Association of DNA methylation with age, gender, and smoking in an Arab population

DNA methylation correlates of chronological age in diverse human tissue types

BACKGROUND

While the association of chronological age with DNA methylation (DNAm) in whole blood has been extensively studied, the tissue-specificity of age-related DNAm changes remains an active area of research. Studies investigating the association of age with DNAm in tissues such as brain, skin, immune cells, fat, and liver have identified tissue-specific and non-specific effects, thus, motivating additional studies of diverse human tissue and cell types.

RESULTS

Here, we performed an epigenome-wide association study, leveraging DNAm data (Illumina EPIC array) from 961 tissue samples representing 9 tissue types (breast, lung, colon, ovary, prostate, skeletal muscle, testis, whole blood, and kidney) from the Genotype-Tissue Expression (GTEx) project. We identified age-associated CpG sites (false discovery rate < 0.05) in 8 tissues (all except skeletal muscle, n = 47). This included 162,002 unique hypermethylated and 90,626 hypomethylated CpG sites across all tissue types, with 130,137 (80%) hypermethylated CpGs and 74,703 (82%) hypomethylated CpG sites observed in a single tissue type. While the majority of age-associated CpG sites appeared tissue-specific, the patterns of enrichment among genomic features, such as chromatin states and CpG islands, were similar across most tissues, suggesting common mechanisms underlying cellular aging. Consistent with previous findings, we observed that hypermethylated CpG sites are enriched in regions with repressed polycomb signatures and CpG islands, while hypomethylated CpG sites preferentially occurred in non-CpG islands and enhancers. To gain insights into the functional effects of age-related DNAm changes, we assessed the correlation between DNAm and local gene expression changes to identify age-related expression quantitative trait methylation (age-eQTMs). We identified several age-eQTMs present in multiple tissue-types, including in the CDKN2A, HENMT1, and VCWE regions.

CONCLUSION

Overall, our findings will aid future efforts to develop biomarkers of aging and understand mechanisms of aging in diverse human tissue types.

Age-related promoter-switch regulates Runx1 expression in adult rat hearts

BACKGROUND

Runt-related transcription factor-1 (RUNX1), a key member of the core-binding factor family of transcription factors, has emerged as a novel therapeutic target for cardiovascular disease. There is an urgent need to fully understand the expression pattern of Runx1 in the heart and the mechanisms by which it is controlled under normal conditions and in response to disease. The expression of Runx1 is regulated at the transcriptional level by two promoters designated P1 and P2. Alternative usage of these two promoters creates differential mRNA transcripts diversified in distribution and translational potential. While the significance of P1/P2 promoter-switch in the transcriptional control of Runx1 has been highlighted in the embryogenic process, very little is known about the level of P1- and P2-specific transcripts in adult hearts, and the underlying mechanisms controlling the promoter-switch.

METHODS

To amplify P1/P2 specific sequences in the heart, we used two different sense primers complementary to either P1 or P2 5'-regions to monitor the expression of P1/P2 transcripts. DNA methylation levels were assessed at the Runx1 promoter regions. Rats were grouped by age.

RESULTS

The expression levels of both P1- and P2-derived Runx1 transcripts were decreased in older rats when compared with that in young adults, paralleled with an age-dependent decline in Runx1 protein level. Furthermore, older rats demonstrated a higher degree of DNA methylation at Runx1 promoter regions. Alternative promoter usage was observed in hearts with increased age, as reflected by altered P1:P2 mRNA ratio.

CONCLUSION

Our data demonstrate that the expression of Runx1 in the heart is age-dependent and underscore the importance of gene methylation in the promoter-mediated transcriptional control of Runx1, thereby providing new insights to the role of epigenetic regulation in the heart.

The application of epiphenotyping approaches to DNA methylation array studies of the human placenta

BACKGROUND

Genome-wide DNA methylation (DNAme) profiling of the placenta with Illumina Infinium Methylation bead arrays is often used to explore the connections between in utero exposures, placental pathology, and fetal development. However, many technical and biological factors can lead to signals of DNAme variation between samples and between cohorts, and understanding and accounting for these factors is essential to ensure meaningful and replicable data analysis. Recently, "epiphenotyping" approaches have been developed whereby DNAme data can be used to impute information about phenotypic variables such as gestational age, sex, cell composition, and ancestry. These epiphenotypes offer avenues to compare phenotypic data across cohorts, and to understand how phenotypic variables relate to DNAme variability. However, the relationships between placental epiphenotyping variables and other technical and biological variables, and their application to downstream epigenome analyses, have not been well studied.

RESULTS

Using DNAme data from 204 placentas across three cohorts, we applied the PlaNET R package to estimate epiphenotypes gestational age, ancestry, and cell composition in these samples. PlaNET ancestry estimates were highly correlated with independent polymorphic ancestry-informative markers, and epigenetic gestational age, on average, was estimated within 4 days of reported gestational age, underscoring the accuracy of these tools. Cell composition estimates varied both within and between cohorts, as well as over very long placental processing times. Interestingly, the ratio of cytotrophoblast to syncytiotrophoblast proportion decreased with increasing gestational age, and differed slightly by both maternal ethnicity (lower in white vs. non-white) and genetic ancestry (lower in higher probability European ancestry). The cohort of origin and cytotrophoblast proportion were the largest drivers of DNAme variation in this dataset, based on their associations with the first principal component.

CONCLUSIONS

This work confirms that cohort, array (technical) batch, cell type proportion, self-reported ethnicity, genetic ancestry, and biological sex are important variables to consider in any analyses of Illumina DNAme data. We further demonstrate the specific utility of epiphenotyping tools developed for use with placental DNAme data, and show that these variables (i) provide an independent check of clinically obtained data and (ii) provide a robust approach to compare variables across different datasets. Finally, we present a general framework for the processing and analysis of placental DNAme data, integrating the epiphenotype variables discussed here.

Getting the chronological age out of DNA: using insights of age-dependent DNA methylation for forensic DNA applications

BACKGROUND

DNA analysis for forensic investigations has a long tradition with important developments and optimizations since its first application. Traditionally, short tandem repeats analysis has been the most powerful method for the identification of individuals. However, in addition, epigenetic changes, i.e., DNA methylation, came into focus of forensic DNA research. Chronological age prediction is one promising application to allow for narrowing the pool of possible individuals who caused a trace, as well as to support the identification of unknown bodies and for age verification of living individuals.

OBJECTIVE

This review aims to provide an overview of the current knowledge, possibilities, and (current) limitations about DNA methylation-based chronological age prediction with emphasis on forensic application.

METHODS

The development, implementation and application of age prediction tools requires a deep understanding about the biological background, the analysis methods, the age-dependent DNA methylation markers, as well as the mathematical models for age prediction and their evaluation. Furthermore, additional influences can have an impact. Therefore, the literature was evaluated in respect to these diverse topics.

CONCLUSION

The numerous research efforts in recent years have led to a rapid change in our understanding of the application of DNA methylation for chronological age prediction, which is now on the way to implementation and validation. Knowledge of the various aspects leads to a better understanding and allows a more informed interpretation of DNAm quantification results, as well as the obtained results by the age prediction tools.

Accelerated epigenetic aging at birth interacts with parenting hostility to predict child temperament and subsequent psychological symptoms

In an effort to elucidate new factors that may contribute to developmental psychopathology, the current study examined whether accelerated epigenetic aging at birth related to children's differential susceptibility to the effects of aversive parenting on early emerging mental health risk. Using data from a multiethnic birth cohort, the interaction between Horvath's methylation age in umbilical cord blood and hostile parenting behaviors was examined in relation to perceptions of infant's temperament at 6 months and to children's psychological symptoms at 3 years in 154 families. Results broadly revealed that children with higher levels of accelerated methylation aging evinced more unpredictable temperaments and more psychological symptoms if their mothers reported more hostile parenting, but showed fewer difficulties if mothers engaged in less hostile parenting; children with lower levels of accelerated methylation age did not show associations between hostility and temperament or psychological symptoms. Effects were not accounted for by gestational age at birth, demographic factors, or the distribution of cell subtypes. These findings suggest that accelerated epigenetic age may function as a form of differential susceptibility, signaling increased risk for psychopathology in more aversive contexts but decreased risk in less aversive early environments. Taken together, they point to a novel biological process to consider within risk for psychopathology.

