Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

forestglip said:
It looks like what we want is in the qced.var.gz file. It just has a list of SNPs, which I assume are those that passed QC. When I filter the main summary stats file (gwas_1.regenie.gz) to only include the SNPs in this list, then it looks like it matches the reported data:
Click to expand...
Oh yeah, I should have read the readme file first. It's got all the info that confirms that.
The shared summary statistics (*.regenie.gz) are the raw output from REGENIE v4.1. only filtered on the fly for minor allele frequency (MAF ≥ 0.01). Two additional files are provided, one listing the variants that passed QC (MAF ≥ 0.01, INFO ≥ 0.9 and dentist-like test) and the other one with the imputation quality (INFO score) of each variants. The published GWAS were QCed we therefore recommend user to keep only the QCed variants.

In all these files, the genomic positions reported are based on genome build GRCh38/hg38.

All GWAS fit the 20 first PCs and genetic sex. The latter is not use for the sex separate analysis.

Files descrtiption

Summary statisitc: *.regenie.gz
REGENIE (v4.1) output one file per phenotype containig one line per variant after a header line (description below). The output is a single gzipped file.

Column Description
CHROM Chromosomes
GENPOS Genomic positions GRCh38 (bp)
ID Variant IDs formated as chromosome: position:allele0:allele1
ALLELE0 Non effect alleles (reference)
ALLELE1 Effect alleles (alternate)
A1FREQ Effect allele frequencies
A1FREQ_CASES Effect allele frequencies in cases
A1FREQ_CONTROLS Effect allele frequencies in controls (not to be use as incorrect calculation in REGENIE v4.1)
N Number of samples tested
N_CASES Number of cases tested
N_CONTROLS Number of controls tested
TEST Test performed here additive “ADD”
BETA Estimated effect sizes (for allele 1)
SE Standard errors
CHISQ Chi-square test statistics
LOG10P −log10(p-value)
EXTRA specifies if Firth corrections failed

List of variants that passed QC: gwas_qced.var
gzipped file with one variant ID (chromosome: position: ref_allele:alt_allele) per line.

Imputation quality (INFO score) for all the variants: imputed.info.gz
gzipped tab-separeted file with one variant per line.
header: chromosome, position, reference allele, alternate allele, imputation variant ID, INFO score
Click to expand...

@ME/CFS Science Blog you were wondering which SNPs were imputed. The imputed.info.gz file might have that.

Also, I noted the issue with the reported frequencies not making sense. The readme says not to use A1FREQ_CONTROLS because it is incorrect.
 
forestglip said:
Oh yeah, I should have read the readme file first. It's got all the info that confirms that.
Click to expand...
I was just about to mention the readme :) That’s as far I got into the data though, so seem to have come from the opposite side of it as you.
Great that you reproduced the manhattan plot. Are you using straight python or r for this?
 
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