My baseline is much more narrow and technical. It is "we look at the the genome of a baby and have no idea what will be its IQ when it grows up". That is still largely the case and the paper's ability to forecast does not look impressive to me.
This paper validates the approach (something a lot of people, for a lot of different reasons, were skeptical of), and even on its own merits we still get some predictive power out of it: the 3 top hits cover a range of ~1.5 points, and the 69 variants with 90% confidence predict even more. (I'm not sure how much since they don't bother to use all their data, but if we assume the 69 are evenly distributed between 0-0.5 points, then the mean is 0.25 and the total predictive power is more than a few points.)
What use is this result? Well, what use is a new-born baby? As the cryptographers say, 'attacks only get better'.
I think getting there will take a bit more than just engineering.
And, uh, why would you think that? There's no secret sauce here. Just take a lot of samples and run a regression. I don't think they even used anything particularly complex like a lasso or elastic net.
Keep in mind that the outside view of biological complexity is that
The known unknowns have tended to end up lower in complexity than we've predicted. But unknown unknowns continue to blindside us, unabated, adding to the total complexity of the human body.
Or to phrase this another way:
people accurately estimate the total complexity and then apportion it among the known unknowns, thus creating an overestimate.
If it's worth saying, but not worth its own post (even in Discussion), then it goes here.
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