Who's afraid of the X? Incorporating the X and Y chromosomes into the analysis of DNA methylation array data

BACKGROUND

Many human disease phenotypes manifest differently by sex, making the development of methods for incorporating X and Y-chromosome data into analyses vital. Unfortunately, X and Y chromosome data are frequently excluded from large-scale analyses of the human genome and epigenome due to analytical complexity associated with sex chromosome dosage differences between XX and XY individuals, and the impact of X-chromosome inactivation (XCI) on the epigenome. As such, little attention has been given to considering the methods by which sex chromosome data may be included in analyses of DNA methylation (DNAme) array data.

RESULTS

With Illumina Infinium HumanMethylation450 DNAme array data from 634 placental samples, we investigated the effects of probe filtering, normalization, and batch correction on DNAme data from the X and Y chromosomes. Processing steps were evaluated in both mixed-sex and sex-stratified subsets of the analysis cohort to identify whether including both sexes impacted processing results. We found that identification of probes that have a high detection p-value, or that are non-variable, should be performed in sex-stratified data subsets to avoid over- and under-estimation of the quantity of probes eligible for removal, respectively. All normalization techniques investigated returned X and Y DNAme data that were highly correlated with the raw data from the same samples. We found no difference in batch correction results after application to mixed-sex or sex-stratified cohorts. Additionally, we identify two analytical methods suitable for XY chromosome data, the choice between which should be guided by the research question of interest, and we performed a proof-of-concept analysis studying differential DNAme on the X and Y chromosome in the context of placental acute chorioamnionitis. Finally, we provide an annotation of probe types that may be desirable to filter in X and Y chromosome analyses, including probes in repetitive elements, the X-transposed region, and cancer-testis gene promoters.

CONCLUSION

While there may be no single "best" approach for analyzing DNAme array data from the X and Y chromosome, analysts must consider key factors during processing and analysis of sex chromosome data to accommodate the underlying biology of these chromosomes, and the technical limitations of DNA methylation arrays.

Longitudinal profiling in patients undergoing cardiac surgery reveals postoperative changes in DNA methylation

BACKGROUND

Cardiac surgery and cardiopulmonary bypass induce a substantial immune and inflammatory response, the overactivation of which is associated with significant pulmonary, cardiovascular, and neurologic complications. Commensurate with the immune and inflammatory response are changes in the heart and vasculature itself, which together drive postoperative complications through mechanisms that are poorly understood. Longitudinal DNA methylation profiling has the potential to identify changes in gene regulatory mechanisms that are secondary to surgery and to identify molecular processes that predict and/or cause postoperative complications. In this study, we measure DNA methylation in preoperative and postoperative whole blood samples from 96 patients undergoing cardiac surgery on cardiopulmonary bypass.

RESULTS

While the vast majority of DNA methylation is unchanged by surgery after accounting for changes in cell-type composition, we identify several loci with statistically significant postoperative changes in methylation. Additionally, two of these loci are associated with new-onset postoperative atrial fibrillation, a significant complication after cardiac surgery. Paired statistical analysis, use of FACS data to support sufficient control of cell-type heterogeneity, and measurement of IL6 levels in a subset of patients add rigor to this analysis, allowing us to distinguish cell-type variability from actual changes in methylation.

CONCLUSIONS

This study identifies significant changes in DNA methylation that occur immediately after cardiac surgery and demonstrates that these acute alterations in DNA methylation have the granularity to identify processes associated with major postoperative complications. This research also establishes methods for controlling for cell-type variability in a large human cohort that may be useful to deploy in other longitudinal studies of epigenetic marks in the setting of acute and chronic disease.

Characterization of methylation patterns associated with lifestyle factors and vitamin D supplementation in a healthy elderly cohort from Southwest Sweden

Numerous studies have shown that lifestyle factors, such as regular physical activity and vitamin D intake, may remarkably improve overall health and mental wellbeing. This is especially important in older adults whose vitamin D deficiency occurs with a high prevalence. This study aimed to examine the influence of lifestyle and vitamin D on global DNA methylation patterns in an elderly cohort in Southwest of Sweden. We also sought to examine the methylation levels of specific genes involved in vitamin D's molecular and metabolic activated pathways. We performed a genome wide methylation analysis, using Illumina Infinium DNA Methylation EPIC 850kBeadChip array, on 277 healthy individuals from Southwest Sweden at the age of 70–95. The study participants also answered queries on lifestyle, vitamin intake, heart medication, and estimated health. Vitamin D intake did not in general affect methylation patterns, which is in concert with other studies. However, when comparing the group of individuals taking vitamin supplements, including vitamin D, with those not taking supplements, a difference in methylation in the solute carrier family 25 (SCL25A24) gene was found. This confirms a previous finding, where changes in expression of SLC25A24 were associated with vitamin D treatment in human monocytes. The combination of vitamin D intake and high physical activity increased methylation of genes linked to regulation of vitamin D receptor pathway, the Wnt pathway and general cancer processes. To our knowledge, this is the first study detecting epigenetic markers associated with the combined effects of vitamin D supplementation and high physical activity. These results deserve to be further investigated in an extended, interventional study cohort, where also the levels of 25(OH)D₃ can be monitored.

Hibernation slows epigenetic ageing in yellow-bellied marmots

Species that hibernate generally live longer than would be expected based solely on their body size. Hibernation is characterized by long periods of metabolic suppression (torpor) interspersed by short periods of increased metabolism (arousal). The torpor–arousal cycles occur multiple times during hibernation, and it has been suggested that processes controlling the transition between torpor and arousal states cause ageing suppression. Metabolic rate is also a known correlate of longevity; we thus proposed the ‘hibernation–ageing hypothesis’ whereby ageing is suspended during hibernation. We tested this hypothesis in a well-studied population of yellow-bellied marmots (Marmota flaviventer), which spend 7–8 months per year hibernating. We used two approaches to estimate epigenetic age: the epigenetic clock and the epigenetic pacemaker. Variation in epigenetic age of 149 samples collected throughout the life of 73 females was modelled using generalized additive mixed models (GAMM), where season (cyclic cubic spline) and chronological age (cubic spline) were fixed effects. As expected, the GAMM using epigenetic ages calculated from the epigenetic pacemaker was better able to detect nonlinear patterns in epigenetic ageing over time. We observed a logarithmic curve of epigenetic age with time, where the epigenetic age increased at a higher rate until females reached sexual maturity (two years old). With respect to circannual patterns, the epigenetic age increased during the active season and essentially stalled during the hibernation period. Taken together, our results are consistent with the hibernation–ageing hypothesis and may explain the enhanced longevity in hibernators.

Species that hibernate generally have longer lifespans than expected based on their body size. The authors show epigenetic ageing patterns from a natural population of hibernating yellow-bellied marmots consistent with the hypothesis that ageing is suspended during hibernation.

Methylation patterns of the CDKN2B and CDKN2A genes in an indigenous population exposed to pesticides

The INK4 -ARF locus includes the CDKN2B and CDKN2A genes and is functionally relevant in the regulation of both cell proliferation and senescence. Studies have reported modifications of DNA methylation in this locus by exposure to environmental contaminants including pesticides; however, until now, specific methylation profiles have not been reported in genetically conserved populations exposed to occupational pesticides. The aim of this study was to determine the methylation profiles of the CDKN2B and CDKN2A genes in a genetically conserved population exposed to pesticides. A cross-sectional and analytical study was carried out in 190 Huichol indigenous persons. Information related to pesticide exposure, diet and other variables were obtained through the use of a structured questionnaire. Blood and urine samples were collected for methylation test and dialkylphosphates (DAP) determination, respectively. DNA methylation was measured by the pyrosequencing of bisulfite-treated DNA and DAP concentrations by gas chromatography-tandem mass spectrometry (GC/MS). The most frequent metabolite in the population was dimethylthiophosphate. The farmer group presented a higher methylation percentage of CDKN2B than the non-farmer group, but no differences in CDKN2A were observed between groups. A positive correlation between methylation of CpG site 3 of CDKN2B and time working in the field was observed in the farmer group. An association between methylation percentage of CDKN2B and age was also observed in the non-farmer group. These results suggest that pesticide exposure and exposure time in Huichol indigenous individuals could modify the methylation pattern of the CDKN2B gene.

