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BACKGROUND: DNA methylation may play a role in progression from normative to problematic drinking and underlie adverse health outcomes associated with alcohol misuse. In the current study, we examined the association between alcohol consumption and DNA methylation patterns using three approaches: a conventional epigenome-wide association study (EWAS); a co-twin comparison design, which controls for genetic and environmental influences that twins share; and a regression of age acceleration, defined as a discrepancy between chronological age and DNA methylation age, on alcohol consumption. METHODS: Participants came from the Finnish Twin Cohorts (FinnTwin12/FinnTwin16; N = 1004; 55% female; average age = 23 years). Individuals reported the number of alcoholic beverages consumed in the past week, and epigenome-wide DNA methylation was assessed in whole-blood using the Infinium HumanMethylation450 BeadChip. RESULTS: In the EWAS, alcohol consumption was significantly related to methylation at 24 CpG sites. When evaluating whether differences between twin siblings (185 monozygotic pairs) in alcohol consumption predicted differences in DNA methylation, co-twin comparisons replicated four CpG sites from the EWAS and identified 23 additional sites. However, when we examined qualitative differences in drinking patterns between twins (heavy drinker versus light drinker/abstainer or moderate drinker versus abstainer; 44 pairs), methylation patterns did not significantly differ within twin pairs. Finally, individuals who reported higher alcohol consumption also exhibited greater age acceleration, though results were no longer significant after controlling for genetic and environmental influences shared by co-twins. CONCLUSIONS: Our analyses offer insight into the associations between epigenetic variation and levels of alcohol consumption in young adulthood.

DNA methylation is an epigenetic modification that has consistently been shown to be linked with a variety of human traits and diseases. Because DNA methylation is dynamic and potentially reversible in nature and can reflect environmental exposures and predict the onset of diseases, it has piqued interest as a potential disease biomarker. DNA methylation patterns are more stable than transcriptomic or proteomic patterns, and they are relatively easy to measure to track exposure to different environments and risk factors. Importantly, technologies for DNA methylation quantification have become increasingly cost effective-accelerating new research in the field-and have enabled the development of novel DNA methylation biomarkers. Quite a few DNA methylation-based predictors for a number of traits and diseases already exist. Such predictors show potential for being more accurate than self-reported or measured phenotypes (such as smoking behavior and body mass index) and may even hold potential for applications in clinics. In this review, we will first discuss the advantages and challenges of DNA methylation biomarkers in general. We will then review the current state and future potential of DNA methylation biomarkers in two human traits that show rather consistent alterations in methylome-obesity and smoking. Lastly, we will briefly speculate about the future prospects of DNA methylation biomarkers, and possible ways to achieve them.

Self-reported smoking status is prone to misclassification due to under-reporting, while biomarkers like cotinine can only measure recent exposure. Smoking strongly influences DNA methylation, with current, former and never smokers exhibiting different methylation profiles. Recently, two approaches were proposed to calculate scores based on smoking-responsive DNA methylation loci, to serve as reliable indicators of long-term exposure and potential biomarkers to estimate smoking behavior. However, these two methodologies need significant improvements to make them globally applicable to all populations and to achieve an optimal classification of individuals with unknown smoking habits. To advance the practical applicability of the smoking-associated methylation signals, we used machine learning methodology to train a classifier for smoking status prediction. We show the prediction performance of our classifier on three independent whole-blood test datasets demonstrating its robustness and global applicability. Furthermore, we show the cross-tissue functionality of our classifier in tissues other than blood. Additionally, we provide the community with an R package, EpiSmokEr, facilitating implementation of our classifier to predict smoking status in future studies.

To date the locus with the most robust human genetic association to COVID-19 susceptibility is 3p21.31. Here, we integrate genome-scale CRISPR loss-of-function screens and eQTLs in diverse cell types and tissues to pinpoint genes underlying COVID-19 risk. Our findings identify SLC6A20 and CXCR6 as putative causal genes that mediate COVID-19 risk and highlight the usefulness of this integrative approach to bridge the divide between correlational and causal studies of human biology.

