Handling High-Throughput Omics Data for Systems Genetics Analysis

Abstract

Omics data are being generated and collected at unprecedented scale. During the last decade, single omics, such as genomics, transcriptomics, proteomics, and metabolomics, have already highlighted pathophysiological pathways underpinning a variety of conditions across all the fields of medicine.In fact, high-throughput data generated by the comprehensive and unbiased analysis of an entire segment of the flow of genetic information (i.e., genetic variants in the case of genomics, or gene expression in transcriptomics) certainly provide a plethora of information and a precious support to dissect the mechanisms involved in complex diseases.Yet the most effective approach, set to fully exploit the potential of such big data, lies in the possibility to integrate various omics to unveil previously unappreciated pathways. This approach is the foundation of Systems Biology and allows to overcome the limitations inherent to single omics and traditional biology analyses.A robust and powerful strategy has been developed to integrate genetics and gene expression data in the framework of Systems Genetics. With this technique the first two layers of the flow of genetic information are integrated and specifically it is possible to pinpoint which genetic variants are associated with gene co-expression networks.Here we present a versatile bioinformatic protocol that can be used to study the Systems Genetics of CTLs, in order to identify genes (also known as master regulators) that influence the activation of biological pathways in these cells in a particular state or condition.

Keywords: Bioinformatics; Biostatistics; Gene expression; Omics; Systems Biology; Systems Genetics.