Predicting Chronological Age from DNA Methylation Data: A Machine Learning Approach for Small Datasets and Limited Predictors

Recent research studies using epigenetic data have been exploring whether it is possible to estimate how old someone is using only their DNA. This application stems from the strong correlation that has been observed in humans between the methylation status of certain DNA loci and chronological age. While genome-wide methylation sequencing has been the most prominent approach in epigenetics research, recent studies have shown that targeted sequencing of a limited number of loci can be successfully used for the estimation of chronological age from DNA samples, even when using small datasets. Following this shift, the need to investigate further into the appropriate statistics behind the predictive models used for DNA methylation-based prediction has been identified in multiple studies. This chapter will look into an example of basic data manipulation and modeling that can be applied to small DNA methylation datasets (100-400 samples) produced through targeted methylation sequencing for a small number of predictors (10-25 methylation sites). Data manipulation will focus on converting the obtained methylation values for the different predictors to a statistically meaningful dataset, followed by a basic introduction into importing such datasets in R, as well as randomizing and splitting into appropriate training and test sets for modeling. Finally, a basic introduction to R modeling will be outlined, starting with feature selection algorithms and continuing with a simple modeling example (linear model) as well as a more complex algorithm (Support Vector Machine).

FYB methylation in peripheral blood as a potential marker for the early-stage lung cancer: a case-control study in Chinese population

BACKGROUND

Lung cancer (LC) is the leading cause of cancer-related morbidity and mortality in China. Exploring novel biomarkers for the early detection of LC is important.

MATERIALS AND METHODS

We quantified DNA methylation levels of three CpG sites of FYB gene in peripheral blood in 163 early-stage LC cases (88.3% at stage I) and 187 age- and gender-matched healthy controls. Covariates-adjusted odds ratios (ORs) for -10% methylation were calculated by binary logistic regression.

RESULTS

With multiple testing corrections, hypomethylation of FYB_CpG_4 was significantly associated with LC (OR = 2.04, p = 4.50E-04) even with LC at stage I (OR = 1.41, p = 0.003) without obvious bias between genders, but it mainly affected the subjects older than 55 years (OR = 2.04, p = 0.015). Hypomethylation of FYB_CpG_2 was also associated with LC, but only for the males (OR = 1.76, p = 0.018). FYB_CpG_3 methylation had no association with LC, but interestingly its methylation level in the males was only half of that in the females.

DISCUSSION AND CONCLUSIONS

We proposed a novel association between blood-based abnormal FYB methylation and very early-stage LC. The age- and gender-related DNA methylation patterns also revealed the diversity and precision of epigenetic regulations.

Combining current knowledge on DNA methylation-based age estimation towards the development of a superior forensic DNA intelligence tool

The estimation of chronological age from biological fluids has been an important quest for forensic scientists worldwide, with recent approaches exploiting the variability of DNA methylation patterns with age in order to develop the next generation of forensic 'DNA intelligence' tools for this application. Drawing from the conclusions of previous work utilising massively parallel sequencing (MPS) for this analysis, this work introduces a DNA methylation-based age estimation method for blood that exhibits the best combination of prediction accuracy and sensitivity reported to date. Statistical evaluation of markers from 51 studies using microarray data from over 4000 individuals, followed by validation using in-house generated MPS data, revealed a final set of 11 markers with the greatest potential for accurate age estimation from minimal DNA material. Utilising an algorithm based on support vector machines, the proposed model achieved an average error (MAE) of 3.3 years, with this level of accuracy retained down to 5 ng of starting DNA input (~ 1 ng PCR input). The accuracy of the model was retained (MAE = 3.8 years) in a separate test set of 88 samples of Spanish origin, while predictions for donors of greater forensic interest (< 55 years of age) displayed even higher accuracy (MAE = 2.6 years). Finally, no sex-related bias was observed for this model, while there were also no signs of variation observed between control and disease-associated populations for schizophrenia, rheumatoid arthritis, frontal temporal dementia and progressive supranuclear palsy in microarray data relating to the 11 markers.

Connecting the epigenome, metabolome and proteome for a deeper understanding of disease

Epigenome-wide association studies (EWAS) identify genes that are dysregulated by the studied clinical endpoints, thereby indicating potential new diagnostic biomarkers, drug targets and therapy options. Combining EWAS with deep molecular phenotyping, such as approaches enabled by metabolomics and proteomics, allows further probing of the underlying disease-associated pathways. For instance, methylation of the TXNIP gene is associated robustly with prevalent type 2 diabetes and further with metabolites that are short-term markers of glycaemic control. These associations reflect TXNIP's function as a glucose uptake regulator by interaction with the major glucose transporter GLUT1 and suggest that TXNIP methylation can be used as a read-out for the organism's exposure to glucose stress. Another case is the association between DNA methylation of the AHRR and F2RL3 genes with smoking and a protein that is involved in the reprogramming of the bronchial epithelium. These examples show that associations between DNA methylation and intermediate molecular traits can open new windows into how the body copes with physiological challenges. This knowledge, if carefully interpreted, may indicate novel therapy options and, together with monitoring of the methylation state of specific methylation sites, may in the future allow the early diagnosis of impending disease. It is essential for medical practitioners to recognize the potential that this field holds in translating basic research findings to clinical practice. In this review, we present recent advances in the field of EWAS with metabolomics and proteomics and discuss both the potential and the challenges of translating epigenetic associations, with deep molecular phenotypes, to biomedical applications.

Ageing-associated changes in DNA methylation in X and Y chromosomes

Background

Ageing displays clear sexual dimorphism, evident in both morbidity and mortality. Ageing is also associated with changes in DNA methylation, but very little focus has been on the sex chromosomes, potential biological contributors to the observed sexual dimorphism. Here, we sought to identify DNA methylation changes associated with ageing in the Y and X chromosomes, by utilizing datasets available in data repositories, comprising in total of 1240 males and 1191 females, aged 14–92 years.

Results

In total, we identified 46 age-associated CpG sites in the male Y, 1327 age-associated CpG sites in the male X, and 325 age-associated CpG sites in the female X. The X chromosomal age-associated CpGs showed significant overlap between females and males, with 122 CpGs identified as age-associated in both sexes. Age-associated X chromosomal CpGs in both sexes were enriched in CpG islands and depleted from gene bodies and showed no strong trend towards hypermethylation nor hypomethylation. In contrast, the Y chromosomal age-associated CpGs were enriched in gene bodies, and showed a clear trend towards hypermethylation with age.

Conclusions

Significant overlap in X chromosomal age-associated CpGs identified in males and females and their shared features suggest that despite the uneven chromosomal dosage, differences in ageing-associated DNA methylation changes in the X chromosome are unlikely to be a major contributor of sex dimorphism in ageing. While age-associated CpGs showed good replication across datasets in the present study, only a limited set of previously reported age-associated CpGs were replicated. One contributor to the limited overlap are differences in the age range of individuals included in each data set. Further study is needed to identify biologically significant age-associated CpGs in the sex chromosomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13072-021-00407-6.

Age Prediction of Human Based on DNA Methylation by Blood Tissues

In recent years, scientists have found a close correlation between DNA methylation and aging in epigenetics. With the in-depth research in the field of DNA methylation, researchers have established a quantitative statistical relationship to predict the individual ages. This work used human blood tissue samples to study the association between age and DNA methylation. We built two predictors based on healthy and disease data, respectively. For the health data, we retrieved a total of 1191 samples from four previous reports. By calculating the Pearson correlation coefficient between age and DNA methylation values, 111 age-related CpG sites were selected. Gradient boosting regression was utilized to build the predictive model and obtained the R² value of 0.86 and MAD of 3.90 years on testing dataset, which were better than other four regression methods as well as Horvath’s results. For the disease data, 354 rheumatoid arthritis samples were retrieved from a previous study. Then, 45 CpG sites were selected to build the predictor and the corresponded MAD and R² were 3.11 years and 0.89 on the testing dataset respectively, which showed the robustness of our predictor. Our results were better than the ones from other four regression methods. Finally, we also analyzed the twenty-four common CpG sites in both healthy and disease datasets which illustrated the functional relevance of the selected CpG sites.

DNA Methylation in Babies Born to Nonsmoking Mothers Exposed to Secondhand Smoke during Pregnancy: An Epigenome-Wide Association Study

BACKGROUND

Maternal smoking during pregnancy is related to altered DNA methylation in infant umbilical cord blood. The extent to which low levels of smoke exposure among nonsmoking pregnant women relates to offspring DNA methylation is unknown.

OBJECTIVE

This study sought to evaluate relationships between maternal prenatal plasma cotinine levels and DNA methylation in umbilical cord blood in newborns using the Infinium HumanMethylation 450K BeadChip.

