DNA Markers Evaluation to Preparing a Genetic Database for Identification in Crimes and Incidents

Document Type : Mini Review


Human Genetics Research Center, Baqiyatallah University of Medical Sciences Tehran, Iran


Natural disasters and man-made incidents take the lives of many individuals and leave them unable to be identified. In such cases, the only possible way to detect an unidentified body or its parts is through genetic markers. This technology is based on the theory that inter-individual differences result from differences in the genetic information held in their DNA. A genetic database covering a target population can be used whenever necessary to identify otherwise unidentifiable individuals. A total of 99% of the DNA of every human is the same; in fact, only a relatively small amount is different. Genetic tests can be carried out to distinguish individuals from each other. One percent of the variable genomic regions that frequently and sporadically emerge are genetic markers. Several types of sequences, including variable number of tandem repeats (VNTRs), short tandem repeats (STRs), single nucleotide polymorphisms (SNPs), Y chromosomes, and mitochondrial DNAs (mtDNAs) at the DNA level, can be used to identify individuals. Each of these sequences has its own capabilities and limitations and can be used by anyone depending on the type of database application. With a genetic database of individuals working in high-risk occupations such as the armed forces and the software-based management of this information, it will be possible to identify genetically the bodies of unidentifiable victims of accidents and incidents. This study introduces and evaluates the sequences that can be used in genetic databases.


