ME/CFS Bioinformatics Repository

[tralfamadorian97]

Since the DecodeME preprint was released, I have been teaching myself bioinformatics by analyzing the summary statistics from DecodeME and other GWAS. I’ve been publishing my code on GitHub here, and documenting my results here.

While I don’t have dramatic headline results, I still thought that this work would be of interest to Science4ME forum members, because there are a few analyses that supplement recent forum discussions. For example, I ran gene-level H-MAGMA on the DecodeME summary statistics.

If anyone wants to contribute, I will happily accept fixes or additions to the documentation or codebase. For minor changes, you can just create a GitHub pull request. For major additions, it is probably better to first create a GitHub issue with a brief proposal, which we can discuss.
@forestglip already found the repo and has made some very helpful contributions

 

[forestglip]

It's been incredible watching this project being developed. I stumbled across it a few months ago, and it was immediately obvious tralfamadorian97 is remarkably motivated, organized, and intelligent.

There are a whole lot of different results and even lessons about bioinformatics tools in the documentation which I found very interesting to explore.

 

[hotblack]

Very impressive! Looks like a great resource, thanks for all your work @tralfamadorian97 and for sharing it.

 

[ME/CFS Science Blog]

Impressive @tralfamadorian97 , thanks.

Do check out Paolo Maccallini's meta-analysis which found some stronger results than DecodeME alone:

A

Preprint Thread 'Biological Insights from Genome-Wide Association Studies and Whole Genome Sequencing of [ME/CFS], 2026, Maccallini et al'

May 15, 2026

Interesting paper
Mods move if better location

• Amw66
• ampa anterior cingulate cortex brain cerebellum genes glutamate glutamatergic synapses gwas hypothalamus pituitary synapse white matter
• Replies: 82
• Forum: ME/CFS research

• 

I'm particularly interesting in the cell type eccentric medium spinal neuron that was significant in the meta-analysis:

Thread 'Eccentric medium spiny neuron (eMSN)'

Monday at 10:15 PM

Eccentric medium spiny neuron (eMSN) sometimes called eccentric spiny projection neurons (eSPNs) is the cell type that came up in Paolo Maccallini’s genetic analysis for ME/CFS. He used a meta-anaysis of DecodeME and the Million Veteran Program (Neff = 74,219). Using a tool called MAGMA, Paolo could match the DNA results with gene expression data from the human brain atlas published by Siletti et al. 2023. More about the methodology can be discussed in the thread on Paolo’s paper...

• ME/CFS Science Blog
• decodeme emsn gaba glutamate neuron paolo maccallini
• Replies: 68
• Forum: Possible causes and predisposing factor discussion

• 

We've also been trying to use tools such as FLAMES to help identify the causal genes but havent' really managed to make it work. Perhaps you might be able to do it?

Thread 'Running FLAMES on DecodeME data'

Dec 11, 2025

Hi all,

Something that has been in the back of my head since the DecodeME data was released was running the newer FLAMES analysis:

GitHub - Marijn-Schipper/FLAMES: FLAMES: Accurate gene prioritization in GWAS loci

FLAMES: Accurate gene prioritization in GWAS loci. Contribute to Marijn-Schipper/FLAMES development by creating an account on GitHub.

github.com

Somewhat discussed here: https://www.s4me.info/threads/initi...2025-decodeme-collaboration.45490/post-636145

My intention with this thread is that if enough of us get our heads together, I think we me able to run this? I wanted to make a separate thread as I believe if we start working on this it...

• ChronicallyOverIt
• decodeme flames magma
• Replies: 41
• Forum: Other research methodology topics

• 

 

[tralfamadorian97]

Do check out Paolo Maccallini's meta-analysis which found some stronger results than DecodeME alone:

Yes, I did see Paolo's paper. Impressive. I'll read it in more detail when I get a chance.

We've also been trying to use tools such as FLAMES to help identify the causal genes but havent' really managed to make it work. Perhaps you might be able to do it?

