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The difference between an individual’s predicted and chronological age, predicted age difference (PAD), is a phenotype of relevance to aging and brain disease. Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders and tested on two datasets, IXI and UK Biobank, utilizing transfer learning to improve accuracy on new sites. A genome-wide association study (GWAS) of PAD in the UK Biobank data (discovery set: $$N=12378$$ N = 12378 , replication set: $$N=4456$$ N = 4456 ) yielded two sequence variants, rs1452628-T ( $$\\beta =-0.08$$ β = − 0.08 , $$P=1.15\\times{10}^{-9}$$ P = 1.15 × 10 − 9 ) and rs2435204-G ( $$\\beta =0.102$$ β = 0.102 , $$P=9.73\\times 1{0}^{-12}$$ P = 9.73 × 1 0 − 12 ). 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Gyða Björnsdóttir 人物

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