Evaluation of Informative SNPs in Iranian Azeri Population

Document Type : Original Article

Authors

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

Abstract

Introduction: Two of the most important tests used in the forensic genetics are DNA fingerprinting and paternity testing. Short tandem repeats (STRs) are very frequently used in identification. Nevertheless, there are limitations on the use of these markers in identifying badly degraded DNA. In these cases, single nucleotide polymorphisms (SNPs) are used because of their shorter amplicon lengths. Recognition of SNPs of high informativeness is a vital step in preparing a list of suitable SNPs. The present research aimed to determine the maximum informative SNPs to be used for identification in Iranian Azari population.
Materials and Methods: Four SNPs developed by the SNPforID Consortium were selected. The allele frequencies of the SNPs were obtained using HRM Analysis on DNA samples taken from 100 different individuals. The SNPs that satisfied the Hardy-Weinberg equilibrium and had heterozygosity of higher than 0.50 and equal allele frequency were selected as SNPs of the maximum informativeness.
Results: Two polymorphisms (Rs2107612 and Rs1355366) had equal allele frequencies and heterozygosity of higher than 50 percent. Therefore, these 2 polymorphisms are considered highly informative among the studied Azeri population and can be considered in preparing a list of suitable SNPs.
Conclusions: Results of the present study can be used along with other SNPs to increase the identification power for some samples. It can also help preparing a database of suitable SNPs to be used for identification in Azeri population in Iran.

Keywords

References

  1. Mohammadi A, Ghorbani Alvanegh A, Khafaei M, et al. A New and Efficient Method for DNA Extraction from Human Skeletal Remains Usable in DNA Typing. J Appl Biotechnol Rep. 2017;4(2):609-614.
  2. Amigo J, Phillips C, Lareu M, Carracedo A. The SNPforID browser: an online tool for query and display of frequency data from the SNPforID project. Int J Legal Med. 2008;122(5):435-440. doi:10.1007/s00414-008-0233-7.
  3. Amorim A, Pereira L. Pros and cons in the use of SNPs in forensic kinship investigation: a comparative analysis with STRs. Forensic Sci Int. 2005;150(1):17-21. doi:10.1016/j.forsciint.2004.06.018.
  4. Kayser M, de Knijff P. Improving human forensics through advances in genetics, genomics and molecular biology. Nat Rev Genet. 2011;12(3):179-192. doi:10.1038/nrg2952.
  5. Farjadian S, Sazzini M, Tofanelli S, et al. Discordant patterns of mtDNA and ethno-linguistic variation in 14 Iranian Ethnic groups. Hum Hered. 2011;72(2):73-84. doi:10.1159/000330166.
  6. Twist GP, Gaedigk R, Leeder JS, Gaedigk A. High-resolution melt analysis to detect sequence variations in highly homologous gene regions: application to CYP2B6. Pharmacogenomics. 2013;14(8):913-922. doi:10.2217/pgs.13.66.
  7. Wang J, Pan X, Liang X. Assessment for Melting Temperature Measurement of Nucleic Acid by HRM. J Anal Methods Chem. 2016;2016:5318935. doi:10.1155/2016/5318935.
  8. Pakstis AJ, Speed WC, Kidd JR, Kidd KK. Candidate SNPs for a universal individual identification panel. Hum Genet. 2007;121(3- 4):305-317. doi:10.1007/s00439-007-0342-2.
  9. Saremi MA, Saremi M, Tavallaei M. Rapid genomic DNA extraction (RGDE). Forensic Sci Int Genet Suppl Ser. 2008;1(1):63- 65. doi:10.1016/j.fsigss.2007.12.001.
  10. Quintana-Murci L, Chaix R, Wells RS, et al. Where west meets east: the complex mtDNA landscape of the southwest and Central Asian corridor. Am J Hum Genet. 2004;74(5):827-845. doi:10.1086/383236.
  11. Kim JJ, Han BG, Lee HI, Yoo HW, Lee JK. Development of SNP-based human identification system. Int J Legal Med. 2010;124(2):125-131. doi:10.1007/s00414-009-0389-9.
  12. Lee HY, Park MJ, Yoo JE, Chung U, Han GR, Shin KJ. Selection of twenty-four highly informative SNP markers for human identification and paternity analysis in Koreans. Forensic Sci Int. 2005;148(2-3):107-112. doi:10.1016/j.forsciint.2004.04.073.
  13. Sharafi Farzad M, Tomas C, Borsting C, et al. Analysis of 49 autosomal SNPs in three ethnic groups from Iran: Persians, Lurs and Kurds. Forensic Sci Int Genet. 2013;7(4):471-473. doi:10.1016/j. fsigen.2013.04.001.
  14. Heaton MP, Harhay GP, Bennett GL, et al. Selection and use of SNP markers for animal identification and paternity analysis in U.S. beef cattle. Mamm Genome. 2002;13(5):272-281. doi:10.1007/s00335-001-2146-3.
  15. Parson W, Ballard D, Budowle B, et al. Massively parallel sequencing of forensic STRs: Considerations of the DNA commission of the International Society for Forensic Genetics (ISFG) on minimal nomenclature requirements. Forensic Sci Int Genet. 2016;22:54-63. doi:10.1016/j.fsigen.2016.01.009.
  16. de la Puente M, Santos C, Fondevila M, et al. The Global AIMs Nano set: A 31-plex SNaPshot assay of ancestry-informative SNPs. Forensic Sci Int Genet. 2016;22:81-88. doi:10.1016/j. fsigen.2016.01.015.
  17. Ambers AD, Churchill JD, King JL, et al. More comprehensive forensic genetic marker analyses for accurate human remains identification using massively parallel DNA sequencing. BMC Genomics. 2016;17(Suppl 9):750. doi:10.1186/s12864-016- 3087-2.
  18. Bulbul O, Cherni L, Khodjet-El-Khil H, Rajeevan H, Kidd KK. Evaluating a subset of ancestry informative SNPs for discriminating among Southwest Asian and circum-Mediterranean populations. Forensic Sci Int Genet. 2016;23:153-158. doi:10.1016/j.fsigen.2016.04.010.
  19. Santos HC, Horimoto AV, Tarazona-Santos E, et al. A minimum set of ancestry informative markers for determining admixture proportions in a mixed American population: the Brazilian set. Eur J Hum Genet. 2016;24(5):725-731. doi:10.1038/ejhg.2015.187.
  20. Huang E, Liu C, Zheng J, et al. Genome-wide screen for universal individual identification SNPs based on the HapMap and 1000 Genomes databases. Sci Rep. 2018;8(1):5553. doi:10.1038/s41598-018-23888-0.
Journal of Applied Biotechnology Reports
Volume 5, Issue 3
September 2018
Pages 125-130

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History

  • Receive Date: 26 March 2018
  • Revise Date: 26 June 2018
  • Accept Date: 03 July 2018
  • First Publish Date: 30 September 2018

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