Potential Predictor of Epigenetic Age Acceleration in Men: 2D:4D Finger Pattern.

OBJECTIVES: Second to fourth digit ratio is widely known indicator of prenatal sex hormones proportion. Higher prenatal androgenization results in longer fourth finger and lower 2D:4D index. The aim of this study was to determine whether the 2D:4D digit ratio is associated with DNA methylation (DNAm) age dependently on sex. MATERIAL AND METHODS: The study included 182 adults (106 females and 76 males) with a mean age of 51.5 ± 13 years. The investigation consisted of three main parts: a survey, anthropometric dimensions measurements (fingers length) and methylome analysis using collected blood samples. Genome-wide methylation was analyzed using EPIC microarray technology. Epigenetic age and epigenetic age acceleration were calculated using several widely applied algorithms. RESULTS: Males with the female left hand pattern had more accelerated epigenetic age than those with the male pattern as calculated with PhenoAge and DNAmTL clocks. CONCLUSIONS: Finger female pattern 2D:4D above or equal to 1 in males is associated with epigenetic age acceleration, indicating that prenatal exposure to estrogens in males may be related to aging process in the later ontogenesis.

The SARS-CoV-2 ORF10 is not essential in vitro or in vivo in humans.

SARS-CoV-2 genome annotation revealed the presence of 10 open reading frames (ORFs), of which the last one (ORF10) is positioned downstream of the N gene. It is a hypothetical gene, which was speculated to encode a 38 aa protein. This hypothetical protein does not share sequence similarity with any other known protein and cannot be associated with a function. While the role of this ORF10 was proposed, there is growing evidence showing that the ORF10 is not a coding region. Here, we identified SARS-CoV-2 variants in which the ORF10 gene was prematurely terminated. The disease was not attenuated, and the transmissibility between humans was maintained. Also, in vitro, the strains replicated similarly to the related viruses with the intact ORF10. Altogether, based on clinical observation and laboratory analyses, it appears that the ORF10 protein is not essential in humans. This observation further proves that the ORF10 should not be treated as the protein-coding gene, and the genome annotations should be amended.

Replication of Severe Acute Respiratory Syndrome Coronavirus 2 in Human Respiratory Epithelium.

Currently, there are four seasonal coronaviruses associated with relatively mild respiratory tract disease in humans. However, there is also a plethora of animal coronaviruses which have the potential to cross the species border. This regularly results in the emergence of new viruses in humans. In 2002, severe acute respiratory syndrome coronavirus (SARS-CoV) emerged and rapidly disappeared in May 2003. In 2012, Middle East respiratory syndrome coronavirus (MERS-CoV) was identified as a possible threat to humans, but its pandemic potential so far is minimal, as human-to-human transmission is ineffective. The end of 2019 brought us information about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emergence, and the virus rapidly spread in 2020, causing an unprecedented pandemic. At present, studies on the virus are carried out using a surrogate system based on the immortalized simian Vero E6 cell line. This model is convenient for diagnostics, but it has serious limitations and does not allow for understanding of the biology and evolution of the virus. Here, we show that fully differentiated human airway epithelium cultures constitute an excellent model to study infection with the novel human coronavirus SARS-CoV-2. We observed efficient replication of the virus in the tissue, with maximal replication at 2 days postinfection. The virus replicated in ciliated cells and was released apically.IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged by the end of 2019 and rapidly spread in 2020. At present, it is of utmost importance to understand the biology of the virus, rapidly assess the treatment potential of existing drugs, and develop new active compounds. While some animal models for such studies are under development, most of the research is carried out in Vero E6 cells. Here, we propose fully differentiated human airway epithelium cultures as a model for studies on SARS-CoV-2.

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.

Impact of excessive alcohol abuse on age prediction using the VISAGE enhanced tool for epigenetic age estimation in blood.