Supplemental digital content is available in the text., Purpose Greater leisure-time physical activity (LTPA) associates with healthier lives, but knowledge regarding occupational physical activity (OPA) is more inconsistent. DNA methylation (DNAm) patterns capture age-related changes in different tissues. We aimed to assess how LTPA and OPA are associated with three DNAm-based epigenetic age estimates, namely, DNAm age, PhenoAge, and GrimAge. Methods The participants were young adult (21–25 yr, n = 285) and older (55–74 yr, n = 235) twin pairs, including 16 pairs with documented long-term LTPA discordance. Genome-wide DNAm from blood samples was used to compute DNAm age, PhenoAge, and GrimAge Age acceleration (Acc), which describes the difference between chronological and epigenetic ages. Physical activity was assessed with sport, leisure-time, and work indices based on the Baecke Questionnaire. Genetic and environmental variance components of epigenetic age Acc were estimated by quantitative genetic modeling. Results Epigenetic age Acc was highly heritable in young adult and older twin pairs (~60%). Sport index was associated with slower and OPA with faster DNAm GrimAge Acc after adjusting the model for sex. Genetic factors and nonshared environmental factors in common with sport index explained 1.5%–2.7% and 1.9%–3.5%, respectively, of the variation in GrimAge Acc. The corresponding proportions considering OPA were 0.4%–1.8% and 0.7%–1.8%, respectively. However, these proportions were minor (

DNA methylation quantitative trait locus (mQTL) analyses on 32,851 participants identify genetic variants associated with DNA methylation at 420,509 sites in blood, resulting in a database of >270,000 independent mQTLs. Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. Here we describe results of DNA methylation-quantitative trait loci (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTL of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We reveal that the genetic architecture of DNAm levels is highly polygenic and DNAm exhibits signatures of negative and positive natural selection. Using shared genetic control between distal DNAm sites we construct networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic factors are associated with both blood DNAm levels and complex diseases but in most cases these associations do not reflect causal relationships from DNAm to trait or vice versa indicating a more complex genotype-phenotype map than has previously been hypothesised.

We combined clinical, cytokine, genomic, methylation and dietary data from 43 young adult monozygotic twin pairs (aged 22–36 years, 53% female), where 25 of the twin pairs were substantially weight discordant (delta body mass index > 3 kg m −2 ). These measurements were originally taken as part of the TwinFat study, a substudy of The Finnish Twin Cohort study. These five large multivariate datasets (comprising 42, 71, 1587, 1605 and 63 variables, respectively) were jointly analysed using an integrative machine learning method called group factor analysis (GFA) to offer new hypotheses into the multi-molecular-level interactions associated with the development of obesity. New potential links between cytokines and weight gain are identified, as well as associations between dietary, inflammatory and epigenetic factors. This encouraging case study aims to enthuse the research community to boldly attempt new machine learning approaches which have the potential to yield novel and unintuitive hypotheses. The source code of the GFA method is publically available as the R package GFA.

The older Finnish Twin Cohort (FTC) was established in 1974. The baseline survey was in 1975, with two follow-up health surveys in 1981 and 1990. The fourth wave of assessments was done in three parts, with a questionnaire study of twins born during 1945–1957 in 2011–2012, while older twins were interviewed and screened for dementia in two time periods, between 1999 and 2007 for twins born before 1938 and between 2013 and 2017 for twins born in 1938–1944. The content of these wave 4 assessments is described and some initial results are described. In addition, we have invited twin-pairs, based on response to the cohortwide surveys, to participate in detailed in-person studies; these are described briefly together with key results. We also review other projects based on the older FTC and provide information on the biobanking of biosamples and related phenotypes.

Few studies have examined both gene expression and DNA methylation profiles in subcutaneous adipose tissue (SAT) during long-term weight loss. Thus, molecular mechanisms in weight loss and regain remain elusive. We performed a 1-year weight loss intervention on 19 healthy obese participants (mean body mass index (BMI) 34.6 kg m−2) and studied longitudinal gene expression (Affymetrix Human Genome U133 Plus 2.0) and DNA methylation (Infinium HumanMethylation450 BeadChip) in SAT at 0, 5 and 12 months. To examine whether weight loss and acquired obesity produce reciprocal profiles, we verified our findings in 26 BMI-discordant monozygotic twin pairs. We found altered expression of 69 genes from 0 to 5’ months (short-term) weight loss. Sixty of these genes showed reversed expression in acquired obesity (twins). Altogether 21/69 genes showed significant expression–DNA methylation correlations. Pathway analyses revealed increased high-density lipoprotein-mediated lipid transport characteristic to short-term weight loss. After the fifth month, two groups of participants evolved: weight losers (WLs) and weight regainers (WRs). In WLs five genes were differentially expressed in 5 vs 12 months, three of which significantly correlated with methylation. Signaling by insulin receptor pathway showed increased expression. We further identified 35 genes with differential expression in WLs from 0 to 12 months (long-term) weight loss, with 20 showing opposite expression patterns in acquired obesity, and 16/35 genes with significant expression–DNA methylation correlations. Pathway analyses demonstrated changes in signal transduction, metabolism, immune system and cell cycle. Notably, seven genes (UCHL1, BAG3, TNMD, LEP, BHMT2, EPDR1 and OSTM1) were found to be downregulated during both short- and long-term weight loss. Our study indicates short- and long-term weight loss influences in transcription and DNA methylation in SAT of healthy participants. Moreover, we demonstrate that same genes react in an opposite manner in weight loss and acquired obesity.

