Overlapping association signals in the genetics of hair-related phenotypes in humans and their relevance to predictive DNA analysis.

Genetic prediction of different hair phenotypes can help reconstruct the physical appearance of an individual whose biological sample is analyzed in criminal and identification cases. Up to date, forensic prediction models for hair colour, hair shape, hair loss and hair greying have been developed, but studies investigating predictability of hair thickness and density traits are missing. First data suggesting overlapping associations in various hair features have emerged in recent years, suggesting partially common genetic basis and molecular mechanisms, and this knowledge can be used for predictive purposes. Here we aim to broaden our understanding of the genetics underlying head, facial and body hair thickness and density traits and examine the association for a set of literature SNPs. We characterize the overlap in SNP association for various hair phenotypes, the extent of genetic interactions and the potential for genetic prediction. The study involved 999 samples from Poland, genotyped for 240 SNPs with targeted next-generation sequencing. Logistic regression methods were applied for association and prediction analyses while entropy-based approach was used for interaction testing. As a result, we refined known associations for monobrow and hairiness (PAX3, 5q13.2, TBX) and identified two novel association signals in IGFBP5 and VDR. Both genes were among top significant loci, showed broad association with different hair-related traits and were implicated in multiple interaction effects. Overall, for 14.7% of SNPs previously associated with head hair loss and/or hair shape, a positive signal of association was revealed with at least one hair feature studied in the current research. Overlap in association with at least two hair-related traits was demonstrated for 24 distinct loci. We showed that the associated SNPs explain ∼5-30% of the variation observed in particular hair traits and allow moderate accuracy of prediction. The highest accuracy was achieved for hairiness level prediction in females (AUC = 0.69 for the "none", 0.69 for the "low" and 0.76 for the "excessive" hairiness category) and monobrow (AUC = 0.69 for the "none", 0.62 for the "slight" and 0.70 for the "significant" monobrow category) with 33% of the variation in hairiness level in females explained by 7 SNPs and age, and 20% of the variation in monobrow captured by 7 SNPs and sex. Our study presents clear evidence of pleiotropy and epistasis in the genetics of hair traits. The acquired knowledge may have practical application in forensics, as well as in the cosmetic industry and anthropological research.

Searching for improvements in predicting human eye colour from DNA.

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.

Circulating MicroRNAs and Novel Proteins as Potential Biomarkers of Neurological Complications after Heart Bypass Surgery.

Postoperative recovery can be impaired by many conditions, some of which are difficult to diagnose clinically. These include type 2 neurological complications such as hypoactive subtype of postoperative delirium (PD) and early postoperative cognitive dysfunction (ePOCD). Hope for their timely detection may lie with novel biomarkers. Plasma concentrations of microRNA-1-3p, microRNA-21-5p, glial fibrillary acidic protein (GFAP), neuroserpin (NSP), phosphorylated axonal neurofilament subunit H (pNfH) and visinin-like protein 1 (VILIP-1) were investigated in 30 patients undergoing elective off-pump coronary artery bypass grafting. Blood samples were collected at the start and end of a surgery as well as 24 h postoperatively. Associations between the studied biomarkers' perioperative expression and type 2 neurological complications were analyzed. PD was associated with postoperative expression of GFAP; ePOCD was associated with postoperative expression of microRNA-21-5p and GFAP as well as intraoperative expression of NSP. The predictive accuracy of these molecules was found acceptable, with all their areas under the curve (AUC) values above 0.7. Multivariable regression indicated that microRNA-21-5p, GFAP and NSP were the only significant predictors of ePOCD. Evaluation of a multi-marker model including these three molecules revealed its outstanding predictive accuracy for ePOCD (AUC = 0.95). The use of microRNA-21-5p, GFAP and NSP for monitoring postoperative recovery warrants further research considering their potential to predict PD and ePOCD.

Exploring the possibility of predicting human head hair greying from DNA using whole-exome and targeted NGS data.

BACKGROUND: 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.

Epidemiology and chronobiology of out-of-hospital cardiac arrest in a subpopulation of southern Poland: A two-year observation.

