Abstract
Dosage-sensitive genes have characteristic patterns of evolution that include being refractory to small-scale duplication, depleted on human benign copy number variants (CNVs) and enriched on pathogenic CNVs. This intolerance to copy number change is likely due to an expression constraint that exists in one or more tissues. While genomic copy number changes alter the encompassed genes' expression across all tissues, expression quantitative trait loci (eQTLs)-genomic regions harboring sequence variants that influence the expression level of one or more genes-can act in a tissue-specific manner. In this work, we examine expression variation of presumed dosage-sensitive and nondosage-sensitive genes to discover how the locus duplicability constraints translate into gene expression constraints. Here, we test the hypothesis that expression changes due to the presence of eQTLs acting in unconstrained tissues will not be deleterious and thus allow dosage-sensitive genes to vary expression while obeying constraints in other tissues. Using eQTLs across 48 human tissues from The Genotype-Tissue Expression project, we find that dosage-sensitive genes are enriched for being affected by eQTLs and that the eQTLs affecting dosage-sensitive genes are biased towards having narrow tissue-specificity with these genes having fewer eQTL-affected tissues than nondosage-sensitive genes. Additionally, we find that dosage-sensitive genes are depleted for being affected by broad tissue breadth eQTLs, likely due to the increased chance of these eQTLs conflicting with expression constraints and being removed by purifying selection. These patterns suggest that the dosage-sensitivity that shapes the evolution of these genes by precluding copy number evolution also restricts their evolutionary trajectories to changes in expression regulation compatible with their functional constraints. Thus, deeper interpretation of the patterns of constraints can be informative of the temporal or spatial location of the gene dosage sensitivity and contribute to our understanding of functional genomics.