Commentary: The Seven Plagues of Epigenetic Epidemiology
Commentary: The Seven Plagues of Epigenetic Epidemiology
The results of GWASs are relatively easy to judge. Quality-control steps are well-defined and reported, individually testing every genetic variant [i.e. single nucleotide polymorphism (SNP)] is straightforward, and levels of genome-wide statistical significance are clear. For EWASs, the analytical methodology is very much under construction. For example, in the current study it was not possible to attain genome-wide levels of significance, which is acceptable for an exploratory study, but makes it difficult to fully interpret the reported differences. Because of the vast range of methods currently being used to assess DNA methylation, meta-analyses across different studies are difficult. The adoption of a common technology platform, such as the new Illumina 450 k Methylation Beadchip, across multiple studies would provide an excellent opportunity to converge on widely accepted guidelines for the analysis and integration of EWAS data. Apart from pre-processing procedures (quality control, normalization, handling different probe types, accounting for genetic variation, etc.), elements of these guidelines should deal with the analysis of individual CG dinucleotides vs groups of (correlated) adjacent CGs, the use of genome annotations in the analysis (histone states, promoter types, CG content, etc.), and levels of epigenome-wide significance for various analyses. An important aspect will be the exploration of the previously mentioned gene-set testing methods in the context of DNA methylation since they will be vital to obtain meaningful interpretations of genome-wide data in terms of underlying biological processes or genomic functions [e.g. promoters, enhancers, inter/intragenic CG island (shores), etc.]. For example, commonly used enrichment methods assume independence within a gene set and, apart from consistency in biological signal in a gene set, statistical significance may reflect consistency in other characteristics such as GC content, coverage or other sequence features. Alternative implementations of gene-set testing methods include global testing approaches. Finally, it will be important to adopt an integrative paradigm based on the combination of genetic and epigenetic epidemiological data. Of particular relevance in this respect is evidence for the widespread occurrence of allele-specific DNA methylation (ASM) across the genome. Recent studies have shown that there are considerable inter-individual differences in ASM, which are frequently associated with genetic variation but can also be mediated by genomic imprinting (i.e. the parent-of-origin dependent silencing of expression by epigenetic mechanisms), environmental influences and apparently stochastic factors in the cell. ASM can mask the effect of risk alleles by silencing their expression, and also provides a potential mechanism underlying gene–environment interactions. Furthermore, ASM may contribute towards the apparent 'missing heritability' of many complex diseases and the low penetrance often reported for SNPs identified by GWASs.
We Lack a Framework for the Analysis of Genome-wide Epigenetic Data
The results of GWASs are relatively easy to judge. Quality-control steps are well-defined and reported, individually testing every genetic variant [i.e. single nucleotide polymorphism (SNP)] is straightforward, and levels of genome-wide statistical significance are clear. For EWASs, the analytical methodology is very much under construction. For example, in the current study it was not possible to attain genome-wide levels of significance, which is acceptable for an exploratory study, but makes it difficult to fully interpret the reported differences. Because of the vast range of methods currently being used to assess DNA methylation, meta-analyses across different studies are difficult. The adoption of a common technology platform, such as the new Illumina 450 k Methylation Beadchip, across multiple studies would provide an excellent opportunity to converge on widely accepted guidelines for the analysis and integration of EWAS data. Apart from pre-processing procedures (quality control, normalization, handling different probe types, accounting for genetic variation, etc.), elements of these guidelines should deal with the analysis of individual CG dinucleotides vs groups of (correlated) adjacent CGs, the use of genome annotations in the analysis (histone states, promoter types, CG content, etc.), and levels of epigenome-wide significance for various analyses. An important aspect will be the exploration of the previously mentioned gene-set testing methods in the context of DNA methylation since they will be vital to obtain meaningful interpretations of genome-wide data in terms of underlying biological processes or genomic functions [e.g. promoters, enhancers, inter/intragenic CG island (shores), etc.]. For example, commonly used enrichment methods assume independence within a gene set and, apart from consistency in biological signal in a gene set, statistical significance may reflect consistency in other characteristics such as GC content, coverage or other sequence features. Alternative implementations of gene-set testing methods include global testing approaches. Finally, it will be important to adopt an integrative paradigm based on the combination of genetic and epigenetic epidemiological data. Of particular relevance in this respect is evidence for the widespread occurrence of allele-specific DNA methylation (ASM) across the genome. Recent studies have shown that there are considerable inter-individual differences in ASM, which are frequently associated with genetic variation but can also be mediated by genomic imprinting (i.e. the parent-of-origin dependent silencing of expression by epigenetic mechanisms), environmental influences and apparently stochastic factors in the cell. ASM can mask the effect of risk alleles by silencing their expression, and also provides a potential mechanism underlying gene–environment interactions. Furthermore, ASM may contribute towards the apparent 'missing heritability' of many complex diseases and the low penetrance often reported for SNPs identified by GWASs.
