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Description: Neuroimaging and machine learning are opening up new opportunities in studying biological aging mechanisms. In this field, ‘brain age gap’ has emerged as promising MRI-based biomarker quantifying the deviation between an individual’s biological and chronological age of the brain – an indicator of accelerated/decelerated aging. Here, we investigated the genetic architecture of brain age gap and its relationships with over 1,000 health traits. Genome-wide analyses in 32,634 UK Biobank individuals unveiled a 30% SNP-based heritability and highlighted 25 associated loci. Of these, 23 showed sign-consistency and 16 replicated in another 7,259 individuals. The leading locus encompasses MAPT, encoding the tau protein central to Alzheimer's disease. Genetic correlations revealed relationships with various mental health (depression), physical health (diabetes), and socioeconomic variables (education). Mendelian Randomization indicated a causal role of enhanced blood pressure on accelerated brain aging. This work refines our understanding of genetically modulated brain aging and its implications for human health. Keywords: aging, genetics, machine learning, mental health, MRI Please find our preprint on medRxiv: https://doi.org/10.1101/2023.12.26.23300533 All analysis scripts have been made publicly available on GitHub: https//github.com/pjawinski/ukb_brainage