1
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Lan Q, Lin Y, Wang X, Yuan X, Shen C, Zhu B. Targeted sequencing of high-density SNPs provides an enhanced tool for forensic applications and genetic landscape exploration in Chinese Korean ethnic group. Hum Genomics 2023; 17:107. [PMID: 38008719 PMCID: PMC10680316 DOI: 10.1186/s40246-023-00541-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 10/05/2023] [Indexed: 11/28/2023] Open
Abstract
BACKGROUND In this study, we present a NGS-based panel designed for sequencing 1993 SNP loci for forensic DNA investigation. This panel addresses unique challenges encountered in forensic practice and allows for a comprehensive population genetic study of the Chinese Korean ethnic group. To achieve this, we combine our results with datasets from the 1000 Genomes Project and the Human Genome Diversity Panel. RESULTS We demonstrate that this panel is a reliable tool for individual identification and parentage testing, even when dealing with degraded DNA samples featuring exceedingly low SNP detection rates. The performance of this panel for complex kinship determinations, such as half-sibling and grandparent-grandchild scenarios, is also validated by various kinship simulations. Population genetic studies indicate that this panel can uncover population substructures on both global and regional scales. Notably, the Han population can be distinguished from the ethnic minorities in the northern and southern regions of East Asia, suggesting its potential for regional ancestry inference. Furthermore, we highlight that the Chinese Korean ethnic group, along with various Han populations from different regional areas and certain northern ethnic minorities (Daur, Tujia, Japanese, Mongolian, Xibo), exhibit a higher degree of genetic affinities when examined from a genomic perspective. CONCLUSION This study provides convincing evidence that the NGS-based panel can serve as a reliable tool for various forensic applications. Moreover, it has helped to enhance our knowledge about the genetic landscape of the Chinese Korean ethnic group.
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Affiliation(s)
- Qiong Lan
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Yifeng Lin
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Xi Wang
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Xi Yuan
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Chunmei Shen
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Bofeng Zhu
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, China.
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China.
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2
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Zhang N, Shi S, Lin S, Bai Z, Ling X, Gao J, Yan R, Ou X. Application of SNPs with low minor allele frequencies in missing person identification (MPI) through kinship analysis of DNA mixtures. Electrophoresis 2023; 44:1569-1578. [PMID: 37454302 DOI: 10.1002/elps.202300111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/18/2023] [Accepted: 07/07/2023] [Indexed: 07/18/2023]
Abstract
The need to identify a missing person (MP) through kinship analysis of DNA samples found at a crime scene has become increasingly prevalent. DNA samples from MPs can be severely degraded, contain little DNA and mixed with other contributors, which often makes it difficult to apply conventional methods in practice. This study developed a massively parallel sequencing-based panel that contains 1661 single-nucleotide polymorphisms (SNPs) with low minor allele frequencies (MAFs) (averaged at 0.0613) in the Chinese Han population, and the strategy for relationship inference from DNA mixtures comprising different numbers of contributors (NOCs) and of varying allele dropout probabilities. Based on the simulated dataset and genotyping results of 42 artificial DNA mixtures (NOC = 2-4), it was observed that the present SNP panel was sufficient for balanced mixtures when referenced to the closest relatives (parents/offspring and full siblings). When the mixture profiles suffered from dropout, incorrect assignments were markedly associated with relatedness, NOC and the dropout level. We, therefore, indicate that SNPs with low MAFs could be reliably interpreted for MP identification through the kinship analysis of complex DNA mixtures. Further studies should be extended to more possible scenarios to test the feasibility of this present approach.
