H-MAGMA can be expanded into many different forms. For example, while we decided to use MAGMA among many other tools as it is most widely used, this framework is applicable to any other tools that convert SNP-level P-values into gene-level association statistics16. Moreover, H-MAGMA can be built on Hi-C datasets from multiple tissue- and cell-types to distill biological mechanisms of any GWAS (e.g. Hi-C datasets from immune cells for rheumatoid arthritis GWAS). Finally, while we primarily used Hi-C datasets to link SNPs to target genes, other functional genomics tools such as chromatin accessibility correlations and machine learning-based enhancer-promoter predictions can be used to generate SNP-gene pairs. In fact, a similar approach using eQTLs (eMAGMA) has been recently reported46.
