A new approach to quantify the adaptive potential of gene expression variation in gymnosperms

Variation in patterns of gene expression contributes to phenotypic diversity and can ultimately predict adaptive responses. However, in many cases, the consequences of regulatory mutations on patterns of gene expression and ultimately phenotypic differences remain elusive. A standard way to study the genetic architecture of expression variation in model systems has been to map gene expression variation to genetic loci (Fig. 1a). At the same time, in many nonmodel species, especially for long-lived organisms, controlled crosses are not feasible. If we are to expand our understanding of the role of regulatory mutations on phenotypes, we need to develop new methodologies to study species under ecologically relevant conditions. In this issue of Molecular Ecology, Verta et al. (2013) present a new approach to analyse gene expression variation and regulatory networks in gymnosperms (Fig. 1b). They capitalized on the fact that gymnosperm seeds contain an energy storage tissue (the megagametophyte) that is directly derived from a single haploid cell (the megaspore). The authors identified over 800 genes for which expression segregated in this maternally inherited haploid tissue. Based on the observed segregation patterns, these genes (Mendelian Expression Traits) are most probably controlled by biallelic variants at a single locus. Most of these genes also belonged to different regulatory networks, except for one large group of 180 genes under the control of a putative trans-acting factor. In addition, the approach developed here may also help to uncover the effect of rare recessive mutations, which usually remain hidden in a heterozygous state in diploid individuals. The appeal of the work by Verta et al. (2013) to study gene expression variation is in its simplicity, which circumvents several of the hurdles behind traditional expression quantitative trait locus (eQTL) studies, and could potentially be applied to a large number of species.