METHODS

Participants from the Newborn Epigenetics Study cohort who reported not smoking during pregnancy had verified low levels of cotinine from maternal prenatal plasma (0 ng / mL to < 4  ng / mL ), and offspring epigenetic data from umbilical cord blood were included in this study (n = 79 ). Multivariable linear regression models were fit to the data, controlling for cell proportions, age, race, education, and parity. Estimates represent changes in response to any 1 -ng / mL unit increase in exposure.

RESULTS

Multivariable linear regression models yielded 29,049 CpGs that were differentially methylated in relation to increases in cotinine at a 5% false discovery rate. Top CpGs were within or near genes involved in neuronal functioning (PRKG1, DLGAP2, BSG), carcinogenesis (FHIT, HSPC157) and inflammation (AGER). Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses suggest cotinine was related to methylation of gene pathways controlling neuronal signaling, metabolic regulation, cell signaling and regulation, and cancer. Further, enhancers associated with transcription start sites were enriched in altered CpGs. Using an independent sample from the same study population (n = 115 ), bisulfite pyrosequencing was performed with infant cord blood DNA for two genes within our top 20 hits (AGER and PRKG1). Results from pyrosequencing replicated epigenome results for PRKG1 (cg17079497, estimate = - 1.09 , standard error  ( SE ) = 0.45 , p = 0.018 ) but not for AGER (cg09199225; estimate = - 0.16 , SE = 0.21 , p = 0.44 ).

DISCUSSION

Secondhand smoke exposure among nonsmoking women may alter DNA methylation in regions involved in development, carcinogenesis, and neuronal functioning. These novel findings suggest that even low levels of smoke exposure during pregnancy may be sufficient to alter DNA methylation in distinct sites of mixed umbilical cord blood leukocytes in pathways that are known to be altered in cord blood from pregnant active smokers. https://doi.org/10.1289/EHP8099.

Cigarette smoke-induced alterations in blood: A review of research on DNA methylation and gene expression

Worldwide, smoking remains a threat to public health, causing preventable diseases and premature mortality. Cigarette smoke is a powerful inducer of DNA methylation and gene expression alterations, which have been associated with negative health consequences. Here, we review the current knowledge on smoking-related changes in DNA methylation and gene expression in human blood samples. We identified 30 studies focused on the association between active smoking, DNA methylation modifications, and gene expression alterations. Overall, we identified 1,758 genes with differentially methylated sites (DMS) and differentially expressed genes (DEG) between smokers and nonsmokers, of which 261 were detected in multiple studies (≥4). The most frequently (≥10 studies) reported genes were AHRR, GPR15, GFI1, and RARA. Functional enrichment analysis of the 261 genes identified the aryl hydrocarbon receptor repressor and T cell pathways (T helpers 1 and 2) as influenced by smoking status. These results highlight specific genes for future mechanistic and translational research that may be associated with cigarette smoke exposure and smoking-related diseases. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

DNA Methylation Markers in Lung Cancer

Lung cancer is the most common cancer and the leading cause of cancer-related morbidity and mortality worldwide. As early symptoms of lung cancer are minimal and non-specific, many patients are diagnosed at an advanced stage. Despite a concerted effort to diagnose lung cancer early, no biomarkers that can be used for lung cancer screening and prognosis prediction have been established so far. As global DNA demethylation and gene-specific promoter DNA methylation are present in lung cancer, DNA methylation biomarkers have become a major area of research as potential alternative diagnostic methods to detect lung cancer at an early stage. This review summarizes the emerging DNA methylation changes in lung cancer tumorigenesis, focusing on biomarkers for early detection and their potential clinical applications in lung cancer.

DNA methylation-based age prediction using cell separation algorithm

The age of each individual can be predicted based on the alteration rule of DNA methylation with age. In this paper, an age prediction method is developed in order to solve multivariate regression problems from DNA methylation data, by optimizing the artificial neural network (ANN) model using a new proposed algorithm named the Cell Separation Algorithm (CSA). The CSA imitates cell separation action by using a differential centrifugation process involving multiple centrifugation steps and increasing the rotor speed in each step. The CSA performs similar to the centrifugal force in separating the solutions based on their objective function in different steps, with velocity increasing in each step. Firstly, 25 test functions are used to test the CSA. Secondly, the CSA is examined on three forms of age prediction problems from two body fluids (blood and saliva). The healthy blood samples, diseased blood samples and saliva samples are used to test the method's capability. The results of the CSA are compared not only with other methods proposed in previous studies, but also with the results from stochastic gradient descent (SGD), ADAM, and genetic algorithm (GA). The model results of CSA are extremely better than the four methods proposed in previous works that have not used ANN training process. The CSA also outperformed SGD, ADAM that employ the ANN model without ANN optimization by meta-heuristics. The CSA results are comparable (even superior) to the GA model which takes the advantages of both ANN and meta-heuristics.

Smoking and blood DNA methylation: an epigenome-wide association study and assessment of reversibility

We conducted a genome-wide association study of blood DNA methylation and smoking, attempted replication of previously discovered associations, and assessed the reversibility of smoking-associated methylation changes. DNA methylation was measured in baseline peripheral blood samples for 5,044 participants in the Melbourne Collaborative Cohort Study. For 1,032 participants, these measures were repeated using blood samples collected at follow-up, a median of 11 years later. A cross-sectional analysis of the association between smoking and DNA methylation and a longitudinal analysis of changes in smoking status and changes in DNA methylation were conducted. We used our cross-sectional analysis to replicate previously reported associations for current (N = 3,327) and former (N = 172) smoking. A comprehensive smoking index accounting for the biological half-life of smoking compounds and several aspects of smoking history was constructed to assess the reversibility of smoking-induced methylation changes. This measure of lifetime exposure to smoking allowed us to detect more associations than comparing current with never smokers. We identified 4,496 cross-sectional associations at P < 10-7, including 3,296 annotated to 1,326 genes that were not previously implicated in smoking-associated DNA methylation changes at this significance threshold. We replicated the majority of previously reported associations (P < 10-7) for current and former smokers. In our data, we observed for former smokers a substantial degree of return to the methylation levels of never smokers, compared with current smokers (median: 74%, IQR = 63-86%), corresponding to small values (median: 2.75, IQR = 1.5-5.25) for the half-life parameter of the comprehensive smoking index. Longitudinal analyses identified 368 sites at which methylation changed upon smoking cessation. Our study demonstrates the usefulness of the comprehensive smoking index to detect associations between smoking and DNA methylation at CpGs across the genome, replicates the vast majority of previously reported associations, and quantifies the reversibility of smoking-induced methylation changes.

Epigenetics meets proteomics in an epigenome-wide association study with circulating blood plasma protein traits

DNA methylation and blood circulating proteins have been associated with many complex disorders, but the underlying disease-causing mechanisms often remain unclear. Here, we report an epigenome-wide association study of 1123 proteins from 944 participants of the KORA population study and replication in a multi-ethnic cohort of 344 individuals. We identify 98 CpG-protein associations (pQTMs) at a stringent Bonferroni level of significance. Overlapping associations with transcriptomics, metabolomics, and clinical endpoints suggest implication of processes related to chronic low-grade inflammation, including a network involving methylation of NLRC5, a regulator of the inflammasome, and associated pQTMs implicating key proteins of the immune system, such as CD48, CD163, CXCL10, CXCL11, LAG3, FCGR3B, and B2M. Our study links DNA methylation to disease endpoints via intermediate proteomics phenotypes and identifies correlative networks that may eventually be targeted in a personalized approach of chronic low-grade inflammation.

MapReduce-Based Parallel Genetic Algorithm for CpG-Site Selection in Age Prediction

Genomic biomarkers such as DNA methylation (DNAm) are employed for age prediction. In recent years, several studies have suggested the association between changes in DNAm and its effect on human age. The high dimensional nature of this type of data significantly increases the execution time of modeling algorithms. To mitigate this problem, we propose a two-stage parallel algorithm for selection of age related CpG-sites. The algorithm first attempts to cluster the data into similar age ranges. In the next stage, a parallel genetic algorithm (PGA), based on the MapReduce paradigm (MR-based PGA), is used for selecting age-related features of each individual age range. In the proposed method, the execution of the algorithm for each age range (data parallel), the evaluation of chromosomes (task parallel) and the calculation of the fitness function (data parallel) are performed using a novel parallel framework. In this paper, we consider 16 different healthy DNAm datasets that are related to the human blood tissue and that contain the relevant age information. These datasets are combined into a single unioned set, which is in turn randomly divided into two sets of train and test data with a ratio of 7:3, respectively. We build a Gradient Boosting Regressor (GBR) model on the selected CpG-sites from the train set. To evaluate the model accuracy, we compared our results with state-of-the-art approaches that used these datasets, and observed that our method performs better on the unseen test dataset with a Mean Absolute Deviation (MAD) of 3.62 years, and a correlation (R²) of 95.96% between age and DNAm. In the train data, the MAD and R² are 1.27 years and 99.27%, respectively. Finally, we evaluate our method in terms of the effect of parallelization in computation time. The algorithm without parallelization requires 4123 min to complete, whereas the parallelized execution on 3 computing machines having 32 processing cores each, only takes a total of 58 min. This shows that our proposed algorithm is both efficient and scalable.

