1
|
Jian H, Wang L, Lv M, Tan Y, Zhang R, Qu S, Wang J, Zha L, Zhang L, Liang W. A Novel SNP-STR System Based on a Capillary Electrophoresis Platform. Front Genet 2021; 12:636821. [PMID: 33613649 PMCID: PMC7893108 DOI: 10.3389/fgene.2021.636821] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 01/14/2021] [Indexed: 11/13/2022] Open
Abstract
Various compound markers encompassing two or more variants within a small region can be regarded as generalized microhaplotypes. Many of these markers have been investigated for various forensic purposes, such as individual identification, deconvolution of DNA mixtures, or forensic ancestry inference. SNP-STR is a compound biomarker composed of a single nucleotide polymorphism (SNP) and a closely linked short tandem repeat polymorphism (STR), and possess the advantages of both SNPs and STRs. In addition, in conjunction with a polymerase chain reaction (PCR) technique based on the amplification refractory mutation system (ARMS), SNP-STRs can be used for forensic unbalanced DNA mixture analysis based on capillary electrophoresis (CE), which is the most commonly used platform in worldwide forensic laboratories. Our previous research reported 11 SNP-STRs, but few of them are derived from the commonly used STR loci, for which existing STR databases can be used as a reference. For maximum compatibility with existing DNA databases, in this study, we screened 18 SNP-STR loci, of which 14 were derived from the expanded CODIS core loci set. Stable and sensitive SNP-STR multiplex PCR panels based on the CE platform were established. Assays on simulated two-person DNA mixtures showed that all allele-specific primers could detect minor DNA components in 1:500 mixtures. Population data based on 113 unrelated Chengdu Han individuals were investigated. A Bayesian framework was developed for the likelihood ratio (LR) evaluation of SNP-STR profiling results obtained from two-person mixtures. Furthermore, we report on the first use of SNP-STRs in casework to show the advantages and limitations for use in practice. Compared to 2.86 × 103 for autosomal STR kits, the combined LR reached 7.14 × 107 using the SNP-STR method in this casework example.
Collapse
Affiliation(s)
- Hui Jian
- Department of Forensic Genetics, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, China
| | - Li Wang
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Meili Lv
- Department of Immunology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, China
| | - Yu Tan
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Ranran Zhang
- Department of Forensic Genetics, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, China
| | - Shengqiu Qu
- Department of Forensic Genetics, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, China
| | - Jijun Wang
- HI-TECH Industrial Sub-Branch of Chengdu Municipal Public Security Bureau, Chengdu, China
| | - Lagabaiyila Zha
- Department of Forensic Medicine, School of Basic Medical Sciences, Central South University, Changsha, China
| | - Lin Zhang
- Department of Forensic Genetics, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, China
| | - Weibo Liang
- Department of Forensic Genetics, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, China
| |
Collapse
|
2
|
Ma G, Cong B, Li S. AUCP: An indicator for system effectiveness of panels in pairwise distant kinship identification. Forensic Sci Int 2020; 316:110539. [DOI: 10.1016/j.forsciint.2020.110539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 09/15/2020] [Accepted: 10/05/2020] [Indexed: 11/26/2022]
|
3
|
Schuerman C, Kalafut T, Buchanan C, Sutton J, Bright JA. Using the Nondonor Distribution to Improve Communication and Inform Decision Making for Low LRs from Minor Contributors in Mixed DNA Profiles. J Forensic Sci 2020; 65:1072-1084. [PMID: 32134501 DOI: 10.1111/1556-4029.14306] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 01/24/2020] [Accepted: 01/30/2020] [Indexed: 12/20/2022]
Abstract
The reporting of a likelihood ratio (LR) calculated from probabilistic genotyping software has become more popular since 2015 and has allowed for the use of more complex mixtures at court. The meaning of "inconclusive" LRs and how to communicate the significance of low LRs at court is now important. We present a method here using the distribution of LRs obtained from nondonors. The nondonor distribution is useful for examining calibration and discrimination for profiles that have produced LRs less than about 104 . In this paper, a range of mixed DNA profiles of varying quantity were constructed and the LR distribution considering the minor contributor for a number of nondonors was compared to the expectation given a calibrated system. It is demonstrated that conditioning genotypes should be used where reasonable given the background information to decrease the rate of nondonor LRs above 1. In all 17 cases examined, the LR for the minor donor was higher than the nondonor LRs, and in 12 of the 17 cases, the 99.9 percentile of the nondonor distribution was lower when appropriate conditioning information was used. The output of the tool is a graph that can show the position of the LR for the person of interest set against the nondonor LR distribution. This may assist communication between scientists and the court.
