Synerg 2024; 8:100466. [PMID: 38645839 PMCID: PMC11031735 DOI: 10.1016/j.fsisyn.2024.100466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 03/07/2024] [Accepted: 03/29/2024] [Indexed: 04/23/2024]
Abstract
There is increasing support for reporting evidential strength as a likelihood ratio (LR) and increasing interest in (semi-)automated LR systems. The log-likelihood ratio cost (Cllr) is a popular metric for such systems, penalizing misleading LRs further from 1 more. Cllr = 0 indicates perfection while Cllr = 1 indicates an uninformative system. However, beyond this, what constitutes a "good" Cllr is unclear. Aiming to provide handles on when a Cllr is "good", we studied 136 publications on (semi-)automated LR systems. Results show Cllr use heavily depends on the field, e.g., being absent in DNA analysis. Despite more publications on automated LR systems over time, the proportion reporting Cllr remains stable. Noticeably, Cllr values lack clear patterns and depend on the area, analysis and dataset. As LR systems become more prevalent, comparing them becomes crucial. This is hampered by different studies using different datasets. We advocate using public benchmark datasets to advance the field.
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Affiliation(s)
- Stijn van Lierop
- Netherlands Forensic Institute, Laan van Ypenburg 6, The Hague, 2497GB, the Netherlands
| | - Daniel Ramos
- AUDIAS Lab, Universidad Autonoma de Madrid, Escuela Politécnica Superior, Calle Francisco Tomàs y Valiente 11, 28049, Madrid, Spain
| | - Marjan Sjerps
- Netherlands Forensic Institute, Laan van Ypenburg 6, The Hague, 2497GB, the Netherlands
- University of Amsterdam, KdVI, PO Box 94248, Amsterdam, 1090 GE, the Netherlands
| | - Rolf Ypma
- Netherlands Forensic Institute, Laan van Ypenburg 6, The Hague, 2497GB, the Netherlands
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11
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Hahn M, Courts C, Eckert M, Fimmers R, Grethe S, Kranz S, Leuker C, Oppelt C, Razbin S, Templin M, Vennemann M, Zimmermann P, Anslinger K. Authors' response. J Forensic Sci 2024; 69:736-738. [PMID: 37986631 DOI: 10.1111/1556-4029.15426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/22/2023]
Affiliation(s)
- Meinhard Hahn
- State Criminal Police Office of Lower Saxony, Hanover, Germany
| | - Cornelius Courts
- Institute of Legal Medicine, University Hospital of Cologne, Cologne, Germany
| | | | - Rolf Fimmers
- Institute for Forensic Statistics and Quality Assurance, St. Augustin, Germany
| | - Stefanie Grethe
- State Criminal Police Office of Rhineland-Palatinate, Mainz, Germany
| | | | - Christoph Leuker
- State Criminal Police Office of North Rhine-Westphalia, Dusseldorf, Germany
| | - Claus Oppelt
- State Criminal Police Office of Lower Saxony, Hanover, Germany
| | - Sven Razbin
- State Criminal Police Office of Bremen, Bremen, Germany
| | - Michael Templin
- State Criminal Police Office of Lower Saxony, Hanover, Germany
| | | | - Peter Zimmermann
- State Criminal Police Office of Baden-Wuerttemberg, Stuttgart, Germany
| | - Katja Anslinger
- Institute of Legal Medicine, Ludwig Maximilian University, Munich, Germany
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12
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Reither JB, Taylor D, Szkuta B, van Oorschot RAH. Determining the number and size of background samples derived from an area adjacent to the target sample that provide the greatest support for a POI in a target sample. Forensic Sci Int Genet 2024; 68:102977. [PMID: 38000160 DOI: 10.1016/j.fsigen.2023.102977] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 10/10/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023]
Abstract
When sampling an item or surface for DNA originating from an action of interest, one is likely to collect DNA unrelated to the action of interest (background DNA). While adding to the complexity of a generated DNA profile, background DNA has been shown to aid in resolving the genotypes of contributors in a targeted sample, and where references of donors to the background DNA are not available, strengthen the LR supporting a person of interest contributing to the targeted sample. This is possible thanks to advances in probabilistic genotyping, where forensic labs are able to deconvolute complex DNA profiles to obtain lists of genotypes and their associated weights. Coupled with DBLR™, one can then compare multiple evidentiary profiles to each other to determine the contribution of common, but unknown, contributors. Here, we consider factors associated with taking background samples and whether one should collect multiple background samples that all relate to a single target sample, or if one should collect larger background samples rather than smaller samples. Background samples consisted of DNA accumulated on the items primarily by one or both occupants of a single household, while targeted samples were generated from touch deposits, or saliva deposits that had been left to air dry. Samples were collected from areas of various sizes, consisting of only the background, the target and the background directly beneath it, and the target and additional surrounding background. A broad range of DNA quantities were recovered, with larger background samples (400 cm2) yielding significantly more DNA than smaller background samples (30 cm2). Significant differences in DNA quantities between target samples were not observed. Generated DNA profiles were interpreted using STRmix™ and DBLR™, and where there was support for a common donor between the background and target sample, pairwise comparisons were performed to observe the effect on the LR supporting the target DNA donor contributing to the targeted sample when conditioning on one (or two) common donor between the targeted sample and 1-8 background samples. Multiple background samples gave significantly higher LRs compared to a single background sample, the larger sampled background area resulted in larger LR gains than the smaller areas, and four or more background samples reduced LR variability considerably. Here we provide recommendations for the minimum and ideal number of additional background samples that should be collected, and that several smaller samples may be more beneficial than a single larger sample.
