Abstract
DNA sequence variation causes changes in gene expression, which in turn has profound effects on cellular states. These variations affect tissue development and may ultimately lead to pathological phenotypes. A genetic locus containing a sequence variation that affects gene expression is called an “expression quantitative trait locus” (eQTL). Whereas the impact of cellular context on expression levels in general is well established, a lot less is known about the cell-state specificity of eQTL. Previous studies differed with respect to how “dynamic eQTL” were defined. Here, we propose a unified framework distinguishing static, conditional and dynamic eQTL and suggest strategies for mapping these eQTL classes. Further, we introduce a new approach to simultaneously infer eQTL from different cell types. By using murine mRNA expression data from four stages of hematopoiesis and 14 related cellular traits, we demonstrate that static, conditional and dynamic eQTL, although derived from the same expression data, represent functionally distinct types of eQTL. While static eQTL affect generic cellular processes, non-static eQTL are more often involved in hematopoiesis and immune response. Our analysis revealed substantial effects of individual genetic variation on cell type-specific expression regulation. Among a total number of 3,941 eQTL we detected 2,729 static eQTL, 1,187 eQTL were conditionally active in one or several cell types, and 70 eQTL affected expression changes during cell type transitions. We also found evidence for feedback control mechanisms reverting the effect of an eQTL specifically in certain cell types. Loci correlated with hematological traits were enriched for conditional eQTL, thus, demonstrating the importance of conditional eQTL for understanding molecular mechanisms underlying physiological trait variation. The classification proposed here has the potential to streamline and unify future analysis of conditional and dynamic eQTL as well as many other kinds of QTL data.
Highlights
Natural genetic variation affects gene expression levels and thereby impacts on molecular and physiological phenotypes such as protein levels, cell morphology or disease phenotypes
Complex physiological traits are affected through subtle changes of molecular traits like gene expression in the relevant tissues, which in turn are caused by genetic variation
Understanding the tissue and cell type specificity of expression quantitative trait locus (eQTL) effects is essential for revealing the molecular mechanisms underlying disease phenotypes
Summary
Natural genetic variation affects gene expression levels and thereby impacts on molecular and physiological phenotypes such as protein levels, cell morphology or disease phenotypes In this respect, gene expression has proven instrumental as an intermediate phenotype from which conclusions about the emergence of high level traits can be drawn. Since cells forming different tissues must have very different morphology, organization and function, distinct patterns of gene expression are required for each cell type. This variation of gene expression between cell types is under the influence of natural genetic variation. There is clear evidence for cross-tissue differences in genetic variation influencing transcript levels This raises the question whether conclusions drawn from an eQTL study in one cell type or even a cell line translate to other cell types. Understanding the tissue and cell type-specificity of molecular traits is essential for revealing the molecular mechanisms underlying disease phenotypes
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