Genome-wide association studies (GWAS) successfully identified hundreds of genomic risk loci that are associated with psychiatric disorders, whereas translating these genetic findings to interpretable biological principles has been a significant challenge. For example, because GWAS-identified common variants are often co-inherited with the nearby variants due to the structure called linkage disequilibrium (LD), GWAS do not provide a single causal variant, but a genomic region that contains up to several hundred variants. Moreover, roughly 90% of genetic risk factors identified by GWAS reside in the non-coding genome [1]. While the common practice is to link these risk factors to nearest genes, accumulating evidence points to the fact that these risk factors can regulate distal genes via distal enhancer–promoter interactions [2, 3]. Therefore, an imperative goal is to establish a computational framework that functionally annotates GWAS risk variants based on (distal) gene regulatory interactions while controlling for LD.

Multi-marker Analysis of GenoMic Annotation (MAGMA) is one of the most widely used tools that convert SNP associations into gene-level associations while taking LD into account [4]. However, it annotates SNPs to the nearest genes and ignores pivotal gene regulatory landscape. Given that disease risk variants can regulate distal genes via tissue-specific enhancer–promoter interaction, we developed Hi-C coupled MAGMA (H-MAGMA) that assigns non-coding SNPs to their regulatory targets based on chromatin interaction profiles from the human brain tissue [5].

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