Literature DB >> 30884346

Performance of four models for eye color prediction in an Italian population sample.

Cecilia Salvoro1, Christian Faccinetto2, Luca Zucchelli1, Marika Porto1, Alberto Marino3, Gianluca Occhi1, Gustavo de Los Campos4, Giovanni Vazza5.   

Abstract

Forensic DNA phenotyping (FDP) has recently provided important advancements in forensic investigations, by predicting the physical appearance of a subject from a biological sample, using SNP markers. The majority of operable prediction models have been developed for iris color; however, replication studies to understand their applicability on a worldwide scale are still limited for many of them. In this work, 4 models for eye color prediction (IrisPlex, Ruiz, Allwood and Hart models) were systematically evaluated in a sample of 296 subjects of Italian origin. Genotypes were determined by a custom NGS-based panel targeting all the predictive SNPs included in the 4 tested models. Overall, 60-69% of the Italian sample could be correctly predicted with the IrisPlex, Ruiz and Allwood models, applying the recommended threshold. The IrisPlex model showed the lowest frequency of errors (17%), but also the highest number of inconclusive results (18%). In the absence of the threshold, the highest proportion of correct predictions was again obtained with the IrisPlex model (76%), followed by the Allwood (73%) and the Ruiz (65%) models. Lastly, the Hart predictive algorithm had the lowest error rate (2%), but the majority of predictions (87%) were restricted to the less informative categories of "not-blue" and "not-brown", and correct color predictions were obtained only for 11% of the sample. As observed in previous studies, the majority of incorrect and undefined predictions were ascribable to the intermediate category, which represented 25% of the Italian sample. An adjustment of the IrisPlex (multinomial logistic regression) and Ruiz models (Snipper Bayesian classifier) with Italian allele frequencies gave only minor improvements in predicting intermediate eye color and no remarkable overall changes in performance. This suggests an incomplete knowledge underlying the intermediate colors. Considering the impact of this phenotype in the Italian sample as well as in other admixed populations, future improvements of eye color prediction methods should include a better genetic and phenotypic characterization of this category.
Copyright © 2019 Elsevier B.V. All rights reserved.

Keywords:  Eye color prediction; Forensic DNA phenotyping; Models comparison; NGS SNP genotyping

Mesh:

Year:  2019        PMID: 30884346     DOI: 10.1016/j.fsigen.2019.03.008

Source DB:  PubMed          Journal:  Forensic Sci Int Genet        ISSN: 1872-4973            Impact factor:   4.882


  5 in total

1.  Prediction of eye and hair pigmentation phenotypes using the HIrisPlex system in a Brazilian admixed population sample.

Authors:  Thássia Mayra Telles Carratto; Letícia Marcorin; Guilherme do Valle-Silva; Maria Luiza Guimarães de Oliveira; Eduardo Antônio Donadi; Aguinaldo Luiz Simões; Erick C Castelli; Celso Teixeira Mendes-Junior
Journal:  Int J Legal Med       Date:  2021-04-22       Impact factor: 2.686

Review 2.  Interpol review of forensic biology and forensic DNA typing 2016-2019.

Authors:  John M Butler; Sheila Willis
Journal:  Forensic Sci Int       Date:  2020-02-20       Impact factor: 2.395

3.  A new approach to broaden the range of eye colour identifiable by IrisPlex in DNA phenotyping.

Authors:  Ersilia Paparazzo; Anzor Gozalishvili; Vincenzo Lagani; Silvana Geracitano; Alessia Bauleo; Elena Falcone; Giuseppe Passarino; Alberto Montesanto
Journal:  Sci Rep       Date:  2022-07-27       Impact factor: 4.996

4.  Prediction of Eye Colour in Scandinavians Using the EyeColour 11 (EC11) SNP Set.

Authors:  Olivia Strunge Meyer; Nina Mjølsnes Salvo; Anne Kjærbye; Marianne Kjersem; Mikkel Meyer Andersen; Erik Sørensen; Henrik Ullum; Kirstin Janssen; Niels Morling; Claus Børsting; Gunn-Hege Olsen; Jeppe Dyrberg Andersen
Journal:  Genes (Basel)       Date:  2021-05-27       Impact factor: 4.096

5.  Interpreting Mixture Profiles: Comparison between Precision ID GlobalFiler™ NGS STR Panel v2 and Traditional Methods.

Authors:  Michele Ragazzo; Stefania Carboni; Valerio Caputo; Carlotta Buttini; Laura Manzo; Valeria Errichiello; Giulio Puleri; Emiliano Giardina
Journal:  Genes (Basel)       Date:  2020-05-26       Impact factor: 4.096

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.