Autism spectrum disorder (ASD) is associated with high structural heterogeneity in magnetic resonance imaging (MRI). This work uncovers three neuroanatomical dimensions of ASD (N=307) using machine learning methods and constructs their characteristic MRI signatures. The presence of these signatures, along with their clinical profiles and genetic architectures, are investigated in the general population. High expression of the first dimension (A1, “aging-related”) is associated with globally reduced brain volume, cognitive dysfunction, and aging-related genetic variants. The second dimension (A2, “schizophrenia-like”) is characterized by enlarged subcortical volume, antipsychotic medication use, and partially overlapping genetic underpinnings to schizophrenia. The third dimension (A3, “classical ASD”) is distinguished by enlarged cortical volume, high non-verbal cognitive performance, and genes and biological pathways implicating brain development and abnormal apoptosis. Thus, we propose a three-dimensional endophenotypic representation to construe the heterogeneity in ASD, which can support precision medicine and the discovery of the biological mechanisms of ASD..