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I was alerted to your package recently and it looks extremely valuable, congratulations!
I did have a couple of feature requests, apologies if this is already implemented I didn't see the documentation.
Clumping - where SNPs are ordered based on their p-value in GWAS and are iteratively filtered by removing any SNPs in LD with the SNP with the lowest p-value
Creating an LD matrix for a list of SNPs (e.g. rather than a region)
The text was updated successfully, but these errors were encountered:
Hi @mklarqvist I just wanted to follow up on this. We have a service that performs LD calculations on the fly, currently using plink 1.9. This is the service: https://gwas-api.mrcieu.ac.uk/
The order of operations is typically pretty small. There are say 5000 SNPs which reach genome-wide significance, and we need to clump them, meaning
Rank the p-values from lowest to highest
Any SNP that is in LD at some threshold and within a physical distance window with the top hit is removed
return to (2) with the new remaining top hit
Once no more SNPs left, return each independent top hit
It's quite a simple algorithm, and plink 1.9 provides good performance on the LD reference panel that we're using which is ~500 european individuals from the 1000 genomes data, retaining only SNPs with maf > 0.01
Running clumping on say 2000 SNPs in this reference dataset in plink takes around 5 seconds.
The next thing that we want to do is increase the sample size of this reference dataset so that more precise estimates of LD can be obtained. Tomahawk looks like a potentially good choice, but I just wanted to get your advice on this before I explore further.
If clumping isn't implemented I'm happy to try implementing it in a fork and create a pull request.
Also - do you have plans to allow indels to be included?
Thanks!
I was alerted to your package recently and it looks extremely valuable, congratulations!
I did have a couple of feature requests, apologies if this is already implemented I didn't see the documentation.
The text was updated successfully, but these errors were encountered: