A twin approach to unraveling epigenetics

Abstract

The regulation of gene expression plays a pivotal role in complex phenotypes, and epigenetic mechanisms such as DNA methylation are essential to this process. The availability of next-generation sequencing technologies allows us to study epigenetic variation at an unprecedented level of resolution. Even so, our understanding of the underlying sources of epigenetic variability remains limited. Twin studies have played an essential role in estimating phenotypic heritability, and these now offer an opportunity to study epigenetic variation as a dynamic quantitative trait. High monozygotic twin discordance rates for common diseases suggest that unexplained environmental or epigenetic factors could be involved. Recent genome-wide epigenetic studies in disease-discordant monozygotic twins emphasize the power of this design to successfully identify epigenetic changes associated with complex traits. We describe how large-scale epigenetic studies of twins can improve our understanding of how genetic, environmental and stochastic factors impact upon epigenetics, and how such studies can provide a comprehensive understanding of how epigenetic variation affects complex traits.

Figure 1

Epigenetic changes and their effects on transcription and disease. Epigenetic variants across multiple levels of chromatin structure, shown here at different levels of cell resolution in nanometers (nm), associate with gene expression and disease status in a sample of MZ twins. Top, higher-order chromatin loop configurations and attachment to the nuclear lamina can represent active and repressed chromatin domains that associate with differential gene expression. The next level represents the chromatin ‘beads on a string’ configuration, which reflects structural organization into loosely structured (active) and densely packed (repressed) chromatin states. Histone modifications associated with active transcription (green) and transcription silencing (red) are indicated by colored dots. The next levels of cell resolution depict DNA methylation (red M) in the promoter regions of the silenced genes and the corresponding differences in gene expression. Bottom, possible effects of these changes on disease status in a sample of MZ twins, highlighting unaffected-concordant, discordant, and disease-concordant MZ twins.

Figure 2

Transgenerational changes in epigenetic variation in twins. The figure provides illustrative examples of epigenetic heritability estimates in hypothetical families that include pairs of MZ or DZ twins across three generations. This highlights the idea that epigenetic heritability estimates from twin studies are expected to be higher than those obtained from transgenerational families. At each of three generation (G1, G2, G3) we represent MZ (blue) and DZ (red) intra-class correlation distributions for genome-wide DNA methylation patterns, and these are consistent with reported correlation estimates . We compare within-generation correlation distributions to hypothetical transgenerational correlations in DNA methylation from parent–offspring (G1 versus G2) and grandparent–grandchild (G1 versus G3) pairs to illustrate the fact that epigenetic heritability becomes diluted over generations. In addition, the figure also emphasizes the time-specific aspect of epigenetic heritability estimates and specifically the reduction in correlation of genome-wide DNA methylation patterns for one pair of twins (at generation G1) with age, where genome-wide methylation correlations are slightly lower at later (T_N) than at earlier (T₁) stages in life. This is consistent with the observed increase in epigenetic variance in older twins .

Figure 3

Epigenetic and phenotypic heritability. Path diagrams detailing the proposed contribution of latent variables to the methylation status of an individual at one genomic region (M) and to their phenotype (P). The left panel represents latent variables contributing to DNA methylation status at one genomic region in one individual: effects will be specific to the age, sex, and population of the individual and the tissue sampled. Methylation latent factors include additive genetic factors (A), common environmental factors (C), unique environment (E), and heritable and stable epigenetic factors that are not DNA-sequence dependent (Ep). The right panel represents the path model in twins, depicting the contribution of DNA methylation and other factors to the phenotype (P) in twin i with correlation estimates in MZ (left) and DZ (right) twins for latent variables including additive genetic effects (A_i), common environment (C_i), DNA methylation (M_i) and unique environment (E_i). Correlation estimates were obtained from previous genetic and epigenetic studies in twins. In siblings, the correlation in M will probably be lower than that observed in DZ twins due to age differences and a higher proportion of stochastic changes.