The EWAS Catalog: a database of epigenome-wide association studies

Battram, Thomas; Yousefi, Paul; Crawford, Gemma; Prince, Claire; Babaei, Mahsa Sheikhali; Sharp, Gemma C; Hatcher, Charlie; Vega‐Salas, María Jesús; Khodabakhsh, Sahar; Whitehurst, Oliver; Langdon, Ryan; Mahoney, Luke; Elliott, Hannah R; Mancano, Giulia; Lee, Matthew A.; Watkins, Sarah; Lay, Abigail C; Hemani, Gibran; Gaunt, Tom R.; Relton, Caroline; Staley, James R; Suderman, Matthew

doi:10.12688/wellcomeopenres.17598.1

Cited by 78 publications

(74 citation statements)

References 12 publications

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“…We checked the CpGs that we identified ( Table 2 ) in the EWAS catalog ( 29 ), which documents (suggestive) associations ( P < 10 −4 ). Cord blood DNAm at cg26974062 had a nominal association with maternal 1-h glucose in the UK Pregnancies Better Eating and Activity Trial (UPBEAT) ( 5 ).…”

Section: Resultsmentioning

confidence: 99%

“…All probes were annotated to the human reference genome version 19, build 37. Meta-analysis results are deposited to the EWAS catalog ( 29 ), Zenodo DOI https://doi.org/10.5281/zenodo.5886997 . The presence of differentially met-hylated regions (DMR) in relation to the glycemic exposures was evaluated with the R packages ipDMR ( 30 ) and DMRcate ( 31 ), with use of each respective meta-analysis test statistic file.…”

Section: Methodsmentioning

confidence: 99%

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Maternal Glycemic Dysregulation During Pregnancy and Neonatal Blood DNA Methylation: Meta-analyses of Epigenome-Wide Association Studies

et al. 2022

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OBJECTIVE Maternal glycemic dysregulation during pregnancy increases the risk of adverse health outcomes in her offspring, a risk thought to be linearly related to maternal hyperglycemia. It is hypothesized that changes in offspring DNA methylation (DNAm) underline these associations. RESEARCH DESIGN AND METHODS To address this hypothesis, we conducted fixed-effects meta-analyses of epigenome-wide association study (EWAS) results from eight birth cohorts investigating relationships between cord blood DNAm and fetal exposure to maternal glucose (Nmaximum = 3,503), insulin (Nmaximum = 2,062), and area under the curve of glucose (AUCgluc) following oral glucose tolerance tests (Nmaximum = 1,505). We performed lookup analyses for identified cytosine-guanine dinucleotides (CpGs) in independent observational cohorts to examine associations between DNAm and cardiometabolic traits as well as tissue-specific gene expression. RESULTS Greater maternal AUCgluc was associated with lower cord blood DNAm at neighboring CpGs cg26974062 (β [SE] = 0.013 [2.1 × 10−3], P value corrected for false discovery rate [PFDR] = 5.1 × 10−3) and cg02988288 (β [SE]−0.013 [2.3 × 10−3], PFDR = 0.031) in TXNIP. These associations were attenuated in women with GDM. Lower blood DNAm at these two CpGs near TXNIP was associated with multiple metabolic traits later in life, including type 2 diabetes. TXNIP DNAm in liver biopsies was associated with hepatic expression of TXNIP. We observed little evidence of associations between either maternal glucose or insulin and cord blood DNAm. CONCLUSIONS Maternal hyperglycemia, as reflected by AUCgluc, was associated with lower cord blood DNAm at TXNIP. Associations between DNAm at these CpGs and metabolic traits in subsequent lookup analyses suggest that these may be candidate loci to investigate in future causal and mediation analyses.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Maternal Glycemic Dysregulation During Pregnancy and Neonatal Blood DNA Methylation: Meta-analyses of Epigenome-Wide Association Studies

et al. 2022

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“…We selected traits for which EWAS had been conducted with over 4500 samples, had more than 10 associations at P<1×10 −7 and for which corresponding well-powered GWAS summary data were available. Suitable traits were identified using The EWAS Catalog (23) on 2021-03-01. Traits and study data information is in Table 1 .…”