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Affiliation(s)
- Jack B Reither
- School of Life and Environmental Sciences, Deakin University, Geelong, VIC 3220, Australia; Office of the Chief Forensic Scientist, Victoria Police Forensic Services Department, Macleod, VIC 3085, Australia.
| | - Duncan Taylor
- College of Science and Engineering, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia; Forensic Science SA, GPO Box 2790, Adelaide, SA 5001, Australia
| | - Bianca Szkuta
- School of Life and Environmental Sciences, Deakin University, Geelong, VIC 3220, Australia
| | - Roland A H van Oorschot
- Office of the Chief Forensic Scientist, Victoria Police Forensic Services Department, Macleod, VIC 3085, Australia; School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC 3086, Australia
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13
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Huffman K, Ballantyne J. Single cell genomics applications in forensic science: Current state and future directions. iScience 2023; 26:107961. [PMID: 37876804 PMCID: PMC10590970 DOI: 10.1016/j.isci.2023.107961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2023] Open
Abstract
Standard methods of mixture analysis involve subjecting a dried crime scene sample to a "bulk" DNA extraction method such that the resulting isolate compromises a homogenized DNA mixture from the individual donors. If, however, instead of bulk DNA extraction, a sufficient number of individual cells from the mixed stain are subsampled prior to genetic analysis then it should be possible to recover highly probative single source, non-mixed scDNA profiles from each of the donors. This approach can detect low DNA level minor donors to a mixture that otherwise would not be identified using standard methods and can also resolve rare mixtures comprising first degree relatives and thereby also prevent the false inclusion of non-donor relatives. This literature landscape review and associated commentary reports on the history and increasing interest in current and potential future applications of scDNA in forensic genomics, and critically evaluates opportunities and impediments to further progress.
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Affiliation(s)
- Kaitlin Huffman
- Graduate Program in Chemistry, Department of Chemistry, University of Central Florida, PO Box 162366, Orlando, FL 32816-2366, USA
| | - Jack Ballantyne
- National Center for Forensic Science, PO Box 162367, Orlando, FL 32816-2367, USA
- Department of Chemistry, University of Central Florida, PO Box 162366, Orlando, FL 32816-2366, USA
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14
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Fantinato C, Fonneløp AE, Bleka Ø, Vigeland MD, Gill P. The invisible witness: air and dust as DNA evidence of human occupancy in indoor premises. Sci Rep 2023; 13:19059. [PMID: 37925517 PMCID: PMC10625553 DOI: 10.1038/s41598-023-46151-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/28/2023] [Indexed: 11/06/2023] Open
Abstract
Humans constantly shed deoxyribonucleic acid (DNA) into the surrounding environment. This DNA may either remain suspended in the air or it settles onto surfaces as indoor dust. In this study, we explored the potential use of human DNA recovered from air and dust to investigate crimes where there are no visible traces available-for example, from a recently vacated drugs factory where multiple workers had been present. Samples were collected from three indoor locations (offices, meeting rooms and laboratories) characterized by different occupancy types and cleaning regimes. The resultant DNA profiles were compared with the reference profiles of 55 occupants of the premises. Our findings showed that indoor dust samples are rich sources of DNA and provide an historical record of occupants within the specific locality of collection. Detectable levels of DNA were also observed in air and dust samples from ultra-clean forensic laboratories which can potentially contaminate casework samples. We provide a Bayesian statistical model to estimate the minimum number of dust samples needed to detect all inhabitants of a location. The results of this study suggest that air and dust could become novel sources of DNA evidence to identify current and past occupants of a crime scene.
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Affiliation(s)
- Chiara Fantinato
- Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway.
