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

2015Journal article Restricted access
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dc.contributor.authorDario, Paulo
dc.contributor.authorMouriño, Helena
dc.contributor.authorOliveira, Ana Rita
dc.contributor.authorLucas, Isabel
dc.contributor.authorRibeiro, Teresa
dc.contributor.authorPorto, Maria João
dc.contributor.authorSantos, Jorge Costa
dc.contributor.authorDias, Deodália
dc.contributor.authorCorte Real, Francisco
dc.date.accessioned2022-03-08T18:02:26Z
dc.date.available2022-03-08T18:02:26Z
dc.date.issued2015
dc.description© Springer-Verlag Berlin Heidelberg 2015pt_PT
dc.description.abstractDNA 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationInt J Legal Med (2015) 129:1191-1200pt_PT
dc.identifier.doi10.1007/s00414-015-1248-5pt_PT
dc.identifier.eissn1437-1596
dc.identifier.issn0937-9827
dc.identifier.urihttp://hdl.handle.net/10451/51659
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relation.publisherversionhttps://www.springer.com/journal/414pt_PT
dc.subjectEVCpt_PT
dc.subjectEye color predictionpt_PT
dc.subjectForensic DNA phenotypingpt_PT
dc.subjectIrisPlexpt_PT
dc.subjectMultinomial logistic regressionpt_PT
dc.subjectSkin color predictionpt_PT
dc.titleAssessment of IrisPlex-based multiplex for eye and skin color prediction with application to a Portuguese populationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage1200pt_PT
oaire.citation.issue6pt_PT
oaire.citation.startPage1191pt_PT
oaire.citation.titleInternational Journal of Legal Medicinept_PT
oaire.citation.volume129pt_PT
person.familyNamePaisana da Silva Dario
person.familyNameMouriño Silva Nunes
person.familyNameSantos
person.familyNameDias
person.givenNamePaulo Alexandre
person.givenNameMaria Helena
person.givenNameJorge
person.givenNameDeodália
person.identifierE-2474-2013
person.identifier.ciencia-id0C1A-BD80-FA0F
person.identifier.ciencia-id551F-A4E0-1728
person.identifier.ciencia-id5614-F011-7D56
person.identifier.orcid0000-0002-4203-9179
person.identifier.orcid0000-0001-7606-9643
person.identifier.orcid0000-0002-8925-3068
person.identifier.orcid0000-0002-8771-6727
person.identifier.scopus-author-id24729210300
person.identifier.scopus-author-id7402389332
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication.latestForDiscoveryc7ede27d-cfbb-452c-b2bf-8b2b8166d5be

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