Modeling dependency structures in 450k DNA methylation data.

MOTIVATION: DNA methylation has been shown to be spatially dependent across chromosomes. Previous studies have focused on the influence of genomic context on the dependency structure, while not considering differences in dependency structure between individuals. RESULTS: We modeled spatial dependency with a flexible framework to quantify the dependency structure, focusing on inter-individual differences by exploring the association between dependency parameters and technical and biological variables. The model was applied to a subset of the Finnish Twin Cohort study (N = 1611 individuals). The estimates of the dependency parameters varied considerably across individuals, but were generally consistent across chromosomes within individuals. The variation in dependency parameters was associated with bisulfite conversion plate, zygosity, sex and age. The age differences presumably reflect accumulated environmental exposures and/or accumulated small methylation differences caused by stochastic mitotic events, establishing recognizable, individual patterns more strongly seen in older individuals. AVAILABILITY AND IMPLEMENTATION: The twin dataset used in the current study are located in the Biobank of the National Institute for Health and Welfare, Finland. All the biobanked data are publicly available for use by qualified researchers following a standardized application procedure (https://thl.fi/en/web/thl-biobank/for-researchers). A R-script for fitting the dependency structure to publicly available DNA methylation data with the software used in this article is provided in supplementary data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Genetic Determinants of Epigenetic Patterns: Providing Insight into Disease.

The field of epigenetics and our understanding of the mechanisms that regulate the establishment, maintenance and heritability of epigenetic patterns continue to grow at a remarkable rate. This information is providing increased understanding of the role of epigenetic changes in disease, insight into the underlying causes of these epigenetic changes and revealing new avenues for therapeutic intervention. Epigenetic modifiers are increasingly being pursued as therapeutic targets in a range of diseases, with a number of agents targeting epigenetic modifications already proving effective in diseases such as cancer. Although it is well established that DNA mutations and aberrant expression of epigenetic modifiers play a key role in disease, attention is now turning to the interplay between genetic and epigenetic factors in complex disease etiology. The role of genetic variability in determining epigenetic profiles, which can then be modified by environmental and stochastic factors, is becoming more apparent. Understanding the interplay between genetic and epigenetic factors is likely to aid in identifying individuals most likely to benefit from epigenetic therapies. This goal is coming closer to realization because of continual advances in laboratory and statistical tools enabling improvements in the integration of genomic, epigenomic and phenotypic data.

The mitochondrial protein Opa1 promotes adipocyte browning that is dependent on urea cycle metabolites.

White to brown/beige adipocytes conversion is a possible therapeutic strategy to tackle the current obesity epidemics. While mitochondria are key for energy dissipation in brown fat, it is unknown if they can drive adipocyte browning. Here, we show that the mitochondrial cristae biogenesis protein optic atrophy 1 (Opa1) facilitates cell-autonomous adipocyte browning. In two cohorts of patients with obesity, including weight discordant monozygotic twin pairs, adipose tissue OPA1 levels are reduced. In the mouse, Opa1 overexpression favours white adipose tissue expandability as well as browning, ultimately improving glucose tolerance and insulin sensitivity. Transcriptomics and metabolomics analyses identify the Jumanji family chromatin remodelling protein Kdm3a and urea cycle metabolites, including fumarate, as effectors of Opa1-dependent browning. Mechanistically, the higher cyclic adenosine monophosphate (cAMP) levels in Opa1 pre-adipocytes activate cAMP-responsive element binding protein (CREB), which transcribes urea cycle enzymes. Flux analyses in pre-adipocytes indicate that Opa1-dependent fumarate accumulation depends on the urea cycle. Conversely, adipocyte-specific Opa1 deletion curtails urea cycle and beige differentiation of pre-adipocytes, and is rescued by fumarate supplementation. Thus, the urea cycle links the mitochondrial dynamics protein Opa1 to white adipocyte browning.

Making Sense of the Epigenome Using Data Integration Approaches.

Epigenetic research involves examining the mitotically heritable processes that regulate gene expression, independent of changes in the DNA sequence. Recent technical advances such as whole-genome bisulfite sequencing and affordable epigenomic array-based technologies, allow researchers to measure epigenetic profiles of large cohorts at a genome-wide level, generating comprehensive high-dimensional datasets that may contain important information for disease development and treatment opportunities. The epigenomic profile for a certain disease is often a result of the complex interplay between multiple genetic and environmental factors, which poses an enormous challenge to visualize and interpret these data. Furthermore, due to the dynamic nature of the epigenome, it is critical to determine causal relationships from the many correlated associations. In this review we provide an overview of recent data analysis approaches to integrate various omics layers to understand epigenetic mechanisms of complex diseases, such as obesity and cancer. We discuss the following topics: (i) advantages and limitations of major epigenetic profiling techniques, (ii) resources for standardization, annotation and harmonization of epigenetic data, and (iii) statistical methods and machine learning methods for establishing data-driven hypotheses of key regulatory mechanisms. Finally, we discuss the future directions for data integration that shall facilitate the discovery of epigenetic-based biomarkers and therapies.

Comparison of pre-processing methodologies for Illumina 450k methylation array data in familial analyses.

BACKGROUND: Human methylome mapping in health and disease states has largely relied on Illumina Human Methylation 450k array (450k array) technology. Accompanying this has been the necessary evolution of analysis pipelines to facilitate data processing. The majority of these pipelines, however, cater for experimental designs where matched 'controls' or 'normal' samples are available. Experimental designs where no appropriate 'reference' exists remain challenging. Herein, we use data generated from our study of the inheritance of methylome profiles in families to evaluate the performance of eight normalisation pre-processing methods. Fifty individual samples representing four families were interrogated on five 450k array BeadChips. Eight normalisation methods were tested using qualitative and quantitative metrics, to assess efficacy and suitability. RESULTS: Stratified quantile normalisation combined with ComBat were consistently found to be the most appropriate when assessed using density, MDS and cluster plots. This was supported quantitatively by ANOVA on the first principal component where the effect of batch dropped from p < 0.01 to p = 0.97 after stratified QN and ComBat. Median absolute differences between replicated samples were the lowest after stratified QN and ComBat as were the standard error measures on known imprinted regions. Biological information was preserved after normalisation as indicated by the maintenance of a significant association between a known mQTL and methylation (p = 1.05e-05). CONCLUSIONS: A strategy combining stratified QN with ComBat is appropriate for use in the analyses when no reference sample is available but preservation of biological variation is paramount. There is great potential for use of 450k array data to further our understanding of the methylome in a variety of similar settings. Such advances will be reliant on the determination of appropriate methodologies for processing these data such as established here.