부산대학교 도서관

Expression QTL (eQTL) analyses have suggested many genes mediating genome-wide association study (GWAS) signals but most GWAS signals still lack compelling explanatory genes. We have leveraged an adipose-specific gene regulatory network to infer expression regulator activities and phenotypic master regulators (MRs), which were used to detect activity QTLs (aQTLs) at cardiometabolic trait GWAS loci. Regulator activities were inferred with the VIPER algorithm that integrates enrichment of expected expression changes among a regulator's target genes with confidence in their regulator-target network interactions and target overlap between different regulators (i.e., pleiotropy). Phenotypic MRs were identified as those regulators whose activities were most important in predicting their respective phenotypes using random forest modeling. While eQTLs were typically more significant than aQTLs in cis, the opposite was true among candidate MRs in trans. Several GWAS loci colocalized with MR trans-eQTLs/aQTLs in the absence of colocalized cis-QTLs. Intriguingly, at the 1p36.1 BMI GWAS locus the EPHB2 cis-aQTL was stronger than its cis-eQTL and colocalized with the GWAS signal and 35 BMI MR trans-aQTLs, suggesting the GWAS signal may be mediated by effects on EPHB2 activity and its downstream effects on a network of BMI MRs. These MR and aQTL analyses represent systems genetic methods that may be broadly applied to supplement standard eQTL analyses for suggesting molecular effects mediating GWAS signals. Author summary: Most human genetic variants lie outside of genes (the functional units of the genome that directly affect a cell's biology) making it unclear which genes are responsible for influencing their associated traits. The gold-standard for linking genetic variants to genes is expression QTL (or eQTL) analysis, which tests for associations between genetic variants and the expression of genes. However, this approach often fails to identify gene(s) potentially mediating the effects of trait-associated variants. Here we propose the use of a supplementary approach called activity QTL (or aQTL) analysis using existing eQTL data. We first inferred the activities of genes that affect other genes' expression based on a gene regulatory network and then tested associations between genetic variants and these inferred regulator activities. This can be advantageous when a gene's measured expression level is a poor indicator of its downstream activity or when multiple genetic influences are funneled through key regulators in a gene regulatory network to affect the trait of interest. Using this approach, we identified genes expressed in adipose tissue (i.e., fat) potentially mediating genetic effects on BMI, fat distribution, diabetes risk and blood cholesterol levels. More broadly, this work highlights the benefits of leveraging relational (i.e., topological) information in addressing complex biological problems. [ABSTRACT FROM AUTHOR]