BACKGROUND: Although recent studies indicate temporal variations in the incidence of out-of-hospital cardiac arrest (OHCA), the Polish experience in this research is scarce to date. We evaluated the epidemiology of OHCA and circadian, weekly and seasonal variations of OHCA frequency among the adult population of the Opole district, Poland. METHODS: The retrospective analysis of 815 OHCA cases with presumed cardiac etiology was made based on dispatch cards from the Emergency Medical Center in Opole registered during a 2 year period (2006-2007). RESULTS: The incidence of OHCA in the studied population was 1.56/1000 inhabitants per year. Mean age of the group was 69.2 ± 14.2 years, with the majority of men (63%), younger than women (66.1 vs. 74 years, p = 0.0001). The OHCA occurrence increased with age reaching a peak between 71 and 75 years. The incidence of OHCA stayed at stable low levels between 22:00 and 4:59 and started to increase at 5:00, with trimodal peaks: 8:00-10:59, 14:00-15:59 and 18.00-21.59. The lowest number of OHCA occurred from 00:00 to 5:59, the highest from 6:00 to 11:59 (13% vs. 32.4%, p < 0.001). The day with the lowest occurrence of OHCA was Friday, the highest Saturday (10.9% vs. 16%, p = 0.01). Summer was the season of the lowest incidence of OHCA, while winter - the highest (22.6% vs. 26%, p = 0.04). These seasons were the warmest and the coldest one, respectively (average temperature 18.5°C vs. 0°C, p < 0.001). CONCLUSIONS: Circadian and less marked, weekly variability in OHCA occurrence were confirmed. Existing seasonal differences may be affected by temperature. This is the first Polish analysis of a large subpopulation, which also includes seasonal temperature data.

DNA-based predictive models for the presence of freckles.

Freckles or ephelides are hyperpigmented spots observed on skin surface mainly in European and Asian populations. Easy recognition and external visibility make prediction of ephelides, the potentially useful target in the field of forensic DNA phenotyping. Prediction of freckles would be a step forward in sketching the physical appearance of unknown perpetrators or decomposed cadavers for the forensic DNA intelligence purposes. Freckles are especially common in people with pale skin and red hair and therefore it is expected that predisposition to freckles may partially share the genetic background with other pigmentation traits. The first proposed freckle prediction model was developed based on investigation that involved variation of MC1R and 8 SNPs from 7 genes in a Spanish cohort [19]. In this study we examined 113 DNA variants from 46 genes previously associated with human pigmentation traits and assessed their impact on freckles presence in a group of 960 individuals from Poland. Nineteen DNA variants revealed associations with the freckle phenotype and the study also revealed that females have ∼1.8 higher odds of freckles presence comparing to males (p-value = 9.5 × 10-5). Two alternative prediction models were developed using regression methods. A simplified binomial 12-variable model predicts the presence of ephelides with cross-validated AUC = 0.752. A multinomial 14-variable model predicts one of three categories - non-freckled, medium freckled and heavily freckled. The two extreme categories, non-freckled and heavily freckled were predicted with moderately high accuracy of cross-validated AUC = 0.754 and 0.792, respectively. Prediction accuracy of the intermediate category was lower, AUC = 0.657. The study presents novel DNA models for prediction of freckles that can be used in forensic investigations and emphasizes significance of pigmentation genes and sex in predictive DNA analysis of freckles.

Investigating the impact of age-depended hair colour darkening during childhood on DNA-based hair colour prediction with the HIrisPlex system.

Predictive DNA analysis of externally visible characteristics exerts an increasing influence on contemporary forensic and anthropological investigations, with pigmentation traits currently being the most advanced for predictive modelling. Since pigmentation prediction error in some cases may be due to the result of age-related hair colour darkening, and sex influence in eye colour, this study aims to investigate these less explored phenomena on a group of juvenile individuals. Pigmentation phenotypes of children between the age of 6-13 years old were evaluated, in addition to data about their hair colour during early childhood from a select number of these individuals. The HIrisPlex models for DNA-based eye and hair colour prediction were used with input from SNP genotyping using massive parallel sequencing. Analysis of the total group of 476 children showed high accuracy in blue (AUC = 0.89) and brown (AUC = 0.91) eye colour prediction, while hair colour was predicted with AUC = 0.64 for blond, AUC = 0.64 for brown and AUC = 0.97 for red. 70.8% (n = 143) of the total number of children phenotypically blond for hair colour during early childhood progressed to brown during advanced childhood. In 70.6% (n = 101) of those cases, an incorrect blond hair prediction was made during the time of analysis. A noticeable decline in AUC values for blond (from 0.76 to 0.65) and brown (from 0.72 to 0.64) were observed when comparing hair colour prediction outcomes for the phenotypes recorded for the two different time points (at the age of 2-3 and 6-13). The number of incorrect blond hair colour predictions was significantly higher in children with brown hair at age 6-13 who were blond at early childhood (n = 47, 32.9%), relative to children who had brown hair at both time points (n = 6, 9.4%). However, in 28.0% (n = 40) of children who did experience hair colour darkening, HIrisPlex provided the correct prediction for the darkened hair colour phenotype, despite them being blond in early childhood. Our study implies that HIrisPlex can correctly predict adult hair colour in some individuals who experience age-related hair colour darkening during adolescence. However, in most instances prediction seems to default to the pre-adolescent hair colour for individuals with this phenomenon. In the future, the full adolescent age range in which hair colour darkening can occur should be considered in the study samples used for training hair colour prediction models to obtain a more complete picture of the phenomenon and its impact on DNA-based hair colour prediction in adults.

