Epigenetic Profiling of Obesity and Smoking

Obesity and smoking are the two major preventable causes of global mortality associated with a multitude of comorbidities, inflicting greater public health and economic burden. Complex interactions between genetic and environmental factors influence susceptibility to obesity and smoking. Epigenetic modifications provide a mechanistic link between genetic and non-genetic factors causing complex diseases or traits. Epigenetic modifications also function as an additional layer of gene regulation by modifying the structure and accessibility of DNA and chromatin. The fundamental objective of this thesis is to elucidate the role of epigenetic and transcriptomic markers in obesity and smoking. Hence, this thesis focuses on identifying epigenetic and transcriptomic markers associated with weight loss and smoking behavior using different study designs and by applying computational and statistical approaches. Genome-wide transcriptome and methylome were assessed in an unbiased, hypothesis-free setting to identify weight-loss and smoking-associated signals in Study I and II, respectively. Validation of the main findings from the discovery analyses and integration of transcriptomic and methylation data were performed to assess the validity and biological significance of the identified markers. A machine learning approach was employed in Study III to develop a robust smoking status classifier based on DNA methylation profiles. The performance of the classifier was tested in three different test datasets and also in comparison with two other existing approaches. Therefore, this thesis encompasses both application and method development aspects to achieve the corresponding aims of the studies. In Study I, clinical parameters, genome-wide transcriptome, and methylome analyses were assessed longitudinally at three time points during a one-year weight loss intervention study, to understand the temporal changes in transcriptome and methylome of subcutaneous adipose tissue (SAT) in response to weight-loss. Results from the discovery analyses were validated using monozygotic (MZ) twin pairs discordant for acquired obesity, to examine whether weight loss and acquired obesity exhibit reciprocal transcriptome and methylome profiles. Gene expression and methylation profiles of the SAT at the three time points were also integrated to enhance our understanding of their interaction and thereby their contribution in weight loss. Based on the weight loss trajectory of the participants, three comparisons were performed: short-term (baseline to the fifth month), continuous (fifth to twelfth month), and long-term weight loss (baseline to twelfth month). Clinical parameters were improved with the weight loss (e.g. from baseline to fifth month, total and low-density lipoprotein cholesterol; triglycerides; and systolic blood pressure decreased and insulin sensitivity increased) and several significant transcriptome profiles were identified in response to weight loss at the three comparisons. No genome-wide significant methylation profiles were identified for the three comparisons. However, several significant correlations were observed between expression and methylation, indicating a potential regulatory role of DNA methylation in weight loss -associated transcriptome profiles. At the pathway level, short-term weight loss was implicated in lipoprotein metabolism and long-term weight loss associated with various pathways associated with multiple functions of the SAT. Furthermore, several weight loss -associated genes exhibited opposite direction of expression in acquired obesity in the validation cohort of MZ twins, validating the robustness of identified associations. In Study II, discovery analyses focused on understanding the widespread effects of smoking on SAT by simultaneous assessment of genome-wide transcriptome and methylome of SAT. Discovery analyses performed on the current (n=54) and never (n=291) smokers in the TwinsUK cohort identified 42 significantly differentially methylated signals and 42 significant differentially expressed genes (DEG) indicating a substantial impact of smoking on metabolically important SAT. Integration of these results revealed an overlap at five genes (AHRR, CYP1A1, CYP1B1, CYTL1, and F2RL3) comprising 14 CpG sites. To further characterize the widespread effects of smoking on metabolic disease risk three adiposity phenotypes (total fat mass [TFM], android-to-gynoid fat ratio [AGR] and visceral fat mass [VFM]) were assessed with regards to the identified smoking-associated methylation and expression signals. Three CpG sites in CYP1A1 showed significant associations with VFM and AGR, and an inverse association was identified between methylation levels of cg14120703 (NOTCH1) and AGR. To validate these associations, a subset of younger Finnish twins (n=69, 21 current smokers) was used as a replication cohort. The overall inverse association between cg10009577 (CYP1A1) and AGR was replicated and exhibited a similar direction for interaction effects between smoking status and AGR. However, this association did not reach the genome-wide significance level. Expression levels of F2RL3 showed a significant association with all three adiposity phenotypes. While OR51E1 expression levels were significantly associated with AGR and VFM. Our results show that smoking affects both the methylome and transcriptome of the SAT with overlapping signals. Furthermore, smoking-associated methylation and transcriptome profiles are also associated with adiposity phenotypes indicating a broader impact of smoking on human metabolic health. In Study III, I developed a methylation-based smoking status classifier using a machine learning approach to overcome the limitations of cotinine and carbon monoxide biomarkers (i.e. limited to measuring recent exposure to smoking due to their short half-lives in body fluids) and the existing DNA methylation score-based approaches and to advance the practical applicability of smoking-associated methylation signals. I considered three smoking status categories (current, former and never) and used multinomial LASSO regression coupled with internal cross-validation to build the classifier. I demonstrated the global applicability and robustness of our classifier by evaluation of its performance in three independent test datasets from different populations and also compared the performance with two existing approaches. Our classifier differs from the existing approaches by curtailing the need to compute a threshold value specific to each dataset to predict smoking status. Our classifier showed good discriminative ability in identifying current and never smokers compared to other approaches. I also performed an extensive phenotypic evaluation to understand the results of our classifier. Accurate classification of former smokers is challenging as their classification is affected by cessation time and smoking intensity prior to quitting. I provide the functionalities of our classifier including other the two methods as an R package EpiSmokEr (Epigenetic Smoking status Estimator), facilitating prediction of smoking status in future studies. In conclusion, this doctoral thesis (1) enhances our understanding of obesity and smoking by integrating methylation and transcriptome data and identifying several weight-loss and smoking-associated signals, (2) shows wide-spread impact of smoking on metabolic health risk by evaluating the associations between smoking-associated signals and adiposity measures, and (3) demonstrates the role of DNA methylation profiles as a robust biomarker to predict smoking status by developing a smoking-status classifier

