Browsing by Author "Pośpiech, Ewelina"

Now showing 1 - 4 of 4
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    The HIrisPlex-S system for eye, hair and skin colour prediction from DNA: Introduction and forensic developmental validation
    (Elsevier, 2018) Chaitanya, Lakshmi; Breslin, Krystal; Zuñiga, Sofia; Wirken, Laura; Pośpiech, Ewelina; Kukla-Bartoszek, Magdalena; Sijen, Titia; de Knijff, Peter; Liu, Fan; Branicki, Wojciech; Kayser, Manfred; Walsh, Susan; Biology, School of Science
    Forensic DNA Phenotyping (FDP), i.e. the prediction of human externally visible traits from DNA, has become a fast growing subfield within forensic genetics due to the intelligence information it can provide from DNA traces. FDP outcomes can help focus police investigations in search of unknown perpetrators, who are generally unidentifiable with standard DNA profiling. Therefore, we previously developed and forensically validated the IrisPlex DNA test system for eye colour prediction and the HIrisPlex system for combined eye and hair colour prediction from DNA traces. Here we introduce and forensically validate the HIrisPlex-S DNA test system (S for skin) for the simultaneous prediction of eye, hair, and skin colour from trace DNA. This FDP system consists of two SNaPshot-based multiplex assays targeting a total of 41 SNPs via a novel multiplex assay for 17 skin colour predictive SNPs and the previous HIrisPlex assay for 24 eye and hair colour predictive SNPs, 19 of which also contribute to skin colour prediction. The HIrisPlex-S system further comprises three statistical prediction models, the previously developed IrisPlex model for eye colour prediction based on 6 SNPs, the previous HIrisPlex model for hair colour prediction based on 22 SNPs, and the recently introduced HIrisPlex-S model for skin colour prediction based on 36 SNPs. In the forensic developmental validation testing, the novel 17-plex assay performed in full agreement with the Scientific Working Group on DNA Analysis Methods (SWGDAM) guidelines, as previously shown for the 24-plex assay. Sensitivity testing of the 17-plex assay revealed complete SNP profiles from as little as 63 pg of input DNA, equalling the previously demonstrated sensitivity threshold of the 24-plex HIrisPlex assay. Testing of simulated forensic casework samples such as blood, semen, saliva stains, of inhibited DNA samples, of low quantity touch (trace) DNA samples, and of artificially degraded DNA samples as well as concordance testing, demonstrated the robustness, efficiency, and forensic suitability of the new 17-plex assay, as previously shown for the 24-plex assay. Finally, we provide an update of the publically available HIrisPlex website https://hirisplex.erasmusmc.nl/, now allowing the estimation of individual probabilities for 3 eye, 4 hair, and 5 skin colour categories from HIrisPlex-S input genotypes. The HIrisPlex-S DNA test represents the first forensically validated tool for skin colour prediction, and reflects the first forensically validated tool for simultaneous eye, hair and skin colour prediction from DNA.
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    Meta-analysis of genome-wide association studies identifies 8 novel loci involved in shape variation of human head hair
    (Oxford University Press, 2018-02-01) Liu, Fan; Chen, Yan; Zhu, Gu; Hysi, Pirro G.; Wu, Sijie; Adhikari, Kaustubh; Breslin, Krystal; Pośpiech, Ewelina; Hamer, Merel A.; Peng, Fuduan; Muralidharan, Charanya; Acuna-Alonzo, Victor; Canizales-Quinteros, Samuel; Bedoya, Gabriel; Gallo, Carla; Poletti, Giovanni; Rothhammer, Francisco; Bortolini, Maria Catira; Gonzalez-Jose, Rolando; Zeng, Changqing; Xu, Shuhua; Jin, Li; Uitterlinden, André G.; Ikram, M. Arfan; van Duijn, Cornelia M.; Nijsten, Tamar; Walsh, Susan; Branicki, Wojciech; Wang, Sijia; Ruiz-Linares, Andrés; Spector, Timothy D.; Martin, Nicholas G.; Medland, Sarah E.; Kayser, Manfred; Biology, School of Science
    Shape variation of human head hair shows striking variation within and between human populations, while its genetic basis is far from being understood. We performed a series of genome-wide association studies (GWASs) and replication studies in a total of 28 964 subjects from 9 cohorts from multiple geographic origins. A meta-analysis of three European GWASs identified 8 novel loci (1p36.23 ERRFI1/SLC45A1, 1p36.22 PEX14, 1p36.13 PADI3, 2p13.3 TGFA, 11p14.1 LGR4, 12q13.13 HOXC13, 17q21.2 KRTAP, and 20q13.33 PTK6), and confirmed 4 previously known ones (1q21.3 TCHH/TCHHL1/LCE3E, 2q35 WNT10A, 4q21.21 FRAS1, and 10p14 LINC00708/GATA3), all showing genome-wide significant association with hair shape (P < 5e-8). All except one (1p36.22 PEX14) were replicated with nominal significance in at least one of the 6 additional cohorts of European, Native American and East Asian origins. Three additional previously known genes (EDAR, OFCC1, and PRSS53) were confirmed at the nominal significance level. A multivariable regression model revealed that 14 SNPs from different genes significantly and independently contribute to hair shape variation, reaching a cross-validated AUC value of 0.66 (95% CI: 0.62-0.70) and an AUC value of 0.64 in an independent validation cohort, providing an improved accuracy compared with a previous model. Prediction outcomes of 2504 individuals from a multiethnic sample were largely consistent with general knowledge on the global distribution of hair shape variation. Our study thus delivers target genes and DNA variants for future functional studies to further evaluate the molecular basis of hair shape in humans.
