Epigenome-Wide Association Studies for common human diseases

By Vardhman K. Rakyan, Thomas A. Down, David J. Balding, Stephan Beck. Nature reviews genetics, 2011

1,272 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1a854842f18c5e8ba68f22b3bc24991a380f1c10

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Citations in the corpus

Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays (2014, positive): “For example, the Illumina products that preceded the 450k included the widely used Infinium 27k array exclusively targeted CGI, primarily at gene promoters. However, Irizarry, et al. (2008), using a non-CGI-centric array design, referred to as Comprehensive Arrays for Relative Methylation (CHARM), d”
De novo identification of differentially methylated regions in the human genome (2015, background): “au 1CSIRO Digital Productivity Flagship, Riverside Life Sciences Centre, 11 Julius Avenue, North Ryde, New South Wales 2113, Australia Full list of author information is available at the end of the article that differ between phenotypes [6-8].”
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LFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies (2019, background): “For example, some epigenome-wide association studies (EWAS) measure DNA methylation levels in different individuals to derive associations between epigenetic variation and exposure levels or phenotypes (Rakyan et al. 2011; Teschendorff and Relton 2018).”
A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data (2012, methodology): “Results: Here we propose a novel model-based intra-array normal- ization strategy for 450 k data, called BMIQ (Beta MIxture Quantile dilation), to adjust the beta-values of type2 design probes into a stat- istical distribution characteristic of type1 probes.”
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A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies (2017, positive): “This is particularly relevant for Epigenome-Wide Association Studies (EWAS) where the effect sizes of interest could be small compared to changes in tissue composition [3, 4].”
Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution (2016, background): “Background The large-scale analysis of epigenome and transcriptome data in population studies is thought to answer fundamental questions about genome biology and will be instrumental in linking genetic and environmental influences to disease etiology [1, 2].”
Accounting for cellular heterogeneity is critical in epigenome-wide association studies (2014, background): “In these studies, DNAm levels are measured genome-wide at thousands to millions of sites in hundreds of individuals to identify loci where these levels are associated with quantitative traits or disease [1,2].”
ChAMP: 450k Chip Analysis Methylation Pipeline (2014, neutral): “Based on Pubmed and GEO submissions, the 450k array has established itself as the platform of choice for epigenome-wide association studies (Rakyan et al., 2011).”
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Association Mapping in Plant Genomes (2012, background): “Since epigenetic factors are also likely to contribute to common complex traits, epigenome-wide association studies (EWASs) have been proposed to uncover another missing piece of heritability unexplained by common variants (Rakyan et al., 2011), specifically involving the study of variation in DNA m”
DNA methylation and healthy human aging (2015, background): “A number of studies have published lists of epigenetic clock sites in blood (Bocklandt et al., 2011; Rakyan et al., 2011; Bell et al., 2012; Numata et al., 2012; Hannum et al., 2013; Horvath, 2013; Florath et al., 2014; Weidner et al., 2014).”