{"id":8288,"date":"2020-01-20T02:16:05","date_gmt":"2020-01-20T01:16:05","guid":{"rendered":"https:\/\/emilkirkegaard.dk\/en\/?p=8288"},"modified":"2020-02-21T02:45:36","modified_gmt":"2020-02-21T01:45:36","slug":"predicting-iq-from-genetics-how-far-have-we-come-january-2020","status":"publish","type":"post","link":"https:\/\/emilkirkegaard.dk\/en\/2020\/01\/predicting-iq-from-genetics-how-far-have-we-come-january-2020\/","title":{"rendered":"Predicting IQ from genetics: how far have we come? [January 2020]"},"content":{"rendered":"
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Results from Allegrini et al 2019<\/figcaption><\/figure>\n

Counting significant hits was always a dumb way to measure progress in genomic prediction of a trait. Breeders using animals and plants never bothered with this approach and they used ridge regression for best predictive power (a “two cultures” problem no doubt<\/a>). Researchers in human genetics are starting to catch up, implementing clever Enet<\/a> approach for array datafiles (Qian et al 2019<\/a>, called snpnet<\/strong>, based on glmnet<\/strong>). We are still waiting for this method to be widely used. It is possible to do summary statistics based Enet too (Mak et al 2017<\/a>, called lassosum<\/strong>), but again, not many have done it yet.<\/p>\n

That being said, we still see a lot of progress owing to larger datasets and some improvements in using the output from ‘single-variant-at-a-time’ regression that they use in regular GWASs. A brief summary. I focus on the TEDS sample<\/a> (a bug UK twin sample with good DNA and cognitive testing) because this is the largest dataset not used to train GWASs with that has great cognitive testing. It’s someone could use the new subset of UK Biobank with improved cognitive testing to replicate the below (Cox et al 2019<\/a>, n=29k).<\/p>\n