DNA methylation-based clocks provide the most accurate age estimates with practical implications for clinical and forensic genetics. However, the effects of external factors that may influence the estimates are poorly studied. Here, we evaluated the effect of alcohol consumption on epigenetic age prediction in a cohort of extreme alcohol abusers. Blood samples from deceased alcohol abusers and age- and sex-matched controls were analyzed using the VISAGE enhanced tool for age prediction from somatic tissues that enables examination of 44 CpGs within eight age markers. Significantly altered DNA methylation was recorded for alcohol abusers in MIR29B2CHG. This resulted in a mean predicted age of 1.4 years higher compared to the controls and this trend increased in older individuals. The association of alcohol abuse with epigenetic age acceleration, as determined by the prediction analysis performed based on MIR29B2CHG, was small but significant (ß = 0.190; P-value = 0.007). However, the observed alteration in DNA methylation of MIR29B2CHG had a non-significant effect on age estimation with the VISAGE age prediction model. The mean absolute error in the alcohol-abusing cohort was 3.1 years, compared to 3.3 years in the control group. At the same time, upregulation of MIR29B2CHG expression may have a biological function, which merits further studies.

Expression profile of lncRNAs and miRNAs in esophageal cancer: Implications in diagnosis, prognosis, and therapeutic response.

Esophageal cancer is the seventh most common cancer worldwide. Although a number of environmental and lifestyle-related risk factors have been identified for this kind of cancer, the exact molecular mechanisms of tumor evolution have not been clarified yet. Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) as important regulators of gene expression and chromatin configuration have essential roles in the pathogenesis of esophageal cancer. They have been shown to alter the function of cancer-related signaling pathways such as phosphoinositide 3-kinase/protein kinase B and Wnt pathway, thus they might modulate the response of patients to pathway-targeted therapies. Moreover, a number of lncRNAs, such as AFAP1-AS1, UCA1, HOTAIR, LOC285194, and TUSC7, are involved in conferring chemoresistant/radioresistant in esophageal cancer cells. A complex network of interaction exists between lncRNAs and miRNAs in the context of esophageal cancer. Finally, various panels of lncRNAs and miRNAs have been introduced that can predict the survival of esophageal cancer patients. In this review article, we summarize the recent findings regarding the role of miRNAs and lncRNAs in the pathogenesis of esophageal cancer with the special focus on their regulatory roles on signaling pathways, their potential as diagnostic/prognostic markers, and their relevance with therapeutic response.

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.

Meta-analysis of genome-wide association studies identifies 8 novel loci involved in shape variation of human head hair.

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.

Altered cytokine levels and immune responses in patients with SARS-CoV-2 infection and related conditions.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic in early 2020. The infection has been associated with a wide range of clinical symptoms. In the severely affected patients, it has caused dysregulation of immune responses including over-secretion of inflammatory cytokines and imbalances in the proportion of naïve helper T cells, memory helper T cells and regulatory T cells. Identification of the underlying mechanism of such aberrant function of immune system would help in the prediction of disease course and selection of susceptible patients for more intensive cares. In the current review, we summarize the results of studies which reported alterations in cytokine levels and immune cell functions in patients affected with SARS-CoV-2 and related viruses.

Non-coding RNA profile in lung cancer.

Lung cancer is the most frequently diagnosed malignancy and the leading source of cancer-associated mortality. This kind of cancer has heterogeneous nature and is divided into two broad classes of small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). In addition to aberrant expression of several signaling pathways and oncogenes, lung cancer is associated with dysregulation of expression of non-coding RNAs including both long non-coding RNAs (lncRNAs) and miRNAs. These aberrantly expressed transcripts are putative therapeutic targets and diagnostic/ prognostic markers. Integrative assessment of expression of lncRNAs, miRNAs and mRNAs has led to construction of competing endogenous RNA networks in which several lncRNAs act as molecular sponges to inhibit regulatory function of miRNAs on mRNAs. Notably, some of these networks seem to have subtype-specific functions in lung cancer. In this review, we summarize recent findings about the importance of these networks in the pathogenesis of lung cancer and provide a list of onco-miRNAs, tumor suppressor miRNAs, oncogenic lncRNAs and tumor suppressor lncRNAs based on their roles in the carcinogenic process in lung cancer.