Collapse
Affiliation(s)
- Curt Schuerman
- US Army Criminal Investigation Laboratory (USACIL), 4930 N 31st Street, Forest Park, GA, 30297
| | - Tim Kalafut
- US Army Criminal Investigation Laboratory (USACIL), 4930 N 31st Street, Forest Park, GA, 30297
| | - Clint Buchanan
- US Army Criminal Investigation Laboratory (USACIL), 4930 N 31st Street, Forest Park, GA, 30297
| | - Joel Sutton
- US Army Criminal Investigation Laboratory (USACIL), 4930 N 31st Street, Forest Park, GA, 30297
| | - Jo-Anne Bright
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, 1142, New Zealand
| |
Collapse
|
4
|
Exploring the probative value of mixed DNA profiles. Forensic Sci Int Genet 2019; 41:1-10. [DOI: 10.1016/j.fsigen.2019.03.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 03/13/2019] [Accepted: 03/13/2019] [Indexed: 12/19/2022]
|
5
|
Four model variants within a continuous forensic DNA mixture interpretation framework: Effects on evidential inference and reporting. PLoS One 2018; 13:e0207599. [PMID: 30458020 PMCID: PMC6245789 DOI: 10.1371/journal.pone.0207599] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 10/13/2018] [Indexed: 12/11/2022] Open
Abstract
Continuous mixture interpretation methods that employ probabilistic genotyping to compute the Likelihood Ratio (LR) utilize more information than threshold-based systems. The continuous interpretation schemes described in the literature, however, do not all use the same underlying probabilistic model and standards outlining which probabilistic models may or may not be implemented into casework do not exist; thus, it is the individual forensic laboratory or expert that decides which model and corresponding software program to implement. For countries, such as the United States, with an adversarial legal system, one can envision a scenario where two probabilistic models are used to present the weight of evidence, and two LRs are presented by two experts. Conversely, if no independent review of the evidence is requested, one expert using one model may present one LR as there is no standard or guideline requiring the uncertainty in the LR estimate be presented. The choice of model determines the underlying probability calculation, and changes to it can result in non-negligible differences in the reported LR or corresponding verbal categorization presented to the trier-of-fact. In this paper, we study the impact of model differences on the LR and on the corresponding verbal expression computed using four variants of a continuous mixture interpretation method. The four models were tested five times each on 101, 1-, 2- and 3-person experimental samples with known contributors. For each sample, LRs were computed using the known contributor as the person of interest. In all four models, intra-model variability increased with an increase in the number of contributors and with a decrease in the contributor’s template mass. Inter-model variability in the associated verbal expression of the LR was observed in 32 of the 195 LRs used for comparison. Moreover, in 11 of these profiles there was a change from LR > 1 to LR < 1. These results indicate that modifications to existing continuous models do have the potential to significantly impact the final statistic, justifying the continuation of broad-based, large-scale, independent studies to quantify the limits of reliability and variability of existing forensically relevant systems.
Collapse
|
6
|
Perlin MW. Efficient construction of match strength distributions for uncertain multi-locus genotypes. Heliyon 2018; 4:e00824. [PMID: 30310872 PMCID: PMC6176789 DOI: 10.1016/j.heliyon.2018.e00824] [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: 07/09/2018] [Revised: 09/18/2018] [Accepted: 09/24/2018] [Indexed: 11/02/2022] Open
Abstract
Natural variation in biological evidence leads to uncertain genotypes. Forensic comparison of a probabilistic genotype with a person's reference gives a numerical strength of DNA association. The distribution of match strength for all possible references usefully represents a genotype's potential information. But testing more genetic loci exponentially increases the number of multi-locus possibilities, making direct computation infeasible. At each locus, Bayesian probability can quickly assemble a match strength random variable. Multi-locus match strength is the sum of these independent variables. A multi-locus genotype's match strength distribution is efficiently constructed by convolving together the separate locus distributions. This convolution construction can accurately collate all trillion trillion reference outcomes in a fraction of a second. This paper shows how to rapidly construct multi-locus match strength distributions by convolution. Function convergence demonstrates that distribution accuracy increases with numerical resolution. Convolution construction has quadratic computational complexity, relative to the exponential number of reference genotypes. A suitably defined random variable reduces high-dimensional computational cost to fast real-line arithmetic. Match strength distributions are used in forensic validation studies. They provide error rates for match results. The convolution construction applies to discrete or continuous variables in the forensic, natural and social sciences. Computer-derived match strength distributions elicit the information inherent in DNA evidence, often overlooked by human analysis.