- Department of Forensic Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Ane Elida Fonneløp
- Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo, Norway
| | - Øyvind Bleka
- Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway
| | | | - Peter Gill
- Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway
- Department of Forensic Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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15
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Diepenbroek M, Bayer B, Anslinger K. Phenotype predictions of two-person mixture using single cell analysis. Forensic Sci Int Genet 2023; 67:102938. [PMID: 37832204 DOI: 10.1016/j.fsigen.2023.102938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/19/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023]
Abstract
Over a decade after the publication of the first forensic DNA phenotyping (FDP) studies, DNA-based appearance predictions are now becoming a reality in routine crime scene investigations. The significant number of publications dedicated to the subject of FDP clearly demonstrates a sustained interest and a strong need for further method development. However, the implementation of FDP in routine work still encounters obstacles, and one of these challenges is making phenotype predictions from DNA mixtures. In this study, we examined single-cell sequencing as a potential tool to enable reliable phenotyping of contributors within mixtures. Two mock mixtures, each containing two contributors with similar and different physical appearances, were analyzed using two different workflows. In the first workflow, the mixtures were sequenced using the Ion AmpliSeq™ PhenoTrivium Panel, which includes 41 HIrisPlex-S (HPS) markers. Subsequently, the genotypes were analyzed using the HPS Deconvolution Tool to predict the phenotypes of both contributors. The second workflow involved the introduction of single-cell separation and collection using the DEPArray™ PLUS System. Two different PhenoTrivium amplification protocols were tested, and the phenotype predictions from single cells were compared with the results obtained using the HPS Tool. Our results suggest that the approach presented here allows for the obtainment of nearly complete HIrisPlex-S profiles with accurate genotypes and reliable phenotype predictions from single cells. This method proves successful in deconvoluting mixtures submitted to forensic DNA phenotyping.
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Affiliation(s)
- Marta Diepenbroek
- Institute of Legal Medicine LMU Munich, Nussbaumstrasse 26, 80336 Munich, Germany.
| | - Birgit Bayer
- Institute of Legal Medicine LMU Munich, Nussbaumstrasse 26, 80336 Munich, Germany
| | - Katja Anslinger
- Institute of Legal Medicine LMU Munich, Nussbaumstrasse 26, 80336 Munich, Germany
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16
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Kruijver M, Kelly H, Taylor D, Buckleton J. Addressing uncertain assumptions in DNA evidence evaluation. Forensic Sci Int Genet 2023; 66:102913. [PMID: 37453205 DOI: 10.1016/j.fsigen.2023.102913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 07/03/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023]
Abstract
Evidential value of DNA mixtures is typically expressed by a likelihood ratio. However, selecting appropriate propositions can be contentious, because assumptions may need to be made around, for example, the contribution of a complainant's profile, or relatedness between contributors. A choice made one way or another disregards any uncertainty that may be present about such an assumption. To address this, a complex proposition that considers multiple sub-propositions with different assumptions may be more appropriate. While the use of complex propositions has been advocated in the literature, the uptake in casework has been limited. We provide a mathematical framework for evaluating DNA evidence given complex propositions and discuss its implementation in the DBLR™ software. The software simultaneously handles multiple mixed samples, reference profiles and relationships as described by a pedigree, which unlocks a variety of applications. We provide several examples to illustrate how complex propositions can efficiently evaluate DNA evidence. The addition of this feature to DBLR™ provides a tool to approach the long-accepted, but often impractical suggestion that propositions should be exhaustive within a case context.
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Affiliation(s)
- Maarten Kruijver
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142 New Zealand.
| | - Hannah Kelly
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142 New Zealand
| | - Duncan Taylor
- Forensic Science SA, GPO Box 2790, Adelaide, SA 5001, Australia; College of Science and Engineering, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia
| | - John Buckleton
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142 New Zealand; University of Auckland, Department of Statistics, Private Bag 92019, Auckland 1142, New Zealand
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17
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Reither JB, Taylor D, Szkuta B, van Oorschot RA. Exploring how the LR of a POI in a target sample is impacted by awareness of the profile of the background derived from an area adjacent to the target sample. Forensic Sci Int Genet 2023; 65:102868. [DOI: 10.1016/j.fsigen.2023.102868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 03/22/2023] [Accepted: 03/24/2023] [Indexed: 03/30/2023]
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18
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Taylor D, Abarno D. A lights-out forensic DNA analysis workflow for no-suspect crime. Forensic Sci Int Genet 2023; 66:102907. [PMID: 37379740 DOI: 10.1016/j.fsigen.2023.102907] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/13/2023] [Accepted: 06/15/2023] [Indexed: 06/30/2023]
Abstract
An automated system of DNA profile processing (termed a 'lights-out' workflow) was trialled for no-suspect cases over a three-month period at Forensic Science SA (FSSA). The lights-out workflow utilised automated DNA profile reading using the neural network reading feature in FaSTR™ DNA with no analytical threshold. The profile information from FaSTR™ DNA was then processed in STRmix™ using a top-down analysis and automatically compared to a de-identified South Australian searchable DNA database. Computer scripts were used to generate link reports and upload reports and these were compared to the links and uploads that were obtained for the cases during their standard processing within the laboratory. The results of the lights-out workflow was an increase in both uploads and links compared to the standard workflow, with minimal adventitious links or erroneous uploads. Overall, the proof-of-concept study shows the potential for using automated DNA profile reading and top-down analysis to improve workflow efficiency in a no-suspect workflow.
