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These analyses were a bit above my level as an amateur so don't take them too seriously.

There is one caveat to this analysis, the olisalgers et al. 2021 paper found that this linear MAGMA analysis with quantiles performed worse (did not align with their other methods). They prefer LDSC or MAGMA with the top 10% of genes as binary annotation. They also restrict their GWAS to the...

The data is available in the R package MAGMA.celltyping by the authors. neurogenomics/MAGMA_Celltyping: Find causal cell-types underlying complex trait genetics Mouse gene have been mapped to human equivalents by them. They also changed the gene expression specificity into 40 quantile (I...

Another cell type study that I wanted to test the DecodeME data to. They used an older Mouse Brain Atlas from the Linnarson lab at the Karolinska Institute (the same group that published the human brain atlas). The data from different dissections was spread out over multiple publications such...

Abstract With few exceptions, the marked advances in knowledge about the genetic basis of schizophrenia have not converged on findings that can be confidently used for precise experimental modeling. By applying knowledge of the cellular taxonomy of the brain from single-cell RNA sequencing, we...

A note on the FUMA platform datasets The FUMA platform contains many of these dataset and makes it easy to test for them by uploading your data. Their approach is explained in this preprint...

DESCARTES This is also a major dataset with a lot of strong points: multiple tissues, in humans, 4 million cells. The main limitation is that it is from foetuses so may not directly apply to the cell types in adults. The main paper is Cao et al. 2020...

Human Brain Cell Atlas v1.0 This is currently the most extensive dataset on brain cells in humans. Published in 2023 by the lab of Sten Linnarsson at the Karolinska Institute, one of the important teams that have been making multiple RNA datasets over the years. The relevant paper is Siletti...

GTEx6 + Franke Lab An older version of the GTEx (version 6) was combined with RNA data from Franke's lab in the study by Hilary Finucane et al. 2018. This is the paper where they introduced the LDSC-SEG approach to testing cell type enrichment and where they tested multiple diseases and traits...

Genotype-Tissue Expression (GTEx) This analysis was done in the DecodeME preprint using the MAGMA tool on the FUMA website. The preprint only posted some of the most significant regions, but the entire plot looks something like this if you upload the DecodeME data. All significant tissue types...

I thought it would be useful to make an overview of RNA datasets that we can match to the DecodeME DNA results. This could give us clues to which tissues or cell types are potentially involved in ME/CFS. I've made an overview of datasets below and plan to update it as we find new ones. The Chan...

This is a recent preprint from the researchers who maintain the FUMA website on the cell type analysis they do, matching GWAS data to RNA databases. They created 388 datasets from 36 studies and tested these in 14 brain traits and diseases. Here's one of their graphs. I noticed that...

Abstract Genome-wide association studies (GWAS) have yielded unprecedented insight into the genetic variants associated with many human traits. However, translating this insight into knowledge about causal biological mechanisms remains challenging. One promising recent strategy is to link...

Thanks, updated. Here it is again: https://www.nature.com/articles/s41588-018-0081-4

Many of the GWAS in this analyses were underpowered so no significant result could simply mean too small sample size. Migraine without aura did have a significant result for the cardiovascular system. EDIT: accidentaly wrote migraine with aura, but it is without aura that I meant.

Yes these are in the supplementary material of the paper, table 6.

One interpretation could be that what sticks out in the (still underpowered) DecodeME results is what every ME/CFS patient has in common. So this may point to the neural networks that produce sickness behavior symptoms such as fatigue. Hence the strong overlap with fibromyalgia. Behind there...

I also did the gene expression data from the Immunological Genome (ImmGen) project that Finucane et al. prepared for their pipeline. Immunological Genome Project Similar results to trafalmadorian97, with the top hit reaching a p-value around 0.005 for 292 cell types tested, not even close to...

I've added the DecodeME into the same pipeline. LDSC-SEG is quite complex to set up (their baseline model has 52 annotations), but I got results that are very similar to what @tralfamadorian97 found...

This was one of the early papers that matched GWAS data with scRNA-seq data from cell types. They used the LDSC method which is based on a regression between the chi-square statistic from GWAS (the association with a SNP and the disease) and LD (Linkage disequilibrium score, how much an SNP is...