SPIRE, a modular pipeline for eQTL analysis of RNA-Seq data, reveals a regulatory hotspot controlling miRNA expression in C. elegans

QTL Analysis Beyond eQTLs

Expression quantitative trait locus (eQTL) analysis is a powerful method to understand the association between genetic variant and gene expression; it also has potential impact for the study of transcription medicine for human complex disease. In the past two decades, the researchers focus on studying the eQTL, while more and more evidence shows that the regulatory genetic variants locating noncoding region have strong effect for the gene expression. More and more researchers working on eQTL analysis realize the importance of other types of QTLs beyond eQTL. In this chapter, we will explore some QTLs beyond eQTLs that show the regulatory association with eQTLs and explain the underlying link among these types of QTLs.

The Genome Conformation As an Integrator of Multi-Omic Data: The Example of Damage Spreading in Cancer

Publicly available multi-omic databases, in particular if associated with medical annotations, are rich resources with the potential to lead a rapid transition from high-throughput molecular biology experiments to better clinical outcomes for patients. In this work, we propose a model for multi-omic data integration (i.e., genetic variations, gene expression, genome conformation, and epigenetic patterns), which exploits a multi-layer network approach to analyse, visualize, and obtain insights from such biological information, in order to use achieved results at a macroscopic level. Using this representation, we can describe how driver and passenger mutations accumulate during the development of diseases providing, for example, a tool able to characterize the evolution of cancer. Indeed, our test case concerns the MCF-7 breast cancer cell line, before and after the stimulation with estrogen, since many datasets are available for this case study. In particular, the integration of data about cancer mutations, gene functional annotations, genome conformation, epigenetic patterns, gene expression, and metabolic pathways in our multi-layer representation will allow a better interpretation of the mechanisms behind a complex disease such as cancer. Thanks to this multi-layer approach, we focus on the interplay of chromatin conformation and cancer mutations in different pathways, such as metabolic processes, that are very important for tumor development. Working on this model, a variance analysis can be implemented to identify normal variations within each omics and to characterize, by contrast, variations that can be accounted to pathological samples compared to normal ones. This integrative model can be used to identify novel biomarkers and to provide innovative omic-based guidelines for treating many diseases, improving the efficacy of decision trees currently used in clinic.