Collapse
|
7
|
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.
Collapse
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.
| |
Collapse
|
8
|
Jiang H, Xie Y, Li X, Ge H, Deng Y, Mu H, Feng X, Yin L, Du Z, Chen F, He N. Noninvasive Prenatal Paternity Testing (NIPAT) through Maternal Plasma DNA Sequencing: A Pilot Study. PLoS One 2016; 11:e0159385. [PMID: 27631491 PMCID: PMC5025199 DOI: 10.1371/journal.pone.0159385] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Accepted: 07/03/2016] [Indexed: 11/18/2022] Open
Abstract
Short tandem repeats (STRs) and single nucleotide polymorphisms (SNPs) have been already used to perform noninvasive prenatal paternity testing from maternal plasma DNA. The frequently used technologies were PCR followed by capillary electrophoresis and SNP typing array, respectively. Here, we developed a noninvasive prenatal paternity testing (NIPAT) based on SNP typing with maternal plasma DNA sequencing. We evaluated the influence factors (minor allele frequency (MAF), the number of total SNP, fetal fraction and effective sequencing depth) and designed three different selective SNP panels in order to verify the performance in clinical cases. Combining targeted deep sequencing of selective SNP and informative bioinformatics pipeline, we calculated the combined paternity index (CPI) of 17 cases to determine paternity. Sequencing-based NIPAT results fully agreed with invasive prenatal paternity test using STR multiplex system. Our study here proved that the maternal plasma DNA sequencing-based technology is feasible and accurate in determining paternity, which may provide an alternative in forensic application in the future.
Collapse
Affiliation(s)
- Haojun Jiang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
- BGI-Shenzhen, Shenzhen, 518000, China
| | - Yifan Xie
- BGI Education Center, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI-Shenzhen, Shenzhen, 518000, China
| | - Xuchao Li
- BGI Education Center, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Huijuan Ge
- BGI Education Center, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yongqiang Deng
- Department of stomatology, The Second People’s Hospital of Shenzhen, Shenzhen, 518000, China
| | - Haofang Mu
- BGI Education Center, University of Chinese Academy of Sciences, Beijing, 100049, China
- Center of Forensic Sciences, Beijing Genomics Institute, Beijing, 100049, China
| | - Xiaoli Feng
- BGI Education Center, University of Chinese Academy of Sciences, Beijing, 100049, China
- Center of Forensic Sciences, Beijing Genomics Institute, Beijing, 100049, China
| | - Lu Yin
- Public Security Bureau of Shenzhen Municipality, Shenzhen, 518000, China
- Joint Laboratory of Gene-associated Application Research in Forensics, Shenzhen, 518000, China
| | - Zhou Du
- Public Security Bureau of Shenzhen Municipality, Shenzhen, 518000, China
- Joint Laboratory of Gene-associated Application Research in Forensics, Shenzhen, 518000, China
| | - Fang Chen
- BGI Education Center, University of Chinese Academy of Sciences, Beijing, 100049, China
- Public Security Bureau of Shenzhen Municipality, Shenzhen, 518000, China
- Joint Laboratory of Gene-associated Application Research in Forensics, Shenzhen, 518000, China
- Shenzhen Municipal Key Laboratory of Birth Defects Screening and Engineering, BGI-Shenzhen, Shenzhen, 518000, China
- Guangdong Provincial Key Laboratory of human diseases genome, BGI-Shenzhen, Shenzhen, 518000, China
- Section of Molecular Disease Biology, Department of Veterinary Disease Biology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 1165 København K, Denmark
- * E-mail: (FC); (NH)
| | - Nongyue He
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
- * E-mail: (FC); (NH)
| |
Collapse
|
9
|
de Zoete J, Oosterman W, Kokshoorn B, Sjerps M. Cell type determination and association with the DNA donor. Forensic Sci Int Genet 2016; 25:97-111. [PMID: 27552692 DOI: 10.1016/j.fsigen.2016.08.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Revised: 06/07/2016] [Accepted: 08/09/2016] [Indexed: 11/18/2022]
Abstract
In forensic casework, evidence regarding the type of cell material contained in a stain can be crucial in determining what happened. For example, a DNA match in a sexual offense can become substantially more incriminating when there is evidence supporting that semen cells are present. Besides the question which cell types are present in a sample, also the question who donated what (association) is very relevant. This question is surprisingly difficult, even for stains with a single donor. The evidential value of a DNA profile needs to be combined with knowledge regarding the specificity and sensitivity of cell type tests. This, together with prior probabilities for the different donor-cell type combinations, determines the most likely combination. We present a Bayesian network that can assist in associating donors and cell types. A literature overview on the sensitivity and specificity of three cell type tests (PSA test for seminal fluid, RSID saliva and RSID semen) is helpful in assigning conditional probabilities. The Bayesian network is linked with a software package for interpreting mixed DNA profiles. This allows for a sensitivity analysis that shows to what extent the conclusion depends on the quantity of available research data. This can aid in making decisions regarding further research. It is shown that the common assumption that an individual (e.g. the victim) is one of the donors in a mixed DNA profile can have unwanted consequences for the association between donors and cell types.
