IrisPlex specifications


Unique identifier OMICS_26231
Name IrisPlex
Interface Web user interface
Restrictions to use None
Input format CSV
Computer skills Basic
Stability Stable
Maintained Yes



  • person_outline Manfred Kayser <>

Publications for IrisPlex

IrisPlex in publications

PMCID: 5901501
PMID: 29658972
DOI: 10.1590/1678-4685-GMB-2017-0175

[…] population of buenos aires province (argentina), and to assess the usefulness of current methods of analysis for this country. we studied five single nucleotide polymorphisms (snps) included in the irisplex kit, in 118 individuals, and we quantified eye color with digital iris analysis tool. the markers fit hardy-weinberg equilibrium for the whole sample, but not for rs12913832 within the group […]

PMCID: 5487854
PMID: 28500464
DOI: 10.1007/s00439-017-1808-5

[…] al. , ; zhu et al. ), and subsequent step-wise ranking on how suitable they were for phenotype prediction (liu et al. ) led to the introduction, further development, and forensic validation of the irisplex system (chaitanya et al. ; walsh et al. , , ). it achieved average prediction accuracies, expressed as area under the receiver-operating characteristic curve (auc), of 0.94 for blue, 0.95 […]

PMCID: 5327401
PMID: 28240252
DOI: 10.1038/srep43359

[…] this simplified phenotyping approach. moreover, it provides an accurate prediction from dna with reasonably high accuracies for at least the extreme categories of blue and brown demonstrated via the irisplex system, a system consisting of only six single nucleotide polymorphisms (snps) from six genes. consequently, eye colour was one of the first externally visible characteristic […]

PMCID: 5193413
PMID: 28030571
DOI: 10.1371/journal.pone.0168014

[…] genotype in bone sample a. the eye and hair colour prediction probabilities were calculated for all four options, and resulted in almost identical probability values. we used the enhanced irisplex eye colour prediction model based on a model-underlying reference database of genotypes and phenotypes from >9000 individuals and the enhanced hirisplex hair colour prediction model based […]

PMCID: 4912978
PMID: 27221533
DOI: 10.1007/s00414-016-1388-2

[…] neglected in some populations. in the present study, we re-investigated the data for 1020 polish individuals and using neural networks and logistic regression methods explored predictive capacity of irisplex snps and gender in this population sample. in general, neural networks provided higher prediction accuracy comparing to logistic regression (auc increase by 0.02–0.06). four out of six […]

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IrisPlex institution(s)
Department of Forensic Molecular Biology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands; Centre for Vision and Vascular Science, The Queen’s University Belfast, Belfast, UK; Department of Epidemiology and Biostatistics, National Institute for Health Development, Tallinn, Estonia; Department of Ophthalmology, University of Bergen, School of Medicine, Bergen, Norway; Clinique Ophthalmologique, Universitaire De Creteil, Paris, France; Clinica Oculistica, Universita degli studi di Verona, Italy; Department of Ophthalmology, Aristotle University of Thessaloniki, School of Medicine, Thessaloniki, Greece; Department of Ophthalmology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands; Dpto. Salud Publica Universidad Miguel Hernandez, Alicante, El Centro de Investigacion Biomedica en Red de Epidemiologıa y Salud Publica (CIBERESP), Elche, Spain; Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, UK
IrisPlex funding source(s)
Supported, in part, by the Netherlands Forensic Institute (NFI), a grant from the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) within the framework of the Forensic Genomics Consortium Netherlands (FGCN), the European Commission 5th Framework (QLK6-CT-1999-02094) and by the Estonian Ministry of Education and Science (target funding SF0940026s07).

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