The potential of DNA methylation as a biomarker for obesity and smoking

Peer reviewe

Associations of Alcohol Consumption With Epigenome-Wide DNA Methylation and Epigenetic Age Acceleration : Individual-Level and Co-twin Comparison Analyses

Background DNA methylation may play a role in the progression from normative to problematic drinking and underlie adverse health outcomes associated with alcohol misuse. We examined the association between alcohol consumption and DNA methylation patterns using 3 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 = 1,004; 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 4 CpG sites from the EWAS and identified 23 additional sites. However, when we examined qualitative differences in drinking patterns between twins (heavy drinker vs. light drinker/abstainer or moderate drinker vs. 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.Peer reviewe

An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs

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.Peer reviewe

Integrative approach identifies SLC6A20 and CXCR6 as putative causal genes for the COVID-19 GWAS signal in the 3p21.31 locus

To date, the locus with the most robust human genetic association to COVID-19 severity 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 modulate COVID-19 risk and highlight the usefulness of this integrative approach to bridge the divide between correlational and causal studies of human biology

Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation

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.Peer reviewe

Leisure-Time and Occupational Physical Activity Associates Differently with Epigenetic Aging

PurposeGreater 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.MethodsThe 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.ResultsEpigenetic 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 (ConclusionsLTPA associates with slower and OPA with faster epigenetic aging. However, adjusting the models for smoking status, which may reflect the accumulation of unhealthy lifestyle habits, attenuated the associations.</p

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes(1). Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel(2)) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries. A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants.Peer reviewe

The Older Finnish Twin Cohort-45 Years of Follow-up

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.Peer reviewe

Smoking induces coordinated DNA methylation and gene expression changes in adipose tissue with consequences for metabolic health

Abstract Background Tobacco smoking is a risk factor for multiple diseases, including cardiovascular disease and diabetes. Many smoking-associated signals have been detected in the blood methylome, but the extent to which these changes are widespread to metabolically relevant tissues, and impact gene expression or metabolic health, remains unclear. Methods We investigated smoking-associated DNA methylation and gene expression variation in adipose tissue biopsies from 542 healthy female twins. Replication, tissue specificity, and longitudinal stability of the smoking-associated effects were explored in additional adipose, blood, skin, and lung samples. We characterized the impact of adipose tissue smoking methylation and expression signals on metabolic disease risk phenotypes, including visceral fat. Results We identified 42 smoking-methylation and 42 smoking-expression signals, where five genes (AHRR, CYP1A1, CYP1B1, CYTL1, F2RL3) were both hypo-methylated and upregulated in current smokers. CYP1A1 gene expression achieved 95% prediction performance of current smoking status. We validated and replicated a proportion of the signals in additional primary tissue samples, identifying tissue-shared effects. Smoking leaves systemic imprints on DNA methylation after smoking cessation, with stronger but shorter-lived effects on gene expression. Metabolic disease risk traits such as visceral fat and android-to-gynoid ratio showed association with methylation at smoking markers with functional impacts on expression, such as CYP1A1, and at tissue-shared smoking signals, such as NOTCH1. At smoking-signals, BHLHE40 and AHRR DNA methylation and gene expression levels in current smokers were predictive of future gain in visceral fat upon smoking cessation. Conclusions Our results provide the first comprehensive characterization of coordinated DNA methylation and gene expression markers of smoking in adipose tissue. The findings relate to human metabolic health and give insights into understanding the widespread health consequence of smoking outside of the lung