Collapse
Affiliation(s)
- Jacob de Zoete
- University of Amsterdam, Korteweg de Vries Instituut voor Wiskunde, Postbus 94248, 1090 GE Amsterdam, The Netherlands.
| | - Wessel Oosterman
- University of Amsterdam, Korteweg de Vries Instituut voor Wiskunde, Postbus 94248, 1090 GE Amsterdam, The Netherlands.
| | - Bas Kokshoorn
- Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB Den Haag, The Netherlands(1).
| | - Marjan Sjerps
- Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB Den Haag, The Netherlands(1).
| |
Collapse
|
10
|
Bright JA, Taylor D, McGovern C, Cooper S, Russell L, Abarno D, Buckleton J. Developmental validation of STRmix™, expert software for the interpretation of forensic DNA profiles. Forensic Sci Int Genet 2016; 23:226-239. [DOI: 10.1016/j.fsigen.2016.05.007] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Revised: 05/09/2016] [Accepted: 05/10/2016] [Indexed: 11/16/2022]
|
11
|
Kruijver M. Characterizing the genetic structure of a forensic DNA database using a latent variable approach. Forensic Sci Int Genet 2016; 23:130-149. [DOI: 10.1016/j.fsigen.2016.03.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Revised: 02/24/2016] [Accepted: 03/21/2016] [Indexed: 12/11/2022]
|
12
|
Swaminathan H, Garg A, Grgicak CM, Medard M, Lun DS. CEESIt: A computational tool for the interpretation of STR mixtures. Forensic Sci Int Genet 2016; 22:149-160. [DOI: 10.1016/j.fsigen.2016.02.005] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Revised: 01/25/2016] [Accepted: 02/10/2016] [Indexed: 12/18/2022]
|
13
|
Slooten K. Familial searching on DNA mixtures with dropout. Forensic Sci Int Genet 2016; 22:128-138. [DOI: 10.1016/j.fsigen.2016.02.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Revised: 12/11/2015] [Accepted: 02/01/2016] [Indexed: 11/25/2022]
|
14
|
Marsden CD, Rudin N, Inman K, Lohmueller KE. An assessment of the information content of likelihood ratios derived from complex mixtures. Forensic Sci Int Genet 2016; 22:64-72. [PMID: 26851613 DOI: 10.1016/j.fsigen.2016.01.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Revised: 01/06/2016] [Accepted: 01/16/2016] [Indexed: 11/17/2022]
Abstract
With the increasing sensitivity of DNA typing methodologies, as well as increasing awareness by law enforcement of the perceived capabilities of DNA typing, complex mixtures consisting of DNA from two or more contributors are increasingly being encountered. However, insufficient research has been conducted to characterize the ability to distinguish a true contributor (TC) from a known non-contributor (KNC) in these complex samples, and under what specific conditions. In order to investigate this question, sets of six 15-locus Caucasian genotype profiles were simulated and used to create mixtures containing 2-5 contributors. Likelihood ratios were computed for various situations, including varying numbers of contributors and unknowns in the evidence profile, as well as comparisons of the evidence profile to TCs and KNCs. This work was intended to illustrate the best-case scenario, in which all alleles from the TC were detected in the simulated evidence samples. Therefore the possibility of drop-out was not modeled in this study. The computer program DNAMIX was then used to compute LRs comparing the evidence profile to TCs and KNCs. This resulted in 140,000 LRs for each of the two scenarios. These complex mixture simulations show that, even when all alleles are detected (i.e. no drop-out), TCs can generate LRs less than 1 across a 15-locus profile. However, this outcome was rare, 7 of 140,000 replicates (0.005%), and associated only with mixtures comprising 5 contributors in which the numerator hypothesis includes one or more unknown contributors. For KNCs, LRs were found to be greater than 1 in a small number of replicates (75 of 140,000 replicates, or 0.05%). These replicates were limited to 4 and 5 person mixtures with 1 or more unknowns in the numerator. Only 5 of these 75 replicates (0.004%) yielded an LR greater than 1,000. Thus, overall, these results imply that the weight of evidence that can be derived from complex mixtures containing up to 5 contributors, under a scenario in which no drop-out is required to explain any of the contributors, is remarkably high. This is a useful benchmark result on top of which to layer the effects of additional factors, such as drop-out, peak height, and other variables.