Characterization of DNA methylation-based markers for human body fluid identification in forensics: a critical review

Body fluid identification in crime scene investigations aids in reconstruction of crime scenes. Several studies have identified and reported differentially methylated sites (DMSs) and regions (DMRs) which differ between forensically relevant tissues (tDMRs) and body fluids. Diverse factors affect methylation patterns such as the environment, diets, lifestyle, disease, ethnicity, genetic variation, amongst others. Thus, it is important to analyse the stability of markers employed for forensic identification. Furthermore, even though epigenetic modifications are described as stable and heritable, epigenetic inheritance of potential markers for body fluid identification needs to be assessed in the long term. Here, we discuss the current status of reported DNA methylation-based markers and their verification studies. Such thorough investigation is crucial to develop a stable panel of DNA methylation-based markers for accurate body fluid identification.

The role of cigarette smoke-induced epigenetic alterations in inflammation

Background

Exposure to cigarette smoke (CS) is a major threat to human health worldwide. It is well established that smoking increases the risk of respiratory diseases, cardiovascular diseases and different forms of cancer, including lung, liver, and colon. CS-triggered inflammation is considered to play a central role in various pathologies by a mechanism that stimulates the release of pro-inflammatory cytokines. During this process, epigenetic alterations are known to play important roles in the specificity and duration of gene transcription.

Main text

Epigenetic alterations include three major modifications: DNA modifications via methylation; various posttranslational modifications of histones, namely, methylation, acetylation, phosphorylation, and ubiquitination; and non-coding RNA sequences. These modifications work in concert to regulate gene transcription in a heritable fashion. The enzymes that regulate these epigenetic modifications can be activated by smoking, which further mediates the expression of multiple inflammatory genes. In this review, we summarize the current knowledge on the epigenetic alterations triggered by CS and assess how such alterations may affect smoking-mediated inflammatory responses.

Conclusion

The recognition of the molecular mechanisms of the epigenetic changes in abnormal inflammation is expected to contribute to the understanding of the pathophysiology of CS-related diseases such that novel epigenetic therapies may be identified in the near future.

A systematic review of smoking-related epigenetic alterations

The aim of this study is to provide a systematic review of the known epigenetic alterations caused by cigarette smoke; establish an evidence-based perspective of their clinical value for screening, diagnosis, and treatment of smoke-related disorders; and discuss the challenges and ethical concerns associated with epigenetic studies. A well-defined, reproducible search strategy was employed to identify relevant literature (clinical, cellular, and animal-based) between 2000 and 2019 based on AMSTAR guidelines. A total of 80 studies were identified that reported alterations in DNA methylation, histone modifications, and miRNA expression following exposure to cigarette smoke. Changes in DNA methylation were most extensively documented for genes including AHRR, F2RL3, DAPK, and p16 after exposure to cigarette smoke. Likewise, miR16, miR21, miR146, and miR222 were identified to be differentially expressed in smokers and exhibit potential as biomarkers for determining susceptibility to COPD. We also identified 22 studies highlighting the transgenerational effects of maternal and paternal smoking on offspring. This systematic review lists the epigenetic events/alterations known to occur in response to cigarette smoke exposure and identifies the major genes and miRNAs that are potential targets for translational research in associated pathologies. Importantly, the limitations and ethical concerns related to epigenetic studies are also highlighted, as are the effects on the ability to address specific questions associated with exposure to tobacco/cigarette smoke. In the future, improved interpretation of epigenetic signatures will lead to their increased use as biomarkers and/or in drug development.

Validated inference of smoking habits from blood with a finite DNA methylation marker set

Inferring a person’s smoking habit and history from blood is relevant for complementing or replacing self-reports in epidemiological and public health research, and for forensic applications. However, a finite DNA methylation marker set and a validated statistical model based on a large dataset are not yet available. Employing 14 epigenome-wide association studies for marker discovery, and using data from six population-based cohorts (N = 3764) for model building, we identified 13 CpGs most suitable for inferring smoking versus non-smoking status from blood with a cumulative Area Under the Curve (AUC) of 0.901. Internal fivefold cross-validation yielded an average AUC of 0.897 ± 0.137, while external model validation in an independent population-based cohort (N = 1608) achieved an AUC of 0.911. These 13 CpGs also provided accurate inference of current (average AUC_{crossvalidation} 0.925 ± 0.021, AUC_{externalvalidation}0.914), former (0.766 ± 0.023, 0.699) and never smoking (0.830 ± 0.019, 0.781) status, allowed inferring pack-years in current smokers (10 pack-years 0.800 ± 0.068, 0.796; 15 pack-years 0.767 ± 0.102, 0.752) and inferring smoking cessation time in former smokers (5 years 0.774 ± 0.024, 0.760; 10 years 0.766 ± 0.033, 0.764; 15 years 0.767 ± 0.020, 0.754). Model application to children revealed highly accurate inference of the true non-smoking status (6 years of age: accuracy 0.994, N = 355; 10 years: 0.994, N = 309), suggesting prenatal and passive smoking exposure having no impact on model applications in adults. The finite set of DNA methylation markers allow accurate inference of smoking habit, with comparable accuracy as plasma cotinine use, and smoking history from blood, which we envision becoming useful in epidemiology and public health research, and in medical and forensic applications.

Novel age-associated DNA methylation changes and epigenetic age acceleration in middle-aged African Americans and whites

Background

African Americans (AAs) experience premature chronic health outcomes and longevity disparities consistent with an accelerated aging phenotype. DNA methylation (DNAm) levels at specific CpG positions are hallmarks of aging evidenced by the presence of age-associated differentially methylated CpG positions (aDMPs) that are the basis for the epigenetic clock for measuring biological age acceleration. Since DNAm has not been widely studied among non-European populations, we examined the association between DNAm and chronological age in AAs and whites, and the association between race, poverty, sex, and epigenetic age acceleration.

Results

We measured genome-wide DNA methylation (866,836 CpGs) using the Illumina MethylationEPIC BeadChip in blood DNA extracted from 487 middle-aged AA (N = 244) and white (N = 243), men (N = 248), and women (N = 239). The mean (sd) age was 48.4 (8.8) in AA and 49.0 (8.7) in whites (p = 0.48). We identified 4930 significantly associated aDMPs in AAs and 469 in whites. Of these, 75.6% and 53.1% were novel, largely driven by the increased number of measured CpGs in the EPIC array, in AA and whites, respectively. AAs had more age-associated DNAm changes than whites in genes implicated in age-related diseases and cellular pathways involved in growth and development. We assessed three epigenetic age acceleration measures (universal, intrinsic, and extrinsic). AAs had a significantly slower extrinsic aging compared to whites. Furthermore, compared to AA women, both AA and white men had faster aging in the universal age acceleration measure (+ 2.04 and + 1.24 years, respectively, p < 0.05).

Conclusions

AAs have more wide-spread methylation changes than whites. Race and sex interact to underlie biological age acceleration suggesting altered DNA methylation patterns may be important in age-associated health disparities.

Electronic supplementary material

The online version of this article (10.1186/s13148-019-0722-1) contains supplementary material, which is available to authorized users.

Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array

Background

There has been a steady increase in the number of studies aiming to identify DNA methylation differences associated with complex phenotypes. Many of the challenges of epigenetic epidemiology regarding study design and interpretation have been discussed in detail, however there are analytical concerns that are outstanding and require further exploration. In this study we seek to address three analytical issues. First, we quantify the multiple testing burden and propose a standard statistical significance threshold for identifying DNA methylation sites that are associated with an outcome. Second, we establish whether linear regression, the chosen statistical tool for the majority of studies, is appropriate and whether it is biased by the underlying distribution of DNA methylation data. Finally, we assess the sample size required for adequately powered DNA methylation association studies.