DNA methylation in ELOVL2 and C1orf132 correctly predicted chronological age of individuals from three disease groups.

Improving accuracy of the available predictive DNA methods is important for their wider use in routine forensic work. Information on age in the process of identification of an unknown individual may provide important hints that can speed up the process of investigation. DNA methylation markers have been demonstrated to provide accurate age estimation in forensics, but there is growing evidence that DNA methylation can be modified by various factors including diseases. We analyzed DNA methylation profile in five markers from five different genes (ELOVL2, C1orf132, KLF14, FHL2, and TRIM59) used for forensic age prediction in three groups of individuals with diagnosed medical conditions. The obtained results showed that the selected age-related CpG sites have unchanged age prediction capacity in the group of late onset Alzheimer's disease patients. Aberrant hypermethylation and decreased prediction accuracy were found for TRIM59 and KLF14 markers in the group of early onset Alzheimer's disease suggesting accelerated aging of patients. In the Graves' disease patients, altered DNA methylation profile and modified age prediction accuracy were noted for TRIM59 and FHL2 with aberrant hypermethylation observed for the former and aberrant hypomethylation for the latter. Our work emphasizes high utility of the ELOVL2 and C1orf132 markers for prediction of chronological age in forensics by showing unchanged prediction accuracy in individuals affected by three diseases. The study also demonstrates that artificial neural networks could be a convenient alternative for the forensic predictive DNA analyses.

MCPIP1 contributes to clear cell renal cell carcinomas development.

Monocyte Chemoattractant protein-induced protein 1 (MCPIP1), also known as Regnase-1, is encoded by the ZC3H12a gene, and it mediates inflammatory processes by regulating the stability of transcripts coding for proinflammatory cytokines and controlling activity of transcription factors, such as NF-κB and AP1. We found that MCPIP1 transcript and protein levels are strongly downregulated in clear cell renal cell carcinoma (ccRCC) samples, which were derived from patients surgically treated for renal cancer compared to surrounded normal tissues. Using Caki-1 cells as a model, we analyzed the role of MCPIP1 in cancer development. We showed that MCPIP1 expression depends on the proteasome activity; however, hypoxia and hypoxia inducible factor 2 alfa (HIF2α) are key factors lowering MCPIP1 expression. Furthermore, we found that MCPIP1 negatively regulates HIF1α and HIF2α levels and in the case of the last one, the mechanism is based on the regulation of the half time of transcript coding for HIF2α. Enhanced expression of MCPIP1 in Caki-1 cells results in a downregulation of transcripts encoding VEGFA, GLUT1, and IL-6. Furthermore, MCPIP1 decreases the activity of mTOR and protein kinase B (Akt) in normoxic conditions. Taken together, MCPIP1 contributes to the ccRCC development.

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.

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.

MC1R variants increased the risk of sporadic cutaneous melanoma in darker-pigmented Caucasians: a pooled-analysis from the M-SKIP project.

The MC1R gene is a key regulator of skin pigmentation. We aimed to evaluate the association between MC1R variants and the risk of sporadic cutaneous melanoma (CM) within the M-SKIP project, an international pooled-analysis on MC1R, skin cancer and phenotypic characteristics. Data included 5,160 cases and 12,119 controls from 17 studies. We calculated a summary odds ratio (SOR) for the association of each of the nine most studied MC1R variants and of variants combined with CM by using random-effects models. Stratified analysis by phenotypic characteristics were also performed. Melanoma risk increased with presence of any of the main MC1R variants: the SOR for each variant ranged from 1.47 (95%CI: 1.17-1.84) for V60L to 2.74 (1.53-4.89) for D84E. Carriers of any MC1R variant had a 66% higher risk of developing melanoma compared with wild-type subjects (SOR; 95%CI: 1.66; 1.41-1.96) and the risk attributable to MC1R variants was 28%. When taking into account phenotypic characteristics, we found that MC1R-associated melanoma risk increased only for darker-pigmented Caucasians: SOR (95%CI) was 3.14 (2.06-4.80) for subjects with no freckles, no red hair and skin Type III/IV. Our study documents the important role of all the main MC1R variants in sporadic CM and suggests that they have a direct effect on melanoma risk, independently on the phenotypic characteristics of carriers. This is of particular importance for assessing preventive strategies, which may be directed to darker-pigmented Caucasians with MC1R variants as well as to lightly pigmented, fair-skinned subjects.

