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The credible set made using SUSIE consists of 77 SNPs in high correlation with each other. The region they span is full of regulatory elements which do not point to particular tissue or cell type. The 3D links data from ENCODE had multiple links to CCDC92 and ZNF664, so I suspect these are the...

This is one of the signals in DecodeME that was just below the significance threshold (top hit p = 2.43e-7). It lies in a dense region with lots of genes. The credible set lies above CCDC92 and ZNF664 and to a lesser extent DNAH10.

Another potential candidate here is ERICH3, a protein associated with cilium assembly but also neurotransmitter storage and vesicular trafficking in the brain. ERICH3: vesicular association and antidepressant treatment response - PubMed

Here's what it looks like, very similar to the ENCODE data and not supportive of NEGR1 The data file was too big we I sampled 2000 random nuclei for 7 major cell types: oligodendrocytes (ODC), VIP interneurons (Vip), D1 and D2 medium spiny neurons (MSN-D1, MSN-D2), L2/3 IT neurons (L2/3-IT)...

Haven't delved deep into what UNC13C does, it gene card says: UNC13C Gene - GeneCards

eQTL Looking at gene expression datasets, there wasn't really a result that matched the DecodeME signal well (all PP.H4 < 0.8). Phewas Looking at genetic studies of other traits, gave almost no results - very different from other loci. Two reached a PP.H4 of 0.8 or higher, dementia in the...

3D contacts with Hi-C The domains that show how the region often folds and touches in 3D points also points to the western region where UNC13C lies, and none includes the genes to the east. More info and background about this analysis is given here. The link to UNC13C in the west is also seen...

Had a closer look at this locus. The top SNP is a bit of an outlier ( lower p-value and imputation score than the SNPs it is in near perfect correlation with). So I think the signal is slightly below the significance threshold, but that doesn't matter much. chromatin openess and histone marks...

Here's what this plot looks like, it did not replicate in the Nott et al. dataset of neurons. More info about the datasets and methodology I used here. The Hi-C data from 5 ENCODE brain samples show a different picture though. No special connection to NEGR1 but to ERICH3 and LRRC53. So...

Yes. I mainly tested the neuronal cells because of the MAGMA enrichment analysis in the brain but also because POU3F2 is mainly expressed there: Tissue expression of POU3F2 - Summary - The Human Protein Atlas

Yes, I plan to do these analyses (eqtl, phewas, AlphaGenome, chromatin openness, Hi-C) on all DecodeME hits with p < 10^-6. Most of the time these analyzes don't give much extra insight, but sometimes they help refine the predictions of the FLAMES algorithm for what the most plausible causal...

Chromatin conformation I've been looking more into databases on 3-D contact: based on how the chromatin is folded, locations that are far apart on the DNA string can actually be close. It is particularly useful when the GWAS signal is in a gene desert, far away from protein-coding genes. The...

Anyone has any insights on this one. My first thought is that it might be a big deal if LLM can improve itself by tinkering with the weights directly. Sparks of Weight-Space Programming: LLMs can natively read & write neural network weights

I think the high genetic correlations with fibromyalgia, irritable bowel syndrome and self-reported CFS (rg > 0.7) compared to a much lower correlation for measures of intelligence and education (rg < 0.2) suggest that the majority of the signal reflects the diseases and not participation bias.I...

Some more studies here https://s4me.info/threads/abnormalities-in-response-to-vasopressin-infusion-in-chronic-fatigue-syndrome-2001-altemus-et-al.48566/post-676043

1) We’ve written a new article about the spectacular progress in artificial intelligence (AI), the billions that AI companies are investing to transform drug discovery, and why this is a reason for hope for ME/CFS patients. https://mecfsscience.org/the-ai-revolution/ Here's a brief summary...

That's what I used. I suspect the results could come from one of the enhancers here such as EH38E3437846. It points to T-cell but the z-score didn't really stood out (< 3) in my analysis.

Thought this was an interesting aspect of NEGR1. It reminded me of ARFGEF2 bringing GABA-A receptors to the cell surface. Source: Neuronal growth regulator 1 (NEGR1) promotes synaptic targeting of glutamic acid decarboxylase 65 (GAD65) | bioRxiv

My analysis didn't reveal much in this region. The credible set was large with 385 SNPs. The eQTL didn't show any gene expression dataset with a similar signal (coloc PP.H4 > 0.8). The closest signal was a weak one for NEGR1 in the esophagus GTEx_v10 dataset (PP.H4 = 0.62, p-value 0.000039)...

The FLAMES algorithm predicted MMS22L for this region with high confidence. But I think it's because FLAMES is restricted to a 750 kb window (filter = 7500000 in this file) so it didn't consider other genes such as POU3F2. It probably only saw MMS22L in the 750 kb window and gave it a high score...