Transcriptome wide profiling illustrates the prevalence of pleiotropic architectures
Strongly correlated traits within biological networks often experience selective constraints, as variants occurring in the genes underlying these traits could cause deleterious pleiotropic effects (Jordanet al ., 2004). On the other hand, traits with lower connectivity will often be involved in adaptive evolution and in GEI, as selection can fine tune population responses to local environments without disrupting key functional components (Cork & Purugganan, 2004; Josephset al ., 2017). Despite this, our work demonstrates an overrepresentation of GEI transcripts at the core of networks and of the modules, thereby leading us to reject our fourth hypothesis of adaptive evolution being dominated by weakly connected peripheral traits. Following Fisher’s geometric model (Fisher, 1930), if populations are further from their fitness optima, variants that impart pleiotropic influences are advantageous during the initial stages of the adaptive walk (Orr, 1998). We suggest that this is likely the case for the expanding hybrid zone populations (Menon et al ., 2020) that are exposed to novel selective pressures. Under this scenario of demographic and climatic mismatch, pleiotropy speeds up evolution to fitness optima. However, our study uses a narrow definition of pleiotropy restricted to the number of associated expression traits which could have a multitude of different functions. While gene expression is widely treated as a quantitative trait, its association with other genes within a co-expression network may not always reflect pleiotropy and could be confounded by the effect size of unaccounted for cis-variants (Josephset al ., 2017. However, considering the large genome size of conifers, and that most variants in our study are located in non-genic regions, this would be a challenging task to accomplish without further improvement in genomic resources.