Epigenome-wide association studies for common human diseases

Abstract

Despite the success of genome-wide association studies (GWASs) in identifying loci associated with common diseases, a substantial proportion of the causality remains unexplained. Recent advances in genomic technologies have placed us in a position to initiate large-scale studies of human disease-associated epigenetic variation, specifically variation in DNA methylation. Such epigenome-wide association studies (EWASs) present novel opportunities but also create new challenges that are not encountered in GWASs. We discuss EWAS design, cohort and sample selections, statistical significance and power, confounding factors and follow-up studies. We also discuss how integration of EWASs with GWASs can help to dissect complex GWAS haplotypes for functional analysis.

Figure 1. The different types of sample cohorts that could be used in an EWAS

Refer to text for a full discussion.

Figure 2. Hypothetical DNA methylation frequency spectra in cases and controls

Methylation states in controls (solid curve) and cases for four effect sizes (other curves) are shown under three scenarios. For (a) and (b), the proportions of individuals who are, respectively, unmethylated, intermediate, or methylated in controls and the four sets of cases are shown in the keys. The distributions of measured methylation states are assumed to follow the following beta distributions (i) unmethylated individuals: beta(1.5,6) distribution, which has mean = 0.2, SD = 0.14; (ii) intermediate individuals: beta(2,2), mean = 0.50, SD = 0.22; (iii) methylated individuals: beta(6,1.5), mean = 0.80, SD = 0.14. For (c), the methylation spectrum is assumed to follow a single beta distribution for controls and each set of cases, and its parameters are shown in the key