International Journal of Legal Medicine

, Volume 129, Issue 6, pp 1191–1200 | Cite as

Assessment of IrisPlex-based multiplex for eye and skin color prediction with application to a Portuguese population

  • Paulo DarioEmail author
  • Helena Mouriño
  • Ana Rita Oliveira
  • Isabel Lucas
  • Teresa Ribeiro
  • Maria João Porto
  • Jorge Costa Santos
  • Deodália Dias
  • Francisco Corte Real
Original Article


DNA phenotyping research is one of the most emergent areas of forensic genetics. Predictions of externally visible characteristics are possible through analysis of single nucleotide polymorphisms. These tools can provide police with “intelligence” in cases where there are no obvious suspects and unknown biological samples found at the crime scene do not result in any criminal DNA database hits. IrisPlex, an eye color prediction assay, revealed high prediction rates for blue and brown eye color in European populations. However, this is less predictive in some non-European populations, probably due to admixing. When compared to other European countries, Portugal has a relatively admixed population, resulting from a genetic influx derived from its proximity to and historical relations with numerous African territories. The aim of this work was to evaluate the utility of IrisPlex in the Portuguese population. Furthermore, the possibility of supplementing this multiplex with additional markers to also achieve skin color prediction within this population was evaluated. For that, IrisPlex was augmented with additional SNP loci. Eye and skin color prediction was estimated using the multinomial logistic regression and binomial logistic regression models, respectively. The results demonstrated eye color prediction accuracies of the IrisPlex system of 90 and 60 % for brown and blue eye color, respectively, and 77 % for intermediate eye color, after allele frequency adjustment. With regard to skin color, it was possible to achieve a prediction accuracy of 93 %. In the future, phenotypic determination multiplexes must include additional loci to permit skin color prediction as presented in this study as this can be an advantageous tool for forensic investigation.


Eye color prediction Skin color prediction EVC Forensic DNA phenotyping IrisPlex Multinomial logistic regression 



The authors would like to express their sincere gratitude to all the volunteers who contributed to this work by providing samples. The authors would also like to thank two anonymous reviewers who gave valuable comments and advice on how to improve the content of this article.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

All experiments were approved by the Governing board of the National Institute of Legal Medicine and Forensic Sciences, Portugal.