I've create a GitHub Issue to track this here. This might take a while, but at the moment I don't see any insurmountable barriers to running this.

 

[forestglip]

I'm particularly interesting in the cell type eccentric medium spinal neuron that was significant in the meta-analysis:

See this page for the results of MAGMA using DecodeME sumstats on a brain cell-type dataset, like Paolo did. The same dataset as one of the two Paolo used actually: Siletti 2023. And the most significant finding was eccentric MSNs.

I don't know the details of the cell-type data, but I think it might be slightly different in this analysis because Paolo's analysis gives specific brain regions, while this seems to be more focused on cell-type in general. Maybe tralfamadorian can clarify. It looks more significant in this analysis.

 

[ChronicallyOverIt]

@tralfamadorian97 if I remember correctly I got the pops analysis running, I can put a pull request for you to check that if you want.


The fine mapping is far more difficult than it seems, using Susie-r is one thing but if I remember correctly you need a large file, possibly many gb’s to make this, called a linkage disequilibrium…It’s been a while, unfortunately I’ve been in a bit of a down trend since the beginning of the year so I just haven’t had the clarity of mind to learn something so complex.

 

[ME/CFS Science Blog]

See this page for the results of MAGMA using DecodeME sumstats on a brain cell-type dataset, like Paolo did. The same dataset as one of the two Paolo used actually: Siletti 2023. And the most significant finding was eccentric MSNs.

Thanks, I see this as confirmation that the data really points to eMSN and that it wasn't a fluke or error from the meta-analysis Paolo did.

 

[ME/CFS Science Blog]

See this page for the results of MAGMA using DecodeME sumstats on a brain cell-type dataset, like Paolo did.

Think it's worth looking into these other cell types as well. Seems like there's a link to splatter cells as well which are also poorly understood.

I also have a question @tralfamadorian97: does the signal for Amygdala excitatory (Cluster419) point to the amygdala's intercalated cells. Cause I read that they have the same developmental origin as the eMSN, with some saying they "represent a ventral extension of the dorsal striatum."

 

[tralfamadorian97]

The fine mapping is far more difficult than it seems, using Susie-r is one thing but if I remember correctly you need a large file, possibly many gb’s to make this, called a linkage disequilibrium…It’s been a while, unfortunately I’ve been in a bit of a down trend since the beginning of the year so I just haven’t had the clarity of mind to learn something so complex.

Yes, I was able to run SUSIE-R. Some example results are here. I did have to to download the linkage disequilibrium (LD) matrices, which as you say are quite large. I got the LD matrices from here.

Overall, I found that SUSIE produced rather diffuse credible sets. That is, it returned about 50-100 possible causal variants, and could not narrow things down beyond this. I believe this is just a sample-size issue: I think you often need >50k cases to get narrow credible sets.

Nevertheless, these diffuse credible sets may still be useful. When I get around to it, my plan is to feed them into FLAMES.

Hope the downward trend improves, @ChronicallyOverIt !

 

[tralfamadorian97]

I also have a question @tralfamadorian97: does the signal for Amygdala excitatory (Cluster419) point to the amygdala's intercalated cells. Cause I read that they have the same developmental origin as the eMSN, with some saying they "represent a ventral extension of the dorsal striatum."

Interesting question! Unfortunately, I don't know enough neuroscience to give a good answer. I do see that eccentric medium spiny neurons are labeled as originating mostly from the Amygdala. Their top three regions are: Amygdala: 75.9%, Cerebral cortex: 14.6%, Thalamus: 5.4%. It might be helpful to go back to the original Siletti paper and its supplementary material to understand more.

The HBA reference data I used for that MAGMA plot with the eccentric medium spiny neurons was prepared the authors of this paper. They preprocessed the raw Siletti 2023 scRNAseq data to produce a matrix suitable for consumption by MAGMA, as described in their github repo.