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 use a Quantitative Trait Loci (QTL) mapping approach to identify genomic loci associated with FVT traits. To capture the mechanistic and regulatory connections between genotype and FVT phenotype, we combine FVT with transcriptomic gene expression data by seeding Mutual Rank (MR) co-expression network models with FVT parameters to identify candidate genes. We compare FVT QTL and MR-identified gene expression to determine which better explains phenotypic trait variation. We employ a targeted eQTL analysis of MR genes to ascertain the regulatory eQTL architecture influencing FVT trait variation.
Results/Conclusions
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 QTL and MR-identified gene expression best characterized phenotypic variation. Further, we identified regulatory hotspots within the genome using a targeted eQTL approach. Hotspots included cis-eQTL that are candidate structural genes influencing plant height, and trans-eQTL that indicate putative upstream regulators of FVT QTL. Taken together, our data mechanistically link FVT QTL with structural trait variation throughout development in agroecologically relevant field settings.
Trans acting factors can influence multiple downstream gene expression events whereas cis-elements often regulate a single gene. Depending on network topology and interconnectivity, trans factors may be effective targets for quickly improving multiple aspects of plant productivity or they can have numerous ‘off-target’ effects. Understanding the genomic architecture and regulatory networks influencing fitness and yield related developmental traits such as plant height is important for evolutionary ecology and plant breeding programs aimed at more sustainable agricultural systems.