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Corrigendum to “Evaluation of the IrisPlex DNA-based eye color prediction assay in a United States population” [Forensic Sci. Int. Genet. (2014) 111–117]

  • Gina M. Dembinski
    Affiliations
    Department of Biology and Forensic and Investigative Sciences Program, Indiana University-Purdue University Indianapolis, 723 W. Michigan Street, Indianapolis, IN 46202, USA
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  • Christine J. Picard
    Correspondence
    Corresponding author at: 723 W. Michigan Street, SL 306, Indianapolis, IN 46202, USA. Tel.: +1 317 278 1050.
    Affiliations
    Department of Biology and Forensic and Investigative Sciences Program, Indiana University-Purdue University Indianapolis, 723 W. Michigan Street, Indianapolis, IN 46202, USA
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      After further scrutiny of the genotype and eye color data of the 200 samples used in the dataset while analyzing it for post-published purposes, some inconsistencies were noted which the authors would like to further report here. There were 9 samples (5 in training set, 4 in the validation set) that had their genotypes shifted by one sample in the data so that the true genotype of the sample was not correctly aligned. The erroneously shifted genotypes were corrected and allele frequencies were recalculated for 4 SNPs. These corrections were made to the model parameters and all analyses repeated.
      Maximum prediction accuracies following allele frequency adjustment went from 58% to 77% for brown, from 95% to 79% for blue, and 11% to 65% for intermediate (Table 3). The figures and tables were corrected and are shown below (Fig. 2, Fig. 3 and Table 3, Table 4, Table 5). Briefly to summarize the changes, there was a small increase of sensitivity for blue eye color with all models with an increase from 93% to 95%, and a decrease for brown and intermediate eye colors, 97% to 89% and 93% to 0%, respectively (Table 5). For all models, there was an increase in correct prediction rates for intermediate eye color from 11% to 65% and blue eye color from 55% to 79%, but a decrease in brown eye color from 98% to 57%. Overall frequencies of predictions (Fig. 2, Fig. 3) mostly decreased in inconclusive results, but also increased in the number of incorrect predictions. AUC values were approximately the same across all models, except with a decrease in intermediate from 0.88 to 0.77 (Table 4). There was no overall change from original conclusions made that the Bayesian network model should still be considered as an optimal prediction model method (over MLR), this is especially true for intermediate sample predictions.
      Table 3The corrected prediction rates (%) by color category of the verification set (N = 100) was evaluated against the IrisPlex regression parameters and the adjusted regression parameters. The verification set was then evaluated using the Bayesian network with either set of a priori odds.
      ParametersThresholdOriginalCorrected
      Brown (%)Intermediate (%)Blue (%)Brown (%)Intermediate (%)Blue (%)
      MLR: IrisPlex0.58809590093
      0.77609579091
      MLR: Adjusted0.5581993795377
      0.7421195794164
      Bayesian: Equal oddsc0.5552080796564
      0.7552098744127
      Bayesian: Adjustedc0.5673098796566
      0.7551598724157
      cEqual odds = 0.33 each eye color category, adjusted odds = 0.39 brown, 0.44 blue, 0.17 intermediate.
      Fig. 2
      Fig. 2Corrected frequency of overall correct, incorrect, and inconclusive eye color predictions using the multinomial regression model. a) Predictions under IrisPlex parameters at the 0.5 threshold, b) predictions under adjusted parameters at the 0.5 threshold, c) predictions under IrisPlex parameters at the 0.7 threshold, and d) predictions under adjusted parameters at the 0.7 threshold.
      Fig. 3
      Fig. 3The corrected frequencies of overall correct, incorrect, and inconclusive eye color predictions using the Bayesian model. a) Predictions under equal odds at the 0.5 threshold, b) predictions under adjusted frequency odds at the 0.5 threshold, c) predictions under equal odds at the 0.7 threshold, and d) predictions under adjusted frequency odds at the 0.7 threshold.
      Table 4Corrected AUC values of each prediction model evaluating the training set (N = 100). AUC reflects model performance (ability to make accurate predictions). Higher AUC value indicates better model performance.
      Prediction ModelOriginalCorrected
      BlueIntermediateBrownBlueIntermediateBrown
      Liu et al.
      • Liu F.
      • van Duijn K.
      • Vingerling J.
      • Hofman A.
      • Uitterlinden A.
      • Janssens A.
      • Kayser M.
      Eye color and the prediction of complex phenotypes from genotypes.
      0.910.730.930.910.730.93
      IrisPlex parameters
      • Walsh S.
      • Liu F.
      • Ballantyne K.N.
      • van Oven M.
      • Lao O.
      • Kayser M.
      IrisPlex: a sensitiveDNA tool for accurate prediction of blue and brown eye colour in the absence of ancestryinformation.
      0.970.840.950.980.830.95
      Adjusted parameters0.970.890.970.970.770.95
      Bayesian: Equal Odds0.970.880.960.970.750.95
      Bayesian: Adjusted0.970.860.960.970.770.95
      Table 5Corrected prediction model performance test characteristics (%) of both regression and Bayesian parameter sets after analysis of the training set (N = 100).
      ModelTest CharacteristicsOriginalCorrected
      BlueIntermediateBrownBlueIntermediateBrown
      MLR: IrisPlexSensitivity959384959487
      Specificity914185912985
      PPV935477935080
      NPV938989938790
      MLR: AdjustedSensitivity959593969889
      Specificity86648586682
      PPV937389953382
      NPV909391908389
      Bayesian: Equal oddsSensitivity93929795092
      Specificity9176778910079
      PPV91659493086
      NPV939587918388
      Bayesian: AdjustedSensitivity93938795092
      Specificity9341828910079
      PPV91548093086
      NPV958988918388
      PPV = positive prediction value (correctly predicted positives), NPV = negative prediction value (correctly predicted negatives).

      References

        • Liu F.
        • van Duijn K.
        • Vingerling J.
        • Hofman A.
        • Uitterlinden A.
        • Janssens A.
        • Kayser M.
        Eye color and the prediction of complex phenotypes from genotypes.
        Curr. Biol. 2009; 19: R192-R193
        • Walsh S.
        • Liu F.
        • Ballantyne K.N.
        • van Oven M.
        • Lao O.
        • Kayser M.
        IrisPlex: a sensitiveDNA tool for accurate prediction of blue and brown eye colour in the absence of ancestryinformation.
        Forensic Sci. Int. Genet. 2011; 5: 170-180

      Linked Article

      • Evaluation of the IrisPlex DNA-based eye color prediction assay in a United States population
        Forensic Science International: GeneticsVol. 9
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          DNA phenotyping is a rapidly developing area of research in forensic biology. Externally visible characteristics (EVCs) can be determined based on genotype data, specifically based on single nucleotide polymorphisms (SNPs). These SNPs are chosen based on their association with genes related to the phenotypic expression of interest, with known examples in eye, hair, and skin color traits. DNA phenotyping has forensic importance when unknown biological samples at a crime scene do not result in a criminal database hit; a phenotypic profile of the sample can therefore be used to develop investigational leads.
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