Section: Resultsmentioning

confidence: 99%

A comparison of the genes and genesets identified by EWAS and GWAS of fourteen complex traits

Battram

Gaunt

Relton

et al. 2022

Preprint

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Identifying the genes, properties of these genes and pathways to understand the underlying biology of complex traits responsible for differential health states in the population is a common goal of epigenome-wide and genome-wide association studies (EWAS and GWAS). GWAS identify genetic variants that effect the trait of interest or variants that are in linkage disequilibrium with the true causal variants. EWAS identify variation in DNA methylation, a complex molecular phenotype, associated with the trait of interest. Therefore, while GWAS in principle will only detect variants within or near causal genes, EWAS can also detect genes that confound the association between a trait and a DNA methylation site, or are reverse causal. Here we systematically compare association EWAS and GWAS results of 14 complex traits (N > 4500). A small fraction of detected genomic regions were shared by both EWAS and GWAS (0-9%). We evaluated if the genes or gene ontology terms flagged by GWAS and EWAS overlapped, and after a multiple testing correction, found substantial overlap for diastolic blood pressure (gene overlap P = 5.2x10 -6, term overlap P = 0.001). We superimposed our empirical findings against simulated models of varying genetic and epigenetic architectures and observed that in a majority of cases EWAS and GWAS are likely capturing distinct genesets, implying that genes identified by EWAS are not generally causally upstream of the trait. Overall our results indicate that EWAS and GWAS are capturing different aspects of the biology of complex traits.

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“…We also repeated REML and LASSO regression using EWAS Catalog CpGs (31). EWAS Catalog CpGs contained CpGs with an mQTL, CpGs without an mQTL and hypo- and hypermethylated CpGs.…”

Section: Methodsmentioning

confidence: 99%

The influence of biological and statistical properties of CpGs on epigenetic predictions of eighteen traits

Hillary

McCartney

McRae

et al. 2022

Preprint

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BackgroundCpG methylation levels can help to explain inter-individual differences in phenotypic traits. Few studies have explored whether identifying CpG subsets based on biological and statistical properties can maximise predictions while minimising array content.MethodsVariance component analyses and penalised regression (epigenetic predictors) were used to test the influence of (i) the number of CpGs considered, (ii) mean CpG methylation variability and (iii) methylation QTL status on the variance captured in eighteen traits by blood DNA methylation. Training and test sets comprised ≤4,450 and ≤2,578 unrelated individuals from Generation Scotland, respectively.ResultsAs the number of CpG sites under consideration decreased, so too did the estimates from the variance components and prediction analyses. Methylation QTL status and mean CpG variability did not influence variance components. However, relative effect sizes were 15% larger for epigenetic predictors based on CpGs with methylation QTLs compared to sites without methylation QTLs. Relative effect sizes were 45% larger for predictors based on CpGs with mean beta-values between 10%-90% compared to those using hypo- or hypermethylated CpGs (beta-value ≤10% or ≥90%).ConclusionArrays with fewer CpGs could reduce costs, leading to increased sample sizes for analyses. Our results show that reducing array content can restrict prediction metrics and careful attention must be given to the biological and distribution properties of CpGs in array content selection.

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The EWAS Catalog: a database of epigenome-wide association studies

Cited by 78 publications

References 12 publications

Maternal Glycemic Dysregulation During Pregnancy and Neonatal Blood DNA Methylation: Meta-analyses of Epigenome-Wide Association Studies

Maternal Glycemic Dysregulation During Pregnancy and Neonatal Blood DNA Methylation: Meta-analyses of Epigenome-Wide Association Studies

A comparison of the genes and genesets identified by EWAS and GWAS of fourteen complex traits

The influence of biological and statistical properties of CpGs on epigenetic predictions of eighteen traits

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