A heritage of considerable research into such phenomena as parental imprinting and carcinogenesis is an almost axiomatic association of the DNA methylation epigenetic mark with the silencing of gene expression. However, the increasing technical resolution afforded by burgeoning -omics technologies reveals that a more elaborate interaction may exist between DNA methylation, within sub-regions of gene structure and/or specific dinucleotide sites, and levels of gene activity. Furthermore, seminal observations from the field of DOHaD, which clearly define the alignment of sequential epigenetic modifications and gene activity appear not to support a strictly causal relationship between DNA methylation and gene silencing. The temporal element implicit within DOHaD studies provides a useful framework within which to further explore the role of epigenetic mechanisms and in particular perhaps, to address the question of ‘deterministic intent’ when implicating the epigenetic regulation of gene activity in the manifestation of phenotype.

Analysis of DNA methylation data in epigenome-wide association studies provides many bioinformatics and statistical challenges. Not least of these, are the non-independence of individual DNA methylation marks from each other, from genotype and from technical sources of variation. In this review we discuss DNA methylation data from the Infinium450K array and processing methodologies to reduce technical variation. We describe recent approaches to harness the concordance of neighbouring DNA methylation values to improve power in association studies. We also describe how the non-independence of genotype and DNA methylation has been used to infer causality (in the case of Mendelian randomization approaches); suggest the mediating effect of DNA methylation in linking intergenic single nucleotide polymorphisms, identified in genome-wide association studies, to phenotype; and to uncover the widespread influence of gene and environment interactions on methylation levels.

The principles embodied by the Developmental Origins of Health and Disease (DOHaD) view of ‘life history’ trajectory are increasingly underpinned by biological data arising from molecular-based epigenomic and transcriptomic studies. Although a number of ‘omic’ platforms are now routinely and widely used in biology and medicine, data generation is frequently confounded by a frequency distribution in the measurement error (an inherent feature of the chemistry and physics of the measurement process), which adversely affect the accuracy of estimation and thus, the inference of relationships to other biological measures such as phenotype. Based on empirical derived data, we have previously derived a probability density function to capture such errors and thus improve the confidence of estimation and inference based on such data. Here we use published open source data sets to calculate parameter values relevant to the most widely used epigenomic and transcriptomic technologies Then by using our own data sets, we illustrate the benefits of this approach by specific application, to measurement of DNA methylation in this instance, in cases where levels of methylation at specific genomic sites represents either (1) a response variable or (2) an independent variable. Further, we extend this formulation to consideration of the ‘bivariate’ case, in which the co-dependency of methylation levels at two distinct genomic sites is tested for biological significance. These tools not only allow greater accuracy of measurement and improved confidence of functional inference, but in the case of epigenomic data at least, also reveal otherwise cryptic information.

There has been a substantial body of evidence, which has shown that genetic variation is an important determinant of disease risk. However, there is now increasing evidence that alterations in epigenetic processes also play a role in determining susceptibility to disease. Epigenetic processes, which include DNA methylation, histone modifications and non-coding RNAs play a central role in regulating gene expression, determining when and where a gene is expressed as well as the level of gene expression. The epigenome is highly sensitive to a variety of environmental factors, especially in early life. One factor that has been shown consistently to alter the epigenome is maternal diet. This review will focus on how maternal diet can modify the epigenome of the offspring, producing different phenotypes and altered disease susceptibilities.

The Developmental Origins of Health and Disease (DOHaD) hypothesis refers to the concept that ‘malnutrition during the fetal period induces a nature of thrift in fetuses, such that they have a higher change of developing non-communicable diseases, such as obesity and diabetes, if they grow up in the current well-fed society.’ Epigenetics is a chemical change in DNA and histones that affects how genes are expressed without alterations of DNA sequences. Several lines of evidence suggest that malnutrition during the fetal period alters the epigenetic expression status of metabolic genes in the fetus and that this altered expression can persist, and possibly lead to metabolic disorders. Similarly, mental stress during the neonatal period can alter the epigenetic expression status of neuronal genes in neonates. Moreover, such environmental, stress-induced, epigenetic changes are transmitted to the next generation via an acquired epigenetic status in sperm. The advantage of epigenetic modifications over changes in genetic sequences is their potential reversibility; thus, epigenetic alterations are potentially reversed with gene expression. Therefore, we potentially establish ‘preemptive medicine,’ that, in combination with early detection of abnormal epigenetic status and early administration of epigenetic-restoring drugs may prevent the development of disorders associated with the DOHaD.

In rats, maternal exposure to restraint stress during pregnancy can induce abnormalities in the cardiovascular and central nervous systems of the offspring. These effects are mediated by long-lasting hyperactivation of the hypothalamic–pituitary–adrenal axis. However, little is known about the potential effects of stress during pregnancy on metabolic systems. We examined the effect of restraint stress in pregnant mice on the liver function of their offspring. The offspring of stressed mothers showed significantly higher lipid accumulation in the liver after weaning than did the controls; this accumulation was associated with increased expression of lipid metabolism-related proteins such as alanine aminotransferase 2 diglyceride acyltransferase 1, peroxisome proliferator-activated receptor gamma and glucocorticoid receptor. Additionally, we observed increased levels of 11β-hydroxysteroid dehydrogenase type 1, an intercellular mediator that converts glucocorticoid from the inactive to the active form, in the foetal and postnatal periods. These results indicate that restraint stress in pregnancy in mice induces metabolic abnormalities via 11β-hydroxysteroid dehydrogenase type 1-related pathways in the foetal liver. It is therefore possible that exposure to stress in pregnant women may be a risk factor for metabolic syndromes (e.g. fatty liver) in children.

In vitro fertilization (IVF) and its subset intracytoplasmic sperm injection (ICSI), are widely used medical treatments for conception. There has been controversy over whether IVF is associated with adverse short- and long-term health outcomes of offspring. As with other prenatal factors, epigenetic change is thought to be a molecular mediator of any in utero programming effects. Most studies focused on DNA methylation at gene-specific and genomic level, with only a few on associations between DNA methylation and IVF. Using buccal epithelium from 208 twin pairs from the Peri/Postnatal Epigenetic Twin Study (PETS), we investigated associations between IVF and DNA methylation on a global level, using the proxies of Alu and LINE-1 interspersed repeats in addition to two locus-specific regulatory regions within IGF2/H19, controlling for 13 potentially confounding factors. Using multiple correction testing, we found strong evidence that IVF-conceived twins have lower DNA methylation in Alu, and weak evidence of lower methylation in one of the two IGF2/H19 regulatory regions and LINE-1, compared with naturally conceived twins. Weak evidence of a relationship between ICSI and DNA methylation within IGF2/H19 regulatory region was found, suggesting that one or more of the processes associated with IVF/ICSI may contribute to these methylation differences. Lower within- and between-pair DNA methylation variation was also found in IVF-conceived twins for LINE-1, Alu and one IGF2/H19 regulatory region. Although larger sample sizes are needed, our results provide additional insight to the possible influence of IVF and ICSI on DNA methylation. To our knowledge, this is the largest study to date investigating the association of IVF and DNA methylation.