I already put up
a blog post on the preprint of
this new study last year:
Variable prediction accuracy of polygenic scores within an ancestry group
Here is the Abstract:
Fields as diverse as human genetics and sociology are increasingly using polygenic
scores based on genome-wide association studies (GWAS) for phenotypic prediction. However,
recent work has shown that polygenic scores have limited portability across groups of different
genetic ancestries, restricting the contexts in which they can be used reliably and potentially
creating serious inequities in future clinical applications. Using the UK Biobank data, we
demonstrate that even within a single ancestry group (i.e., when there are negligible differences in
linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores
can depend on characteristics such as the socio-economic status, age or sex of the individuals in
which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings
highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to
their broad use.
Damning on its face, but the authors appear to not want to give up the ship, and give only a few passing mentions of pop/strat and other confounding issues with these large genetic databases. At what point do you reject the model if the studies aren't giving you the expected results? Time will tell...
The most damning thing for me is that the prediction variability dramatically lowered for EA and income but not height or Pulse rate, so that throws the "eventually the pgs for EA" will be as high as height out the window
ReplyDeleteYes, although it's interesting how limited even physical traits like height are in that regard, too. In any case, they've maxed out, because they are not even going to get these paltry results when they broaden their reach and don't have a stratified population to study.
ReplyDelete