Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to structural covariance patterns across brain regions and individuals. We present a mega-analysis of structural covariance with magnetic resonance imaging of 50,699 healthy and diseased individuals (12 studies, 130 sites, and 12 countries) over their lifespan (ages 5 through 97). Patterns of structural covariance (PSC) were highly heritable (0.05< h2 <0.78) and significantly associated with 1610 independent significant variants after Bonferroni correction (10.3 > -log10p-value > 8.8): 1245 previously unreported, and 69% of them independently replicated (-log10p-value = 4.5). Associations revealed an imaging phenotypic landscape between 2003 PSCs and 49 clinical and cognitive traits at multiple scales. We constructed machine learning-derived individualized imaging signatures for various disease diagnoses using PSC features at multiple scales, suggesting that disease effects on the brain were better manifested in a multi-scale continuum than on any single scale. Experimental results were integrated into the Multi-scale Structural Imaging Covariance (MuSIC) atlas and made publicly accessible through the BRIDGEPORT web portal (https://www.cbica.upenn.edu/bridgeport/). Our results reveal strong associations between brain structural covariance, genetics, and clinical phenotypes, supporting that PSCs can serve as an endophenotypic anatomic dictionary in future research.