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It looks like this now, with many regions shifted upwards. The Upper_layer_intratelencephalic no longer stands out. They are now on par with (e)MSN.

Great, thanks. So the results are largely comparably when using absolute gene expression? Think it was worth checking so that we can be more confident about the results. How did you avoid adding the dissection average gene expression as covariate in MAGMA: is there a option for that, that you...

I made this quick overview of case definitions for 2-day CPET studies in ME/CFS. Quite a few, including the one by Davenport's group used Fukuda criteria as inclusion. The studies by Van Campen/Visser mention that both ICC and Fukuda were assessed and it isn't very clear if Fukuda alone was...

Interesting, thanks for posting this. Based on genetic evidence on ME/CFS, I had the impression that the illness might involve altered cortico-striatal communication, as this has been found repeatedly in studies on fatigue and sickness behavior. Most of these brain studies have very small...

For what it's worth, I made these plots of the MAGMA analyses that have been done using the FUMA version of the Siletti brain atlas and ME/CFS data. This one is the data that forestglip posted using DecodeME. I ordered the results according to the 31 superclusters. It looks similar to the...

I suspect it's because without a covariate, pretty much all the signal will be for genes that are common in every cell, making it harder to notice the signals for genes that have more specific functions. It could be that it then picks up cells that don't have much specificity and mainly have...

Forestglip's FUMA cell type analysis of Siletti datasets level 2 has 2,037 results, of which 52 are for eMSN, exactly the same as for Paolo. So those are the 31 superclusters measured in each dissection separately. It's strange that eMSN stand out much more in Paolo's analysis but I think this...

I'm not able to comment on the new thread that discusses MAGMA (It says: "You have insufficient privileges to reply here.") Using MAGMA on ME/CFS genetic data | Science for ME EDIT: Fixed now! But having downloaded the FUMA processed files here, I think Forestglip is right...

There was a paper from the group of Andreas Goebel who first demonstrated this transfer of IgG effect in fibromyalgia. They argue that "FMS-IgG binds to mast cells in a MRGPRX2/b2-dependent manner, leading to mast cell recruitment and IL-6 secretion. [...] The ablation of mice Mrgprb2 mast...

My guess is that it only tested one of the less notable eMSN such as those in the cerebral cortex but not those in the striatum and amygdala, which is where most eMSNs are and where most of the significant results came from.

The inclusion criteria were Fukuda, but with PEM required. Here are the main results, showing almost no decline at all in the ME/CFS patients. The text also mentions workload at VT, which was the most replicated finding thus far. The authors argue that the previous study by Keller et al...

Thanks. Do you know which eMSN cell type this was and from which database? Because both the 461 and 2082 clustering from the Siletti atlas resulted in eMSN cell types with p < 10^−6, if I recall correctly.

I can see there might be variation in the brain cell samples as Siletti et al. only used 4 donors. So the clustering and gene expression per cluster might be slightly different if this was done again. But the same reasoning likely applies to GTEx tissue expression or eQTL-based mapping that the...

Some counterpoints: - The MAGMA analysis shows that the eMSN clusters have high BETA, BETA_STD and SE. So when estimating the slope between gene-associations with ME/CFS and gene expression in the cluster, they had high effects and high variation. So the p-value likely wasn't due to higher...

Curious what the explanation might be that antibodies from LC are more likely to cause pain sensitivity in mice compared to antibodies from convalescent controls. Several groups have reported this now. Looks like this group did in-depth measurements to figure out what causes this but that they...

Made this summary of the paper. 1) Impressive paper from Iwasaki’s lab pointing at autoimmunity in a subgroup of Long Covid patients. They replicated previous experiments of antibody transfer causing symptoms in mice. A couple of findings that stand out… 2) They extracted antibodies from...

Thanks, quite a lot of differences unfortunately. Did it only focus on the hits above 5*10^-8?

Yes ranked according to p-value of the locus where they were predicted to be the causal gene. The loci with the lowest p-value (strongest signal) are on top. The Excel file on the other thread gives more information.

We managed to apply the FLAMES machine learning algorithm to DecodeME data. It helps to make a prediction which genes the DNA signal points to. For the significance threshold of 5*10^-8 there were only 6 DecodeME hits so I've reduced it to 5*10^-6 to get more potential genes. More info here...

I got slightly different results using the UK biobank LD panel (UKBrelease2b10kEuropean). I specified the full rather than effective sample size (n = 275488). FLAMES highlighted 31 instead of 32 causal genes (and VRK2 appears twice again, so it's 30 genes). The main differences are: No longer...