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Affiliation(s)
- Nan Zhang
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, P. R. China
- Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou, P. R. China
| | - Shanshan Shi
- Fetal Medicine Department, The First Affiliated Hospital of Jinan University, Guangzhou, P. R. China
| | - Shaobin Lin
- Fetal Medicine Center, Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, P. R. China
| | - Zhaochen Bai
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, P. R. China
- Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou, P. R. China
| | - Xiaohua Ling
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, P. R. China
- Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou, P. R. China
| | - Jun Gao
- Reproductive Medicine Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, P. R. China
| | - Ruiling Yan
- Fetal Medicine Department, The First Affiliated Hospital of Jinan University, Guangzhou, P. R. China
| | - Xueling Ou
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, P. R. China
- Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou, P. R. China
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3
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Zhu Q, Wang H, Cao Y, Huang Y, Wei Y, Hu Y, Dai X, Shan T, Wang Y, Zhang J. Evaluation of large-scale highly polymorphic microhaplotypes in complex DNA mixtures analysis using RMNE method. Forensic Sci Int Genet 2023; 65:102874. [PMID: 37075688 DOI: 10.1016/j.fsigen.2023.102874] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 03/19/2023] [Accepted: 04/13/2023] [Indexed: 04/21/2023]
Abstract
DNA mixture interpretation is one of the most challenging problems in forensics. Complex DNA mixtures are more difficult to analyze when there are more than two contributors or related contributors. Microhaplotypes (MHs) are polymorphic genetic markers recently discovered and employed in DNA mixture analysis. However, the evidentiary interpretation of the MH genotyping data needs more debate. The Random Man Not Excluded (RMNE) method analyzes DNA mixtures without using allelic peak height data or the number of contributors (NoC) assumptions. This study aimed to assess how well RMNE interpreted mixed MH genotyping data. We classified the MH loci from the 1000 Genomes Project database into groups based on their Ae values. Then we performed simulations of DNA mixtures with 2-10 unrelated contributors and DNA mixtures with a pair of sibling contributors. For each simulated DNA mixture, incorrectly included ratios were estimated for three types of non-contributors: random men, parents of contributors, and siblings of contributors. Meanwhile, RMNE probability was calculated for contributors and three types of non-contributors, allowing loci mismatch. The results showed that the MH number, the MH Ae values, and the NoC affected the RMNE probability of the mixture and the incorrectly included ratio of non-contributors. When there were more MHs, MHs with higher Ae values, and a mixture with less NoC, the RMNE probability, and the incorrectly included ratio decreased. The existence of kinship in mixtures complicated the mixture interpretation. Contributors' relatives as non-contributors and related contributors in the mixture increased the demands on the genetic markers to identify the contributors correctly. When 500 highly polymorphic MHs with Ae values higher than 5 were used, the four individual types could be distinguished according to the RMNE probabilities. This study reveals the promising potential of MH as a genetic marker for mixed DNA interpretation and the broadening of RMNE as a parameter indicating the relationship of a specific individual with a DNA mixture in the DNA database search.
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Affiliation(s)
- Qiang Zhu
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, PR China
| | - Haoyu Wang
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, PR China
| | - Yueyan Cao
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, PR China
| | - Yuguo Huang
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, PR China
| | - Yifan Wei
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, PR China
| | - Yuhan Hu
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, PR China
| | - Xuan Dai
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, PR China
| | - Tiantian Shan
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, PR China
| | - Yunfeng Wang
- College of Computer Science, Sichuan University, PR China.
| | - Ji Zhang
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, PR China.
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4
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A novel computational strategy to predict the value of the evidence in the SNP-based forensic mixtures. PLoS One 2021; 16:e0247344. [PMID: 34653182 PMCID: PMC8519470 DOI: 10.1371/journal.pone.0247344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 09/30/2021] [Indexed: 11/24/2022] Open
Abstract
This study introduces a methodology for inferring the weight of the evidence (WoE) in the single nucleotide polymorphism (SNP)-typed DNA mixtures of forensic interest. First, we redefined some algebraic formulae to approach the semi-continuous calculation of likelihoods and likelihood ratios (LRs). To address the allelic dropouts, a peak height ratio index (“h,” an index of heterozygous state plausibility) was incorporated into semi-continuous formulae to act as a proxy for the “split-drop” model of calculation. Second, the original ratio at which a person of interest (POI) has entered into the mixture was inferred by evaluating the DNA amounts conferred by unique genotypes to any possible permutation of any locus of the typing protocol (unique genotypes are genotypes that appear just once in the relevant permutation). We compared this expected ratio (MRex) to all the mixing ratios emerging at all other permutations of the mixture (MRobs) using several (1 - χ2) tests to evaluate the probability of each permutation to exist in the mixture according to quantitative criteria. At the level of each permutation state, we multiplied the (1 - χ2) value to the genotype frequencies and the h index. All the products of all the permutation states were finally summed to give a likelihood value that accounts for three independent properties of the mixtures. Owing to the (1 - χ2) index and the h index, this approach qualifies as a fully continuous methodology of LR calculation. We compared the MRs and LRs emerging from our methodology to those generated by the EuroForMix software ver. 3.0.3. When the true contributors were tested as POIs, our procedure generated highly discriminant LRs that, unlike EuroForMix, never overcame the corresponding single-source LRs. When false contributors were tested as POIs, we obtained a much lower LR value than that from EuroForMix. These two findings indicate that our computational method is more reliable and realistic than EuroForMix.