Results

We quantified DNA methylation in the Understanding Society cohort (n = 1175), a large population based study, using the Illumina EPIC array to assess the statistical properties of DNA methylation association analyses. By simulating null DNA methylation studies, we generated the distribution of p-values expected by chance and calculated the 5% family-wise error for EPIC array studies to be 9 × 10^− 8. Next, we tested whether the assumptions of linear regression are violated by DNA methylation data and found that the majority of sites do not satisfy the assumption of normal residuals. Nevertheless, we found no evidence that this bias influences analyses by increasing the likelihood of affected sites to be false positives. Finally, we performed power calculations for EPIC based DNA methylation studies, demonstrating that existing studies with data on ~ 1000 samples are adequately powered to detect small differences at the majority of sites.

Conclusion

We propose that a significance threshold of P < 9 × 10^− 8 adequately controls the false positive rate for EPIC array DNA methylation studies. Moreover, our results indicate that linear regression is a valid statistical methodology for DNA methylation studies, despite the fact that the data do not always satisfy the assumptions of this test. These findings have implications for epidemiological-based studies of DNA methylation and provide a framework for the interpretation of findings from current and future studies.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5761-7) contains supplementary material, which is available to authorized users.

Smoking and DNA methylation: Correlation of methylation with smoking behavior and association with diseases and fetus development following prenatal exposure

Among epigenetic mechanisms, DNA methylation has been widely studied with respect to many environmental factors. Smoking is a common factor which affects both global and gene-specific DNA methylation. It is supported that smoking directly affects DNA methylation, and these effects contribute to the development and progression of various diseases, such as cancer, lung and cardiovascular diseases and male infertility. In addition, prenatal smoking influences the normal development of the fetus via DNA methylation changes. The DNA methylation profile and its smoking-induced alterations helps to distinguish current from former smokers and non-smokers and can be used to predict the risk for the development of a disease. This review summarizes the DNA methylation changes induced by smoking, their correlation with smoking behavior and their association with various diseases and fetus development.

SIPA1L3 methylation modifies the benefit of smoking cessation on lung adenocarcinoma survival: an epigenomic-smoking interaction analysis

Smoking cessation prolongs survival and decreases mortality of patients with non-small-cell lung cancer (NSCLC). In addition, epigenetic alterations of some genes are associated with survival. However, potential interactions between smoking cessation and epigenetics have not been assessed. Here, we conducted an epigenome-wide interaction analysis between DNA methylation and smoking cessation on NSCLC survival. We used a two-stage study design to identify DNA methylation-smoking cessation interactions that affect overall survival for early-stage NSCLC. The discovery phase contained NSCLC patients from Harvard, Spain, Norway, and Sweden. A histology-stratified Cox proportional hazards model adjusted for age, sex, clinical stage, and study center was used to test DNA methylation-smoking cessation interaction terms. Interactions with false discovery rate-q ≤ 0.05 were further confirmed in a validation phase using The Cancer Genome Atlas database. Histology-specific interactions were identified by stratification analysis in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients. We identified one CpG probe (cg02268510SIPA1L3 ) that significantly and exclusively modified the effect of smoking cessation on survival in LUAD patients [hazard ratio (HR)interaction  = 1.12; 95% confidence interval (CI): 1.07-1.16; P = 4.30 × 10-7 ]. Further, the effect of smoking cessation on early-stage LUAD survival varied across patients with different methylation levels of cg02268510SIPA1L3 . Smoking cessation only benefited LUAD patients with low methylation (HR = 0.53; 95% CI: 0.34-0.82; P = 4.61 × 10-3 ) rather than medium or high methylation (HR = 1.21; 95% CI: 0.86-1.70; P = 0.266) of cg02268510SIPA1L3 . Moreover, there was an antagonistic interaction between elevated methylation of cg02268510SIPA1L3 and smoking cessation (HRinteraction  = 2.1835; 95% CI: 1.27-3.74; P = 4.46 × 10-3 ). In summary, smoking cessation benefited survival of LUAD patients with low methylation at cg02268510SIPA1L3 . The results have implications for not only smoking cessation after diagnosis, but also possible methylation-specific drug targeting.

Human age prediction based on DNA methylation of non-blood tissues

BACKGROUND AND OBJECTIVE

The study of human aging contributes to disease prevention, treatment and life extension. Recently, epigenetics studies have evidenced that there is a close association between DNA methylation and human ages. A quantitatively statistical modeling between DNA methylation and ages could predict the person's age more accurately.

METHODS

We propose a regression model to predict human age based on gradient boosting regressor (GBR). We collect a total of 1280 publicly available non-blood tissues samples with ages ranged from 0 to 90 years old. We calculate the Pearson correlation between CpG's DNA methylation level and age to select age-related CpGs.

RESULTS

Thirteen age-related CpG sites are selected. GBR has the smallest mean absolute deviation to the actual age comparing with other three different models including Bayesian ridge, multiple linear regression, and support vector regression. In the training datasets, the cross-validation results show that the correlation R² between predicted age and DNA methylation is 0.89, and the mean absolute deviation is 4.66 years. In an independent testing set with 262 samples, the GBR achieves the mean absolute deviation of 6.08 years. Meanwhile we also briefly describe the function of the selected thirteen CpG sites.

CONCLUSIONS

We build an age predictor to study the association between ages and the DNA methylation of human non-blood tissues. Our new model provides a more accurate estimation of human ages which will be instrumental for understanding the regulation of DNA methylation on human aging and will accurately monitor the individual aging process.

Deep molecular phenotypes link complex disorders and physiological insult to CpG methylation

Epigenetic regulation of cellular function provides a mechanism for rapid organismal adaptation to changes in health, lifestyle and environment. Associations of cytosine-guanine di-nucleotide (CpG) methylation with clinical endpoints that overlap with metabolic phenotypes suggest a regulatory role for these CpG sites in the body's response to disease or environmental stress. We previously identified 20 CpG sites in an epigenome-wide association study (EWAS) with metabolomics that were also associated in recent EWASs with diabetes-, obesity-, and smoking-related endpoints. To elucidate the molecular pathways that connect these potentially regulatory CpG sites to the associated disease or lifestyle factors, we conducted a multi-omics association study including 2474 mass-spectrometry-based metabolites in plasma, urine and saliva, 225 NMR-based lipid and metabolite measures in blood, 1124 blood-circulating proteins using aptamer technology, 113 plasma protein N-glycans and 60 IgG-glyans, using 359 samples from the multi-ethnic Qatar Metabolomics Study on Diabetes (QMDiab). We report 138 multi-omics associations at these CpG sites, including diabetes biomarkers at the diabetes-associated TXNIP locus, and smoking-specific metabolites and proteins at multiple smoking-associated loci, including AHRR. Mendelian randomization suggests a causal effect of metabolite levels on methylation of obesity-associated CpG sites, i.e. of glycerophospholipid PC(O-36: 5), glycine and a very low-density lipoprotein (VLDL-A) on the methylation of the obesity-associated CpG loci DHCR24, MYO5C and CPT1A, respectively. Taken together, our study suggests that multi-omics-associated CpG methylation can provide functional read-outs for the underlying regulatory response mechanisms to disease or environmental insults.

Epigenome-wide association study of serum cotinine in current smokers reveals novel genetically driven loci

Background

DNA methylation alteration extensively associates with smoking and is a plausible link between smoking and adverse health. We examined the association between epigenome-wide DNA methylation and serum cotinine levels as a proxy of nicotine exposure and smoking quantity, assessed the role of SNPs in these associations, and evaluated molecular mediation by methylation in a sample of biochemically verified current smokers (N = 310).

Results

DNA methylation at 50 CpG sites was associated (FDR < 0.05) with cotinine levels, 17 of which are novel associations. As cotinine levels are influenced not only by nicotine intake but also by CYP2A6-mediated nicotine metabolism rate, we performed secondary analyses adjusting for genetic risk score of nicotine metabolism rate and identified five additional novel associations. We further assessed the potential role of genetic variants in the detected association between methylation and cotinine levels observing 124 cis and 3898 trans methylation quantitative trait loci (meQTLs). Nineteen of these SNPs were also associated with cotinine levels (FDR < 0.05). Further, at seven CpG sites, we observed a trend (P < 0.05) that altered DNA methylation mediates the effect of SNPs on nicotine exposure rather than a direct consequence of smoking. Finally, we performed replication of our findings in two independent cohorts of biochemically verified smokers (N = 450 and N = 79).

Conclusions

Using cotinine, a biomarker of nicotine exposure, we replicated and extended identification of novel epigenetic associations in smoking-related genes. We also demonstrated that DNA methylation in some of the identified loci is driven by the underlying genotype and may mediate the causal effect of genotype on cotinine levels.