Further evidence for population specific differences in the effect of DNA markers and gender on eye colour prediction in forensics.

The genetics of eye colour has been extensively studied over the past few years, and the identified polymorphisms have been applied with marked success in the field of Forensic DNA Phenotyping. A picture that arises from evaluation of the currently available eye colour prediction markers shows that only the analysis of HERC2-OCA2 complex has similar effectiveness in different populations, while the predictive potential of other loci may vary significantly. Moreover, the role of gender in the explanation of human eye colour variation should not be neglected in some populations. In the present study, we re-investigated the data for 1020 Polish individuals and using neural networks and logistic regression methods explored predictive capacity of IrisPlex SNPs and gender in this population sample. In general, neural networks provided higher prediction accuracy comparing to logistic regression (AUC increase by 0.02-0.06). Four out of six IrisPlex SNPs were associated with eye colour in the studied population. HERC2 rs12913832, OCA2 rs1800407 and SLC24A4 rs12896399 were found to be the most important eye colour predictors (p < 0.007) while the effect of rs16891982 in SLC45A2 was less significant. Gender was found to be significantly associated with eye colour with males having ~1.5 higher odds for blue eye colour comparing to females (p = 0.002) and was ranked as the third most important factor in blue/non-blue eye colour determination. However, the implementation of gender into the developed prediction models had marginal and ambiguous impact on the overall accuracy of prediction confirming that the effect of gender on eye colour in this population is small. Our study indicated the advantage of neural networks in prediction modeling in forensics and provided additional evidence for population specific differences in the predictive importance of the IrisPlex SNPs and gender.

Evaluation of the predictive capacity of DNA variants associated with straight hair in Europeans.

DNA-based prediction of hair morphology, defined as straight, curly or wavy hair, could contribute to an improved description of an unknown offender and allow more accurate forensic reconstructions of physical appearance in the field of forensic DNA phenotyping. Differences in scalp hair morphology are significant at the worldwide scale and within Europe. The only genome-wide association study made to date revealed the Trichohyalin gene (TCHH) to be significantly associated with hair morphology in Europeans and reported weaker associations for WNT10A and FRAS1 genes. We conducted a study that centered on six SNPs located in these three genes with a sample of 528 individuals from Poland. The predictive capacity of the candidate DNA variants was evaluated using logistic regression; classification and regression trees; and neural networks, by applying a 10-fold cross validation procedure. Additionally, an independent test set of 142 males from six European populations was used to verify performance of the developed prediction models. Our study confirmed association of rs11803731 (TCHH), rs7349332 (WNT10A) and rs1268789 (FRAS1) SNPs with hair morphology. The combined genotype risk score for straight hair had an odds ratio of 2.7 and these predictors explained ∼ 8.2% of the total variance. The selected three SNPs were found to predict straight hair with a high sensitivity but low specificity when a 10-fold cross validation procedure was applied and the best results were obtained using the neural networks approach (AUC=0.688, sensitivity=91.2%, specificity=23.0%). Application of the neural networks model with 65% probability threshold on an additional test set gave high sensitivity (81.4%) and improved specificity (50.0%) with a total of 78.7% correct calls, but a high non-classification rate (66.9%). The combined TTGGGG SNP genotype for rs11803731, rs7349332, rs1268789 (European frequency=4.5%) of all six straight hair-associated alleles was identified as the best predictor, giving >80% probability of straight hair. Finally, association testing of 44 SNPs previously identified to be associated with male pattern baldness revealed a suggestive association with hair morphology for rs4679955 on 3q25.1. The study results reported provide the starting point for the development of a predictive test for hair morphology in Europeans. More studies are now needed to discover additional determinants of hair morphology to improve the predictive accuracy of this trait in forensic analysis.