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Affiliation(s)
- Duncan Taylor
- Forensic Science SA, Adelaide, Australia; Flinders University, Adelaide, Australia.
| | - Damien Abarno
- Forensic Science SA, Adelaide, Australia; Flinders University, Adelaide, Australia
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19
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Hoogenboom J, Sijen T, Benschop C. ProbRank: An efficient DNA database search method for complex mixtures per a quantitative likelihood ratio model. Forensic Sci Int Genet 2023; 65:102884. [PMID: 37150077 DOI: 10.1016/j.fsigen.2023.102884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/04/2023] [Accepted: 04/27/2023] [Indexed: 05/09/2023]
Abstract
Searching a DNA Database with a DNA profile from an evidentiary trace can provide investigative leads in a forensic case. Various searching approaches exist such as conventional methods based on matching alleles or more advanced methods computing likelihood ratios (LR) while considering drop-in and drop-out. Here we examine the potential of using a quantitative LR model (EuroForMix model incorporated in ProbRank method) that takes peak heights into account in comparison to a qualitative LR model (LRmix model implemented in SmartRank method). Both methods present DNA database candidates in order of decreasing LR. Especially regarding minor contributors in complex mixtures, the method using the quantitative model outperforms the method using the qualitative model in terms of sensitivity and specificity as more true donors and less adventitious matches are retrieved. ProbRank is to be implemented in DNAStatistX and is sufficiently fast for daily use.
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Affiliation(s)
- Jerry Hoogenboom
- Division of Biological Traces, Netherlands Forensic Institute, The Hague, the Netherlands.
| | - Titia Sijen
- Division of Biological Traces, Netherlands Forensic Institute, The Hague, the Netherlands; Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands
| | - Corina Benschop
- Division of Biological Traces, Netherlands Forensic Institute, The Hague, the Netherlands
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20
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Damour G, Mauffrey F, Hall D. Identification and characterization of novel DIP-STRs from whole-genome sequencing data. Forensic Sci Int Genet 2023; 64:102849. [PMID: 36827792 DOI: 10.1016/j.fsigen.2023.102849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/13/2023] [Accepted: 02/16/2023] [Indexed: 02/19/2023]
Abstract
In an attempt to enhance forensic DNA mixture deconvolution several alternative DNA typing approaches have been developed. Among these, DIP-STR compound markers can resolve extremely unbalanced two-source DNA mixtures of same-or-opposite sex donors, up to a 1:1000 minor:major DNA ratio. A forensic set of 10 markers was validated for casework and a larger set of 23 DIP-STRs has proven suitable to biogeographic ancestry inference and for prenatal paternity testing. Yet, to promote the widespread use of this original approach, more markers and multiplex panels need to be developed. To this end, here we describe an extended set of forensic DIP-STRs identified using currently available whole-genome sequencing datasets. Complete lists of Indels and STRs were obtained from reported frequencies of genetic variants of 76,156 genomes. About 3000 identified DIP-STRs candidates were shorter than 200 bp and 500 showed high haplotype variability estimated using the genotypes of individuals homozygous for the DIP or the STR. Here, we present 23 additional DIP-STRs validated for sensitivity, specificity and Swiss population variability. Finally, a set of 30 markers comprising seven previously validated ones is proposed for the prospective development of a forensic DIP-STR multiplex panel.
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Affiliation(s)
- Géraldine Damour
- Unité de Génétique Forensique, Centre Universitaire Romand de Médecine Légale, Centre Hospitalier Universitaire Vaudois et Université de Lausanne, Ch. de Vulliette 4, 1000 Lausanne, Switzerland
| | - Florian Mauffrey
- Unité de Génétique Forensique, Centre Universitaire Romand de Médecine Légale, Centre Hospitalier Universitaire Vaudois et Université de Lausanne, Ch. de Vulliette 4, 1000 Lausanne, Switzerland
| | - Diana Hall
- Unité de Génétique Forensique, Centre Universitaire Romand de Médecine Légale, Centre Hospitalier Universitaire Vaudois et Université de Lausanne, Ch. de Vulliette 4, 1000 Lausanne, Switzerland.