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5
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Yang TW, Li YH, Chou CF, Lai FP, Chien YH, Yin HI, Lee TT, Hwa HL. DNA mixture interpretation using linear regression and neural networks on massively parallel sequencing data of single nucleotide polymorphisms. AUST J FORENSIC SCI 2021. [DOI: 10.1080/00450618.2020.1807050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Ta-Wei Yang
- Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan
| | - Yi-Hao Li
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Cheng-Fu Chou
- Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Fei-Pei Lai
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan
| | - Yin-Hsiu Chien
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan
| | - Hsiang-I Yin
- Department and Graduate Institute of Forensic Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Tsui-Ting Lee
- Department and Graduate Institute of Forensic Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Hsiao-Lin Hwa
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan
- Department and Graduate Institute of Forensic Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Obstetrics and Gynecology, National Taiwan University Hospital, Taipei, Taiwan
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6
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Novroski NMM. Exploring new short tandem repeat markers for
DNA
mixture deconvolution. ACTA ACUST UNITED AC 2020. [DOI: 10.1002/wfs2.1390] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Nicole M. M. Novroski
- Forensic Science Program, Department of Anthropology University of Toronto Mississauga Ontario Canada
- Center for Human Identification, Graduate School of Biomedical Sciences University of North Texas Health Science Center, Fort Worth Texas USA
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7
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Petrovick MS, Boettcher T, Fremont-Smith P, Peragallo C, Ricke DO, Watkins J, Schwoebel E. Analysis of complex DNA mixtures using massively parallel sequencing of SNPs with low minor allele frequencies. Forensic Sci Int Genet 2020; 46:102234. [PMID: 32018060 DOI: 10.1016/j.fsigen.2020.102234] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 12/05/2019] [Accepted: 01/02/2020] [Indexed: 11/18/2022]
Abstract
DNA mixtures from 3 or more contributors have proven difficult to analyze using the current state-of-the-art method of short-tandem repeat (STR) amplification followed by capillary electrophoresis (CE). Here we analyze samples from both laboratory-defined mixtures and complex multi-contributor touch samples using a single nucleotide polymorphism (SNP) panel comprised of 2311 low-minor-allele-frequency loci, combined with massively parallel sequencing (MPS). This approach demonstrates that as many as 10 people can be identified in touch samples using a threshold of -Log P(RMNE) of 6, and a detection rate of 18-94 % across 10 different materials using a threshold of -Log P(RMNE) of 2. Thirty-two false positives were observed in 100 touch samples.
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Affiliation(s)
- Martha S Petrovick
- Massachusetts Institute of Technology, Lincoln Laboratory, 244 Wood St., Lexington, MA 02421, United States.
