Regulatory genomic circuitry of brain age by integrative functional genomic analyses

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 estimate brain age from magnetic resonance imaging with improved accuracy using our proposed adversarial convolution network (ACN), followed by applying the ACN model to an elder cohort from UK Biobank. The genetic heritability of BAG is significantly enriched in the regulatory regions and implicated in glial cells. We prioritize a set of BAG-associated genes, and further characterize their expression patterns across brain cell types and regions. Two BAG-associated genes, RUNX2 and KLF3, are found as associated with epigenetic clock and diverse aging-related biological pathways. Finally, two BAG-associated hub transcription factors, KLF3 and SOX10, are identified as regulators of pleiotropic risk genes from diverse brain disorders. Altogether, we improve the estimation of BAG, and identify BAG-associated genes and regulatory networks that are implicated in brain disorders.