The epigenome in Alzheimer's disease: current state and approaches for a new path to gene discovery and understanding disease mechanism

Abstract

The advent of new technologies and analytic approaches is beginning to provide an unprecedented look at features of the human genome that affect RNA expression. These "epigenomic" features are found in a number of different forms: they include DNA methylation, covalent modifications of histone proteins and non-coding RNAs. Some of these features have now been implicated in Alzheimer's disease (AD). Here, we focus on recent studies that have identified robust observations relating to DNA methylation and chromatin in human brain tissue; these findings will ground the next generation of studies and provide a model for the design of such studies. Stemming from observations that compounds with histone deacetylase activity may be beneficial in AD, epigenome-wide studies in cortical samples from large numbers of human subjects have now shown that AD-associated epigenomic changes are reproducible, are not driven by genetic risk factors, and are widespread at specific locations in the genome. A fundamental question of whether such changes are causal remains to be demonstrated, but it is already clear that well-powered investigations of the human epigenome in the target organ of a neurodegenerative disease are feasible, are implicating new areas of the genome in the disease, and will be an important tool for future studies. We are now at an inflection point: as genome-wide association studies of genetic variants come to an end, a new generation of studies exploring the epigenome will provide an important new layer of information with which to enrich our understanding of AD pathogenesis and to possibly guide development of new therapeutic targets.

Keywords: Aging; Alzheimer’s disease; Chromatin; DNA methylation; Epigenomics; Histone.

Figure 1. Genetic and epigenomic variation have independent effects on disease susceptibility in the BIN1 locus

At the top of the figure, we include a diagram of chromosome 2, with a vertical red line denoting the location of the BIN1 locus. Below the chromosome, a double line presents the physical position of the segment of chromosome 2 that is presented in this figure (X axis for all subsequent components of the figure). The first ribbon contains the SNPs (blue dots) that have been interrogated in relation to AD susceptibility in this locus. The Y axis reports the −log₁₀(p value) for the association from AD, taken from the IGAP study for each SNP [33]. The second ribbon presents CG dinucleotides (pink dots) interrogated in relation to AD susceptibility as part of a DNA methylation study of the frontal cortex in AD [DeJager 2014]. The peak of the genetic and epigenomic associations are close to one another but do not overlap exactly. When modeled together, the SNP does not explain the association of the CG methylation level with AD, as reported in [14]. Below the association results, we present the chromatin state of this segment of chromosome 2, based on the Roadmap Epigenomics data and the ChromHMM algorithm [44] for three different brain regions: the prefrontal cortex (PFC), the hippocampus (HC), and the substantia nigra (SN). Each color denotes a different chromatin state, as outlined in the color key. The next ribbon presents the level of sequence conservation across mammalian species. The one below presents the location of CG islands, and the bottom ribbon shows the location of the exons of the gene found in this chromosomal segment, BIN1.

Figure 2. “Epigenetic age” based on DNA methylation and its relation to neuropathology

(a) The age of a subject can be predicted based on the subject’s epigenetic profile. The predicted age is termed “epigenetic age” and, for most tissues, is strongly correlated with the chronological age as indicated in the scatter plot. If the epigenetic age of a subject is larger than the chronological age (red dot), the subject’s epigenome appears to be “older” than the average epigenome of an individual with that chronological age in the reference set that was used to train the prediction model. Hence, the epigenetic age must be interpreted with respect to the reference set, which can comprise various different tissues and cell types or be a homogeneous set of brain tissues, for example. (b) A published method [28] was used to estimate the epigenetic age of each aging subject from whom we have a DNA methylation profile of the dorsolateral prefrontal cortex (n=740) [DeJager 2014]. The scatter plot shows each subject as a dot relative to their estimated epigenetic age based on DNA methylation data and chronological age. While the two ages are strongly correlated (r=0.67) (the regression line is presented in red), the estimated epigenetic age systematically underestimates the chronological age. This may be due to the age range and the various different tissue types from which the algorithm was developed. (c) The diagram illustrates the point made in a recent manuscript [51] that neuropathologies must be considered when studying aging-associated alterations of brain DNA methylation levels. The red circles indicates that 7,336 out of 420,132 CG dinucleotides were significantly associated with age in a statistical model adjusted for gender and technical covariates. When the model was further adjusted for six common neuropathologies (green circle), 39% of those CG dinucletides were no longer significantly associated with age.

Figure 3. Required sample sizes for AD methylome-wide association studies

The plot shows the number of significantly differentially methylated CG dinucleotides detected for various sample sizes between 50 and 740 subjects. The required sample size depends remarkably on the studied variable as demonstrated by the different curves for neuritic plaque (NP, continuous pathologic variable), pathologic Alzheimer diagnosis (pathologic AD, dichotomous pathologic variable) and clinical Alzheimer diagnosis (clinical AD, dichotomous clinical variable). The dashed lines show results after a stringent multiple testing adjustment using the family-wise error rate (FWER); solid lines show results when using the more liberal false discovery rate (FDR) approach. Methylation levels were logit-transformed and all 420,132 CG dinucletides for tested for an association with the variable of interest in a linear model adjusted for gender, age and technical covariates.