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Abstract Allodynia (perceiving touch as painful) is an enduring symptom of neuropathic pain. While acute pain is initiated by afferent signaling from the periphery to spinal cord, pain chronification recruits ongoing activity in supraspinal sites. One such site that has been proposed to be...

Noticed that SPATA25 appears a couple of times in the AstraZeneca analysis of rare mutations. AstraZeneca PheWAS Portal Not sure if there's a relation but it's mainly involved in the development of mature sperm cells.

I noticed that SMCHD1 was one of the genes below the significance threshold that was associated with ME/CFS in DecodeME. it seems to play a role in X-chromosome inactivation SmcHD1, containing a structural-maintenance-of-chromosomes hinge domain, has a critical role in X inactivation - PubMed

Looks like they don't have a letter to the editor format and that a critique would fall under 'General commentary.' https://www.frontiersin.org/journals/immunology/sections/systems-immunology/for-authors/article-types I don't like that they mention "Authors are required to pay a fee (B-type...

Yeah agree here are some other studies pointing to this problem: https://www.nature.com/articles/6604491 https://pmc.ncbi.nlm.nih.gov/articles/PMC4577098/ The mean concentration in patients was more than 7 times higher (126 versus 17 ng/ml) than in controls, suggesting this is likely a...

I think the reason is that since that the dbSNP, which provides a database of rsID for SNPs, no longer provides a file for common variants since version 151. So for their latest release, 157, you have to download and query a massive file vcf file that also includes rare variants and likely...

This 2020 paper on the H-MAGMA method also includes a list of 1,841 pleiotropic genes that were found in 4 or more out of 5 tested neuropsychiatric conditions such as ADHD, autism, schizophrenia, bipolar disorder and depression. I checked the FLAMES list from above to see of any appear in it...

With covariate I meant a data column with the average gene expression in the dataset that you can choose to condition on or not. From what I remember, the Duncan et al. file only gives the relative gene expression in the dataset per cluster, so not the absolute gene expression for each cluster...

Perhaps the file provided by Duncan et al. 2025 paper might be useful? it gives a specificity score matrix file for Siletti et al 2023 atlas. EDIT: so it's each cluster's gene expression relative to the total gene expression of the dataset. It doesn't work with a covariate...

I noticed that the 2019 Watanabe paper on cell type analysis using MAGMA has a supplementary note where they explain their choice of using the average gene expression per dataset as a covariate, rather than just the absolute gene expression per cell type. It's basically what we already saw...

Ah apologies if I misunderstood!

Thanks but I think this is a step in the wrong direction. It mainly argues that treatments like CBT shouldn't be tested in randomised controlled trials.

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...