Háskóli ÍslandsUniversity of IcelandJónsson, Benedikt AtliBjornsdottir, GydaThorgeirsson, ThorgeirEllingsen, Lotta MaríaWalters, G. BragiGudbjartsson, DanielStefansson, HreinnStefansson, KariUlfarsson, Magnus2020-02-102020-02-102019-11-27Jonsson, B.A., Bjornsdottir, G., Thorgeirsson, T.E. et al. Brain age prediction using deep learning uncovers associated sequence variants. Nat Commun 10, 5409 (2019). https://doi.org/10.1038/s41467-019-13163-92041-1723https://hdl.handle.net/20.500.11815/1517Publisher's version (útgefin grein).Machine learning algorithms can be trained to estimate age from brain structural MRI. 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 , replication set: N= 4456) yielded two sequence variants, rs1452628-T (β= − 0.08 , P= 1.15 × 10 − 9) and rs2435204-G (β= 0.102 , P= 9.73 × 1 0 − 12). The former is near KCNK2 and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a well-known inversion at 17q21.31 (H2).5409eninfo:eu-repo/semantics/openAccessHeilinnÖldrunErfðarannsóknirinfo:eu-repo/semantics/articleNature Communications10.1038/s41467-019-13163-9