INFORMATION RELATING TO assoc_meta_all.csv.gz
BACKGROUND METHODS
GoDMC was established with the view of bringing together researchers with an interest in studying the genetic basis of DNA methylation variation, to consolidate as many resources and expertise as possible and thereby expedite this field of research. The initial release of their findings consists of mQTL associations based on a sample size of 27,750 individuals. Summary statistics of these meta analyses are available on request from http://www.godmc.org.uk/projects.html.
Genotype data: Genotype data of all autosomes and chromosome X (if available) was imputed to 1000G and above using hg19/build37. Genotype data was filtered on an info score of 0.8 and a minor allele frequency (MAF) of 0.01. Genotype data was converted to bestguess data without a probability cut-off.
DNA methylation data: DNA methylation was measured in whole blood or cord blood using Illumina 450k or EPIC Beadchips in at least 100 European individuals. Normalized beta values were used, preferable normalized with the R package meffil. Most analysts used meffil to quality control and normalize the DNA methylation data using functional normalization. Protocols can be found here: https://github.com/perishky/meffil/wiki.
A github pipeline was implemented to run the analyses locally. For the genotype data, several standard sample QC steps were performed including a sex check, removal of samples with >5% missingness, and the identification and exclusion of ethnic outliers. In datasets of ostensibly unrelated individuals, those that were found to be related (identity by state > 0.125) were excluded.
The pipeline then residualised the normalized methylation betas by replacing outliers that were 10 standard deviations from the mean (3 iterations) with the probe mean, rank transforming the normalized beta values and regressing out age, sex, predicted cell counts, predicted smoking, genetic principal components and non-genetic methylation principal components. In family-based cohorts, genetic relatedness matrices were constructed and relatedness adjusted for using the GRAMMAR approach. Genomic lambdas were checked by performing a GWAS of cg07959070. These residualised methylation measurements were used in all analyses.
Association analysis: First, every study performed a full analysis of all candidate mQTL associations, returning only associations at a threshold of p<1e-5. All candidate mQTL associations at p<1e-5 were combined to create a unique â€˜candidate listâ€™ of mQTL associations. In total, 102,965,711 candidate mQTL associations in cis (p<1e-5, SNP located within 1Mb of the methylation site) and 710,638,230 candidate mQTL associations in trans were identified in at least one dataset. To avoid computational burden, we included cis associations found in at least one dataset and trans associations in at least two datasets. The candidate list (n=120,212,413) was then sent back to all cohorts and the association estimates obtained for every mQTL association on the candidate list.
Meta analyses: Meta analyses were run using a modified version of METAL using 962 chunks. Candidate mQTL associations were meta-analysed using fixed effects, additive random effects and multiplicative random effects models. 36 datasets from European origin were included in the meta-analyses.
In our analysis we considered a cis pvalue smaller than 1e-8 and a trans pvalue smaller than 1e-14 as significant.
COLUMN NAMES
snp={CHR}:{POS}:{SNP/INDEL}
cpg=450k cpg
Allele1: Effect allele
Allele2: Non effect allele
Freq1: Effect allele frequency
FreqSE: Standard error allele frequency
Effect: Regression coefficient fixed effects meta analysis
StdErr: Standard error fixed effects meta analysis
pval: Pvalue fixed effects meta analysis
Direction: Direction for each of 36 cohorts
HetISq: I2
HetChiSq: Heterogeneity Chi square
HetDf: Degrees of freedom
HetPVal: Heterogeneity pvalue
EffectARE: Regression coefficient additive random effects meta analysis
StdErrARE: Standard error additive random effects meta analysis
PvalueARE: Pvalue random effects meta analysis
tausq: Tau square
StdErrMRE: Standard error multiplicative random effects meta analysis; Effect sizes MRE are the same as in Effect column
PvalueMRE: Pvalue multiplicative random effects meta analysis
TotalSampleSize: Samplesize
snpchr: SNP Chromosome
snppos: SNP Position build 37
snptype: INDEL or SNP
cpgchr: CpG chromosome
cpgpos: CpG position build 37
cis: cis yes/no, Cis: Distance between SNP-CpG <1 MB
chunk: meta analysis chunks