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I contacted the authors and they clarified that FLAMES requires the effective sample size ("the sample size is used for fine-mapping and should ideally be Neff"). So I'll try to do it again with the UK biobank controls and effective sample size.

Here's the cell type analysis for the 461 cell types from the Siletti et al. brain atlas (same pipeline as Duncan et al. 2025). The data below shows the European MVP subgroup only (this dataset includes a column imputation quality so I filtered r2 > 0.6). The one for the MVP-meta analysis is...

So I filtered out rare variants (MAF > 0.01). I also don’t want signals that came from only one ethnicity group only, as this might more likely reflect bias. The meta-analysis hasa column called ‘direction’ that indicates if the signal was found in the three substudies. So I required this and...

There are a lot of significant hits but I don’t think these are reliable because they are dominated by rare variants. You can see this in the QQ-plot, that the rare variants show genomic inflation, probably because they depend on only a small number of cases.

MVP-META I’ll focus on the meta-analysis first. The summary results can be downloaded here on the GWAS catalog: https://www.ebi.ac.uk/gwas/studies/GCST90480593 And there are some visualizations available using pheweb here: https://phenomics.va.ornl.gov/pheweb/gia/meta/pheno/Phe_798_1

There were almost 5000 of these ME/CFS-like cases in the MVP database which is divided into different ethnicity groups: African, Admixed American and European. The numbers are given below (taken from the supplementary material of Verma et al. 2024). EthnicityCasesControlsTotal...

The data on ME/CFS is based on electronic health records using PheCode 798.1. This not only maps to G93.3 in the ICD-10 CM but also a chronic fatigue unspecified (R53.82). So it’s likely a broader group than just ME/CFS. https://phenomics.va.ornl.gov/phecodemap/

I had a closer look at the genetic data on ME/CFS in the Million Veterans Program (MVP). It includes data on more than half a million US veterans. Around 90% are male with a mean age of 62, so a very different cohort to DecodeME. The main results are reported in Verma et al. 2024...

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.