| | - Tara Boettcher
- Massachusetts Institute of Technology, Lincoln Laboratory, 244 Wood St., Lexington, MA 02421, United States
| | - Philip Fremont-Smith
- Massachusetts Institute of Technology, Lincoln Laboratory, 244 Wood St., Lexington, MA 02421, United States
| | - Chelsea Peragallo
- Massachusetts Institute of Technology, Lincoln Laboratory, 244 Wood St., Lexington, MA 02421, United States
| | - Darrell O Ricke
- Massachusetts Institute of Technology, Lincoln Laboratory, 244 Wood St., Lexington, MA 02421, United States
| | - James Watkins
- Massachusetts Institute of Technology, Lincoln Laboratory, 244 Wood St., Lexington, MA 02421, United States
| | - Eric Schwoebel
- Massachusetts Institute of Technology, Lincoln Laboratory, 244 Wood St., Lexington, MA 02421, United States
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8
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Ricke DO, Watkins J, Fremont-Smith P, Petrovick MS, Boettcher T, Schwoebel E. TranslucentID: analysis of complex DNA SNP mixtures with large numbers of donors. AUST J FORENSIC SCI 2019. [DOI: 10.1080/00450618.2019.1699958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Darrell O. Ricke
- Biological and Chemical Technologies, Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA, USA
| | - James Watkins
- Biological and Chemical Technologies, Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA, USA
| | - Philip Fremont-Smith
- Biological and Chemical Technologies, Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA, USA
| | - Martha S. Petrovick
- Biological and Chemical Technologies, Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA, USA
| | - Tara Boettcher
- Biological and Chemical Technologies, Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA, USA
| | - Eric Schwoebel
- Biological and Chemical Technologies, Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA, USA
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9
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Wu L, Chu X, Zheng J, Xiao C, Zhang Z, Huang G, Li D, Zhan J, Huang D, Hu P, Xiong B. Targeted capture and sequencing of 1245 SNPs for forensic applications. Forensic Sci Int Genet 2019; 42:227-234. [DOI: 10.1016/j.fsigen.2019.07.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 05/19/2019] [Accepted: 07/09/2019] [Indexed: 01/06/2023]
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10
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Yang J, Lin D, Deng C, Li Z, Pu Y, Yu Y, Li K, Li D, Chen P, Chen F. The advances in DNA mixture interpretation. Forensic Sci Int 2019; 301:101-106. [PMID: 31153987 DOI: 10.1016/j.forsciint.2019.05.024] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 05/09/2019] [Indexed: 12/16/2022]
Abstract
In forensic genetics, the analysis of DNA in biological samples is a valuable tool for personal identification. There is an increasing demand in analyzing of the mixed DNA which may provide insightful investigative instructions. With the continuous effort for the improvement of individual identification, complicated mixed stains represent a growing fraction of the samples processed by forensic laboratories. Recent technological advances have enabled quantitative analysis of DNA mixture and emerging sequencing approaches to decipher the complicated DNA mixture. Here, we describe the use of different genetic markers, typing approaches and analytical methods in mixture analysis, and how useful information can be obtained from complicated DNA mixture.
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Affiliation(s)
- Jiawen Yang
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, PR China
| | - Donghai Lin
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, PR China
| | - Chuwei Deng
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, PR China
| | - Zheng Li
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, PR China
| | - Yan Pu
- School of Medicine, Southeast University, Nanjing, Jiangsu 210009, PR China
| | - Yanfang Yu
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, PR China
| | - Kai Li
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, PR China
| | - Ding Li
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, PR China
| | - Peng Chen
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, PR China.
| | - Feng Chen
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, PR China; Key Laboratory of Targeted Intervention of Cardiovascular Disease, Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, Jiangsu, 211166, PR China.