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21
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Einsatz vollkontinuierlicher Modelle zur biostatistischen Bewertung forensischer DNA-analytischer Befunde. Rechtsmedizin (Berl) 2023. [DOI: 10.1007/s00194-022-00600-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
ZusammenfassungDie biostatistische Bewertung DNA-analytischer Befunde unterstützt Gerichte bei der Einschätzung des Beweiswertes einer Spur. In der Praxis werden dabei zunehmend Spuren mit minimaler DNA-Menge und möglichen „Drop-in“- und „Drop-out“-Ereignissen sowie komplexe Mischspuren analysiert. Solche Spuren sind mit einer klassischen „binären“ Berechnung biostatistisch häufig nicht oder nur eingeschränkt bewertbar.Die Entwicklung vollkontinuierlicher Modelle (VKM) macht eine Vielzahl dieser bisher nicht berechenbaren Spuren einer biostatistischen Bewertung zugänglich. Dabei werden nahezu sämtliche verfügbaren Informationen einer DNA-Spur in die Berechnung einbezogen. Während diese probabilistischen Verfahren international bereits vielfach zum Einsatz kommen, liegen hierzu im deutschsprachigen Raum nur wenige Erfahrungen vor.Um Funktionsweise, Möglichkeiten und Grenzen von VKM-Berechnungen zu erfassen, wurden Mischspuren bekannter Zusammensetzung mit 4 aktuell verfügbaren VKM-Programmen vergleichend analysiert. Bei der Auswertung wurden zentrale Aspekte betrachtet, wie beispielsweise die Konkordanz von Berechnungsergebnissen, der Einfluss von Drop-in- und Drop-out-Ereignissen auf die berechneten vollkontinuierlichen LR-Werte (LRfc) sowie die Ableitung recherchefähiger DNA-Profile mithilfe wahrscheinlichkeitsbasierter Prognosen (Deconvolution).Die im Rahmen dieser Arbeit gewonnenen Erfahrungen bilden, zusammen mit weiteren bereits international publizierten Studien, eine Basis für Empfehlungen zum Einsatz von VKM-basierter Software bei der biostatistischen Bewertung DNA-analytischer Befunde.
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22
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Duijs FE, Meijers E, Kokshoorn B, Sijen T. Comparison of genotyping and weight of evidence results when applying different genotyping strategies on samples from a DNA transfer experiment. Int J Legal Med 2023; 137:47-56. [PMID: 36416964 DOI: 10.1007/s00414-022-02918-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 11/15/2022] [Indexed: 11/24/2022]
Abstract
In this study, we assessed to what extent data on the subject of TPPR (transfer, persistence, prevalence, recovery) that are obtained through an older STR typing kit can be used in an activity-level evaluation for a case profiled with a more modern STR kit. Newer kits generally hold more loci and may show higher sensitivity especially when reduced reaction volumes are used, and this could increase the evidential value at the source level. On the other hand, the increased genotyping information may invoke a higher number of contributors in the weight of evidence calculations, which could affect the evidential values as well. An activity scenario well explored in earlier studies [1,2] was redone using volunteers with known DNA profiles. DNA extracts were analyzed with three different approaches, namely using the optimal DNA input for (1) an older and (2) a newer STR typing system, and (3) using a standard, volume-based input combined with replicate PCR analysis with only the newer STR kit. The genotyping results were analyzed for various aspects such as percentage detected alleles and relative peak height contribution for background and the contributors known to be involved in the activity. Next, source-level LRs were calculated and the same trends were observed with regard to inclusionary and exclusionary LRs for persons who had or had not been in direct contact with the sampled areas. We subsequently assessed the impact on the outcome of the activity-level evaluation in an exemplary case by applying the assigned probabilities to a Bayesian network. We infer that data from different STR kits can be combined in the activity-level evaluations.
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Affiliation(s)
- Francisca E Duijs
- Division of Biological Traces, Netherlands Forensic Institute, The Hague, The Netherlands
| | - Erin Meijers
- Division of Biological Traces, Netherlands Forensic Institute, The Hague, The Netherlands
| | - Bas Kokshoorn
- Division of Biological Traces, Netherlands Forensic Institute, The Hague, The Netherlands.,Faculty of Technology, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands
| | - Titia Sijen
- Division of Biological Traces, Netherlands Forensic Institute, The Hague, The Netherlands. .,University of Amsterdam, Swammerdam Institute for Life Sciences, Amsterdam, The Netherlands.
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23
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Butler JM. Recent advances in forensic biology and forensic DNA typing: INTERPOL review 2019-2022. Forensic Sci Int Synerg 2022; 6:100311. [PMID: 36618991 PMCID: PMC9813539 DOI: 10.1016/j.fsisyn.2022.100311] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
This review paper covers the forensic-relevant literature in biological sciences from 2019 to 2022 as a part of the 20th INTERPOL International Forensic Science Managers Symposium. Topics reviewed include rapid DNA testing, using law enforcement DNA databases plus investigative genetic genealogy DNA databases along with privacy/ethical issues, forensic biology and body fluid identification, DNA extraction and typing methods, mixture interpretation involving probabilistic genotyping software (PGS), DNA transfer and activity-level evaluations, next-generation sequencing (NGS), DNA phenotyping, lineage markers (Y-chromosome, mitochondrial DNA, X-chromosome), new markers and approaches (microhaplotypes, proteomics, and microbial DNA), kinship analysis and human identification with disaster victim identification (DVI), and non-human DNA testing including wildlife forensics. Available books and review articles are summarized as well as 70 guidance documents to assist in quality control that were published in the past three years by various groups within the United States and around the world.
