Background: Genome wide association studies (GWAS) have contributed significantly to the field of complex disease genetics. However, GWAS only report signals associated with a given trait and do not necessarily identify the precise location of culprit genes. As most association signals occur in non-coding regions of the genome, it is often challenging to assign genomic variants to the underlying causal mechanism(s). Topologically associating domains (TADs) are primarily cell-type independent genomic regions that define interactome boundaries and can aid in the designation of limits within which a GWAS locus most likely impacts gene function. Results: We describe and validate a computational method that uses the genic content of TADs to assign GWAS signals to likely causal genes. Our method, called "TAD_Pathways", performs a Gene Ontology (GO) analysis over all genes that reside within the boundaries of all TADs corresponding to the GWAS signals for a given trait or disease. We applied our pipeline to the GWAS catalog entries associated with bone mineral density (BMD), identifying 'Skeletal System Development' (Benjamini-Hochberg adjusted p = 1.02x10-5) as the top ranked pathway. Often, the causal gene identified at a given locus was well known and/or the nearest gene to the sentinel SNP. In other cases, our method implicated a gene further away. Our molecular experiments describe a novel example: ACP2, implicated at the canonical 'ARHGAP1' locus. We found ACP2 to be an important regulator of osteoblast metabolism, whereas a causal role of ARHGAP1 was not supported. Conclusions: Our results demonstrate how basic principles of three-dimensional genome organization can help define biologically informed windows of signal association. We anticipate that incorporating TADs will aid in refining and improving the performance of a variety of algorithms that linearly interpret genomic content.