Implicating candidate genes at GWAS signals by leveraging topologically associating domains

Genome wide association studies (GWAS) have contributed significantly to the understanding of complex disease genetics. However, GWAS only report associated signals and do not necessarily identify culprit genes. As most 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 an association most likely impacts gene function. We describe and validate a computational method that uses the genic content of TADs to discover candidate genes. Our method, called \"TAD_Pathways,\" performs a Gene Ontology (GO) analysis over genes that reside within TAD boundaries corresponding to 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. In many cases, our method implicated a gene other than the nearest gene. 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 ARHGAP1 was not supported. Our results via the example of BMD demonstrate how basic principles of three-dimensional genome organization can define biologically informed association windows.