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This might warrant its own thread because it has a lot of info and advice that is not available in other formats. I made this thread about it on social media: 1) There's a new document with care advice for patients with (very) severe ME/CFS. It was published by the Austrian Society for...

The DecodeME participants were 85% females themselves. I think it's the default of GWAS because sex chromosomes have additional difficulties to analyze see: eXclusionarY: 10 years later, where are the sex chromosomes in GWASs? - PMC EDIT: added quote from the paper above

I kind of picked examples with high heritability though. There are other diseases with similarly low heritability estimates such as rheumatoid arthritis so the estimate for ME/CFS is nothing weird. But perhaps a bit lower than some expected.

I think it was this study: Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes (2019) Lakhani et al. | Science for ME Here's the overview from Dibble et al.

To clarify: the link to the Neal lab data includes a search function where you can find the heritability estimate for other diseases using UKB methods similar to what DecodeME used. It seems that ME/CFS isn't like schizophrenia, Crohn's disease or diabetes type 1 for example, which have a...

I got the impression that the ME/CFS estimate still seems quite low compared to other diseases using the same method https://nealelab.github.io/UKBB_ldsc/h2_browser.html https://pmc.ncbi.nlm.nih.gov/articles/PMC3469463/

Something that hasn't been discussed much is the low heritability estimate This was estimated using linkage disequilibrium score regression ( LDSC). If I understand correctly the method is relatively simple: it does a regression analysis of linkage disequilibrium (how much SNPs correlate with...

In an interview with David Tuller, Ponting also said something interesting (starting at minute 21:23):

Yes it seems that figuring out where the signal comes from is quite difficult. The MAGMA gene-tissue analysis for example, used a different approach than FUMA + coloc and suggested different genes (given in supplementary table S4). Interestingly it suggests LRRC7 for the location on chromosome...

In the data analysis plan there was the suggestion of combining the DecodeME and UKB ME/CFS cases to get more power. Was this done or was the idea that the UKB ME/CFS cases are too unreliable?

Thanks, and big thanks to the DecodeME team for uploading all this info! This is all quite new to me so apologies for any errors or dumb questions. I think the INFO score give an indication if the imputation went well by comparing the variance of the dosage (expected number of alternative...

That's probably because 90% of texts and papers on ME/CFS talk about weak data and all these possible connections that have no sound basis. I wonder if you would get better results if you call it illnessX and describe it yourself with what are considered the most robust findings. You could also...

Most (around 90%) of the SNP results are imputed if I understand correctly, not just the HLA region. I'm trying to find info that says which ones were actually measured and which ones imputed.

Anyone who can point me to this study? Seem to have missed it.

Not sure, was thinking there might be a higher risk of selection bias and not getting a representative sample of the ME/CFS population.

The replication with existing databases does not seem to go very well: there's likely too much uncertainty about the case definition used. So I suspect that another project with the same approach as DecodeME might be needed to confirm the results. It will need to be in an area were ME/CFS is...

Apologies if this is a stupid question but how do you know which SNPs are measured and which ones are imputed? Do they mention this somewhere in the text or is it somewhere in the data?

EDIT: these probably reflect artefacts that were excluded in the main analysis? I'm looking at the DecodeME summary data, namely the file: gwas_1.regenie.gz taken from here: https://osf.io/rgqs3/files/osfstorage When I arrange by lowest p-value however, I get 8320 rows with a p-value lower...

Yes strange. Perhaps the overall frequency was taken from another database and does not reflect the data in DecodeME?

Here's an overview of the replication cohorts' sample sizes (taken from the supplementary material): Replication Cohort Cases Controls R1 Lifelines 3,440 17,080 R1 UK Biobank 10,327 195,103 R2 Estonian Biobank 1,926 195,103 R2 FinnGen 283 463,029 R2 Michigan Genomics Initiative...