It remains challenging to boost statistical power of genome-wide association studies (GWASs) to identify more risk variants or loci that can account for “missing heritability.” Furthermore, since most identified variants are not in gene-coding regions, a biological interpretation of their function is largely lacking. On the other hand, recent biotechnological advances have made it feasible to experimentally measure the three-dimensional organization of the genome, including enhancer–promoter interactions in high resolutions. Due to the well-known critical roles of enhancer–promoter interactions in regulating gene expression programs, such data have been applied to link GWAS risk variants to their putative target genes, gaining insights into underlying biological mechanisms. However, their direct use in GWAS association testing is yet to be exploited. Here we propose integrating enhancer–promoter interactions into GWAS association analysis to both boost statistical power and enhance interpretability. We demonstrate that through an application to two large-scale schizophrenia (SCZ) GWAS summary data sets, the proposed method could identify some novel SCZ-associated genes and pathways (containing no significant SNPs). For example, after the Bonferroni correction, for the larger SCZ data set with 36,989 cases and 113,075 controls, our method applied to the gene body and enhancer regions identified 27 novel genes and 11 novel KEGG pathways to be significant, all missed by the transcriptome-wide association study (TWAS) approach. We conclude that our proposed method is potentially useful and is complementary to TWAS and other standard gene-and pathway-based methods.
Bibliographical noteFunding Information:
We are grateful to the reviewers for constructive comments. We thank Hui Li for helping with the MCF7 data. This research was supported by National Institutes of Health (NIH) grants R21AG057038, R01HL116720, R01GM113250, R01HL105397, and R01GM126002, NSF grant DMS 1711226, and by the Minnesota Supercomputing Institute.
- Gene-based testing