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Affiliation(s)
- John M. Butler
- National Institute of Standards and Technology, Special Programs Office, 100 Bureau Drive, Mail Stop 4701, Gaithersburg, MD, USA
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24
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Gemeinsame Empfehlungen der Projektgruppe „Biostatistische DNA-Berechnungen“ und der Spurenkommission zur biostatistischen Bewertung forensischer DNA-analytischer Befunde mit vollkontinuierlichen Modellen (VKM). Rechtsmedizin (Berl) 2022. [DOI: 10.1007/s00194-022-00599-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
ZusammenfassungDie biostatistische Bewertung DNA-analytischer Befunde unterstützt Gerichte bei der Einschätzung des Beweiswertes hinsichtlich einer möglichen Spurenbeteiligung durch eine zu betrachtende Person (engl. „Person Of Interest“; POI). Um die Vergleichbarkeit derartiger Berechnungen auf Grundlage etablierter wissenschaftlicher Standards zu gewährleisten, wurden bereits in der Vergangenheit entsprechende Empfehlungen im nationalen Konsens formuliert.Mit Einführung sog. vollkontinuierlicher Modelle (VKM) für die probabilistische Genotypisierung, die u. a. die Signalintensitäten eines Elektropherogramms berücksichtigen, wurde eine Ergänzung zu den damaligen Empfehlungen erforderlich. VKM erlauben eine biostatistische Bewertung von Spuren mit möglichen Drop-in- und Drop-out-Ereignissen und wahrscheinlichkeitsbasierte Prognosen der zu einer Mischspur beitragenden Genotypen („Deconvolution“).Die vorliegende Veröffentlichung enthält Empfehlungen zum Einsatz VKM-basierter Software und zur Berichterstattung vollkontinuierlicher LR-Werte (engl. „Fully Continuous Likelihood Ratios“; LRfc). Sie empfiehlt bei schwierig zu interpretierenden Befunden eine VKM-Berechnung zur Bewertung einer Spurenlegerschaft. Die VKM-Berechnung ersetzt die bisher in Ausnahmefällen als hinnehmbar erachtete Vorgehensweise einer binären Berechnung unter Ausklammern einzelner Merkmalssysteme. Der Einsatz von VKM erfordert eine umfassende Anwenderschulung sowie eine Validierung und Verifizierung gemäß den Vorgaben der Programmanbieter. Mit der Empfehlung von LRfc-Schwellenwerten soll eine sichere, vergleichbare Anwendung von VKM gewährleistet werden.
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25
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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. [PMID: 35853341 DOI: 10.1016/j.fsigen.2022.102744] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 06/14/2022] [Accepted: 06/28/2022] [Indexed: 11/15/2022]
Abstract
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 default settings (including default chain lengths) are used, as large as a 10-fold changes in inferred log-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 the use of 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 for the inference process. In the benchmark mixtures, on consumer-grade hardware, the runtime is less than 7 min for 3 contributors, less than 35 min for 4 contributors, and less than an hour for 5 contributors with one known contributor.
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Affiliation(s)
- Mateusz Susik
- Biotype GmbH, Dresden, 01109, Germany; Technische Universität Dresden, Faculty of Computer Science, Dresden, 01187, Germany.
| | | | - Ivo F Sbalzarini
- Technische Universität Dresden, Faculty of Computer Science, Dresden, 01187, Germany; Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, 01307, Germany; Center for Systems Biology Dresden, Dresden, 01307, Germany
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26
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Kruijver M, Bright JA. A tool for simulating single source and mixed DNA profiles. Forensic Sci Int Genet 2022; 60:102746. [PMID: 35843122 DOI: 10.1016/j.fsigen.2022.102746] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/22/2022] [Accepted: 07/05/2022] [Indexed: 11/04/2022]
Abstract
Simulation studies play an important role in the study of probabilistic genotyping systems, as a low cost and fast alternative to in vitro studies. With ongoing calls for further study of the behaviour of probabilistic genotyping systems, there is a continuous need for such studies. In most cases, researchers use simplified models, for example ignoring complexities such as peak height variability due to lack of availability of advanced tools. We fill this void and describe a tool that can simulate DNA profiles in silico for the validation and investigation of probabilistic genotyping software. Contributor genotypes are simulated by randomly sampling alleles from selected allele frequencies. Some or all contributors may be related to a pedigree and the genotypes of non-founders are obtained by random gene dropping. The number of contributors per profile, and ranges for parameters such as DNA template amount and degradation parameters can be configured. Peak height variability is modelled using a lognormal distribution or a gamma distribution. Profile behaviour of simulated profiles is shown to be broadly similar to laboratory generated profiles though the latter shows more variation. Simulation studies do not remove the need for experimental data. The tool has been made available as an R-package named simDNAmixtures.
