PLINK clump behavior on missing SNPs? I have a long list of autoimmune-associated SNPs, and I want to boil it down so that I get one SNP representing each LD block. I chose to use PLINK's --clump option for this. I'm roughly following this tutorial (but analyzing my own data).

I don't have the full data from the original GWAS's, so I am using 1000 Genomes data as the reference for LD estimation. But, the 1000 Genomes data don't have all the SNPs I need. I end up a few hundred warnings:

Warning: 'rs12345678' is missing from the main dataset, and is a top variant.

How does PLINK handle this situation? More importantly, how should I handle this situation? I don't want to omit those SNPs, but I also don't want to take them all, for the same reason that I am doing this in the first place.
Most likely, the GWASs that generated your summary statistics used other imputation panels than 1000G, like HRC. Clearly, PLINK can't estimate the LD for SNPs that aren't found in the reference, and most likely just ignores them. I think the easiest solution is to use whatever individual-level dataset you have to estimate the correlations - even if it's small or from a different population. The estimate will be biased, but you don't really need high precision for clumping.

Then again, you can skip PLINK and try searching for SNP prioritization methods based on summary statistics - it's an area of active development, and something might be available to suit your needs.