Little is known about epigenetic alterations associated with subcutaneous adipose tissue (SAT) in obesity. Our aim was to study genome-wide DNA methylation and gene expression differences in SAT in monozygotic (MZ) twin pairs who are discordant for body mass index (BMI). This design completely matches lean and obese groups for genetic background, age, gender and shared environment.14We analyzed DNA methylome and gene expression from SAT, together with body composition (magnetic resonance imaging/spectroscopy) and glucose tolerance test, lipids and C-reactive protein from 26 rare BMI-discordant (intrapair difference in BMI ⩾3 kg m(-2)) MZ twin pairs identified from 10 birth cohorts of young adult Finnish twins.We found 17 novel obesity-associated genes that were differentially methylated across the genome between heavy and lean co-twins. Nine of them were also differentially expressed. Pathway analyses indicated that dysregulation of SAT in obesity includes a paradoxical downregulation of lipo/adipogenesis and upregulation of inflammation and extracellular matrix remodeling. Furthermore, CpG sites whose methylation correlated with metabolically harmful fat depots (intra-abdominal and liver fat) also correlated with measures of insulin resistance, dyslipidemia and low-grade inflammation, thus suggesting that epigenetic alterations in SAT are associated with the development of unhealthy obesity.This is the first study in BMI-discordant MZ twin pairs reporting genome-wide DNA methylation and expression profiles in SAT. We found a number of novel genes and pathways whose methylation and expression patterns differ within the twin pairs, suggesting that the pathological adaptation of SAT to obesity is, at least in part, epigenetically regulated.

Figure S1. Scatterplot of correlations between EWAS -log10P-values from the linear mixed effect model used in the current study adipose discovery sample (y-axis) and results from Reference-free EWAS approach proposed by Houseman et al. (x-axis) [32]. Figure S2. Smoking cessation and adipose DNA methylation profiles. DNA methylation levels at the 42 smoking-DMS and smoking status in 542 adipose samples. Subject groups include current smoker, subjects who quit smoking within one year, subjects who quit between 1 to 5 years, subjects who quit smoking more than 5 years, and subjects who never smoked. Fourteen CpG sites located in genes with both smoking-DMS and smoking-DES are denoted with asterisks. Figure S3. Smoking cessation and adipose gene expression profiles. Gene expression levels at the 42 smoking-DES and smoking status in 542 adipose samples. Subject groups include current smoker, subjects who quit smoking within one year, subjects who quit between 1 to 5 years, subjects who quit smoking more than 5 years, and subjects who never smoked. Five genes with both smoking-DMS and smoking-DES are denoted with asterisks. Figure S4. Association between DNA methylation levels at the 42 smoking-DMS and future change in visceral fat mass (VFM) in 18 (red solid dots) and 228 subjects (gray hollow dots). Figure S5. Association between gene expression levels at the 42 smoking-DES and future change in visceral fat mass (VFM) in 18 (blue solid dots) and 228 subjects (gray hollow dots). (PDF 1411 kb)

Figure S1. Scatterplot of correlations between EWAS -log10P-values from the linear mixed effect model used in the current study adipose discovery sample (y-axis) and results from Reference-free EWAS approach proposed by Houseman et al. (x-axis) [32]. Figure S2. Smoking cessation and adipose DNA methylation profiles. DNA methylation levels at the 42 smoking-DMS and smoking status in 542 adipose samples. Subject groups include current smoker, subjects who quit smoking within one year, subjects who quit between 1 to 5 years, subjects who quit smoking more than 5 years, and subjects who never smoked. Fourteen CpG sites located in genes with both smoking-DMS and smoking-DES are denoted with asterisks. Figure S3. Smoking cessation and adipose gene expression profiles. Gene expression levels at the 42 smoking-DES and smoking status in 542 adipose samples. Subject groups include current smoker, subjects who quit smoking within one year, subjects who quit between 1 to 5 years, subjects who quit smoking more than 5 years, and subjects who never smoked. Five genes with both smoking-DMS and smoking-DES are denoted with asterisks. Figure S4. Association between DNA methylation levels at the 42 smoking-DMS and future change in visceral fat mass (VFM) in 18 (red solid dots) and 228 subjects (gray hollow dots). Figure S5. Association between gene expression levels at the 42 smoking-DES and future change in visceral fat mass (VFM) in 18 (blue solid dots) and 228 subjects (gray hollow dots). (PDF 1411 kb)