Collapse
Affiliation(s)
- Clare D Marsden
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 621 Charles E. Young Dr. South, Los Angeles, CA 90095-1606, USA
| | - Norah Rudin
- Forensic DNA Consultant, 650 Castro Street, Suite 120-404, Mountain View, CA, 94041, USA
| | - Keith Inman
- Department of Criminal Justice Administration, California State University, East Bay, 4069 Meiklejohn Hall, 25800 Carlos Bee Boulevard, Hayward, CA 94542, USA
| | - Kirk E Lohmueller
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 621 Charles E. Young Dr. South, Los Angeles, CA 90095-1606, USA.
| |
Collapse
|
15
|
Inman K, Rudin N, Cheng K, Robinson C, Kirschner A, Inman-Semerau L, Lohmueller KE. Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles. BMC Bioinformatics 2015; 16:298. [PMID: 26384762 PMCID: PMC4575494 DOI: 10.1186/s12859-015-0740-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2015] [Accepted: 09/12/2015] [Indexed: 11/17/2022] Open
Abstract
Background Technological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists. Results To address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer. Conclusions Lab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0740-8) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Keith Inman
- Department of Criminal Justice Administration, California State University, East Bay, 25800 Carlos Bee Boulevard, Hayward, CA, 94542, USA.
| | - Norah Rudin
- , 650 Castro Street, Suite 120-404, Mountain View, CA, 94041, USA.
| | - Ken Cheng
- , 1224 Burnham Dr, San Jose, CA, 95132, USA.
| | | | | | | | - Kirk E Lohmueller
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 621 Charles E. Young Dr. South, Los Angeles, CA, 90095-1606, USA.
| |
Collapse
|
16
|
p -Values should not be used for evaluating the strength of DNA evidence. Forensic Sci Int Genet 2015; 16:226-231. [DOI: 10.1016/j.fsigen.2015.01.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2014] [Revised: 12/30/2014] [Accepted: 01/14/2015] [Indexed: 01/08/2023]
|
17
|
Gill P, Haned H, Bleka O, Hansson O, Dørum G, Egeland T. Genotyping and interpretation of STR-DNA: Low-template, mixtures and database matches-Twenty years of research and development. Forensic Sci Int Genet 2015; 18:100-17. [PMID: 25866376 DOI: 10.1016/j.fsigen.2015.03.014] [Citation(s) in RCA: 92] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Revised: 03/19/2015] [Accepted: 03/24/2015] [Indexed: 12/17/2022]
Abstract
The introduction of Short Tandem Repeat (STR) DNA was a revolution within a revolution that transformed forensic DNA profiling into a tool that could be used, for the first time, to create National DNA databases. This transformation would not have been possible without the concurrent development of fluorescent automated sequencers, combined with the ability to multiplex several loci together. Use of the polymerase chain reaction (PCR) increased the sensitivity of the method to enable the analysis of a handful of cells. The first multiplexes were simple: 'the quad', introduced by the defunct UK Forensic Science Service (FSS) in 1994, rapidly followed by a more discriminating 'six-plex' (Second Generation Multiplex) in 1995 that was used to create the world's first national DNA database. The success of the database rapidly outgrew the functionality of the original system - by the year 2000 a new multiplex of ten-loci was introduced to reduce the chance of adventitious matches. The technology was adopted world-wide, albeit with different loci. The political requirement to introduce pan-European databases encouraged standardisation - the development of European Standard Set (ESS) of markers comprising twelve-loci is the latest iteration. Although development has been impressive, the methods used to interpret evidence have lagged behind. For example, the theory to interpret complex DNA profiles (low-level mixtures), had been developed fifteen years ago, but only in the past year or so, are the concepts starting to be widely adopted. A plethora of different models (some commercial and others non-commercial) have appeared. This has led to a confusing 'debate' about the 'best' to use. The different models available are described along with their advantages and disadvantages. A section discusses the development of national DNA databases, along with details of an associated controversy to estimate the strength of evidence of matches. Current methodology is limited to searches of complete profiles - another example where the interpretation of matches has not kept pace with development of theory. STRs have also transformed the area of Disaster Victim Identification (DVI) which frequently requires kinship analysis. However, genotyping efficiency is complicated by complex, degraded DNA profiles. Finally, there is now a detailed understanding of the causes of stochastic effects that cause DNA profiles to exhibit the phenomena of drop-out and drop-in, along with artefacts such as stutters. The phenomena discussed include: heterozygote balance; stutter; degradation; the effect of decreasing quantities of DNA; the dilution effect.