Epigenetic clock in the aorta and age-related endothelial dysfunction in mice.

While epigenetic age (EA) of mouse blood can be determined using DNA methylation analysis at three CpG sites in the Prima1, Hsf4 and Kcns1 genes it is not known whether this approach is useful for predicting vascular biological age. In this study we validated the 3-CpG estimator for age prediction in mouse blood, developed a new predictive model for EA in mouse aorta, and assessed whether epigenetic age acceleration (EAA) measured with blood and aorta samples correlates with age-dependent endothelial dysfunction. Endothelial function was characterized in vivo by MRI in 8-96-week-old C57BL/6 mice. Arterial stiffness was measured by USG-doppler. EA-related changes within 41 CpG sites in Prima1, Kcns1 and Hsf4 loci, were analyzed in the aorta and blood using bisulfite amplicon high-throughput sequencing. Progressive age-dependent endothelial dysfunction and changes in arterial stiffness were observed in 36-96-week-old C57BL/6 mice. Methylation levels of the investigated loci correlated with chronological age in blood and the aorta. The new model for EA estimation in aorta included three cytosines located in the Kcns1 and Hsf4, explained R2 = 87.8% of the variation in age, and predicted age with an mean absolute error of 9.6 weeks in the independent test set. EAA in the aorta was associated with endothelial dysfunction in the abdominal aorta and femoral artery what was consistent with the EAA direction estimated in blood samples. The rate of vascular biological ageing in mice, reflected by the age-dependent systemic endothelial dysfunction, could be estimated using DNA methylation measurements at three loci in aorta and blood samples.

Recommendations of the Polish-speaking Working Group of the International Society for Forensic Genetics (ISFG-PL) regarding the disclosure of biological traces and the handling of evidence for identification tests. / Zalecenia Polskojezycznej Grupy Miedzynarodowego Towarzystwa Genetyki Sadowej (ISFG-PL) dotyczace ujawniania i identyfikacji sladów biologicznych przeznaczonych do badan genetycznych.

The purpose of this paper is to formulate recommendations for the disclosure of biological traces in the laboratory and the handling of forensic evidence submitted for identification tests, recommended by the Polish Speaking Working Group of the International Society for Forensic Genetics. The paper organizes the knowledge of the most relevant stages of preliminary analysis of biological traces based on both literature sources and those resulting from years of research practice. Recommendations formulated in the course of multi-stage expert consultations contained in this study should be used in the development of laboratory procedures applied during the execution.

DNA methylation at AHRR as a master predictor of smoke exposure and a biomarker for sleep and exercise.

BACKGROUND: DNA methylation profiling may provide a more accurate measure of the smoking status than self-report and may be useful in guiding clinical interventions and forensic investigations. In the current study, blood DNA methylation profiles of nearly 800 Polish individuals were assayed using Illuminia EPIC and the inference of smoking from epigenetic data was explored. In addition, we focused on the role of the AHRR gene as a top marker for smoking and investigated its responsiveness to other lifestyle behaviors. RESULTS: We found > 450 significant CpGs associated with cigarette consumption, and overrepresented in various biological functions including cell communication, response to stress, blood vessel development, cell death, and atherosclerosis. The model consisting of cg05575921 in AHRR (p = 4.5 × 10-32) and three additional CpGs (cg09594361, cg21322436 in CNTNAP2 and cg09842685) was able to predict smoking status with a high accuracy of AUC = 0.8 in the test set. Importantly, a gradual increase in the probability of smoking was observed, starting from occasional smokers to regular heavy smokers. Furthermore, former smokers displayed the intermediate DNA methylation profiles compared to current and never smokers, and thus our results indicate the potential reversibility of DNA methylation after smoking cessation. The AHRR played a key role in a predictive analysis, explaining 21.5% of the variation in smoking. In addition, the AHRR methylation was analyzed for association with other modifiable lifestyle factors, and showed significance for sleep and physical activity. We also showed that the epigenetic score for smoking was significantly correlated with most of the epigenetic clocks tested, except for two first-generation clocks. CONCLUSIONS: Our study suggests that a more rapid return to never-smoker methylation levels after smoking cessation may be achievable in people who change their lifestyle in terms of physical activity and sleep duration. As cigarette smoking has been implicated in the literature as a leading cause of epigenetic aging and AHRR appears to be modifiable by multiple exogenous factors, it emerges as a promising target for intervention and investment.

