Previous high throughput screening studies indicated trans-eQTLs tend to be tissue specific. In this study, I probed if this feature can be used to identify tissue-specific gene regulatory networks. eQTL data (P<5e-8) for 16,259 genes were identified and their summary association statistics were obtained from the eQTLGene database. Next, eQTLs that display both cis and trans regulatory effects were selected and the association between their corresponding genes were examined using Mendelian randomization. A total of 169 genes that exerted trans-regulatory impacts on 692 genes were identified. 90% of the genes (N=749) aggregated into a gene regulatory network significantly enriched in hemo-immune processes. The robustness of finding was confirmed through simulation. The identified network displayed the scale-free topology. This provided the reason to examine the association of the network’s hub genes with the phenome. The outcome of analyses revealed GSDMB and ORMDL3 impact several disorders of immune origin and ALDH2 overexpression contributes to obesity. This study reports trans-eQTLs can be used to identify tissue-specific gene regulatory networks and describes a workflow to achieve this purpose. The network identified in this study showed scale-free topology indicating the hub genes of a GRN could be targeted to prevent disease outcomes.

Understanding epigenome paths through which smoking contributes to cardiometabolic traits is important for downstream applications. In this study, I used a SNP-based analytical pipeline to integrate several publicly available datasets and identify CpG sites that mediate the impact of smoking on cardiometabolic traits. After applying stringent statistical criteria, 11 CpG sites were detected that showed significant association (P<5e-8) with cardiometabolic traits at both discovery and replication stage. By integrating eQTL data, I identified genes behind a number of these associations. cg05228408 was hypomethylated in smokers and contributed to higher blood pressure by lowering the expression of CLCN6 gene. cg08639339 was hypermethylated in smokers and lowered metabolic rate by increasing the expression of RAB29; furthermore, I noted TMEM120A mediated the impact of smoking-cg17325771 on LDL, and LTBP3 mediated the smoking-cg07029024 effect on heart rate. This study provides a list of CpG sites that mediates the impact of smoking on cardiometabolic traits and a framework to investigate epigenome path through which a lifestyle habit modifies disease risks.

Following the era of GWAS studies, efforts are being made to identify genes underlying complex traits by merging eQTL and GWAS data and assessing the colocalization of eQTLs and GWAS signals. A problem that sometimes occurs in this context is the observation of association between several genes in a genomic region with a trait. This happens because genes in a region could be under the regulatory impact of common elements and coexpress. As such, computational approaches that rely on cis-eQTL information can not exactly pinpoint the causal gene. Here, I report an alternative solution, based on trans-eQTLs to test the association between a gene and a trait. Through the analyses applied to adjacent genes that coexpress and concordantly impact blood traits, I provide evidence that trans-eQTLs can resolve the problem of coexpression-causality without the interference of shared cis-regulatory SNPs.

Understanding the function of a locus is a challenge in molecular biology. Although numerous molecular data have been generated in the last decades, it remains difficult to grasp, how these data are related at a locus? In this study, we describe an analytical workflow that can solve this problem using the knowledge available at single-nucleotide polymorphisms (SNPs) level. The underlying algorithm uses SNPs as connectors to link omics data and identify correlation between them through a joint bioinformatical/statistical approach. We describe its application in finding the mechanism whereby a mutation causes a phenotype and in revealing the path whereby a gene is being regulated and impacts the phenotypes. We translated our workflow into freely available shell scripts that carry out the analyses. Our approach provides a basic framework to solve the information overload problem in biology.