Abstract
Brain age gap (BAG) is a valuable biomarker for evaluating brain healthy status and detecting age-associated cognitive degeneration. However, the genetic architecture of BAG and the underlying mechanisms are poorly understood. Here, we estimated brain age from magnetic resonance imaging with improved accuracy using our proposed adversarial convolution network (ACN), and applied the ACN model to an elderly cohort from the UK Biobank. The genetic heritability of BAG was significantly enriched in regulatory regions and implicated in glial cells. We prioritized a set of BAG-associated genes, and further characterized their expression patterns across brain cell types and regions. Two BAG-associated genes, RUNX2 and KLF3, were found to be associated with epigenetic clock and diverse aging-related biological pathways. Finally, two BAG-associated hub transcription factor genes, KLF3 and SOX10, were identified as regulators of pleiotropic risk genes for diverse brain disorders. Altogether, we improve the estimation of BAG, and identify BAG-associated genes and regulatory networks implicated in brain disorders.