Here we present a refined framework for gene pathway analysis, H-MAGMA, that aggregates SNP-level summary statistics into the gene-level association statistics. Compared with cMAGMA, H-MAGMA (1) links non-coding SNPs to their target genes based on functional genomic evidence, and (2) adds relevant cellular context to gene mapping by using chromatin interaction data from disease-relevant tissue and cell types. While the basic concept of mapping SNPs to genes using functional genomic resources is similar to FUMA7, H-MAGMA leverages the MAGMA framework to obtain gene-level association statistics in a genome-wide fashion, while FUMA maps a selected set of genomic loci to target genes. Therefore, H-MAGMA can provide an attractive framework to identify genes and biological pathways for low powered GWAS. It also allows comparing different GWAS to elucidate shared biological pathways.