Electronic supplementary material

The online version of this article (10.1186/s13148-018-0606-9) contains supplementary material, which is available to authorized users.

Critical evaluation of linear regression models for cell-subtype specific methylation signal from mixed blood cell DNA

Epigenome-wide association studies seek to identify DNA methylation sites associated with clinical outcomes. Difference in observed methylation between specific cell-subtypes is often of interest; however, available samples often comprise a mixture of cells. To date, cell-subtype estimates have been obtained from mixed-cell DNA data using linear regression models, but the accuracy of such estimates has not been critically assessed. We evaluated linear regression performance for cell-subtype specific methylation estimation using a 450K methylation array dataset of both mixed-cell and cell-subtype sorted samples from six healthy males. CpGs associated with each cell-subtype were first identified using t-tests between groups of cell-subtype sorted samples. Subsequent reduced panels of reliably accurate CpGs were identified from mixed-cell samples using an accuracy heuristic (D). Performance was assessed by comparing cell-subtype specific estimates from mixed-cells with corresponding cell-sorted mean using the mean absolute error (MAE) and the Coefficient of Determination (R²). At the cell-subtype level, methylation levels at 3272 CpGs could be estimated to within a MAE of 5% of the expected value. The cell-subtypes with the highest accuracy were CD56⁺ NK (R² = 0.56) and CD8⁺T (R² = 0.48), where 23% of sites were accurately estimated. Hierarchical clustering and pathways enrichment analysis confirmed the biological relevance of the panels. Our results suggest that linear regression for cell-subtype specific methylation estimation is accurate only for some cell-subtypes at a small fraction of cell-associated sites but may be applicable to EWASs of disease traits with a blood-based pathology. Although sample size was a limitation in this study, we suggest that alternative statistical methods will provide the greatest performance improvements.

Tobacco biomarkers and genetic/epigenetic analysis to investigate ethnic/racial differences in lung cancer risk among smokers

The Multiethnic Cohort Study has demonstrated that African Americans and Native Hawaiians have a higher risk for lung cancer due to cigarette smoking than Whites while Latinos and Japanese Americans have a lower risk. These findings are consistent with other epidemiologic studies in the literature. In this review, we summarize tobacco carcinogen and toxicant biomarker studies and genetic analyses which partially explain these differences. As determined by measurement of total nicotine equivalents in urine, which account for about 85% of the nicotine dose, African Americans take up greater amounts of nicotine than Whites per cigarette while Japanese Americans take up less. There are corresponding differences in the uptake of tobacco smoke carcinogens such as tobacco-specific nitrosamines, polycyclic aromatic hydrocarbons, 1,3-butadiene, and other toxic volatiles. The lower nicotine uptake of Japanese Americans is clearly linked to the preponderance of low activity forms of the primary nicotine metabolizing enzyme CYP2A6 in this ethnic group, leading to more unchanged nicotine in the body and thus lower smoking intensity. But the relatively high risk of Native Hawaiians and the low risk of Latino smokers for lung cancer are not explained by these factors. The possible role of epigenetics in modifying lung cancer risk among smokers is also discussed here. The results of these published studies may lead to a better understanding of susceptibility factors for lung cancer in cigarette smokers thus potentially identifying biomarkers that can detect those individuals at highest risk so that preventive approaches can be initiated at an early stage of the lung cancer development process.

Human Age Prediction Based on DNA Methylation Using a Gradient Boosting Regressor

All tissues of organisms will become old as time goes on. In recent years, epigenetic investigations have found that there is a close correlation between DNA methylation and aging. With the development of DNA methylation research, a quantitative statistical relationship between DNA methylation and different ages was established based on the change rule of methylation with age, it is then possible to predict the age of individuals. All the data in this work were retrieved from the Illumina HumanMethylation BeadChip platform (27K or 450K). We analyzed 16 sets of healthy samples and 9 sets of diseased samples. The healthy samples included a total of 1899 publicly available blood samples (0–103 years old) and the diseased samples included 2395 blood samples. Six age-related CpG sites were selected through calculating Pearson correlation coefficients between age and DNA methylation values. We built a gradient boosting regressor model for these age-related CpG sites. 70% of the data was randomly selected as training data and the other 30% as independent data in each dataset for 25 runs in total. In the training dataset, the healthy samples showed that the correlation between predicted age and DNA methylation was 0.97, and the mean absolute deviation (MAD) was 2.72 years. In the independent dataset, the MAD was 4.06 years. The proposed model was further tested using the diseased samples. The MAD was 5.44 years for the training dataset and 7.08 years for the independent dataset. Furthermore, our model worked well when it was applied to saliva samples. These results illustrated that the age prediction based on six DNA methylation markers is very effective using the gradient boosting regressor.

Novel Epigenomic Biomarkers of Male Infertility Identified by Methylation Patterns of CpG Sites Within Imprinting Control Regions of H19 and SNRPN Genes

Male infertility is an important global health burden that can benefit from novel biomarkers and diagnostics innovation. Aberrant methylation of the imprinted genes H19 and SNRPN (small nuclear ribonucleoprotein polypeptide N) in sperm DNA has been implicated in abnormal sperm parameters and male infertility. However, whether certain methylation patterns of one or multiple CpG sites within an imprinted gene are pathological for multiple sperm defects remains poorly understood. To examine the diagnostic potential of certain methylation patterns of CpG sites for multiphenotype defects in human sperm, the sperm DNA methylation patterns of individual CpG sites within imprinting control regions (ICRs) of imprinted genes H19 and SNRPN were measured by bisulfite pyrosequencing in a Han Chinese population sample: 39 oligoasthenozoospermia (OA) patients, 36 asthenoteratozoospermia (AT) patients, and 50 normozoospermia (N) controls. A partial least squares discriminant analysis model was built with the CpG sites as independent variables. Among the 16 CpG sites screened, the methylation patterns of eight CpG sites within H19-ICR (CpG sites 1, 6-9, 12 and 15-16), and eight CpG sites within SNRPN-ICR (CpG sites 2, 5-6, 8-10, 13, and 16) correctly classified 74.4% and 72.0% of the samples in terms of male fertile status, respectively. Furthermore, by combination of these 16 selected CpG sites within ICRs of H19 and SNRPN, 88.0% of the samples could be successfully classified. Our study demonstrates that methylation profiles of CpG sites within ICRs of imprinted genes H19 and SNRPN may potentially serve as epigenomic biomarkers for assessment of infertility in men with multiple sperm defects. Further studies in independent population samples are called for diagnostic significance of methylation patterns of CpG sites within imprinted genes.

DNA methylation of membrane-bound catechol-O-methyltransferase in Malaysian schizophrenia patients

AIM

This study examined catechol-O-methyltransferase (COMT) DNA methylation in the peripheral blood of schizophrenia patients and also in healthy controls to investigate its potential use as a peripheral biomarker of schizophrenia and its relations with the clinical variables of schizophrenia patients.

METHODS

We examined the DNA methylation levels of COMT using genomic DNA from the peripheral blood of schizophrenia patients (n = 138) and healthy control participants (n = 132); all were Malaysian Malays. The extracted DNA was bisulfite converted, and the percentage methylation ratio value was calculated based on the results following a MethyLight protocol analysis.

RESULTS

The percentage methylation ratio of COMT was lower in schizophrenia than it was in the healthy controls (P < 0.001) and was different between the body mass index (P = 0.003) and antipsychotic (P = 0.004) groups. The COMT DNA methylation rate was lower in patients receiving atypical antipsychotics (P = 0.004) and risperidone (P = 0.049) as compared to typical antipsychotics. The Excitement and Depressed subdomains of the Positive and Negative Syndrome Scale were inversely related (P < 0.001) and therefore predictors (Excitement: b = -11.396, t = -4.760, P < 0.001; Depressed: b = -7.789, t = -3.487, P = 0.001) of COMT DNA methylation.

CONCLUSION

Our results suggested that the methylation level was affected by the severity of the clinical symptoms of schizophrenia and might also be influenced by pharmacological treatment. The epigenetic alteration of COMT in the peripheral blood could be a potential peripheral biomarker of schizophrenia.

