Abstract
ABSTRACTComplementary to traditional gene mapping approaches used to identify the hereditary components of complex diseases, integrative genomics and systems genetics have emerged as powerful strategies to decipher the key genetic drivers of molecular pathways that underlie disease. Broadly speaking, integrative genomics aims to link cellular-level traits (such as mRNA expression) to the genome to identify their genetic determinants. With the characterization of several cellular-level traits within the same system, the integrative genomics approach evolved into a more comprehensive study design, called systems genetics, which aims to unravel the complex biological networks and pathways involved in disease, and in turn map their genetic control points. The first fully integrated systems genetics study was carried out in rats, and the results, which revealed conserved trans-acting genetic regulation of a pro-inflammatory network relevant to type 1 diabetes, were translated to humans. Many studies using different organisms subsequently stemmed from this example. The aim of this Review is to describe the most recent advances in the fields of integrative genomics and systems genetics applied in the rat, with a focus on studies of complex diseases ranging from inflammatory to cardiometabolic disorders. We aim to provide the genetics community with a comprehensive insight into how the systems genetics approach came to life, starting from the first integrative genomics strategies [such as expression quantitative trait loci (eQTLs) mapping] and concluding with the most sophisticated gene network-based analyses in multiple systems and disease states. Although not limited to studies that have been directly translated to humans, we will focus particularly on the successful investigations in the rat that have led to primary discoveries of genes and pathways relevant to human disease.
Highlights
Several gene mapping strategies such as genome-wide association studies (GWAS; see Box 1 for a glossary of terms), whole-exome sequencing (WES) and whole-genome sequencing (WGS) are widely applied to study the genetic etiology of complex diseases (Altshuler et al, 2010)
A study published by Thessen Hedreul et al (Table 1) involved the integration of expression QTL (eQTL) and autoimmune encephalomyelitis Quantitative trait locus (QTL) analysis, using gene expression profiles collected from spleens of an F2 population of rats. This approach identified several candidate genes and pathways involved in the regulation of autoimmune encephalomyelitis (Thessen Hedreul et al, 2013). These integrative genomics studies demonstrate how genome-wide expression analysis in pathophysiologically relevant tissues can be integrated with genetic variation data to identify cis- and trans-eQTLs, which in turn can pinpoint the genetic mechanisms underlying QTLs or GWAS loci associated with complex diseases
Concluding remarks The plethora of disease-predisposing gene variants identified by GWAS, WES and WGS has not been paralleled by a fast and far-reaching characterization of the associated biological processes and pathways
Summary
Several gene mapping strategies such as genome-wide association studies (GWAS; see Box 1 for a glossary of terms), whole-exome sequencing (WES) and whole-genome sequencing (WGS) are widely applied to study the genetic etiology of complex diseases (Altshuler et al, 2010). Data modeling strategies used in system genetics There are two key types of genome-wide data modeling strategies that are used (and often integrated) in systems genetics studies: quantitative trait locus (QTL; Box 1) mapping of cellular-level traits, and gene regulatory network analysis Each of these approaches has been extensively used in isolation in integrative genomics, for instance using expression QTL (eQTL) mapping (see below). In addition to using genetic susceptibility data such as GWAS-inferred SNPs, gene networks can be associated with disease by summarizing the variability of the network [e.g. by principal component (PC) analysis; Box 1] and correlating the major PCs with the trait or disease (Langfelder and Horvath, 2008) This strategy originated from the genome-wide correlation analysis of individual cellular-level phenotypes (e.g. mRNAs, proteins, metabolites) and whole-body quantitative phenotypes (e.g. body mass index). Using a comparative genomics analysis with humans (Box 1), i.e. by comparing the rat eQTLs with the human loci previously identified
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