Supplementary material

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ESM 1 (PDF 118 kb)
414_2015_1248_MOESM2_ESM.xlsx (31 kb)
ESM 2 (XLSX 30 kb)
414_2015_1248_MOESM3_ESM.docx (16 kb)
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  1. 1.
    Poetsch M, Blohm R, Harder M et al (2013) Prediction of people’s origin from degraded DNA—presentation of SNP assays and calculation of probability. Int J Leg Med 127:347–357. doi: 10.1007/s00414-012-0728-0 CrossRefGoogle Scholar
  2. 2.
    Keating B, Bansal AT, Walsh S et al (2013) First all-in-one diagnostic tool for DNA intelligence: genome-wide inference of biogeographic ancestry, appearance, relatedness, and sex with the Identitas v1 Forensic Chip. Int J Leg Med 127:559–572. doi: 10.1007/s00414-012-0788-1 CrossRefGoogle Scholar
  3. 3.
    Walsh S, Liu F, Wollstein A et al (2013) The HIrisPlex system for simultaneous prediction of hair and eye colour from DNA. Forensic Sci Int Genet 7:98–115. doi: 10.1016/j.fsigen.2012.07.005 CrossRefPubMedGoogle Scholar
  4. 4.
    Pospiech E, Draus-Barini J, Kupiec T et al (2011) Gene-gene interactions contribute to eye colour variation in humans. J Hum Genet 56:447–455. doi: 10.1038/jhg.2011.38 CrossRefPubMedGoogle Scholar
  5. 5.
    Pośpiech E, Wojas-Pelc A, Walsh S et al (2014) The common occurrence of epistasis in the determination of human pigmentation and its impact on DNA-based pigmentation phenotype prediction. Forensic Sci Int Genet 11C:64–72. doi: 10.1016/j.fsigen.2014.01.012 CrossRefGoogle Scholar
  6. 6.
    Kayser M, Schneider PM (2009) DNA-based prediction of human externally visible characteristics in forensics: motivations, scientific challenges, and ethical considerations. Forensic Sci Int Genet 3:154–61. doi: 10.1016/j.fsigen.2009.01.012 CrossRefPubMedGoogle Scholar
  7. 7.
    Walsh S, Liu F, Ballantyne KN et al (2011) IrisPlex: a sensitive DNA tool for accurate prediction of blue and brown eye colour in the absence of ancestry information. Forensic Sci Int Genet 5:170–80. doi: 10.1016/j.fsigen.2010.02.004 CrossRefPubMedGoogle Scholar
  8. 8.
    Liu F, van Duijn K, Vingerling JR et al (2009) Eye color and the prediction of complex phenotypes from genotypes. Curr Biol 19:R192–3. doi: 10.1016/j.cub.2009.01.027 CrossRefPubMedGoogle Scholar
  9. 9.
    Walsh S, Lindenbergh A, Zuniga SB et al (2011) Developmental validation of the IrisPlex system: determination of blue and brown iris colour for forensic intelligence. Forensic Sci Int Genet 5:464–71. doi: 10.1016/j.fsigen.2010.09.008 CrossRefPubMedGoogle Scholar
  10. 10.
    Walsh S, Wollstein A, Liu F et al (2012) DNA-based eye colour prediction across Europe with the IrisPlex system. Forensic Sci Int Genet 6:330–40. doi: 10.1016/j.fsigen.2011.07.009 CrossRefPubMedGoogle Scholar
  11. 11.
    (2004) Scientific Working Group on DNA analysis methods, revised validation guidelines. Forensic Sci. Commun. 6Google Scholar
  12. 12.
    Kastelic V, Pospiech E, Draus-Barini J et al (2013) Prediction of eye color in the Slovenian population using the IrisPlex SNPs. Croat Med J 54:381–386PubMedCentralCrossRefPubMedGoogle Scholar
  13. 13.
    Yun L, Gu Y, Rajeevan H, Kidd KK (2014) Application of six IrisPlex SNPs and comparison of two eye color prediction systems in diverse Eurasia populations. Int J Leg Med. doi: 10.1007/s00414-013-0953-1 Google Scholar
  14. 14.
    Dembinski GM, Picard CJ (2014) Evaluation of the IrisPlex DNA-based eye color prediction assay in a United States population. Forensic Sci Int Genet 9:111–117. doi: 10.1016/j.fsigen.2013.12.003 CrossRefPubMedGoogle Scholar
  15. 15.
    Moorjani P, Patterson N, Hirschhorn JN et al (2011) The history of African gene flow into Southern Europeans, Levantines, and Jews. PLoS Genet 7, e1001373. doi: 10.1371/journal.pgen.1001373 PubMedCentralCrossRefPubMedGoogle Scholar
  16. 16.
    Plaza S, Calafell F, Helal A et al (2003) Joining the Pillars of Hercules: mtDNA sequences show multidirectional gene flow in the western Mediterranean. Ann Hum Genet 67:312–328. doi: 10.1046/j.1469-1809.2003.00039.x CrossRefPubMedGoogle Scholar
  17. 17.
    Kennedy H (1996) Muslim Spain and Portugal. A Political History of al-Andalus. LongmanGoogle Scholar
  18. 18.
    Pinto AC, Lloyd-Jones S (2003) The last empire: thirty years of Portuguese decolonization. ix, 156 pGoogle Scholar
  19. 19.
    Han J, Kraft P, Nan H et al (2008) A genome-wide association study identifies novel alleles associated with hair color and skin pigmentation. PLoS Genet 4, e1000074. doi: 10.1371/journal.pgen.1000074 PubMedCentralCrossRefPubMedGoogle Scholar
  20. 20.
    Sulem P, Gudbjartsson DF, Stacey SN et al (2007) Genetic determinants of hair, eye and skin pigmentation in Europeans. Nat Genet 39:1443–1452. doi: 10.1038/ng.2007.13, ng.2007.13 [pii]CrossRefPubMedGoogle Scholar
  21. 21.
    Sulem P, Gudbjartsson DF, Stacey SN et al (2008) Two newly identified genetic determinants of pigmentation in Europeans. Nat Genet 40:835–7. doi: 10.1038/ng.160 CrossRefPubMedGoogle Scholar
  22. 22.
    Branicki W, Brudnik U, Wojas-Pelc A (2009) Interactions between HERC2, OCA2 and MC1R may influence human pigmentation phenotype. Ann Hum Genet 73:160–170. doi: 10.1111/j.1469-1809.2009.00504.x CrossRefPubMedGoogle Scholar
