the foreseeable future. Statistical and computational methods are therefore essential to reconstruct trait-associated causal networks by integrating diverse omics data [@rockman2008reverse; @schadt2009; @ritchie2015methods].

A typical systems genetics study collects genotype and gene, protein and/or metabolite expression data from a large number of individuals segregating for one or more traits of interest. After raw data processing and normalization, eQTLs are identified for each of the expression data types, and a co-expression matrix is constructed. Causal Bayesian gene networks, co-expression modules (i.e. clusters) and/or causal Bayesian module networks are then reconstructed. *In silico* validation of predicted networks and modules using independent data confirms their overall validity, ideally followed by experimental validation of the most promising findings in a relevant cell line or model organism (Figure \[fig-flow\]). Here we review the main analytic principles behind each of the steps from eQTL identification to *in silico* network validation, and present a selection of most commonly used methods and softwares for each step. Throughout this chapter, we tacitly assume that all data has been quality controlled, pre-processed and normalized to suit the assumptions of the analytic methods presented here. For expression data, this usually means working with log-transformed data where each gene expression profile is 