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Here's the social media summary for it: 1) We’ve just published our second instalment on the DecodeME results, this timing zooming in on the genes associated with ME/CFS. 2) The clearest signals point to genes such as CA10, SHISA6, SOX6, LRRC7, and DCC, which are involved in neuronal...

Second blog article on the DecodeME results, this time focusing on genes related to ME/CFS. https://mecfsscience.org/genes-pointing-to-the-brain-decodeme-part-ii/

The outcome measures are hard to interpret because the authors don't report variability or statistical tests.

Just noticed the video already has 170k views!

It depends on how you look at it. The 8 significant SNP signals were not seen before in the same pattern in depression or anxiety. There were however genes such as OLFM4 that are implicated in both depression and ME/CFS, even though the SNP pattern around it is different. In addition, the...

Thanks @EndME They are all the same color (gray) but I lowered the opacity so that if you see black ones it means there are multiple dots in a similar place overlapping each other. Think that having the same SNP signals, suggest the same genes as risk factors which points to similar biological...

This study doesn't warrant all this media attention. Wonder if the SMC is to blame for pushing this?

The team of Deborah Antcliff is an example of researchers applying to chronic pain interpretation of pacing and applying it to ME/CFS as if it didn't have a different meaning there. There is some discussion about it here: Tack 2022 - Pacing: one term, many meanings...

Perhaps simply a correlation matrix of the 8 alleles within the ME/CFS sample?

The summary data that DecodeME has made available doesn't include or allow for subgroup analyses. It doesn't include the raw data and questionnaire data to do this. So it will be up to the DecodeME team to publish more on this.

Thanks @Robert 1973

Updated, thanks!

Thanks

If I understand correctly, the LC GWAS had only 5,768 cases and 2,701 which is really small so no wonder they didn't find any significant hits. Here's what their Manhatten plot looks like: I haven't checked systematically but most of these do not ring a bell in relation to DecodeME. It also...

Reading some GWAS in other illnesses made me appreciate DecodeME even more. Other GWAS are usually based on (1) extracting data from big databases like the UK biobank, Finngen, AllofUS, 23andME etc. where the case definition was often poor or (2) on multiple cohorts that are combined into a...

Thanks I should probably mention that I zoom out a bit more (1Mb) than most tools like LocusZoom (around 250kb) to get an overview of the entire region around the hit. The implicated genes are probably closer to the top SNP then the region I show (so don't pay too much attention to the genes at...

Another issue is that fibromyalgia is usually not about heightened pain sensitivity when there is peripheral input. It's about constant widespread pain even without any peripheral input. So rather than just have an increased response to pain stimuli when these arise, patients seem to have the...

From the article: Looks like pacing was confused with GET.

In contrast to DecodeME, most of the participants were recruited from other cohorts which used the ICD-code M79.7 to select fibromyalgia patients. They basically combined all the major databases (All of us, UK Biobank, FinnGenn, etc.). 87% of patients were female. Most of the implicated genes...

Anyone knows more about this situation in Zwitserland? Seems a positive development, would be interesting to hear from people there what they expect from it and what it means.