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Interesting! I've only read the the first paper so far, but it looks like what they have shown is basically that variants associated with complex traits (GWAS hits) and variants associated with changes in gene expression (eQTL hits) tend to be fundamentally different in several ways. They...

@Chris Ponting I think you might be interested in these other potentially overlapping loci, if you're not already aware of them. It seems like there might end up being a lot of genetic similarity between ME/CFS and pain.

Nice find! Just noting for the thread that this is the study that DecodeME said overlapped at CA10. They didn't look at the other loci because they weren't genome-wide significant. I wanted to compare the other loci, so I downloaded the summary stats for this study from GWAS Catalog, then did...

I think CACNA1E is the gene from DecodeME. Note also this other gene in the same family:

Any chance anyone knows the thread that the original image was discussed on?

The above explanation might have had some hard to understand parts, so I think it might be useful to describe TWAS with a really simple toy example. Hopefully, people can get a feel for what it is, in case we start seeing ME/CFS papers using it. To make it simple, I'll focus on one gene and one...

Now published: Genetic Insights into Circulating Complement Proteins in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Potential Inflammatory Subgroup Abstract Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating multi-system illness with heterogeneity that...

I made a thread with some papers about TWAS: https://www.s4me.info/threads/a-gene-based-association-method-for-mapping-traits-using-reference-transcriptome-data-2015-gamazon-et-al.48810/

The aim of TWAS is to help identify the consequences of genetic variation in a GWAS on gene expression, which could help identify genes and tissues that may causally influence the disease. The basic steps of TWAS 1. First, a prediction model is created using data from large reference datasets...

The results reported from doing a transcriptome-wide association study (TWAS) based on depression GWAS data made me interested in learning more about TWAS. The abstract above is from the paper that introduced the first form of TWAS, PrediXcan. The first paper below introduced S-PrediXcan, a...

A gene-based association method for mapping traits using reference transcriptome data Abstract Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in...

I agree with that last sentence that this seems to be a remarkable finding and supports the potential of GWAS and TWAS, because a decrease in dopamine receptors in the nucleus accumbens seems to be exactly what you would expect to be implicated in depression. DRD2 in nucleus accumbens was...

They looked for drugs that might target the depression-associated genes. It might be an interesting technique to use for ME/CFS genes. Discussion about some of these drugs:

Just copying the top enriched gene sets. It's basically synapse-related.

They talk about one area that might represent such a neuropsychiatric common component: Edit: 3p21 covers a wide area. Here's that range plotted with DecodeME data: A couple small spikes, but not significant.

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.