Deep learning based brain age prediction uncovers associated sequence variants

Abstract

Machine learning algorithms trained to recognize age-related structural changes in magnetic resonance images (MRIs) of healthy individuals can be used to predict biological brain age in independent samples. The difference between predicted and chronological age, predicted age difference (PAD), is a phenotype holding promise for the study of normal brain ageing and brain diseases, and genetic discovery via genome-wide association studies (GWASs). 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 (N = 1264) and tested on two datasets, the IXI (N = 544) and UK Biobank (N = 12395) datasets, utilizing transfer learning to improve accuracy on new sites. A GWAS of PAD in the UK Biobank data (discovery set: N=12395, replication set: N=4453) yielded two sequence variants, rs1452628-T ({beta}=-0.08, P = 1.15 {middle dot} 10-9) and rs2435204-G ({beta}=0.102, P = 9.73 {middle dot} 10-12). 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). The genetic association analysis was also confined to variants known to associate with brain structure, yielding three additional sequence variants associating with PAD.