Jonathan Edwards
Senior Member (Voting Rights)
Fascinating. Keep at it.
Genetics: Chromosome 17 CA10
Fascinating. Keep at it.
It's using an older assembly/coordinate system called GRCh37. The more recent one that DecodeME uses is called GRCh38. Defining where exactly a SNP is on a chromosome isn't an exact science, so as they learn more, they make updates to the positions.
"Liftover" is converting the coordinates to a different assembly. I used liftover for the "Getting up in the morning" GWAS to make it match DecodeME.
On gnomAD, if you look up a SNP, it'll tell you where that SNP is in the other assembly. For example, here's the page for the lead SNP of the neck and shoulder pain GWAS in GRCh37 coordinates, as reported in Table 2: https://gnomad.broadinstitute.org/variant/17-50259142-A-C?dataset=gnomad_r2_1
Partway down the page, it says:
Partway down the page, it says:
ScoutB
Senior Member (Voting Rights)
Thank you for finding/sharing this. These results are so fascinating. I hope I get less foggy soon so I can read more of the details.
One question for you forestglip (and maybe @jnmaciuch if you're not too busy) -- do we have any idea how much of the similarity in 'shape' of the DecodeME stats and the ease-of-getting-up stats is due to variants at all those elevated positions being inherited together? i.e. would we expect that all the dots I circled in orange tend to be inherited together? (In that case I guess any condition where one of the dots is elevated you'd expect to see them all elevated?)
(Crop from post #57)
(Maybe this question translates to is 'is this what linkage disequilibrium looks like in summary statistics'?)
Jonathan Edwards
Senior Member (Voting Rights)
I think that is probably right at first pass.
It intrigued me that if you have a batch of variants tightly linked - so that they produce a flat line of linkages - you cannot simply ask 'did SNP 45 or SNP 52 cause the risk' because 'cause' in this situation is a complicated concept dependent on what options are biologically available. If these variants always go hand in hand, even if you can in theory trace a transcription factor binding being critical at a particular point it may not be legitimate to attribute cause to any one variant (which might be a variant not in your SNP library but closely linked to those in the line I guess).
The others still have a clearer understanding of this than I do so let's see what they say.
Yes, this is showing linkage disequilibrium. The following plot actually shows the strength of LD between each of the variants in the plot with the lead variant (purple diamond).
The variants in red have very strong LD with the lead variant, so would be expected to show up very often in people that have the lead variant. Hence, they are just about as significant as the lead variant. The yellow variants have less LD with the lead variant, so the significance might be further off, as we see here.
Yes, if the causal variants in the two studies were two different high LD "red" variants, the plots would probably look pretty similar. In theory, coloc helps to mathematically determine the probability that there is a shared variant based on the overall pattern, which may subtly change even if the other study's causal variant is a high LD "red" variant.
With a set of variants that have near perfect LD with each other (LD~1), I would think it's probably very difficult to determine which one is causal.
I guess it may be wise to leave open the possibility that the "getting up in the morning" study has a different causal variant that is just in high LD with ME/CFS's causal variant.
For the PheWAS analysis I did a few posts ago (looking up which other traits have significant associations at a variant), I used GWAS Atlas. They have 4,756 GWAS datasets, and they have not added any datasets since 2019.
I found out that IEU Open GWAS also has PheWAS functionality, and they have over 10 times as many GWAS datasets (50,069), so it seemed worth looking for associations there too.
Unfortunately, the Open GWAS website doesn't have a simple PheWAS lookup using the web browser like GWAS Atlas, and instead it requires using their API. But they provide Python and R packages for accessing the API, and at least the Python package is fairly straightforward. I haven't tried the R package.
Keep in mind that the variant being significant in multiple traits doesn't necessarily indicate a shared causal variant between ME/CFS and each of these traits. Though it at least shows that a causal variant is likely in the same general region near CA10 in the two traits.
The most significant in this case is a reaction time trait. The link in the table doesn't take you directly to the BioBank details for the trait, but here are the details for that first trait, including a little video of what the cognitive test is: https://biobank.ndph.ox.ac.uk/ukb/field.cgi?id=404
Basically, it's a "game" where two cards are shown on a screen, and the participant presses a button as quickly as possible if the two cards match. This trait is how long it takes to press the button (durations even if the participant got it wrong are counted here).
The direction of effect is as expected: the T allele is associated with increased risk of ME/CFS, and it is also associated with longer duration to press the button.
Here are all associations with p<1e-6, sorted starting from most significant:
I also did the PheWAS with the other 7 DecodeME variants, and will post the results on their respective threads. [Edit: Not all of them had other significant traits.]
Edit: Note, if you want to look up the details page of a UK BioBank trait (ID starts with "ukb"):
- Go to the Open GWAS page for the trait linked in the table
- In the "note" row of the table, there's a number. For example, for the most significant trait above, it says "404" in the "note" field.
- Replace the number at the end of the following URL with the number for the trait: https://biobank.ndph.ox.ac.uk/ukb/field.cgi?id=404
I found out that IEU Open GWAS also has PheWAS functionality, and they have over 10 times as many GWAS datasets (50,069), so it seemed worth looking for associations there too.
Unfortunately, the Open GWAS website doesn't have a simple PheWAS lookup using the web browser like GWAS Atlas, and instead it requires using their API. But they provide Python and R packages for accessing the API, and at least the Python package is fairly straightforward. I haven't tried the R package.
Keep in mind that the variant being significant in multiple traits doesn't necessarily indicate a shared causal variant between ME/CFS and each of these traits. Though it at least shows that a causal variant is likely in the same general region near CA10 in the two traits.
The most significant in this case is a reaction time trait. The link in the table doesn't take you directly to the BioBank details for the trait, but here are the details for that first trait, including a little video of what the cognitive test is: https://biobank.ndph.ox.ac.uk/ukb/field.cgi?id=404
Basically, it's a "game" where two cards are shown on a screen, and the participant presses a button as quickly as possible if the two cards match. This trait is how long it takes to press the button (durations even if the participant got it wrong are counted here).
The direction of effect is as expected: the T allele is associated with increased risk of ME/CFS, and it is also associated with longer duration to press the button.
Here are all associations with p<1e-6, sorted starting from most significant:
I also did the PheWAS with the other 7 DecodeME variants, and will post the results on their respective threads. [Edit: Not all of them had other significant traits.]
Edit: Note, if you want to look up the details page of a UK BioBank trait (ID starts with "ukb"):
- Go to the Open GWAS page for the trait linked in the table
- In the "note" row of the table, there's a number. For example, for the most significant trait above, it says "404" in the "note" field.
- Replace the number at the end of the following URL with the number for the trait: https://biobank.ndph.ox.ac.uk/ukb/field.cgi?id=404
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