, 14 tweets, 19 min read Read on Twitter
How good is the recently published polygenic risk score for BMI?

Explains ~9% of variance.

What does that mean?

Plot on left is prediction vs. actual BMI in simulated population with amount of info in PRS.

Right is same except using other 91% of information.
@f2harrell @ADAlthousePhD @statsepi @MaartenvSmeden @mikejohansenmd @bogdienache @VPrasadMDMPH @drjohnm Shows this might give us some interesting insights, but not yet ready to predict anything useful in a clinical setting.
@f2harrell @ADAlthousePhD @statsepi @MaartenvSmeden @mikejohansenmd @bogdienache @VPrasadMDMPH @drjohnm This is why Ruth Loos says: "Any score that captures genetic predisposition will never accurately predict future obesity. … You need to account for that environmental part."

@f2harrell @ADAlthousePhD @statsepi @MaartenvSmeden @mikejohansenmd @bogdienache @VPrasadMDMPH @drjohnm Ruth Loos again: “If you have a score that only represents 10% of the overall obesity susceptibility, you can never accurately predict future obesity,”

@f2harrell @ADAlthousePhD @statsepi @MaartenvSmeden @mikejohansenmd @bogdienache @VPrasadMDMPH @drjohnm Ruth Loos again: "I am concerned that the authors may have oversold the genetic score and that their claims of prediction are too bold."

@f2harrell @ADAlthousePhD @statsepi @MaartenvSmeden @mikejohansenmd @bogdienache @VPrasadMDMPH @drjohnm @CardiacJoshi Would you use a test which has this level of accuracy in clinical care?
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Response: This is a toy example. it is to illustrate how little information is in the PRS and how much info is *not* in the PRS. Finding the info to make the right graph is VERY HARD.
I should also acknowledge the first function in the code was lifted directly from here:

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