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    Testing the impact of trait prevalence priors in Bayesian-based genetic prediction modeling of human appearance traits
    (Elsevier, 2021-01) Katsara, Maria-Alexandra; Branicki, Wojciech; Pośpiech, Ewelina; Hysi, Pirro; Walsh, Susan; Kayser, Manfred; Nothnagel, Michael; VISAGE Consortium; Biology, School of Science
    The prediction of appearance traits by use of solely genetic information has become an established approach and a number of statistical prediction models have already been developed for this purpose. However, given limited knowledge on appearance genetics, currently available models are incomplete and do not include all causal genetic variants as predictors. Therefore such prediction models may benefit from the inclusion of additional information that acts as a proxy for this unknown genetic background. Use of priors, possibly informed by trait category prevalence values in biogeographic ancestry groups, in a Bayesian framework may thus improve the prediction accuracy of previously predicted externally visible characteristics, but has not been investigated as of yet. In this study, we assessed the impact of using trait prevalence-informed priors on the prediction performance in Bayesian models for eye, hair and skin color as well as hair structure and freckles in comparison to the respective prior-free models. Those prior-free models were either similarly defined either very close to the already established ones by using a reduced predictive marker set. However, these differences in the number of the predictive markers should not affect significantly our main outcomes. We observed that such priors often had a strong effect on the prediction performance, but to varying degrees between different traits and also different trait categories, with some categories barely showing an effect. While we found potential for improving the prediction accuracy of many of the appearance trait categories tested by using priors, our analyses also showed that misspecification of those prior values often severely diminished the accuracy compared to the respective prior-free approach. This emphasizes the importance of accurate specification of prevalence-informed priors in Bayesian prediction modeling of appearance traits. However, the existing literature knowledge on spatial prevalence is sparse for most appearance traits, including those investigated here. Due to the limitations in appearance trait prevalence knowledge, our results render the use of trait prevalence-informed priors in DNA-based appearance trait prediction currently infeasible.
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    Towards broadening Forensic DNA Phenotyping beyond pigmentation: Improving the prediction of head hair shape from DNA
    (Elsevier, 2018-11) Pośpiech, Ewelina; Chen, Yan; Kukla-Bartoszek, Magdalena; Breslin, Krystal; Aliferi, Anastasia; Andersen, Jeppe D.; Ballard, David; Chaitanya, Lakshmi; Freire-Aradas, Ana; van der Gaag, Kristiaan J.; Girón-Santamaría, Lorena; Gross, Theresa E.; Gysi, Mario; Huber, Gabriela; Mosquera-Miguel, Ana; Muralidharan, Charanya; Skowron, Malgorzata; Carracedo, Ángel; Haas, Cordula; Morling, Niels; Parson, Walther; Phillips, Christopher; Schneider, Peter M.; Sijen, Titia; Syndercombe-Court, Denise; Vennemann, Marielle; Wu, Sijie; Xu, Shuhua; Jin, Li; Wang, Sijia; Zhu, Ghu; Martin, Nick G.; Medland, Sarah E.; Branicki, Wojciech; Walsh, Susan; Liu, Fan; Kayser, Manfred; Biology, School of Science
    Human head hair shape, commonly classified as straight, wavy, curly or frizzy, is an attractive target for Forensic DNA Phenotyping and other applications of human appearance prediction from DNA such as in paleogenetics. The genetic knowledge underlying head hair shape variation was recently improved by the outcome of a series of genome-wide association and replication studies in a total of 26,964 subjects, highlighting 12 loci of which 8 were novel and introducing a prediction model for Europeans based on 14 SNPs. In the present study, we evaluated the capacity of DNA-based head hair shape prediction by investigating an extended set of candidate SNP predictors and by using an independent set of samples for model validation. Prediction model building was carried out in 9674 subjects (6068 from Europe, 2899 from Asia and 707 of admixed European and Asian ancestries), used previously, by considering a novel list of 90 candidate SNPs. For model validation, genotype and phenotype data were newly collected in 2415 independent subjects (2138 Europeans and 277 non-Europeans) by applying two targeted massively parallel sequencing platforms, Ion Torrent PGM and MiSeq, or the MassARRAY platform. A binomial model was developed to predict straight vs. non-straight hair based on 32 SNPs from 26 genetic loci we identified as significantly contributing to the model. This model achieved prediction accuracies, expressed as AUC, of 0.664 in Europeans and 0.789 in non-Europeans; the statistically significant difference was explained mostly by the effect of one EDAR SNP in non-Europeans. Considering sex and age, in addition to the SNPs, slightly and insignificantly increased the prediction accuracies (AUC of 0.680 and 0.800, respectively). Based on the sample size and candidate DNA markers investigated, this study provides the most robust, validated, and accurate statistical prediction models and SNP predictor marker sets currently available for predicting head hair shape from DNA, providing the next step towards broadening Forensic DNA Phenotyping beyond pigmentation traits.