stephenslab / susieR

R package for "sum of single effects" regression.
https://stephenslab.github.io/susieR
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slow convergence example #123

Closed stephens999 closed 3 years ago

stephens999 commented 3 years ago

Here is example 5, which eventually converges with an in-sample LD matrix (generated by plink using 341k unrelated UK biobank samples).

Originally posted by @joshchiou in https://github.com/stephenslab/susieR/issues/122#issuecomment-833220143

stephens999 commented 3 years ago

@joshchiou I'm going to follow up this example separately when i get time....

stephens999 commented 3 years ago

this is an interesting example. First it is clear this region seems complex: susie find 9 CSs! So I think it is not that surprising that convergence is slower than usual.

Some additional comments are probably in order:

i) many of the signals identified have small non-significant marginal z scores. The reason they are identified in the multi-snp analysis is because they are in high LD with another variant with strong z score, and yet don't themselves have a strong z score. The only way a variant can be in high LD with a strongly-associated variant and yet not show a strong z score itself is if there is another compensatory signal that cancels out the strong z score. This is probably the hardest kind of example to finemap. (Of course an alternative possibility is somehow the z scores or ld matrix are wrong, but I do not see any evidence for that.)

ii) To check for convergence issues i reran susie with "refine=TRUE". This takes several hours in this case because there are so many signals. It did find a better solution, but with one additional signal compared with those found in the original run.

I don't think there is a problem per se with these results, but in complex regions like this the space is likely to be quite complex and it is possible that susie could converge to a local optima, so although my best guess is that the CSs identified here are mostly reliable, in complex situations like this -- 9+ signals, some cancelling each other out in the marginal associations -- it is harder to be confident in any given mapped signal.

Thanks for sharing this interesting example with us. Please close this issue if you don't have additional questions or concerns.

joshchiou commented 3 years ago

Thank you for taking a deeper look. Your explanation makes sense - this is one of the regions with the strongest associations so I'm not surprised to see so many signals. I can certainly close the original issue #122 but I think you opened this one, so I can't close it.

stephens999 commented 3 years ago

Thanks!