Collapse
Affiliation(s)
- Peter Gill
- Norwegian Institute of Public Health, Department of Forensic Biology, PO Box 4404 Nydalen, 0403 Oslo, Norway; Department of Forensic Medicine, Sognsvannsveien 20, Rikshospitalet, 0372 Oslo, Norway.
| | - Hinda Haned
- Netherlands Forensic Institute, Department of Human Biological Traces, The Hague, The Netherlands
| | - Oyvind Bleka
- Norwegian Institute of Public Health, Department of Forensic Biology, PO Box 4404 Nydalen, 0403 Oslo, Norway
| | - Oskar Hansson
- Norwegian Institute of Public Health, Department of Forensic Biology, PO Box 4404 Nydalen, 0403 Oslo, Norway
| | - Guro Dørum
- Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Aas, Norway
| | - Thore Egeland
- Norwegian Institute of Public Health, Department of Forensic Biology, PO Box 4404 Nydalen, 0403 Oslo, Norway; Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Aas, Norway
| |
Collapse
|
18
|
Taylor D, Buckleton J, Evett I. Testing likelihood ratios produced from complex DNA profiles. Forensic Sci Int Genet 2015; 16:165-171. [PMID: 25621923 DOI: 10.1016/j.fsigen.2015.01.008] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2014] [Revised: 01/13/2015] [Accepted: 01/16/2015] [Indexed: 11/19/2022]
Abstract
The performance of any model used to analyse DNA profile evidence should be tested using simulation, large scale validation studies based on ground-truth cases, or alignment with trends predicted by theory. We investigate a number of diagnostics to assess the performance of the model using Hd true tests. Of particular focus in this work is the proportion of comparisons to non-contributors that yield a likelihood ratio (LR) higher than or equal to the likelihood ratio of a known contributor (LRPOI), designated as p, and the average LR for Hd true tests. Theory predicts that p should always be less than or equal to 1/LRPOI and hence the observation of this in any particular case is of limited use. A better diagnostic is the average LR for Hd true which should be near to 1. We test the performance of a continuous interpretation model on nine DNA profiles of varying quality and complexity and verify the theoretical expectations.
Collapse
Affiliation(s)
- Duncan Taylor
- Forensic Science South Australia, 21 Divett Place, Adelaide, SA 5000, Australia; School of Biological Sciences, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia.
