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MAGMA gene set associations: I thought the B cell mention might be interesting.

It seems like the most significant hit from the anxiety study image above is GABAergic neurons in the midbrain, not cortex.

Actually, maybe I can do a very simple version. Just checking if specific variants from DecodeME are associated with tissue expression in GTEx. The top variant in the NEGR1 locus is 1:73126414:C:CA (rs34330896). Here's the page for that variant on GTEx...

No, they didn't look at gene expression linked to the SNPs near NEGR1 because that locus didn't quite reach genome-wide significance. I think it might be doable with the freely available DecodeME data by someone who knows how, but I don't think I have the knowledge or energy to do so.

Ha maybe. Though GTEx probably gets a lot of funding because the data can then be used by any other GWAS studies in any diseases, to try to see if results line up with gene expression - as it was used here, and as it was used in DecodeME, for example.

The expression data for hypothalamus, and all other tissues, is from an existing database of variant-expression associations, GTEx. There was no new testing of gene expression in this study, if that's what you mean. From GTEx: So the question they're trying to answer, basically, is "do the...

It wasn't done in the DecodeME paper, though it was attempted by me. It seemed to point to neurons all over the brain (excitatory, "GABAergic", and just "neurons"). I'm hoping they do something like this in the final paper. Edit: Cell types from this study: Edit: Note that the analysis I did...

Yeah, it's possible. It's more meant as one piece of evidence. If the GWAS points to a gene's expression in one specific tissue, and other evidence implicates the same tissue, it adds to the evidence. Though, it could be that a hypothalamus association might just exist due to similarity of...

Considering that this GWAS has many different anxiety phenotypes combined together, and considering the overlap with DecodeME in MAGMA, my suspicion is that this may be getting to a "root" specific component that all these diseases, including ME/CFS might have in common. Maybe further very...

To add to the above post, they are looking for how well SNP associations match with gene expression in specific tissues. Like DecodeME did using a different method, where they found, for example, that ME/CFS SNPs matched up with expression of RABGAP1L expression in several specific tissues.

A transcriptome-wide assocation study (TWAS), used here, does something like trains a machine learning model to predict expression of a given gene based on SNP patterns (using existing expression databases like GTEx), then uses some method (that I don't really understand) on GWAS summary...

2021 study, but I was interested in it because it's so large (>350,000 cases), and to see how the MAGMA tissue enrichment might compare with DecodeME or a large anxiety GWAS, which seem to have similar MAGMA results. Here is the MAGMA tissue enrichment from this study: In order of...

Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions Abstract Major depressive disorder is the most common neuropsychiatric disorder, affecting 11% of veterans. We report results of a large meta-analysis...

This is Scientific Reports, which is not as prestigious as the main Nature journals, like Nature and Nature Medicine. This journal apparently has the largest number of articles/year of any journal, which suggests to me that they probably allow many less rigorous articles.

Email from IACFS/ME (original bolding): Some of you may recall that IACFS/ME previously published a Newsletter several times a year, edited by the amazing Dr. Rosamund Vallings of New Zealand. Given the many developments in ME/CFS, Long COVID, and related conditions, we felt this is the perfect...

I don't know anything about these proteins, but if you want to see how they're related, you should be able to click the lines between proteins on the STRING plot, and it'll give you all the evidence it based the relationships on.

I was waiting to get a reply from someone at mapMECFS to see if it would be okay for me to still post the data I previously analyzed, and I was told I could. I see it's already been mostly discussed at length, but they also suggested I note that there may be reasons for the differences, such as...

68 statistical tests and no multiple test correction. We should expect around 5% of these, or 3.4 tests, to be p<.05 by chance. 5 tests had p<.05. So I think likely most or all of these findings are due to chance.

Interesting that the sex bias is opposite of typically seen.

Some general discussion about AlphaGenome has been moved to: Advancing regulatory variant effect prediction with AlphaGenome 2026 Avsec et al