Integration of genomic and epigenomic DNA methylation data in common complex diseases by haplotype-specific methylation analysis

Abstract

The analysis of complex diseases was revolutionized by the ability to genotype at a genome-wide level tagging common SNPs in sufficiently large, and therefore adequately powered, population sample sets. This technological breakthrough has led to thousands of genetic variants being robustly associated with a multitude of phenotypic traits. These findings have illuminated novel genes and previously unknown pathways in the pathogenesis of disease, although in the majority of loci the functional mechanism remains unknown. The integration of this genomic information with epigenomic and transcriptomic data from these regions is one of the next steps in unraveling their biological significance. Allele-specific methylation influences allele-specific expression; therefore, the methylation state of the haplotypes within genetically associated regions can determine epigenetic differences with potential functional effects. DNA methylation data and association-determined risk and nonrisk haplotypes can be compared by a haplotype-specific methylation analysis. These are the first forays into what will become an increasingly routine multidimensional analysis as whole-genome, epigenome and transcriptome sequencing data become easily obtainable, with existing second- and soon to be available third-generation sequencing analyzers. Concise understanding of the functional implications of these genome-wide association-derived risk factors, plus rare variants discovered from deep sequencing experiments currently underway, will enable personalized risk and prevention profiling, as well as treatment, to come to fruition.