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11
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Clark MA, Stankiewicz SH, Barronette V, Ricke DO. Letter to the Editor – Detecting HTS Barcode Contamination. J Forensic Sci 2019; 64:961-962. [DOI: 10.1111/1556-4029.14027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Mallory A. Clark
- Bioengineering Systems & Technologies Massachusetts Institute of Technology Lincoln Laboratory Lexington MA
| | - Sara H. Stankiewicz
- Bioengineering Systems & Technologies Massachusetts Institute of Technology Lincoln Laboratory Lexington MA
| | - Vincent Barronette
- Bioengineering Systems & Technologies Massachusetts Institute of Technology Lincoln Laboratory Lexington MA
| | - Darrell O. Ricke
- Bioengineering Systems & Technologies Massachusetts Institute of Technology Lincoln Laboratory Lexington MA
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12
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Chen R, Zhao X, Ma K, Li H, Cao Y, Cao Y, Liu W. Separation of SNP profiles from DNA mixtures with two contributors via massively parallel sequencing technology. AUST J FORENSIC SCI 2019. [DOI: 10.1080/00450618.2019.1586997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Ronghua Chen
- Key Laboratory of Forensic Evidence and Science Technology, Ministry of Public Security, Institute of Forensic Science, Shanghai Public Security Bureau, Shanghai, China
| | - Xueying Zhao
- Shanghai Key Laboratory of Crime Scene Evidence, Shanghai Research Institute of Criminal Science and Technology, Shanghai, China
| | - Ke Ma
- Shanghai Key Laboratory of Crime Scene Evidence, Shanghai Research Institute of Criminal Science and Technology, Shanghai, China
| | - Hui Li
- Shanghai Key Laboratory of Crime Scene Evidence, Shanghai Research Institute of Criminal Science and Technology, Shanghai, China
| | - Yu Cao
- Key Laboratory of Forensic Evidence and Science Technology, Ministry of Public Security, Institute of Forensic Science, Shanghai Public Security Bureau, Shanghai, China
- State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Yandong Cao
- Technical department, Analyses Technology Co. Ltd, Beijing, China
| | - Wenbin Liu
- Shanghai Key Laboratory of Crime Scene Evidence, Shanghai Research Institute of Criminal Science and Technology, Shanghai, China
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13
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Ricke DO, Fremont‐Smith P, Watkins J, Stankiewicz S, Boettcher T, Schwoebel E. Estimating Individual Contributions to Complex DNA SNP Mixtures.,. J Forensic Sci 2019; 64:1468-1474. [DOI: 10.1111/1556-4029.14030] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 01/18/2019] [Accepted: 01/28/2019] [Indexed: 01/04/2023]
Affiliation(s)
- Darrell O. Ricke
- Bioengineering Systems & Technologies Massachusetts Institute of Technology Lincoln Laboratory 244 Wood Street Lexington MA
| | - Philip Fremont‐Smith
- Bioengineering Systems & Technologies Massachusetts Institute of Technology Lincoln Laboratory 244 Wood Street Lexington MA
| | - James Watkins
- Bioengineering Systems & Technologies Massachusetts Institute of Technology Lincoln Laboratory 244 Wood Street Lexington MA
| | - Sara Stankiewicz
- Bioengineering Systems & Technologies Massachusetts Institute of Technology Lincoln Laboratory 244 Wood Street Lexington MA
| | - Tara Boettcher
- Bioengineering Systems & Technologies Massachusetts Institute of Technology Lincoln Laboratory 244 Wood Street Lexington MA
| | - Eric Schwoebel
- Bioengineering Systems & Technologies Massachusetts Institute of Technology Lincoln Laboratory 244 Wood Street Lexington MA
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14
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Tao R, Wang S, Zhang J, Zhang J, Yang Z, Sheng X, Hou Y, Zhang S, Li C. Separation/extraction, detection, and interpretation of DNA mixtures in forensic science (review). Int J Legal Med 2018; 132:1247-1261. [PMID: 29802461 DOI: 10.1007/s00414-018-1862-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Accepted: 05/11/2018] [Indexed: 02/08/2023]
Abstract
Interpreting mixed DNA samples containing material from multiple contributors has long been considered a major challenge in forensic casework, especially when encountering low-template DNA (LT-DNA) or high-order mixtures that may involve missing alleles (dropout) and unrelated alleles (drop-in), among others. In the last decades, extraordinary progress has been made in the analysis of mixed DNA samples, which has led to increasing attention to this research field. The advent of new methods for the separation and extraction of DNA from mixtures, novel or jointly applied genetic markers for detection and reliable interpretation approaches for estimating the weight of evidence, as well as the powerful massively parallel sequencing (MPS) technology, has greatly extended the range of mixed samples that can be correctly analyzed. Here, we summarized the investigative approaches and progress in the field of forensic DNA mixture analysis, hoping to provide some assistance to forensic practitioners and to promote further development involving this issue.
