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There were more ME/CFS papers on this then I expected (26 in total for the ME/CFS only category)! The sample size for ME/CFS participants, however, was smaller than 50 in all but three: an old SPECT study from 1992 (Ichise) and then Van Campen/Visser studies. I suspect that the latter two will...

Yes this is one of the top hits for depression, discussed in this big depression GWAS: Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions | Nature Neuroscience

Thanks for this review. It must have been an enormous task: screening 11,218 papers and 367 full-texts! What I'm missing though is some focus on the effect size: how large is the reduction in cerebral blood flow, what is the variation among patients, how big is the overlap with healthy...

In other GWAS I read that they compared their results to those of other conditions using LD Hub. Unfortunately that tool no longer seems to be available but perhaps BIGA GWAS might be an alternative? Bivariate Cross-trait Genetic Architecture Analyses of GWAS

As mentioned in the DecodeME paper OLFM4 is also a clear hit in GWAS on depression. Here's what their Manhatten plot looks like. That big peak on Chromosome 13 is the OLFM4 region. The effect size is quite small though, an odds ratio around 1.04. Genome-wide association analyses identify 44...

This might be a better comparison. Tried to convert the DecodeME data back to the GRCh37 positioning (28151 SNPs failed) and then used the same x-axis range. Insomnia GWAS 2019 DecodeME transferred to GRCh37 using

Here's how the signal looked like (notice that the X-axis is much shorter in DecodeME) EDIT: the graphs are misleading: the Insomnia GWAS uses GRCh37/hg19 positioning versus GRCh38/hg38 in the DecodeME graph. Insomnia GWAS 2019DecodeME data on ME/CFS 2025 The p-values for the insomnia...

On the GENE Atlas site you can click on PheWAS: https://atlas.ctglab.nl/PheWAS This allows you to search on geneID or rsID in the GWAS in their database.

Also noted that the Postuma received a large grant (BRAINSCAPES) of around 20 million euros to work out the biological mechanisms behind genetic results of brain disorders. Might be useful for DecodeME could make a connection to this group to see if ME/CFS could be included in their work...

Apparently CA10 was associated with morningness, in the same study. Suspect that this is something that would be useful: to systematically search if the DecodeME genes show up in other GWAS.

It also makes it possible to look if genes have been found in previous GWAS. For example it helped me find that OLFM4 was a hit in this GWAS on insomnia...

There's also this gene atlas which provides lots of data of previous gwas including manhatten and qqplots for comparison. Unfortunately, it looks like they stopped updating it since 2019. https://atlas.ctglab.nl/

I think it's very unlikely. The ancestry was controlled for by first choosing similar European ethnicity as the UK Biobank controls and then by adding the first 20 principal components as covariates in the regression analysis. These PCs showed no clear pattern anymore between patients and...

This paper gives some data and background on eQTL not being as useful in identifying relevant genes as many anticipated. The missing link between genetic association and regulatory function | eLife Using diseases and genes were the mechanisms are relatively well understood, they found that...

For those with strength, courage and coding skills: I've noticed that the Dutch authors of MAGMA have a new method called FLAME. It combines multiple approaches to finding the effector gene within a significant GWAS locus using a machine-learning framework. Prioritizing effector genes at...

Regarding neuronal damage, there's this paper from last year that reported (slightly) increased Neurofilament Light Chain. Trial Report - Plasma Neurofilament Light Chain: A Potential Biomarker for Neurological Dysfunction in ME/CFS, 2024, Azcue et al | Science for ME This has also been...

Thanks for delving into this! It looks so complicated. I expected that there would be nice packages in R and Python that wrap this into a handy user interface and provide the correct reference data (for LD, for example) automatically. Quite disappointing that the workflow mostly consists of...

I think it would only need to explain why nothing has been found yet on the brain scans (mostly MRI) that have been done. Based on @SNT Gatchaman interesting example of chronic traumatic encephalopathy and others' responses that neural damage would still be possible, I think brain banks and...

This looks like it could be quite important, a new target for pain treatment?

Looks like a labelling issue of the graph. Here's what I got trying to recreate it. The high p-values belong to the SNPs with high MAF values, showing that it was mostly this group that reached significant hits. @Chris Ponting