The age of the Genome-Wide Association Study has left an indelible legacy on the ways upon which researchers associate genomic changes to disease. However, associating variation with disease is only the beginning of the genomic revolution. The next step is to attribute these genetic variations to changes in the pathways that eventually lead to disease manifestation. Thus, here I explore the analysis of human variation through the lens of structural genomics, a novel frontier of genetics that aims to describe how the structure and function of the genome are intertwined. By extending classical genetics, this thesis informs the transition from cataloguing genetic variation (genome-wide association studies) in the context of local genomic landscapes (2D genomics) into understanding how specific variations alter basic aspects of genome regulation (3D genomics). As these genetic changes largely occur in noncoding regions of the genome, function of these SNPs is typically defined according to the function of genes nearby in 2D linear distance. In spatial genomics, the effects of genetic variations are instead defined based on spatial proximity to genes in 3D. These spatial associations implicate long-distance connections; classifying variation based on its modifications of key regulatory sequences. Thus, DNA structure and the subsequent longdistance connections are here related to alterations of gene expression and function in common biological events and diseases (e.g. post-term birth, diabetes, growth, and rheumatoid arthritis). This approach provides a hypothesis for how genetic variation in noncoding regions contributes to the genetic risks of disease onset, progression, and treatment.