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Affiliation(s)
- Ruiyang Tao
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China.,Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Ministry of Justice, Academy of Forensic Sciences, Shanghai, 200063, People's Republic of China
| | - Shouyu Wang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Jiashuo Zhang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Ministry of Justice, Academy of Forensic Sciences, Shanghai, 200063, People's Republic of China.,Department of Forensic Science, Medical School of Soochow University, Suzhou, 215123, People's Republic of China
| | - Jingyi Zhang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Ministry of Justice, Academy of Forensic Sciences, Shanghai, 200063, People's Republic of China.,Department of Forensic Science, Medical School of Soochow University, Suzhou, 215123, People's Republic of China
| | - Zihao Yang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Ministry of Justice, Academy of Forensic Sciences, Shanghai, 200063, People's Republic of China.,Department of Forensic Medicine, School of Basic Medical Science, Wenzhou Medical University, Wenzhou, 325035, People's Republic of China
| | - Xiang Sheng
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Ministry of Justice, Academy of Forensic Sciences, Shanghai, 200063, People's Republic of China.,Department of Forensic Science, Medical School of Soochow University, Suzhou, 215123, People's Republic of China
| | - Yiping Hou
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Suhua Zhang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Ministry of Justice, Academy of Forensic Sciences, Shanghai, 200063, People's Republic of China.
| | - Chengtao Li
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China. .,Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Ministry of Justice, Academy of Forensic Sciences, Shanghai, 200063, People's Republic of China.
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15
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Chen P, Yin C, Li Z, Pu Y, Yu Y, Zhao P, Chen D, Liang W, Zhang L, Chen F. Evaluation of the Microhaplotypes panel for DNA mixture analyses. Forensic Sci Int Genet 2018; 35:149-155. [PMID: 29778046 DOI: 10.1016/j.fsigen.2018.05.003] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 04/12/2018] [Accepted: 05/11/2018] [Indexed: 11/17/2022]
Abstract
The identification of a suspect in a DNA mixture typed with the standard short tandem repeat polymorphism (STR) kits has faced challenges. Several improved methods or technologies have been introduced to address this issue. However, some complex situations in the process remain elusive. In the present study, we presented a panel of 26 tiny microhaplotypes, each generating a relatively high (>3.0) effective number of alleles (Ae) and possessing low (<50 bp) sequence lengths. The average Ae and heterozygosity values among the 9 populations of 26 microhaps were in ranges from 2.60 to 4.54 and 0.59 to 0.96, respectively. Power of discrimination and power of exclusion values were ranged from 0.49 to 0.87 and 0.29 to 0.94, respectively. Significant positive correlations have been found between Ae values and heterozygosity (r = 0.43, p = 0.02) or power of discrimination values (r = 0.55, p = 0.003), respectively. The cumulative probability of detecting a mixture of two unrelated individuals could reach 0.9999998 when using a panel of 26 microhaps with Ae = 3. We further tested the panel by using massively parallel sequencing, and 14 out of 26 microhaps were successfully genotyped in a single multiplex system. 60 unrelated Chinese Han individuals and 2 artificially prepared samples mixed by two unrelated contributors (in duplicate, ie. 4 mixtures) were sequenced. Approximately 32.14% of the 14 loci presented three or four alleles in the two mixtures. The likelihood ratio values to cognizance the mixtures' contributor were in a range from 1.95 × 106 to 1.10 × 107. The results demonstrated that the present panel could offer a valuable complementary tool in forensic applications.
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Affiliation(s)
- Peng Chen
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, 211166, PR China.
| | - Caiyong Yin
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, 211166, PR China
| | - Zheng Li
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, 211166, PR China
| | - Yan Pu
- Department of Forensic Biology, West China School of Basic Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, 610041, PR China
| | - Youjia Yu
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, 211166, PR China
| | - Peng Zhao
- Institute of Forensic Science, Wuxi Public Security Bureau, Wuxi, 214002, Jiangsu, China
| | - Dexin Chen
- Department of Engineering, Columbia University, New York, 10027, USA
| | - Weibo Liang
- Department of Forensic Biology, West China School of Basic Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, 610041, PR China
| | - Lin Zhang
- Department of Forensic Biology, West China School of Basic Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, 610041, PR China
| | - Feng Chen
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, 211166, PR China.
