Organ specificity and transcriptional control of metabolic routes revealed by expression QTL profiling of source--sink tissues in a segregating potato population

Leaf and tuber samples were analysed and screened for the presence of conserved and tissue dependent eQTLs. Expression QTLs present in both tissues are predominantly cis-acting whilst for tissue specific QTLs, the percentage of trans-acting QTLs increases. Tissue dependent eQTLs were assigned to functional classes and visualized in metabolic pathways. We identified a potential regulatory network on chromosome 10 involving genes crucial for maintaining circadian rhythms and controlling clock output genes. In addition, we show that the type of genetic material screened and sampling strategy applied, can have a high impact on the output of genetical genomics studies.Identification of tissue dependent regulatory networks based on mapped differential expression not only gives us insight in tissue dependent gene subfunctionalization but brings new insights into key biological processes and delivers targets for future haplotyping and genetic marker development.The field of associative genomics or genetical genomics attempts to combine the heritability of generated ~ omics data with phenotypic variation through genetic marker associations [1]. The aimed outcome of a genetical genomics study is to define genomic regions that control the expression of single or multiple genes, metabolites and/or proteins (eQTLs, mQTLs and pQTLs, respectively). The amount of population wide ~ omics data for non-model species has increased dramatically over the last few years as costs for data generation have decreased and computational bottlenecks have been largely overcome. Successful application of genetical genomics has been demonstrated for a number of plant species including Arabidopsis, barley, wheat, eucalyptus and poplar [2-8]. In particular, studies with the model plant Arabidopsis have been crucial for the development of the concept of genetical genomics and have pushed software development forward to cope with the increasing amount of data generated by the different profiling platfo