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Woerner AE, Crysup B, Hewitt FC, Gardner MW, Freitas MA, Budowle B. Techniques for estimating genetically variable peptides and semi-continuous likelihoods from massively parallel sequencing data. Forensic Sci Int Genet 2022; 59:102719. [DOI: 10.1016/j.fsigen.2022.102719] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/25/2022] [Accepted: 05/01/2022] [Indexed: 11/25/2022]
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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. [DOI: 10.1016/j.fsigen.2022.102715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/29/2022] [Accepted: 04/21/2022] [Indexed: 11/22/2022]
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Post hoc deconvolution of human mitochondrial DNA mixtures by EMMA 2 using fine-tuned Phylotree nomenclature. Comput Struct Biotechnol J 2022; 20:3630-3638. [PMID: 35860401 PMCID: PMC9283771 DOI: 10.1016/j.csbj.2022.06.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/24/2022] [Accepted: 06/25/2022] [Indexed: 11/23/2022] Open
Abstract
MtDNA mixtures are observed frequently and difficult to deconvolute. Most previous methods require raw data or quantitative information. EMMA 2 produces valid splittings from consensus sequences of any sequencing technology. EMMA 2 can deconvolute 2 and 3 person mixtures in a fast and traceable way.
In this paper we present a new algorithm for splitting (partial) human mitogenomes into components with high similarity to haplogroup motifs of Phylotree. The algorithm reads a (partial) mitogenome coded by the differences to the reference (rCRS) and outputs the estimated haplogroups of the putative components. The algorithm requires no special information on the raw data of the sequencing process and is therefore suited for the post hoc analysis of mixtures of any sequencing technology. The software EMMA 2 implementing the algorithm will be made available via the EMPOP (https://empop.online) database and extends the nine years old software EMMA for haplogrouping single mitogenomes to mixtures with at most three components.
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Benschop CCG, Slagter M, Nagel JHA, Hovers P, Tuinman S, Duijs FE, Grol LJW, Jegers M, Berghout A, van der Zwan AW, Ypma RJF, de Jong J, Kneppers ALJ. Development and validation of a fast and automated DNA identification line. Forensic Sci Int Genet 2022; 60:102738. [PMID: 35691141 DOI: 10.1016/j.fsigen.2022.102738] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/31/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022]
Abstract
The importance of DNA evidence for gaining investigative leads demands a fast workflow for forensic DNA profiling performed in large volumes. Therefore, we developed software solutions for automated DNA profile analysis, contamination check, major donor inference, DNA database (DDB) comparison and reporting of the conclusions. This represents the Fast DNA IDentification Line (FIDL) and this study describes its development, validation and implementation in criminal casework at the authors' institute. This first implementation regards single donor profiles and major contributors to mixtures. The validation included testing of the software components on their own and examination of the performance of different DDB search strategies. Furthermore, end-to-end testing was performed under three conditions: (1) testing of scenarios that can occur in DNA casework practice, (2) tests using three months of previous casework data, and (3) testing in a casework production environment in parallel to standard casework practices. The same DNA database candidates were retrieved by this automated line as by the manual workflow. The data flow was correct, results were reproducible and robust, results requiring manual analysis were correctly flagged, and reported results were as expected. Overall, we found FIDL valid for use in casework practice in our institute. The results from FIDL are automatically reported within three working days from receiving the trace sample. This includes the time needed for registration of the case, DNA extraction, quantification, polymerase chain reaction and capillary electrophoresis. FIDL itself takes less than two hours from intake of the raw CE data to reporting. Reported conclusions are one of five options: (1) candidate retrieved from DDB, (2) no candidate retrieved from DDB, (3) high evidential value with regards to reference within the case, (4) results require examination of expert, or (5) insufficient amount of DNA obtained to generate a DNA profile. In our current process, the automated report is sent within three working days and a complete report, with confirmation of the FIDL results, and signed by a reporting officer is sent at a later time. The signed report may include additional analyses regarding e.g. minor contributors. The automated report with first case results is quickly available to the police enabling them to act upon the DNA results prior to receiving the full DNA report. This line enables a uniform and efficient manner of handling large numbers of traces and cases and provides high value investigative leads in the early stages of the investigation.
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Affiliation(s)
- Corina C G Benschop
- Netherlands Forensic Institute, Division of Biological Traces, Laan van Ypenburg 6, 2497GB The Hague, the Netherlands.
| | - Martin Slagter
- Netherlands Forensic Institute, Division of Biological Traces, Laan van Ypenburg 6, 2497GB The Hague, the Netherlands.
| | - Jord H A Nagel
- Netherlands Forensic Institute, Division of Biological Traces, Laan van Ypenburg 6, 2497GB The Hague, the Netherlands.
| | - Pauline Hovers
- Netherlands Forensic Institute, Division of Biological Traces, Laan van Ypenburg 6, 2497GB The Hague, the Netherlands.
| | - Sietske Tuinman
- Netherlands Forensic Institute, Division of Biological Traces, Laan van Ypenburg 6, 2497GB The Hague, the Netherlands.
| | - Francisca E Duijs
- Netherlands Forensic Institute, Division of Biological Traces, Laan van Ypenburg 6, 2497GB The Hague, the Netherlands.
