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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] [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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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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Green PJ, Mortera J, Prieto L. Casework applications of probabilistic genotyping methods for DNA mixtures that allow relationships between contributors. Forensic Sci Int Genet 2021; 52:102482. [PMID: 33640736 DOI: 10.1016/j.fsigen.2021.102482] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 01/10/2021] [Accepted: 02/08/2021] [Indexed: 10/22/2022]
Abstract
In both criminal cases and civil cases there is an increasing demand for the analysis of DNA mixtures involving relationships. The goal might be, for example, to identify the contributors to a DNA mixture where the unknown donors may be related, or to infer the relationship between individuals based on a DNA mixture. This paper applies a new approach to modelling and computation for DNA mixtures involving contributors with arbitrarily complex relationships to two real cases from the Spanish Forensic Police.
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Affiliation(s)
| | - Julia Mortera
- University of Bristol, UK; Università Roma Tre, Italy.
| | - Lourdes Prieto
- Forensic Sciences Institute, University of Santiago de Compostela, Spain; Comisaría General de Policía Científica, DNA Laboratory, Madrid, Spain
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Hwa HL, Wu MY, Lee JCI, Yin HI, Hsu PM, Li SF, Hwu WL, Su CW. Analysis of nondegraded and degraded DNA mixtures of close relatives using massively parallel sequencing. Leg Med (Tokyo) 2020; 42:101631. [DOI: 10.1016/j.legalmed.2019.101631] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 08/30/2019] [Accepted: 09/21/2019] [Indexed: 10/25/2022]
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