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Genetic variation in gene expression traits contributes to phenotypic diversity and may facilitate adaptation following environmental change. This is especially important in long-lived organisms where adaptation to rapid changes in the environment must rely on standing variation within populations. However, the extent of expression variation in most wild species remains to be investigated. We address this question by measuring the segregation of expression levels in white spruce [Picea glauca (Moench), Voss] in a transcriptome-wide manner and examining the underlying evolutionary and biological processes. We applied a novel approach for the genetic analysis of expression variation by measuring its segregation in haploid meiotic seed tissue. We identified over 800 transcripts whose abundances are most likely controlled by variants in single loci. Cosegregation analysis of allelic expression levels was used to construct regulatory associations between genes and define regulatory networks. The majority (67%) of segregating transcripts were under linkage. Regulatory associations were typically among small groups of genes (2-3 transcripts), indicating that most segregating expression levels can evolve independently from one another. One notable exception was a large putative trans effect that altered the expression of 180 genes that includes key regulators of protein metabolism, highlighting a regulatory cascade affected by variation in a single locus in this conserved metabolic pathway. Overall, segregating expression variation was associated with stress response- and duplicated genes, whose evolution may be linked to functional innovations. These observations indicate that expression variation might be important in facilitating diversity of molecular responses to environmental stresses in wild trees.

Original publication




Journal article


Mol Ecol

Publication Date





2369 - 2379


Adaptation, Physiological, Alleles, Biological Evolution, Environment, Gene Duplication, Gene Expression, Gene Regulatory Networks, Genetic Linkage, Genetic Variation, Microarray Analysis, Phenotype, Picea, Quantitative Trait Loci, Trees