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

[ME/CFS Science Blog]

Can you give more detail for how you used to decodeme data to get these lists of traits? Search for genes? Variants? Ranges?

Searched for genes, like CA10. Then you get a list of other traits and GWAS where the gene has been implicated.

Selected the DecodeME genes mainly based on proximity to the SNP with the lowest p-value in the region. The bottom of the table is from regions that only reached a p value of 10^-7.

 

[forestglip]

Searched for genes, like CA10. Then you get a list of other traits and GWAS where the gene has been implicated.

Selected the DecodeME genes mainly based on proximity to the SNP with the lowest p-value in the region. The bottom of the table is from regions that only reached a p value of 10^-7.

Ok thanks. How did you select traits to highlight? For example, BTN3A2 has 97 traits, some with even lower p values than some of the traits in your table, such as height or teeth issues.

 

[ME/CFS Science Blog]

For example, BTN3A2 has 97 traits, some with even lower p values than some of the traits in your table, such as height or teeth issues.

Yeah good point, it's a bit arbitrary. Selected the ones with the most associations (those that you see if you click on 'Traits'). Those are mostly quantitative traits like height or intelligence that have been tested a lot and have big sample sizes.

So I also looked at traits with a reported odds ratio, because those are often binary traits like having an illness or not. Added those binary traits if they had a p-value lower than 5*10^-8. But I didn't try to be very comprehensive so perhaps I missed some.

EDIT: should probably have made clearer that this was only a selection of associated traits. Will update my post above.