Analysis of epigenetic clocks links yoga, sleep, education, reduced meat intake, coffee, and a SOCS2 gene variant to slower epigenetic aging.

DNA methylation (DNAm) clocks hold promise for measuring biological age, useful for guiding clinical interventions and forensic identification. This study compared the commonly used DNAm clocks, using DNA methylation and SNP data generated from nearly 1000 human blood or buccal swab samples. We evaluated different preprocessing methods for age estimation, investigated the association of epigenetic age acceleration (EAA) with various lifestyle and sociodemographic factors, and undertook a series of novel genome-wide association analyses for different EAA measures to find associated genetic variants. Our results highlighted the Skin&Blood clock with ssNoob normalization as the most accurate predictor of chronological age. We provided novel evidence for an association between the practice of yoga and a reduction in the pace of aging (DunedinPACE). Increased sleep and physical activity were associated with lower mortality risk score (MRS) in our dataset. University degree, vegetable consumption, and coffee intake were associated with reduced levels of epigenetic aging, whereas smoking, higher BMI, meat consumption, and manual occupation correlated well with faster epigenetic aging, with FitAge, GrimAge, and DunedinPACE clocks showing the most robust associations. In addition, we found a novel association signal for SOCS2 rs73218878 (p = 2.87 × 10-8) and accelerated GrimAge. Our study emphasizes the importance of an optimized DNAm analysis workflow for accurate estimation of epigenetic age, which may influence downstream analyses. The results support the influence of genetic background on EAA. The associated SOCS2 is a member of the suppressor of cytokine signaling family known for its role in human longevity. The reported association between various risk factors and EAA has practical implications for the development of health programs to improve quality of life and reduce premature mortality associated with age-related diseases.

Prediction of eye color in the Slovenian population using the IrisPlex SNPs.

AIM: To evaluate the accuracy of eye color prediction based on six IrisPlex single nucleotide polymorphisms (SNP) in a Slovenian population sample. METHODS: Six IrisPlex predictor SNPs (HERC2 - rs12913832, OCA2 - rs1800407, SLC45A2 - rs16891982 and TYR - rs1393350, SLC24A4 - rs12896399, and IRF4 - rs12203592) of 105 individuals were analyzed using single base extension approach and SNaPshot chemistry. The IrisPlex multinomial regression prediction model was used to infer eye color probabilities. The accuracy of the IrisPlex was assessed through the calculation of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver characteristic operating curves (AUC). RESULTS: Blue eye color was observed in 44.7%, brown in 29.6%, and intermediate in 25.7% participants. Prediction accuracy expressed by the AUC was 0.966 for blue, 0.913 for brown, and 0.796 for intermediate eye color. Sensitivity was 93.6% for blue, 58.1% for brown, and 0% for intermediate eye color. Specificity was 93.1% for blue, 89.2% for brown, and 100% for intermediate eye color. PPV was 91.7% for blue and 69.2% for brown color. NPV was 94.7% for blue and 83.5% for brown eye color. These values indicate prediction accuracy comparable to that established in other studies. CONCLUSION: Blue and brown eye color can be reliably predicted from DNA samples using only six polymorphisms, while intermediate eye color defies prediction, indicating that more research is needed to genetically predict the whole variation of eye color in humans.