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  • Springer Science and Business Media LLC  (2)
  • Branicki, Wojciech  (2)
  • 2020-2024  (2)
  • 1
    Online Resource
    Online Resource
    Exploring the possibility of predicting human head hair greying from DNA using whole-exome and targeted NGS data
    Springer Science and Business Media LLC ; 2020
    In:  BMC Genomics Vol. 21, No. 1 ( 2020-12)
    Details
    In: BMC Genomics, Springer Science and Business Media LLC, Vol. 21, No. 1 ( 2020-12)
    Abstract: Greying of the hair is an obvious sign of human aging. In addition to age, sex- and ancestry-specific patterns of hair greying are also observed and the progression of greying may be affected by environmental factors. However, little is known about the genetic control of this process. This study aimed to assess the potential of genetic data to predict hair greying in a population of nearly 1000 individuals from Poland. Results The study involved whole-exome sequencing followed by targeted analysis of 378 exome-wide and literature-based selected SNPs. For the selection of predictors, the minimum redundancy maximum relevance (mRMRe) method was used, and then two prediction models were developed. The models included age, sex and 13 unique SNPs. Two SNPs of the highest mRMRe score included whole-exome identified KIF1A rs59733750 and previously linked with hair loss FGF5 rs7680591. The model for greying vs. no greying prediction achieved accuracy of cross-validated AUC = 0.873. In the 3-grade classification cross-validated AUC equalled 0.864 for no greying, 0.791 for mild greying and 0.875 for severe greying. Although these values present fairly accurate prediction, most of the prediction information was brought by age alone. Genetic variants explained 〈  10% of hair greying variation and the impact of particular SNPs on prediction accuracy was found to be small. Conclusions The rate of changes in human progressive traits shows inter-individual variation, therefore they are perceived as biomarkers of the biological age of the organism. The knowledge on the mechanisms underlying phenotypic aging can be of special interest to the medicine, cosmetics industry and forensics. Our study improves the knowledge on the genetics underlying hair greying processes, presents prototype models for prediction and proves hair greying being genetically a very complex trait. Finally, we propose a four-step approach based on genetic and epigenetic data analysis allowing for i) sex determination; ii) genetic ancestry inference; iii) greying-associated SNPs assignment and iv) epigenetic age estimation, all needed for a final prediction of greying.
    Type of Medium: Online Resource
    ISSN: 1471-2164
    URL: Article
    DOI: 10.1186/s12864-020-06926-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2041499-7
    SSG: 12
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  • 2
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    Searching for improvements in predicting human eye colour from DNA
    Springer Science and Business Media LLC ; 2021
    In:  International Journal of Legal Medicine Vol. 135, No. 6 ( 2021-11), p. 2175-2187
    Details
    In: International Journal of Legal Medicine, Springer Science and Business Media LLC, Vol. 135, No. 6 ( 2021-11), p. 2175-2187
    Abstract: Increasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.
    Type of Medium: Online Resource
    ISSN: 0937-9827 , 1437-1596
    URL: Article
    DOI: 10.1007/s00414-021-02645-5
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 1459222-8
    SSG: 2
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