study and the corresponding softwares. Steps in light yellow are covered in this chapter.\[fig-flow\]](fig1.pdf)

Genetic differences between individuals are responsible for variation in the observable phenotypes. This principle underpins genome-wide association studies (GWAS), which map the genetic architecture of complex traits by measuring genetic variation at single-nucleotide polymorphisms (SNPs) on a genome-wide scale across many individuals [@mackay2009genetics]. GWAS have resulted in major improvements in plant and animal breeding [@goddard2009mapping] and in numerous insights into the genetic basis of complex diseases in human [@manolio2013bringing]. However, quantitative trait loci (QTLs) with large effects are uncommon and a molecular explanation for their trait association rarely exists [@mackay2009genetics]. The vast majority of QTLs indeed lie in non-coding genomic regions and presumably play a gene regulatory role [@hindorff2009potential; @schaub2012linking]. Consequently, numerous studies have identified *cis*- and *trans*-acting DNA variants that influence gene expression levels (i.e., “expression QTLs”; eQTLs) in model organisms, plants, farm animals and human (reviewed in [@rockman2006genetics; @georges2007mapping; @cookson2009mapping; @cheung2009genetics; @cubillos2012lessons]). Gene expression programmes are of course highly tissue- and cell-type specific, and the properties and complex relations of eQTL associations across multiple tissues are only beginning to be mapped [@dimas2009common; @foroughi2015; @greenawalt2011survey; @ardlie2015genotype]. At the molecular level, a mounting body of evidence 