| | - Laurens J W Grol
- Netherlands Forensic Institute, Division of Biological Traces, Laan van Ypenburg 6, 2497GB The Hague, the Netherlands.
| | - Mariëlle Jegers
- Netherlands Forensic Institute, Division of Biological Traces, Laan van Ypenburg 6, 2497GB The Hague, the Netherlands.
| | - Abigayle Berghout
- Netherlands Forensic Institute, Division of Biological Traces, Laan van Ypenburg 6, 2497GB The Hague, the Netherlands.
| | - Anne-Wil van der Zwan
- Netherlands Forensic Institute, Division of Biological Traces, Laan van Ypenburg 6, 2497GB The Hague, the Netherlands.
| | - Rolf J F Ypma
- Netherlands Forensic Institute, Division of Digital and Biometric Traces, Laan van Ypenburg 6, 2497GB The Hague, the Netherlands.
| | - Jeroen de Jong
- Netherlands Forensic Institute, Division of Digital and Biometric Traces, Laan van Ypenburg 6, 2497GB The Hague, the Netherlands.
| | - Alexander L J Kneppers
- Netherlands Forensic Institute, Division of Biological Traces, Laan van Ypenburg 6, 2497GB The Hague, the Netherlands.
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Yin Y, Zhang P, Xing Y. A New Computational Deconvolution Algorithm for the Analysis of Forensic DNA Mixtures with SNP Markers. Genes (Basel) 2022; 13:genes13050884. [PMID: 35627269 PMCID: PMC9141285 DOI: 10.3390/genes13050884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/06/2022] [Accepted: 05/13/2022] [Indexed: 02/01/2023] Open
Abstract
Single nucleotide polymorphisms (SNPs) support robust analysis on degraded DNA samples. However, the development of a systematic method to interpret the profiles derived from the mixtures is less studied, and it remains a challenge due to the bi-allelic nature of SNP markers. To improve the discriminating power of SNPs, this study explored bioinformatic strategies to analyze mixtures. Then, computer-generated mixtures were produced using real-world massively parallel sequencing (MPS) data from the single samples processed with the Precision ID Identity Panel. Moreover, the values of the frequency of major allele reads (FMAR) were calculated and applied as key parameters to deconvolve the two-person mixtures and estimate mixture ratios. Four custom R language scripts (three for autosomes and one for Y chromosome) were designed with the K-means clustering method as a core algorithm. Finally, the method was validated with real-world mixtures. The results indicated that the deconvolution accuracy for evenly balanced mixtures was 100% or close to 100%, which was the same as the deconvolution accuracy of inferring the genotypes of the major contributor of unevenly balanced mixtures. Meanwhile, the accuracy of inferring the genotypes of the minor contributor decreased as its proportion in the mixture decreased. Moreover, the estimated mixture ratio was almost equal to the actual ratio between 1:1 and 1:6. The method proposed in this study provides a new paradigm for mixture interpretation, especially for inferring contributor profiles of evenly balanced mixtures and the major contributor profile of unevenly balanced mixtures.
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Affiliation(s)
- Yu Yin
- Department of Forensic Medicine, Chongqing Medical University, #1 Yixueyuan Road, Chongqing 400016, China; (Y.Y.); (P.Z.)
| | - Peng Zhang
- Department of Forensic Medicine, Chongqing Medical University, #1 Yixueyuan Road, Chongqing 400016, China; (Y.Y.); (P.Z.)
- Public Security Bureau of Chongqing Nanchan District, #11 Jinshan Avenue, Nanchang District, Chongqing 408499, China
| | - Yu Xing
- Department of Forensic Medicine, Chongqing Medical University, #1 Yixueyuan Road, Chongqing 400016, China; (Y.Y.); (P.Z.)
- Correspondence:
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Who Packed the Drugs? Application of Bayesian Networks to Address Questions of DNA Transfer, Persistence, and Recovery from Plastic Bags and Tape. Genes (Basel) 2021; 13:genes13010018. [PMID: 35052357 PMCID: PMC8774669 DOI: 10.3390/genes13010018] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 12/16/2021] [Accepted: 12/17/2021] [Indexed: 11/23/2022] Open
Abstract
When DNA from a suspect is detected in a sample collected at a crime scene, there can be alternative explanations about the activity that may have led to the transfer, persistence and recovery of his/her DNA. Previous studies have shown that DNA can be indirectly transferred via intermediate surfaces and that DNA on a previously used object can persist after subsequent use of another individual. In addition, it has been shown that a person’s shedder status may influence transfer, persistence, prevalence, and recovery of DNA. In this study we have investigated transfer persistence and recovery on zip-lock bags and tape, which are commonly encountered in drug cases and how the shedder status of the participants influenced the results. A probabilistic framework was developed which was based on a previously described Bayesian network with case-specific modifications. Continuous modelling of data was used to inform the Bayesian networks and two case scenarios were investigated. In the specific scenarios only moderate to low support for Hp was obtained. Applying a continuous model based on the profile quality can change the LRs.
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