No, they didn't try to measure non-linear effects. Nor did they try to measure environment. That is all irrelevant to measuring linear effects, which was the main thing I wanted to convey. If you want to understand this, the key phrase is "narrow sense heritability." Try a textbook. Hell, try wikipedia.
That it did well on held-back data should convince you that you don't understand overfitting.
Actually, I would expect a bell curve transformation to be the most linear.
That it did well on held-back data should convince you that you don't understand overfitting.
They didn't do well on the gene level: Analyses of individual SNPs and genes did not result in any replicable genome-wide significant association
No, they didn't try to measure non-linear effects. Nor did they try to measure environment. That is all irrelevant to measuring linear effects, which was the main thing I wanted to convey.
No, the fact that you can calculate a linear model that predicts h_2 in a way that fits 0.4 or 0.5 of the variance doesn't mean t...
If it's worth saying, but not worth its own post (even in Discussion), then it goes here.
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