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Technique and Method

Rapid analyzing mixed STR profiles based on the global minimum residual method

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  • 1. School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
    2. The institute of Forensic Science, Ministry of Public Security, Beijing 100038, China
    3. Department of Biology, Boston University, Boston 02215, USA

Received date: 2023-06-15

  Revised date: 2023-08-14

  Online published: 2023-08-30

Supported by

National Natural Science Foundation of China(12271324);Key Program of Shaanxi Natural Science Foundation(2022ZJ-39);Open Projects of the Key Laboratory of Forensic Genetics of the Ministry of Public Security(2021FGKFKT07)

Abstract

The analysis of mixed short tandem repeat (STR) profiles has been long considered as a difficult challenge in the forensic DNA analysis. In the context of China, the current approach to analyze mixed STR profiles depends mostly on forensic manual method. However, besides the inefficiency, this technique is also susceptible to subjective biases in interpreting analysis results, which can hardly meet up with the growing demand for STR profiles analysis. In response, this study introduces an innovative method known as the global minimum residual method, which not only predicts the proportion of each contributor within a mixture, but also delivers accurate analysis results. The global minimum residual method first gives new definitions to the mixture proportion, then optimizes the allele model. After that, it comprehensively considers all loci present in the STR profile, accumulates and sums the residual values of each locus and selects the mixture proportion with the minimum accumulative sum as the inference result. Furthermore, the grey wolf optimizer is also employed to expedite the search for the optimal value. Notably, for two-person STR profiles, the high accuracy and remarkable efficiency of the global minimum residual method can bring convenience to realize extensive STR profile analysis. The optimization scheme established in this research has exhibited exceptional outcomes in practical applications, boasting significant utility and offering an innovative avenue in the realm of mixed STR profile analysis.

Cite this article

Xin Li, Hong Fan, Xingchun Zhao, Xiaonuo Fan, Ruoxia Yao . Rapid analyzing mixed STR profiles based on the global minimum residual method[J]. Hereditas(Beijing), 2023 , 45(10) : 933 -944 . DOI: 10.16288/j.yczz.23-101

References

[1] Cowell RG, Lauritzen SL, Mortera J. Identification and separation of DNA mixtures using peak area information. Forensic Sci Int, 2007, 166(1): 28-34.
[2] Gill P, Sparkes R, Kimpton C. Development of guidelines to designate alleles using an STR multiplex system. Forensic Sci Int, 1997, 89(3): 185-197.
[3] Bleka ?, Benschop CCG, Storvik G, Gill P. A comparative study of qualitative and quantitative models used to interpret complex STR DNA profiles. Forensic Sci Int Genet, 2016, 25: 85-96.
[4] Budowle B, Onorato AJ, Callaghan TF, Manna AD, Gross AM, Guerrieri RA, Luttman JC, McClure DL. Mixture interpretation: defining the relevant features for guidelines for the assessment of mixed DNA profiles in forensic casework. J Forensic Sci, 2009, 54(4): 810-821.
[5] Taylor D, Bright JA, Buckleton J. The interpretation of single source and mixed DNA profiles. Forensic Sci Int Genet, 2013, 7(5): 516-528.
[6] Swaminathan H, Grgicak CM, Medard M, Lun DC. NOCIt: a computational method to infer the number of contributors to DNA samples analyzed by STR genotyping. Forensic Sci Int Genet, 2015, 16: 172-180.
[7] Curran JM. A MCMC method for resolving two person mixtures. Sci Justice, 2008, 48(4): 168-177.
[8] Gill P, Sparkes R, Pinchin R, Clayton T, Whitaker J, Buckleton J. Interpreting simple STR mixtures using allele peak areas. Forensic Sci Int, 1998, 91(1): 41-53.
[9] Tvedebrink T, Eriksen PS, Mogensen HS, Morling N. Identifying contributors of DNA mixtures by means of quantitative information of STR typing. J Comput Biol, 2012, 19(7): 887-902.
[10] Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Softw, 2014, 69(3): 46-61.
[11] Zhang S, Zhou YQ. Grey wolf optimizer based on powell local optimization method for clustering analysis. Discrete Dyn Nat Soc, 2015, 2015: 1-17.
[12] Zhang S, Zhou YQ, Li ZM, Pan W. Grey wolf optimizer for unmanned combat aerial vehicle path planning. Adv Eng Softw, 2016, 99: 121-136.
[13] Khairuzzaman AKM, Chaudhury S. Multilevel thresholding using grey wolf optimizer for image segmentation. Expert Syst Appl, 2017, 86: 64-76.
[14] Zhang XF, Wang XY. Comprehensive review of grey wolf optimization algorithm. Comput Sci, 2019, 46(3): 30-38.
[14] 张晓凤, 王秀英. 灰狼优化算法研究综述. 计算机科学, 2019, 46(3): 30-38.
[15] Wang T, Xue N, Birdwell JD. Least-square deconvolution: a framework for interpreting short tandem repeat mixtures. J Forensic Sci, 2006, 51(6): 1284-1297.
[16] Tvedebrink T. mixsep: An R-package for DNA mixture separation. Forensic Sci Int: Genet Suppl Ser, 2011, 3(1):e486-e488.
[17] Clayton TM, Whitaker JP, Sparkes R, Gill P. Analysis and interpretation of mixed forensic stains using DNA STR profiling. Forensic Sci Int, 1998, 91(1): 55-70.
[18] B?rsting C, Morling N. Next generation sequencing and its applications in forensic genetics. Forensic Sci Int Genet, 2015, 18: 78-89.
[19] Ji XC, Chi LJ, Xu Z, Peng Z, Ye J, Tu Z, Chen H. SMART: an analysis system for mixed str profiles. Forensic Sci Technol, 2022, 47(1): 1-9.
[19] 季现超, 池连江, 徐珍, 彭柱, 叶健, 凃政, 陈华. SMART:自主研发的混合STR图谱分析系统. 刑事技术, 2022, 47(1): 1-9.
[20] Fan HL, Xie QQ, Wang LX, Ru K, Tan XH, Ding JY, Wang X, Huang J, Wang Z, Li YN, Wang XH, He YT, Gu CH, Liu M, Ma SW, Wen SQ, Qiu PM. Microhaplotype and Y-SNP/STR (MY): A novel MPS-based system for genotype pattern recognition in two-person DNA mixtures. Forensic Sci Int Genet, 2022, 59: 102705.
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