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Epigenome-wide association studies for common human diseases

Nature Reviews Genetics volume 12, pages 529–541 (2011)Cite this article

Subjects

Key Points

  • Epigenetic variation affects genome function and hence can contribute to common disease. To establish a possible link requires systematic studies, such as the proposed epigenome-wide association studies (EWASs).

  • Of the many epigenetic marks, DNA methylation (DNAm) is the most stable and accessible and therefore ideally suited for EWASs.

  • In principle, EWASs should be equally successful as genome-wide association studies (GWASs) for the identification of disease-associated variations. However, there are fundamental differences between GWASs and EWASs that need to be considered for appropriate study design.

  • The key differences for EWASs are tissue specificity and the possibility that some epigenetic changes may occur downstream of the disease process. Both considerations affect the type of cohorts and samples that should be analyzed.

  • Technologies for EWASs are readily available for both array- and sequencing-based platforms but many of the computational and statistical analysis methods remain to be developed.

  • At this early stage, it is challenging to predict the possible effect of DNAm variation. However, if it does exist and if the right study design is used, then much more than the 'low-hanging fruit' should be detectable in fewer samples than are required for a typical GWAS, based on simulations assuming a conservative methylation odds ratio.

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.

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Figure 1: The different types of sample cohorts that could be used in an epigenome-wide association study.
Figure 2: Hypothetical DNA methylation frequency spectra in cases and controls.

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Acknowledgements

S.B. was supported by the Wellcome Trust (084071) and a Royal Society Wolfson Research Merit Award.

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Authors and Affiliations

  1. Blizard Institute of Cell and Molecular Science, Barts and The London School of Medicine and Dentistry, Queen Mary, University of London, 4 Newark Street, London, E1 2AT, UK

    Vardhman K. Rakyan

  2. Wellcome Trust Cancer Research UK Gurdon Institute and Department of Genetics, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK

    Thomas A. Down

  3. Genetics Institute, University College London, Darwin Building, Gower Street, London, WC1E 6BT, UK

    David J. Balding

  4. UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT, UK

    Stephan Beck

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Avon Longitudinal Study of Parents and Children

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Glossary

Genome-wide association studies

(GWASs). These are genome-wide studies that are designed to identify genetic associations with an observable trait, disease or condition, such as diabetes.

Exome

The part of a genome that encodes exons for translation into proteins.

Epigenome

The complete collection of epigenetic marks, such as DNA methylation and histone modifications, and other molecules that can transmit epigenetic information, such as non-coding RNAs, that exist in a cell at any given point in time.

Core histones

The proteins that form the nucleosome, which is composed of two copies each of the histones 2A, 2B, 3 and 4. Together, they form a histone octamer around which 147 bases of genomic DNA are wrapped.

Core promoters

Regions upstream and downstream of the transcriptional start site (TSS), typically defined as the interval −60 to +40 bases from the TSS.

CpG islands

(CGIs). Regions of the genome (typically 500 bp–2 kb) that contain a higher than expected frequency of CpG sites. CGIs are frequently unmethylated and found near promoter regions.

Imprinted

This term refers to genes that are expressed in a parent-of-origin-specific manner.

Loss-of-imprinting

(LOI). Parental imprinting results in the epigenetic silencing of one allele of a gene owing to its parental origin. Aberrant disruption of imprinting leads to both alleles being expressed; that is, loss-of-imprinting.

Satellite DNA

A type of non-coding, repetitive DNA that is a component of functional centromeres and the main structural constituent of heterochromatin.

Methylation quantitative trait loci

(methQTLs). DNA variants that influence the DNA methylation state either in cis or in trans.

Allele-specific methylation

(ASM). The presence of DNA methylation on only one of the two alleles present in a cell. This could be due to parental imprinting, random methylation of one allele or genetic effects.

Reverse causation

Refers to an association between A and B that is due to B causing A rather than the presumed A causing B.

Methylation-sensitive restriction enzyme digestion

Procedure that cleaves dsDNA depending on the methylation status of the enzyme's recognition site. Some enzymes only cleave when the recognition site is methylated and others only when the site is unmethylated.

Affinity enrichment

In this context, this term refers to a procedure to enrich methylated DNA fragments from a pool of methylated and unmethylated fragments using affinity reagents such as antibodies against 5-methylcytosine or other methyl-binding proteins.

RRBS

(Reduced representation bisulphite sequencing). A procedure for single base resolution methylation analysis using bisulphite DNA sequencing of a representative part of a genome, typically 5–10%.

Bayesian

The two main statistical schools are the classical (or frequentist) school, which dominated twentieth century science and measures the strength of evidence against a hypothesis using P values, and the Bayesian school, which was developed in the nineteenth century but is currently undergoing a resurgence and attempts to compute the posterior probability that the hypothesis is true.

Principal coordinates

Analysis of principal coordinates is a multivariate statistical technique that is related to principal components analysis but investigates individuals rather than variables. It is often used to investigate population structure in a sample of individuals whose relatedness has been estimated from genome-wide genotype data.

ChIP–seq

(Chromatin immunoprecipitation followed by sequencing). A method for mapping the distribution of histone modifications and chromatin-associated proteins genome wide that relies on immunoprecipitation with antibodies to modified histones or other chromatin proteins. The enriched DNA is sequenced to create genome-wide profiles.

Bivalency

A property of chromatin that contains both activating and repressing epigenetic modifications at the same locus.

Multivariate hidden Markov analysis

A statistical method for modelling multidimensional data by one of a small number of hidden Markov states, each of which is associated with a multivariate probability distribution.

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Rakyan, V., Down, T., Balding, D. et al. Epigenome-wide association studies for common human diseases. Nat Rev Genet 12, 529–541 (2011). https://doi.org/10.1038/nrg3000

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