| | | | - Ian Evett
- Principal Forensic Services Ltd., London, UK
| |
Collapse
|
19
|
Efficient computations with the likelihood ratio distribution. Forensic Sci Int Genet 2015; 14:116-24. [DOI: 10.1016/j.fsigen.2014.09.018] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2014] [Revised: 08/04/2014] [Accepted: 09/23/2014] [Indexed: 12/15/2022]
|
20
|
Haned H, Benschop CCG, Gill PD, Sijen T. Complex DNA mixture analysis in a forensic context: evaluating the probative value using a likelihood ratio model. Forensic Sci Int Genet 2014; 16:17-25. [PMID: 25485478 DOI: 10.1016/j.fsigen.2014.11.014] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Revised: 11/14/2014] [Accepted: 11/16/2014] [Indexed: 12/01/2022]
Abstract
The interpretation of mixed DNA profiles obtained from low template DNA samples has proven to be a particularly difficult task in forensic casework. Newly developed likelihood ratio (LR) models that account for PCR-related stochastic effects, such as allelic drop-out, drop-in and stutters, have enabled the analysis of complex cases that would otherwise have been reported as inconclusive. In such samples, there are uncertainties about the number of contributors, and the correct sets of propositions to consider. Using experimental samples, where the genotypes of the donors are known, we evaluated the feasibility and the relevance of the interpretation of high order mixtures, of three, four and five donors. The relative risks of analyzing high order mixtures of three, four, and five donors, were established by comparison of a 'gold standard' LR, to the LR that would be obtained in casework. The 'gold standard' LR is the ideal LR: since the genotypes and number of contributors are known, it follows that the parameters needed to compute the LR can be determined per contributor. The 'casework LR' was calculated as used in standard practice, where unknown donors are assumed; the parameters were estimated from the available data. Both LRs were calculated using the basic standard model, also termed the drop-out/drop-in model, implemented in the LRmix module of the R package Forensim. We show how our results furthered the understanding of the relevance of analyzing high order mixtures in a forensic context. Limitations are highlighted, and it is illustrated how our study serves as a guide to implement likelihood ratio interpretation of complex DNA profiles in forensic casework.
Collapse
Affiliation(s)
- Hinda Haned
- Department of Human Biological Traces, Netherlands Forensic Institute, P.O. Box 24044, 2490 AA The Hague, The Netherlands.
| | - Corina C G Benschop
- Department of Human Biological Traces, Netherlands Forensic Institute, P.O. Box 24044, 2490 AA The Hague, The Netherlands.
| | - Peter D Gill
- National Institute of Public Health, Department of Forensic Biology, P.O. Box 4404 Nydalen, 0403 Oslo, Norway; National Institute of Public Health, Department of Forensic Medicine, P.O. Box 4950 Nydalen, 0424 Oslo, Norway.
| | - Titia Sijen
- Department of Human Biological Traces, Netherlands Forensic Institute, P.O. Box 24044, 2490 AA The Hague, The Netherlands.
| |
Collapse
|
21
|
Kaur N, Fonneløp AE, Egeland T. Regression models for DNA-mixtures. Forensic Sci Int Genet 2014; 11:105-10. [PMID: 24709581 DOI: 10.1016/j.fsigen.2014.03.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2013] [Revised: 02/26/2014] [Accepted: 03/02/2014] [Indexed: 10/25/2022]
Abstract
This paper deals with the statistical interpretation of DNA mixture evidence. The conventional methods used in forensic casework today use something like 16 STR-markers. Power can be increased by rather using SNP-markers. New statistical methods are then needed, and we present a regression framework. The basic idea is that the traditional forensic hypotheses, commonly denoted HD and HP, are replaced by parametric versions: a person contributes to a mixture if and only if the fraction he contributes is greater than 0. This contributed fraction is a parameter of the regression model. The regression model uses the peak heights directly and there is no need to specify or estimate the number of contributors to the mixture. Also, drop-in and drop-out pose no principal problems. Data from 25 controlled blinded experiments were used to test the model. The number of contributors varied between 2 and 5, and the fractions contributed ranged from 0.01 to 0.99. The fractions were accurately estimated by the regression analyses. There were no false positives (i.e., in no cases were non-contributors declared to contributors). Some false negatives occurred for fractions of 0.1 or lower. Simulations were performed to test the model further. The analyses show that useful estimates can be obtained from a relatively small number of SNP-markers. Reasonable results are achieved using 300 markers which is close to the 313 SNPs in the controlled experiment. Increasing the number of SNPs, the analyses demonstrate that individuals contributing as little as 1% can reliably be detected, which suggests that cases beyond the reach of conventional forensic methods today can be reported.
Collapse
Affiliation(s)
- Navreet Kaur
- IKBM, Norwegian University of Life Sciences, Ås, Norway.
| | | | - Thore Egeland
- IKBM, Norwegian University of Life Sciences, Ås, Norway; Norwegian Institute of Public Health, Oslo, Norway
| |
Collapse
|
22
|
Likelihood ratios for complex mixtures with relatives. FORENSIC SCIENCE INTERNATIONAL GENETICS SUPPLEMENT SERIES 2013. [DOI: 10.1016/j.fsigss.2013.10.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|