GWAS links variants in neuronal development and actin remodeling related loci with pseudoexfoliation syndrome without glaucoma

Pseudoexfoliation syndrome (PEXS) is an age-related elastosis, strongly associated with the development of secondary glaucoma. It is clearly suggested that PEXS has a genetic component, but this has not been extensively studied. Here, a genome-wide association study (GWAS) using a DNA-pooling approach was conducted to explore the potential association of genetic variants with PEXS in a Polish population, including 103 PEXS patients without glaucoma and 106 perfectly (age- and gender-) matched controls. Individual sample TaqMan genotyping was used to validate GWAS-selected single-nucleotide polymorphism (SNP) associations. Multivariate binary logistic regression analysis was applied to develop a prediction model for PEXS. In total, 15 SNPs representing independent PEXS susceptibility loci were selected for further validation in individual samples. For 14 of these variants, significant differences in the allele and genotype frequencies between cases and controls were identified, of which 12 remained significant after Benjamini-Hochberg adjustment. The minor allele of five SNPs was associated with an increased risk of PEXS development, while for nine SNPs, it showed a protective effect. Beyond the known LOXL1 variant rs2165241, nine other SNPs were located within gene regions, including in OR11L1, CD80, TNIK, CADM2, SORBS2, RNF180, FGF14, FMN1, and RBFOX1 genes. None of these associations with PEXS has previously been reported. Selected SNPs were found to explain nearly 69% of the total risk of PEXS development. The overall risk prediction accuracy for PEXS, expressed by the area under the ROC curve (AUC) value, increased by 0.218, from 0.672 for LOXL1 rs2165241 alone to 0.89 when seven additional SNPs were included in the proposed 8-SNP prediction model. In conclusion, several new susceptibility loci for PEXS without glaucoma suggested that neuronal development and actin remodeling are potentially involved in either PEXS onset or inhibition or delay of its conversion to glaucoma

Model-based prediction of human hair color using DNA variants

Predicting complex human phenotypes from genotypes is the central concept of widely advocated personalized medicine, but so far has rarely led to high accuracies limiting practical applications. One notable exception, although less relevant for medical but important for forensic purposes, is human eye color, for which it has been recently demonstrated that highly accurate prediction is feasible from a small number of DNA variants. Here, we demonstrate that human hair color is predictable from DNA variants with similarly high accuracies. We analyzed in Polish Europeans with single-observer hair color grading 45 single nucleotide polymorphisms (SNPs) from 12 genes previously associated with human hair color variation. We found that a model based on a subset of 13 single or compound genetic markers from 11 genes predicted red hair color with over 0.9, black hair color with almost 0.9, as well as blond, and brown hair color with over 0.8 prevalence-adjusted accuracy expressed by the area under the receiver characteristic operating curves (AUC). The identified genetic predictors also differentiate reasonably well between similar hair colors, such as between red and blond-red, as well as between blond and dark-blond, highlighting the value of the identified DNA variants for accurate hair color prediction

Global skin colour prediction from DNA

Human skin colour is highly heritable and externally visible with relevance in medical, forensic, and anthropological genetics. Although eye and hair colour can already be predicted with high accuracies from small sets of carefully selected DNA markers, knowledge about the genetic predictability of skin colour is limited. Here, we investigate the skin colour predictive value of 77 single-nucleotide polymorphisms (SNPs) from 37 genetic loci previously associated with human pigmentation using 2025 individuals from 31 global populations. We identified a minimal set of 36 highly informative skin colour predictive SNPs and developed a statistical prediction model capable of skin colour prediction on a global scale. Average cross-validated prediction accuracies expressed as area under the receiver-operating characteristic curve (AUC) ± standard deviation were 0.97 ± 0.02 for Light, 0.83 ± 0.11 for Dark, and 0.96 ± 0.03 for Dark-Black. When using a 5-category, this resulted in 0.74 ± 0.05 for Very Pale, 0.72 ± 0.03 for Pale, 0.73 ± 0.03 for Intermediate, 0.87±0.1 for Dark, and 0.97 ± 0.03 for Dark-Black. A comparative analysis in 194 independent samples from 17 populations demonstrated that our model outperformed a previously proposed 10-SNP-classifier approach with AUCs rising from 0.79 to 0.82 for White, comparable at the intermediate level of 0.63 and 0.62, respectively, and a large increase from 0.64 to 0.92 for Black. Overall, this study demonstrates that the chosen DNA markers and prediction model, particularly the 5-category level; allow skin colour predictions within and between continental regions for the first time, which will serve as a valuable resource for future applications in forensic and anthropologic genetics

Fuzzy modelling in supporting investment decisions

Inwestując w akcje z uwzględnieniem analizy fundamentalnej, należy przyjąć odpowiedni okres, z którego pochodzić będą dane. W literaturze można znaleźć sugestie, że w analizach powinno się uwzględnić dane z trzech do pięciu lat. Pozostaje jednak problem określenia oceny wariantów decyzyjnych za badany kilkuletni okres za pomocą jednej wartości. W artykule przedstawione są propozycje zastosowania wielokryterialnej metody TOPSIS w ujęciu rozmytym, w zagadnieniu porządkowania i selekcji walorów giełdowych. Posługując się tą metodą przedstawiono oceny kryterialne jako trójkątne liczby rozmyte.Investing in shares using fundamental analysis means taking into account the appropriate period from which the data will come. In the literature we can find suggestions that in the analysis should be considered data from three to five years. However, there is the problem of determining the assessment of the quoted companies for included several years with one value. In the paper the proposals for use of multi-criteria fuzzy TOPSIS method, in the issue of ordering and selection of shares are presented. Using this method the values of fundamental and market indicators were presented as triangular fuzzy numbers

Quantitative models in constructing investment strategy

Celem artykułu było zastosowanie wybranych modeli optymalizacyjnych w kształtowaniu strategii inwestycyjnych. Porównano portfele skonstruowane na podstawie wybranych wskaźników analizy fundamentalnej z portfelami, w których jako kryterium brano pod uwagę inwestowanie w wartość. Zaproponowano także połączenie ww. podejść w konstruowaniu optymalnych rozwiązań. Zbadano efektywność uzyskanych modeli.The purpose of the article was to apply selected optimization models in building investment strategies. In the analyses, portfolios were compared – portfolios based on fundamental analysis indicators with those which were based on investing in the value strategy. Moreover, both mentioned approaches were combined to build an investment strategy. Additionally, the efficiency of the solutions were analyzed