  1. Prinz M, Carracedo A, Mayr WR, et al. DNA Commission of the International Society for Forensic Genetics (ISFG): recommendations regarding the role of forensic genetics for disaster victim identification (DVI). Forensic Sci Int Genet. 2007;1(1):3-12. doi:10.1016/j.fsigen.2006.10.003.
  2. Goodwin W, Linacre A, Hadi S. An Introduction to Forensic Genetics. Wiley; 2011.
  3. Spelbrink JN. Functional organization of mammalian mitochondrial DNA in nucleoids: history, recent developments, and future challenges. IUBMB Life. 2010;62(1):19-32. doi:10.1002/iub.282.
  4. Nelson K, Melton T. Forensic mitochondrial DNA analysis of 116 casework skeletal samples. J Forensic Sci. 2007;52(3):557-561. doi:10.1111/j.1556-4029.2007.00407.x.
  5. Putkonen MT, Palo JU, Cano JM, Hedman M, Sajantila A. Factors affecting the STR amplification success in poorly preserved bone samples. Investig Genet. 2010;1(1):9. doi:10.1186/2041-2223-1-9.
  6. Loreille OM, Diegoli TM, Irwin JA, Coble MD, Parsons TJ. High efficiency DNA extraction from bone by total demineralization. Forensic Sci Int Genet. 2007;1(2):191-195. doi:10.1016/j.fsigen.2007.02.006.
  7. Milos A, Selmanovic A, Smajlovic L, et al. Success rates of nuclear short tandem repeat typing from different skeletal elements. Croat Med J. 2007;48(4):486-493.
  8. Marjanovic D, Durmic-Pasic A, Bakal N, et al. DNA identification of skeletal remains from the World War II mass graves uncovered in Slovenia. Croat Med J. 2007;48(4):513-519.
  9. Vanek D, Saskova L, Koch H. Kinship and Y-chromosome analysis of 7th century human remains: novel DNA extraction and typing procedure for ancient material. Croat Med J. 2009;50(3):286-295. doi:10.3325/cmj.2009.50.286.
  10. Kalmar T, Bachrati CZ, Marcsik A, Rasko I. A simple and efficient method for PCR amplifiable DNA extraction from ancient bones. Nucleic Acids Res. 2000;28(12):E67. doi:10.1093/nar/28.12.e67.
  11. Loreille OM, Parr RL, McGregor KA, et al. Integrated DNA and fingerprint analyses in the identification of 60-year-old mummified human remains discovered in an Alaskan glacier. J Forensic Sci. 2010;55(3):813-818. doi:10.1111/j.1556-4029.2010.01356.x.
  12. Wood AR, Esko T, Yang J, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014;46(11):1173-1186. doi:10.1038/ng.3097.
  13. Ehret GB, Munroe PB, Rice KM, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478(7367):103-109. doi:10.1038/nature10405.
  14. Estrada K, Styrkarsdottir U, Evangelou E, et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat Genet. 2012;44(5):491-501. doi:10.1038/ng.2249.
  15. Dehghan A, Dupuis J, Barbalic M, et al. Meta-analysis of genome-wide association studies in >80 000 subjects identifies multiple loci for C-reactive protein levels. Circulation. 2011;123(7):731- 738. doi:10.1161/CIRCULATIONAHA.110.948570.
  16. Loth DW, Soler Artigas M, Gharib SA, et al. Genome-wide association analysis identifies six new loci associated with forced vital capacity. Nat Genet. 2014;46(7):669-677. doi:10.1038/ng.3011.
  17. Abecasis GR, Auton A, Brooks LD, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491(7422):56-65. doi:10.1038/nature11632.
  18. Csete K, Beer Z, Varga T. Prenatal and newborn paternity testing with DNA analysis. Forensic Sci Int. 2005;147 Suppl:S57-60. doi:10.1016/j.forsciint.2004.09.101.
  19. Geada H, Brito RM, Ribeiro T, Espinheira R. Portuguese population and paternity investigation studies with a multiplex PCR--the AmpFlSTR Profiler Plus. Forensic Sci Int. 2000;108(1):31-37. doi:10.1016/S0379-0738(99)00091-2.
  20. Kemp BM, Smith DG. Use of bleach to eliminate contaminating DNA from the surface of bones and teeth. Forensic Sci Int. 2005;154(1):53-61. doi:10.1016/j.forsciint.2004.11.017.
  21. Bogdanowicz W, Allen M, Branicki W, Lembring M, Gajewska M, Kupiec T. Genetic identification of putative remains of the famous astronomer Nicolaus Copernicus. Proc Natl Acad Sci U S A. 2009;106(30):12279-12282. doi:10.1073/pnas.0901848106.
  22. The International HapMap Project. Nature. 2003;426(6968):789- 796. doi:10.1038/nature02168.
  23. Willer CJ, Schmidt EM, Sengupta S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274- 1283. doi:10.1038/ng.2797.
  24. Smith NL, Huffman JE, Strachan DP, et al. Genetic predictors of fibrin D-dimer levels in healthy adults. Circulation. 2011;123(17):1864- 1872. doi:10.1161/CIRCULATIONAHA.110.009480.
  25. Locke AE, Kahali B, Berndt SI, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197-206. doi:10.1038/nature14177.
  26. Huang J, Sabater-Lleal M, Asselbergs FW, et al. Genome-wide association study for circulating levels of PAI-1 provides novel insights into its regulation. Blood. 2012;120(24):4873-4881. doi:10.1182/blood-2012-06-436188.
  27. Huang J, Huffman JE, Yamakuchi M, et al. Genome-wide association study for circulating tissue plasminogen activator levels and functional follow-up implicates endothelial STXBP5 and STX2. Arterioscler Thromb Vasc Biol. 2014;34(5):1093-1101. doi:10.1161/ATVBAHA.113.302088.
  28. Deloukas P, Kanoni S, Willenborg C, et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013;45(1):25-33. doi:10.1038/ng.2480.
  29. Smith NL, Chen MH, Dehghan A, et al. Novel associations of multiple genetic loci with plasma levels of factor VII, factor VIII, and von Willebrand factor: The CHARGE (Cohorts for Heart and Aging Research in Genome Epidemiology) Consortium. Circulation. 2010;121(12):1382-1392. doi:10.1161/CIRCULATIONAHA.109.869156.
  30. Zuk O, Schaffner SF, Samocha K, et al. Searching for missing heritability: designing rare variant association studies. Proc Natl Acad Sci U S A. 2014;111(4):E455-E464. doi:10.1073/pnas.1322563111.