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  1. forestglip

    Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

    I think most people could estimate hours upright to within about 2 hours. For step count, I personally have almost zero idea how many exact steps I walk. Whenever I try a tracker, I'm surprised by how many steps there are in a short walk around the house.
  2. forestglip

    Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

    I wonder if a question about hours of time lying down would be good for future studies. I think either time lying down or step count might be the best indicators of severity we currently have (though step count is probably too difficult for people to estimate without using a tracker). Since the...
  3. forestglip

    Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

    Here are all the items (only from UK Biobank analysis) which contain the text "depres" and which were Bonferroni significantly correlated with ME/CFS, with links to descriptions of the items, in order of correlation with highest at the top. The last item is negatively correlated. The most...
  4. forestglip

    Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

    If you consider that the prevalence of ME/CFS is far lower than depression, then most people in the depression studies wouldn't have ME/CFS. My sense is that it'd be difficult to get such high correlations based on people in the depression studies having ME/CFS if only a small portion of the...
  5. forestglip

    Criticisms of DecodeME in the media - and responses to the criticisms

    "Great, maybe people with ME/CFS have a genetic predisposition to unhelpful beliefs. Unfortunately, none of the treatments you have suggested have worked, so lets examine the specific mechanisms through which these genes cause ME/CFS. The genetics seem to be pointing to people with ME/CFS having...
  6. forestglip

    Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

    Yes, just sorted by p-value. (And that enormously significant correlation with depression does come from comparing to a study with an enormous sample size: 371,184 depression cases) Good idea to look at top correlations. I wonder what that milk one is about. Just under your significance...
  7. forestglip

    Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

    The only result I see of these from a search of the words is multiple sclerosis in UK BioBank and it doesn't look to be significant. (Also all results from all tested traits are in the attached files if you want to explore.) From UK BioBank: Multiple Sclerosis: Diagnoses - main ICD10: G35...
  8. forestglip

    Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

    I've now run this tool on a few different datasets using LDSC to find genetic correlations between ME/CFS and other traits. I'll attach all results for download and just mention some specific correlations. Wikipedia: First I ran it on the dataset "PGC (Psychiatric Genomics Consortium) and...
  9. forestglip

    Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

    The above was the 10th most significant locus in the main GWAS (though its p-value of 1.19e-7 didn't pass the genome-wide threshold). NEGR1 appears to be the closest gene. Lead variant: 1:73,126,414:C:CA There appears to be evidence linking NEGR1 to depression. Link to PubMed search for...
  10. forestglip

    Mapping cerebral blood flow in [ME/CFS] and orthostatic intolerance: insights from a systematic review, 2025, Christopoulos, Armstrong et al

    Which part of the conclusion goes beyond the evidence? It seems fairly reserved to me. They saw many studies that found reduced CBF in ME/CFS. They said CBF is reduced in ME/CFS. The reason for the CBF reduction (deconditioning, medications, ME/CFS pathophysiology, etc) is a different...
  11. forestglip

    Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

    Awesome! I'm not yet positive I understand it right, but I've been trying to find if there's any tool to find the best correlations based on raw genetic data from thousands of other traits, and this might be it? And you don't even have to convert to grch37 or rsids.
  12. forestglip

    Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

    Some posts about sleep disturbance in ME/CFS have been moved to: Sleep problems in ME/CFS - discussion thread
  13. forestglip

    Favorable responses to upadacitinib, a JAK1 inhibitor, in [LC] with predominant neuropsychiatric symptoms: case reports [...], 2025, Jyonouchi et al

    Favorable responses to upadacitinib, a JAK1 inhibitor, in long COVID patients with predominant neuropsychiatric symptoms: case reports in 2 autistic patients and one typically developing patient Harumi Jyonouchi, Jeffery Kornitzer, Lee Geng [Line breaks added] Abstract The long-term impact of...
  14. forestglip

    Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

    I see a few reasons: - Different reference panel (1000G vs UK BioBank). - Liftover to another assembly only loses 0.3% of variants, while our mapping to SNP method lost 6% (though it's unclear how many more are lost later in both methods when MAGMA refers to the LD reference panels) - I don't...
  15. forestglip

    Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

    Just a guess, but I think the UK BioBank data might not be open access like 1000 Genomes is. Maybe worth checking though.
  16. forestglip

    DecodeME in the media

    I get a print subscription to Science. I've been excitedly waiting for this issue, and it finally came:
  17. forestglip

    Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

    Yeah, I don't know where to start to find the right files. It's all so interesting. It feels like there's so much hidden treasure in this data file of DNA, and all these free tools across the internet to analyze it. I'm just very lacking in the experience and energy departments, so most of it...
  18. forestglip

    Preprint Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration

    Looks interesting. I tried to see if I could do anything, but it's too much stuff I don't know how to do, like the part about creating credible set files. In other news, based on a suggestion by @hotblack, I tried to use the UK BioBank reference panel for FUMA instead of the 1000 Genomes...
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