Genome-scale approaches to the epigenetics of common human disease

Abstract

Traditionally, the pathology of human disease has been focused on microscopic examination of affected tissues, chemical and biochemical analysis of biopsy samples, other available samples of convenience, such as blood, and noninvasive or invasive imaging of varying complexity, in order to classify disease and illuminate its mechanistic basis. The molecular age has complemented this armamentarium with gene expression arrays and selective analysis of individual genes. However, we are entering a new era of epigenomic profiling, i.e., genome-scale analysis of cell-heritable nonsequence genetic change, such as DNA methylation. The epigenome offers access to stable measurements of cellular state and to biobanked material for large-scale epidemiological studies. Some of these genome-scale technologies are beginning to be applied to create the new field of epigenetic epidemiology.

Fig. 1

Altered DNA methylation of CpG island shores in human colon cancer. Shown are an example of hypomethylation (Gene A, top) and hypermethylation (Gene B, bottom) in cancer revealed by a genome-scale analysis of the cancer methylome. Gene A is normally methylated at the shore and not at the island, and it acquires a hypomethylated pattern at the shore in colon cancer, resembling that of the normal liver. Aberrant expression at an alternate promoter, or for an untranslated RNA, is activated at the shore. Gene B is normally unmethylated at both shore and island, and it acquires a hypermethylated island at the shore, resembling the normal liver, and potentially at the island as well. Aberrant silencing ensues at the shore and potentially at the canonical promoter

Fig. 2

Epigenetic epidemiology. New genome-scale tools for epigenetic analysis will allow us to determine the relationship between genetic variation, epigenetic variation, and disease simultaneously. The area of overlap is deliberately drawn as the larger fraction of the overlap between genetics and phenotype to emphasize that most genetic findings must be considered in an epigenetic context and to highlight that the full value of typical genetic epidemiology studies cannot be realized until the complementary epigenetic measures and statistical tools are developed and performed on these samples