Epigenome-wide association study of smoking and DNA methylation in non-small cell lung neoplasms

Tobacco smoke is a well-established lung cancer carcinogen. We hypothesize that epigenetic processes underlie carcinogenesis. The objective of this study is to examine the effects of smoke exposure on DNA methylation to search for novel susceptibility loci. We obtained epigenome-wide DNA methylation data from lung adenocarcinoma (LUAD) and lung squamous cell (LUSC) tissues in The Cancer Genome Atlas (TCGA). We performed a two-stage discovery (n = 326) and validation (n = 185) analysis to investigate the association of epigenetic DNA methylation level with cigarette smoking pack-years. We also externally validated our findings in an independent dataset. Linear model with least square estimator and spline regression were performed to examine the association between DNA methylation and smoking. We identified five CpG sites highly associated with pack-years of cigarette smoking. Smoking was negatively associated with methylation levels in cg25771041 (WWTR1, p = 3.6 × 10⁻⁹), cg16200496 (NFIX, p = 3.4 × 10⁻¹²), cg22515201 (PLA2G6, p = 1.0 × 10⁻⁹) and cg24823993 (NHP2L1, p = 5.1 × 10⁻⁸) and positively associated with the methylation level in cg11875268 (SMUG1, p = 4.3 × 10⁻⁸). The CpG-smoking association was stronger in LUSC than LUAD. Of the five loci, smoking explained the most variation in cg16200496 (R² = 0.098 [both types] and 0.144 [LUSC]). We identified 5 novel CpG candidates that demonstrate differential methylation patterns associated with smoke exposure in lung neoplasms.

Causal effect of smoking on DNA methylation in peripheral blood: a twin and family study

Background

Smoking has been reported to be associated with peripheral blood DNA methylation, but the causal aspects of the association have rarely been investigated. We aimed to investigate the association and underlying causation between smoking and blood methylation.

Methods

The methylation profile of DNA from the peripheral blood, collected as dried blood spots stored on Guthrie cards, was measured for 479 Australian women including 66 monozygotic twin pairs, 66 dizygotic twin pairs, and 215 sisters of twins from 130 twin families using the Infinium HumanMethylation450K BeadChip array. Linear regression was used to estimate associations between methylation at ~ 410,000 cytosine-guanine dinucleotides (CpGs) and smoking status. A regression-based methodology for twins, Inference about Causation through Examination of Familial Confounding (ICE FALCON), was used to assess putative causation.

Results

At a 5% false discovery rate, 39 CpGs located at 27 loci, including previously reported AHRR, F2RL3, 2q37.1 and 6p21.33, were found to be differentially methylated across never, former and current smokers. For all 39 CpG sites, current smokers had the lowest methylation level. Our study provides the first replication for two previously reported CpG sites, cg06226150 (SLC2A4RG) and cg21733098 (12q24.32). From the ICE FALCON analysis with smoking status as the predictor and methylation score as the outcome, a woman’s methylation score was associated with her co-twin’s smoking status, and the association attenuated towards the null conditioning on her own smoking status, consistent with smoking status causing changes in methylation. To the contrary, using methylation score as the predictor and smoking status as the outcome, a woman’s smoking status was not associated with her co-twin’s methylation score, consistent with changes in methylation not causing smoking status.

Conclusions

For middle-aged women, peripheral blood DNA methylation at several genomic locations is associated with smoking. Our study suggests that smoking has a causal effect on peripheral blood DNA methylation, but not vice versa.

Electronic supplementary material

The online version of this article (10.1186/s13148-018-0452-9) contains supplementary material, which is available to authorized users.

Maternal cigarette smoking during pregnancy and offspring DNA methylation in midlife

Maternal smoking in pregnancy (MSP) has been associated with DNA methylation in specific CpG sites (CpGs) in infants and children. We investigated whether MSP, independent of own personal active smoking, was associated with midlife DNA methylation in CpGs that were previously identified in studies of MSP-DNA methylation in children. We used data on MSP collected from pregnant mothers of 89 adult women born in 1959-1964 and measured DNA methylation in blood (granulocytes) collected in 2001-2007 (mean age: 43 years). Seventeen CpGs were differentially methylated by MSP, with multiple CpGs mapping to CYP1A1, MYO1G, AHRR, and GFI1. These associations were consistent in direction with prior studies (e.g., MSP associated with more and less methylation in AHRR and CYP1A1, respectively) and, with the exception of AHRR CpGs, were not substantially altered by adjustment for active smoking. These preliminary results confirm prior prospective reports that MSP influences the offspring DNA methylation, and extends the timeframe to midlife, and suggest that these effects may persist into adulthood, independently of active smoking.

Relationship of tobacco smoking and smoking-related DNA methylation with epigenetic age acceleration

Recent studies have identified biomarkers of chronological age based on DNA methylation levels. Since active smoking contributes to a wide spectrum of aging-related diseases in adults, this study intended to examine whether active smoking exposure could accelerate the DNA methylation age in forms of age acceleration (AA, residuals of the DNA methylation age estimate regressed on chronological age). We obtained the DNA methylation profiles in whole blood samples by Illumina Infinium Human Methylation450 Beadchip array in two independent subsamples of the ESTHER study and calculated their DNA methylation ages by two recently proposed algorithms. None of the self-reported smoking indicators (smoking status, cumulative exposure and smoking cessation time) or serum cotinine levels was significantly associated with AA. On the contrary, we successfully confirmed that 66 out of 150 smoking-related CpG sites were associated with AA, even after correction for multiple testing (FDR <0.05). We further built a smoking index (SI) based on these loci and demonstrated a monotonic dose-response relationship of this index with AA. In conclusion, DNA methylation-based biological indicators for current and past smoking exposure, but not self-reported smoking information or serum cotinine levels, were found to be related to DNA methylation defined AA. Further research should address potential mechanisms underlying the observed patterns, such as potential reflections of susceptibility to environmental hazards in both smoking related methylation changes and methylation defined AA.

From forensic epigenetics to forensic epigenomics: broadening DNA investigative intelligence

Human genetic variation is a major resource in forensics, but does not allow all forensically relevant questions to be answered. Some questions may instead be addressable via epigenomics, as the epigenome acts as an interphase between the fixed genome and the dynamic environment. We envision future forensic applications of DNA methylation analysis that will broaden DNA-based forensic intelligence. Together with genetic prediction of appearance and biogeographic ancestry, epigenomic lifestyle prediction is expected to increase the ability of police to find unknown perpetrators of crime who are not identifiable using current forensic DNA profiling.

DNA methylation levels in candidate genes associated with chronological age in mammals are not conserved in a long-lived seabird

Most seabirds do not have any outward identifiers of their chronological age, so estimation of seabird population age structure generally requires expensive, long-term banding studies. We investigated the potential to use a molecular age biomarker to estimate age in short-tailed shearwaters (Ardenna tenuirostris). We quantified DNA methylation in several A. tenuirostris genes that have shown age-related methylation changes in mammals. In birds ranging from chicks to 21 years of age, bisulphite treated blood and feather DNA was sequenced and methylation levels analysed in 67 CpG sites in 13 target gene regions. From blood samples, five of the top relationships with age were identified in KCNC3 loci (CpG66: R² = 0.325, p = 0.019). In feather samples ELOVL2 (CpG42: R² = 0.285, p = 0.00048) and EDARADD (CpG46: R² = 0.168, p = 0.0067) were also weakly correlated with age. However, the majority of markers had no clear association with age (of 131 comparisons only 12 had a p-value < 0.05) and statistical analysis using a penalised lasso approach did not produce an accurate ageing model. Our data indicate that some age-related signatures identified in orthologous mammalian genes are not conserved in the long-lived short tailed shearwater. Alternative molecular approaches will be required to identify a reliable biomarker of chronological age in these seabirds.

Forensic age prediction for saliva samples using methylation-sensitive high resolution melting: exploratory application for cigarette butts

There is high demand for forensic age prediction in actual crime investigations. In this study, a novel age prediction model for saliva samples using methylation-sensitive high resolution melting (MS-HRM) was developed. The methylation profiles of ELOVL2 and EDARADD showed high correlations with age and were used to predict age with support vector regression. ELOVL2 was first reported as an age predictive marker for saliva samples. The prediction model showed high accuracy with a mean absolute deviation (MAD) from chronological age of 5.96 years among 197 training samples. The model was further validated with an additional 50 test samples (MAD = 6.25). In addition, the age prediction model was applied to saliva extracted from seven cigarette butts, as in an actual crime scene. The MAD (7.65 years) for these samples was slightly higher than that of intact saliva samples. A smoking habit or the ingredients of cigarettes themselves did not significantly affect the prediction model and could be ignored. MS-HRM provides a quick (2 hours) and cost-effective (95% decreased compared to that of DNA chips) method of analysis. Thus, this study may provide a novel strategy for predicting the age of a person of interest in actual crime scene investigations.