  23. 23.
    Nan H, Kraft P, Qureshi A a et al (2009) Genome-wide association study of tanning phenotype in a population of European ancestry. J Invest Dermatol 129:2250–7. doi: 10.1038/jid.2009.62 PubMedCentralCrossRefPubMedGoogle Scholar
  24. 24.
    Spichenok O, Budimlija ZM, Mitchell AA et al (2011) Prediction of eye and skin color in diverse populations using seven SNPs. Forensic Sci Int Genet 5:472–478. doi: 10.1016/j.fsigen.2010.10.005 CrossRefPubMedGoogle Scholar
  25. 25.
    Sturm RA, Frudakis TN (2004) Eye colour: portals into pigmentation genes and ancestry. Trends Genet 20:327–32. doi: 10.1016/j.tig.2004.06.010 CrossRefPubMedGoogle Scholar
  26. 26.
    Sturm RA, Duffy DL, Zhao ZZ et al (2008) A single SNP in an evolutionary conserved region within intron 86 of the HERC2 gene determines human blue-brown eye color. Am J Hum Genet 82:424–31. doi: 10.1016/j.ajhg.2007.11.005 PubMedCentralCrossRefPubMedGoogle Scholar
  27. 27.
    Leite TK, Fonseca RM, de Franca NM et al (2011) Genomic ancestry, self-reported “color” and quantitative measures of skin pigmentation in Brazilian admixed siblings. PLoS One 6, e27162. doi: 10.1371/journal.pone.0027162 PubMedCentralCrossRefPubMedGoogle Scholar
  28. 28.
    Valenzuela RK, Henderson MS, Walsh MH et al (2010) Predicting phenotype from genotype: normal pigmentation. J Forensic Sci 55:315–322. doi: 10.1111/j.1556-4029.2009.01317.x, JFO1317 [pii]PubMedCentralCrossRefPubMedGoogle Scholar
  29. 29.
    Lamason RL, Mohideen MA, Mest JR (2005) SLC24A5, a putative cation exchanger, affects pigmentation in zebrafish and humans. Science (80-) 310:1782–1786. doi: 10.1126/science.1116238 CrossRefGoogle Scholar
  30. 30.
    Stokowski RP, Pant PV, Dadd T et al (2007) A genomewide association study of skin pigmentation in a South Asian population. Am J Hum Genet 81:1119–1132. doi: 10.1086/522235 PubMedCentralCrossRefPubMedGoogle Scholar
  31. 31.
    Dimisianos G, Stefanaki I, Nicolaou V et al (2009) A study of a single variant allele (rs1426654) of the pigmentation-related gene SLC24A5 in Greek subjects. Exp Dermatol 18:175–177. doi: 10.1111/j.1600-0625.2008.00758.x CrossRefPubMedGoogle Scholar
  32. 32.
    Soejima M, Koda Y (2007) Population differences of two coding SNPs in pigmentation-related genes SLC24A5 and SLC45A2. Int J Leg Med 121:36–39. doi: 10.1007/s00414-006-0112-z CrossRefGoogle Scholar
  33. 33.
    Fitzpatrick TB (1988) The validity and practicality of sun-reactive skin types I through VI. Arch Dermatol 124:869. doi: 10.1001/archderm.1988.01670060015008 CrossRefPubMedGoogle Scholar
  34. 34.
    Walsh PS, Metzger DA, Higuchi R (1991) Chelex 100 as a medium for simple extraction of DNA for PCR-based typing from forensic material. Biotechniques 10:506–513PubMedGoogle Scholar
  35. 35.
    Sturm R (2009) Molecular genetics of human pigmentation diversity. Hum Mol Genet 18:R9–17. doi: 10.1093/hmg/ddp003 CrossRefPubMedGoogle Scholar
  36. 36.
    Dieffenbach CW, Lowe TM, Dveksler GS (1993) General concepts for PCR primer design. PCR Methods Appl 3:S30–7CrossRefPubMedGoogle Scholar
  37. 37.
    Vallone PM, Butler JM (2004) AutoDimer: a screening tool for primer-dimer and hairpin structures. Biotechniques 37:226–31PubMedGoogle Scholar
  38. 38.
    Hosmer DW Jr, Lemeshow S, Sturdivant RX (2013) Applied logistic regression, third edit. Wiley, New JerseyCrossRefGoogle Scholar
  39. 39.
    Croissant Y (2013) mlogit: multinomial logit model. R packageGoogle Scholar
  40. 40.
    R Core Team (2014) R: A language and environment for statistical computingGoogle Scholar
  41. 41.
    Lee K, Ahn H, Moon H et al (2013) Multinomial logistic regression ensembles. J Biopharm Stat 23:681–94. doi: 10.1080/10543406.2012.756500 CrossRefPubMedGoogle Scholar
  42. 42.
    Ferri C, Hernández-Orallo J, Salido M (2003) Volume under the ROC Surface for Multi-class Problems. In: Lavrač N, Gamberger D, Blockeel H, Todorovski L (eds) Mach. Learn. ECML 2003 SE - 12. Springer Berlin Heidelberg, pp 108–120Google Scholar
  43. 43.
    Hand DJ, Till RJ (2001) A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach Learn 45:171–186. doi: 10.1023/A:1010920819831 CrossRefGoogle Scholar
  44. 44.
    Hilbe JM (2009) Logistic regression models. Chapman & Hall/CRC Press, Boca RatonGoogle Scholar
  45. 45.
    McCullagh P, Nelder JA (1989) Generalized linear models, second Edi. Chapman & Hall, LondonCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Paulo Dario
    • 1
    • 2
    • 3
    • 4
    Email author
  • Helena Mouriño
    • 2
  • Ana Rita Oliveira
    • 2
    • 4
  • Isabel Lucas
    • 1
  • Teresa Ribeiro
    • 1
    • 3
  • Maria João Porto
    • 1
    • 3
  • Jorge Costa Santos
    • 1
    • 3
    • 5
  • Deodália Dias
    • 2
    • 4
  • Francisco Corte Real
    • 1
    • 3
    • 6
  1. 1.INMLCF - National Institute of Legal Medicine and Forensic SciencesCoimbraPortugal
  2. 2.Faculty of SciencesUniversity of LisbonLisboaPortugal
  3. 3.CENCIFOR - Forensic Sciences CentreCoimbraPortugal
  4. 4.CESAM - Centre for Environmental and Marine StudiesLisboaPortugal
  5. 5.Faculty of MedicineUniversity of LisbonLisboaPortugal
  6. 6.Faculty of MedicineUniversity of CoimbraCoimbraPortugal

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