-Population differences are important when working with genome-wide association studies, because a model "trained on" one population won't perform as well against the "test set" of a different population. Suppose you do a big study and find a bunch of SNPs that correlate with a trait, like schizophrenia or liking opera. The frequencies of those SNPs for two populations from the same continent (like Japanese and Chinese) will hugely correlate (_r_ ≈ 0.97), but for more genetically-distant populations from different continents, the correlation will still be big but not huge (like _r_ ≈ 0.8 or whatever).
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-[table p. 192]
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-[sickle-cell, lactase persistence]
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-[Tibet, Peru, Ethiopia all have high-altitude adapatations, but they're different adaptations, p. 198]
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-The third part of the book is about genetic influences on class structure!
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-[p. 212-4: A + C + E model and comparing identical and fraternal twins (different from twins raised apart)]
-[ACE model assumes no assortative mating, which leads to an underestimate of A: because it makes fraternal twins resemble each other for non-environmental reasons]
-[equal environments assumption could be violated]
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-[shared environment is zero for personality]
+Population differences are important when working with genome-wide association studies, because a model "trained on" one population won't perform as well against the "test set" of a different population. Suppose you do a big study and find a bunch of SNPs that correlate with a trait, like schizophrenia or liking opera. The frequencies of those SNPs for two populations from the same continent (like Japanese and Chinese) will hugely correlate (Pearson's _r_ ≈ 0.97), but for more genetically-distant populations from different continents, the correlation will still be big but not huge (like _r_ ≈ 0.8 or whatever).