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16
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Ricke DO, Shcherbina A, Michaleas A, Fremont‐Smith P. Grigora
SNP
s: Optimized Analysis of
SNP
s for
DNA
Forensics,. J Forensic Sci 2018; 63:1841-1845. [DOI: 10.1111/1556-4029.13794] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Revised: 02/22/2018] [Accepted: 02/28/2018] [Indexed: 12/30/2022]
Affiliation(s)
- Darrell O. Ricke
- Bioengineering Systems & Technologies Massachusetts Institute of Technology Lincoln Laboratory 244 Wood Street Lexington MA 02421‐6426
| | - Anna Shcherbina
- Bioengineering Systems & Technologies Massachusetts Institute of Technology Lincoln Laboratory 244 Wood Street Lexington MA 02421‐6426
| | - Adam Michaleas
- Bioengineering Systems & Technologies Massachusetts Institute of Technology Lincoln Laboratory 244 Wood Street Lexington MA 02421‐6426
| | - Philip Fremont‐Smith
- Bioengineering Systems & Technologies Massachusetts Institute of Technology Lincoln Laboratory 244 Wood Street Lexington MA 02421‐6426
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17
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A 1204-single nucleotide polymorphism and insertion–deletion polymorphism panel for massively parallel sequencing analysis of DNA mixtures. Forensic Sci Int Genet 2018; 32:94-101. [DOI: 10.1016/j.fsigen.2017.11.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 11/03/2017] [Accepted: 11/06/2017] [Indexed: 11/19/2022]
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18
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Madsen T, Hobolth A, Jensen JL, Pedersen JS. Significance evaluation in factor graphs. BMC Bioinformatics 2017; 18:199. [PMID: 28359297 PMCID: PMC5374669 DOI: 10.1186/s12859-017-1614-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Accepted: 03/24/2017] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Factor graphs provide a flexible and general framework for specifying probability distributions. They can capture a range of popular and recent models for analysis of both genomics data as well as data from other scientific fields. Owing to the ever larger data sets encountered in genomics and the multiple-testing issues accompanying them, accurate significance evaluation is of great importance. We here address the problem of evaluating statistical significance of observations from factor graph models. RESULTS Two novel numerical approximations for evaluation of statistical significance are presented. First a method using importance sampling. Second a saddlepoint approximation based method. We develop algorithms to efficiently compute the approximations and compare them to naive sampling and the normal approximation. The individual merits of the methods are analysed both from a theoretical viewpoint and with simulations. A guideline for choosing between the normal approximation, saddle-point approximation and importance sampling is also provided. Finally, the applicability of the methods is demonstrated with examples from cancer genomics, motif-analysis and phylogenetics. CONCLUSIONS The applicability of saddlepoint approximation and importance sampling is demonstrated on known models in the factor graph framework. Using the two methods we can substantially improve computational cost without compromising accuracy. This contribution allows analyses of large datasets in the general factor graph framework.
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Affiliation(s)
- Tobias Madsen
- Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, Aarhus, Denmark. .,Bioinformatics Research Center, Aarhus University, C.F. Møllers Allé 8, Aarhus, Denmark.
| | - Asger Hobolth
- Bioinformatics Research Center, Aarhus University, C.F. Møllers Allé 8, Aarhus, Denmark
| | - Jens Ledet Jensen
- Department of Mathematics, Aarhus University, Ny Munkegade 118, Aarhus, Denmark
| | - Jakob Skou Pedersen
- Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, Aarhus, Denmark.,Bioinformatics Research Center, Aarhus University, C.F. Møllers Allé 8, Aarhus, Denmark
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19
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Bond JW, Weart JR. The Effectiveness of Trace DNA Profiling-A Comparison Between a U.S. and a U.K. Law Enforcement Jurisdiction. J Forensic Sci 2016; 62:753-760. [DOI: 10.1111/1556-4029.13317] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Revised: 05/17/2016] [Accepted: 08/06/2016] [Indexed: 11/30/2022]
Affiliation(s)
- John W. Bond
- Department of Criminology; University of Leicester; 154 Upper New Walk Leicester LE1 7QA U.K
| | - Jocelyn R. Weart
- Santa Clara County Crime Laboratory; 250 West Hedding Street San Jose CA 95110
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