very interesting! We are particularly interested in (B) bad. Your worst score for bad is not so bad, but the "best" score for bad is really something we would not like to see. In the files there is a column for filtering certain low-quality SNPs, did you use that?
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Nope, I just used all the SNPs in the results files (deliberately naively) which would probably explain excessive "snowiness" in some cases. Worth applying to a cleaner set of results to see how things change. I'll find some time during the less interesting sessions at ASHG :)
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Also, did you filter the data beforehand? For instance on imputation quality and allele frequency? This will impact the Manhattan and therefore the AutoML analysis. Some of those bad are not really that bad once you filtered away some of those obviously ‘bad’ variants.
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Ok so nevermind.
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Neat idea, and thanks for sharing!
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