Discovering eQTL Regulatory Patterns Through eQTLMotif

Abstract

The expression quantitative trait loci (eQTL) analysis has become important for understanding the regulatory function of genomic variants on gene expression in a tissuespecific manner and has been widely applied across species from microbes to mammals. Current eQTL studies mainly focus on the simple one-to-one regulation between variant and gene. Recent research have demonstrated there are also more complex regulatory patterns between eQTLs and genes. However, there is a lack of studies and relevant methods to systematically discover the regulatory patterns between multiple eQTLs and multiple genes. In this regard, this study has proposed a novel computational framework, called eQTLMotif, to discover regulation patterns of eQTLs in a many-to-many manner. This framework mainly consists of two steps: (1) construct a novel eQTL regulatory network by integrating bipartite eQTL network, eQTL mediation effects, and gene regulatory network; (2) perform motif mining through exactly enumerating frequently appeared eQTL regulatory structures. Based on this framework, we for the first time systematically investigated the eQTL regulatory patterns in the human frontal cortex based on a large cohort of postmortem human brains. Experiments have demonstrated that our framework can effectively reveal novel eQTL regulatory patterns. And some are in similar structure to the existing gene regulation patterns, such as feed-forward loop (FFL)-like motif, single input module (SIM)-like motif, and dense overlapping regulons (DOR)- like motif. Our method and findings will further enhance the understanding of regulatory mechanisms of eQTLs in multiple tissues and species.