Integrating transcriptomic network reconstruction and QTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development

Abstract

Plant developmental dynamics can be heritable, genetically correlated with fitness and yield, and undergo selection. Therefore, characterizing the mechanistic connections between the genetic architecture governing plant development and the resulting ontogenetic dynamics of plants in field settings is critically important for agricultural production and evolutionary ecology. We use a hierarchical Bayesian Function-Valued Trait (FVT) approach to estimate Brassica rapa growth curves throughout ontogeny, across two treatments and in two growing seasons. We find that the shape of growth curves is relatively plastic across environments compared to final height, and that there are trade-offs between growth rate and duration. We determined that combining FVT Quantitative Trait Loci (QTL) and genes/eigengene expression identified via transcriptomic co-expression network reconstructions best characterized phenotypic variation. Further, targeted eQTL analyses identified regulatory hotspots that colocalized with FVT QTL and co-expression network identified genes and mechanistically link FVT QTL with structural trait variation throughout development in agroecologically relevant field settings.