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Yeah good point, it's a bit arbitrary. Selected the ones with the most associations (those that you see if you click on 'Traits'). Those are mostly quantitative traits like height or intelligence that have been tested a lot and have big sample sizes. So I also looked at traits with a reported...

Searched for genes, like CA10. Then you get a list of other traits and GWAS where the gene has been implicated. Selected the DecodeME genes mainly based on proximity to the SNP with the lowest p-value in the region. The bottom of the table is from regions that only reached a p value of 10^-7.

Coming back to @Woolie 's argument that the SNPs might reflect differences in non-ME/CFS factors such as ancestry, socio-economic status etc. I think that big genetic differences due to ancestry are controlled for by adding the 20 principal components to the regression. But what I said about...

See quite some similarity between this GWAS on multisite chronic pain. Genome-wide association study of multisite chronic pain in UK Biobank | PLOS Genetics Heritability around 10%, MAGMA only points to brain regions, DCC, CA10 and SOX6 as significant hits, genetic correlation with depression...

As the DecodeME preprint highlights, DCC has repeatedly been associated with chronic pain including in this GWAS of Analgesic Use https://www.medrxiv.org/content/10.1101/2024.12.02.24318312v1.supplementary-material

The source data about immune cells can be downloaded here: https://www.ebi.ac.uk/gwas/publications/32929287 But it seems like quite a lot of work to download and filter all of these (suspect the authors had automated scripts for this).

Interesting that elevated TNF levels were inversely related to ME /CFS risk. The effect size, however, were small and as @forestglip mentioned it doesn't look like they correct for multiple comparisons. So the significant results the paper highlights may simply be false positives. I wonder if...

Don't want to beat the same drum but these secondary hits also point to neuronal development, synapses, synaptic transmission etc.

Highlighting the gene cards of some of the closest genes that have little competition or that have the top SNP inside it (Forestglip already posted about many of these before). LRRC7 LRRC7 Gene - GeneCards | LRRC7 Protein | LRRC7 Antibody SOX6 SOX6 Gene - GeneCards | SOX6 Protein | SOX6...

Did something similar by looking at SNPs that had a p-value below 5*10^-7 but that didn't appear in 8 regions that DecodeME already highlighted. So they were just below the threshold of 5*10^-8 for statistical significance. But because this threshold is a bit rough and arbitrary, it might be...

The coloc analysis doesn't seem that difficult (there's an R package for it, suspect it's mainly about getting the two GWAS summary data in the right format). But unfortunately, it looks like you have to request access to get the GWAS data on depression. PGC major depressive disorder GWAS |...

Also checked for significant hits in Long Covid such as the FOXP4 gene highlighted by Lammi et al. 2025 (rs9367106) Genome-wide association study of long COVID | Nature Genetics And the 3 SNPs highlighted by Chaudhary et al. but DecodeME didn't have a signal anywhere near these regions...

Coming back to OLMF4 and NEGR1, two genes which had significant SNPs close to them in a big depression GWAS: Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression | Nature Genetics DecodeME also has SNPs close to these genes that were...

More info about it here: https://reporter.nih.gov/search/yRt6xcKZp0uEl3CiqgghbA/project-details/10878255

In this interview with David Tuller, Lipkin seems to suggest that the planned GWAS and collaboration with DecodeME was cancelled. Instead they seem to have set up a different genetics study. They will look at genes of families where more than one person is affected, in collaboration with the...

In the graph above the greater variance in ME/CFS could be due to the decline in strength over the 10 repetitions. If I take only the first 3 repetitions, the variation between groups looks similar.

Was looking at one of the reference in the paper, a review Shechtman 2000 which was highly critical rather than supportive of the measure. It also points out what seems to be the issue here: So I think that's whats happening here: the standard deviations between patients and controls are...

Had a closer look at the raw data, available here: https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-021-02774-w EDIT: posted some other graphs earlier but these were incorrect (they focused on the variation per group rather than per individual). Below is a graph of the...

The difference seems quite large. So apart from the authors interpretation what would be the most likely explanation for this? Is it simply that being ill/having lots of symptoms causes the variation in handgrip strength measurements?

Seems like there is an inflation of the term autonomic dysfunction or dysautonomia. In this paper they report that "38% of the healthy controls had autonomic abnormalities". If it is so common, it's probably not an abnormality anymore? Using the lean test (not tilt table) the study found that...