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>use principal components to successfully control away population structure PCA doesn't perfectly control away population structure, especially if you have cry

>use principal components to successfully control away population structure PCA doesn't perfectly control away population structure, especially if you have cryptic tiny subpopulations in your dataset that only few individuals are in. In that case you don't have enough signal to establish the subpopulations. It's also a function of the way you call your SNPs - if you don't have enough SNPs, or focus on a different method, PCA won't magically know about those unobserved populations.

theparanoid 9y ago
The most famous recent study used Iceland [0], which is about as homogeneous as you can imagine. [0] http://www.pnas.org/content/114/5/E727.full http://www.pnas.org/content/114/5/E727.full
AlexCoventry 9y ago
I'm pretty sure he was being sarcastic.
- nathan_f77 9y ago
  Wow, I actually have no idea if gwern was being sarcastic or not. I didn't consider it until I read your comment. There's a few clues in there that do suggest sarcasm, but I really can't tell, because I know nothing about the field. I don't think they were being sarcastic though, because the rest of their replies are pretty serious.
  - AlexCoventry 9y ago
    He says some things with perfect confidence which are unknowable or false, but you do need a background in the field to see that. Also, he starts out with what seems to be an outrageous appeal to emotion, but I suppose "hurtful" could mean "harmful" rather than "mean."
gwern 9y ago
They may not perfectly control, but they do the job well enough that the error from that is minimal and of less importance than other issues like limited SNP panel coverage, sample error, or measurement error (a _huge_ limit on current GWASes and very underappreciated, given how few papers attempt to correct for it or even mention what sort of psychometric properties the available data has). It is certainly not true, as Alex claims, that the results are nothing but systematic measurement error or population structure.

theparanoid 9y ago

The most famous recent study used Iceland [0], which is about as homogeneous as you can imagine. [0] http://www.pnas.org/content/114/5/E727.full http://www.pnas.org/content/114/5/E727.full

AlexCoventry 9y ago

I'm pretty sure he was being sarcastic.

nathan_f77 9y ago
Wow, I actually have no idea if gwern was being sarcastic or not. I didn't consider it until I read your comment. There's a few clues in there that do suggest sarcasm, but I really can't tell, because I know nothing about the field. I don't think they were being sarcastic though, because the rest of their replies are pretty serious.
- AlexCoventry 9y ago
  He says some things with perfect confidence which are unknowable or false, but you do need a background in the field to see that. Also, he starts out with what seems to be an outrageous appeal to emotion, but I suppose "hurtful" could mean "harmful" rather than "mean."

gwern 9y ago

They may not perfectly control, but they do the job well enough that the error from that is minimal and of less importance than other issues like limited SNP panel coverage, sample error, or measurement error (a _huge_ limit on current GWASes and very underappreciated, given how few papers attempt to correct for it or even mention what sort of psychometric properties the available data has). It is certainly not true, as Alex claims, that the results are nothing but systematic measurement error or population structure.