Brassica rapa: crop-types
The evolution and development of structure-function relationships
Brassica rapa has multiple centers of domestication and is an incredibly morphologically diverse species including cabbages and bok choy with large, fleshy leaves, turnips with underground storage organs and mustards & oil seeds that produce copious amounts of fruit and seed. In addition to characteristic morphologies, these crop types also exhibit different life histories and physiologies (Yarkhunova et al, 2016). Current work in the Baker lab focuses on using next generation sequencing to refine our understanding of the relationships between various crop types. We are quantifying the genomic architecture underlying correlations between plant physiological function (photosynthetic capacity and water use efficiency) and structure (anatomy, morphology). Future work includes elucidating the development dynamics that give rise to different structure-function relationships. Understanding the developmental and genetic mechanisms responsible for such morphological and physiological diversity will inform crop breeding efforts aimed at improving agricultural sustainability through, for instance, increased water use efficiency.
Mimulus guttatus* (monkeyflower)
Evolution and development of shoot architecture
Branching is a fundamental process contributing to shoot architecture and the evolution of morphological diversity and life history strategies. The Baker lab has characterized the development of branching patterns in two locally adapted but morphologically divergent populations of Mimulus (Baker and Diggle, 2011). We then correlated these differences with intraspecific differences in gene expression (Baker et al, 2012), and examined the impacts of water availability on branching phenotypic plasticity (Baker et al, 2014).
*currently Erythranthe guttata
Developmental stability and high throughput 3D phenotyping
Plants must cope with the environmental conditions they encounter because they cannot move to alternative environments. On the one hand, plants must buffer their developmental processes against environmental perturbations to function successfully and ultimately reproduce (canalization or developmental stability). On the other hand, plants may benefit from altering development to take advantage of conducive environments or survive harsher environments (phenotypic plasticity). We collect data on leaf growth from large panels of Arabidopsis thaliana mutants in multiple environments (field and growth chamber) to ascertain where and how genetic networks respond to environmental perturbations (Baker et al, in prep). To do so, we use tens of thousands of plants and a high-throughput 3D plant phenotyping platforms and data extraction pipelines (An et al, 2016 & 2017).
Brassica rapa: quantitative genetics
Estimating and predicting non-linear developmental phenotypes
Developmental and growth processes are typically continuous and often follow non-linear trajectories. However, phenotypic data collected over developmental time are necessarily discrete. We use Function Valued Trait (FVT) modeling to estimate trait parameters that describe non-linear developmental phenotypes and uncovered independent genetic modules for change in leaf size vs. shape (Baker et al, 2015). In the Baker lab, we have demonstrated that Bayesian approaches outperform frequentist FVT trait estimation (Baker et al, 2018a). We have also developed hierarchical Bayesian models that incorporate photosynthetic capacity as a cofactor, thereby factoring out genotype-specific differences in carbon availability and exposing the core developmental genetic networks responsible for organ growth (in collaboration with the Welch lab at Kansas State and the Weinig Lab at the University of Wyoming). We answer questions about growth phenotypes using quantitative genetics: are there trade-offs between growth rates and final sizes? Are shape and size genetically correlated? Are there differences in environmental sensitivity (phenotypic plasticity) among growth traits compared to final sizes? Can we detect signatures of selection on development itself (growth rates) or just fully developed structures? What is the genomic architecture of growth rates, and does quantifying the underlying developmental dynamics allow for increased information about the genomic architecture of an organ’s final size (Baker et al, 2015)? In conjunction with a transcriptomic approach to expression QTL mapping and gene expression network construction (in collaboration with the Maloof lab at U.C. Davis) build models that can predict growth phenotypes based on genetic information (Baker et al, 2018b and Baker et al, 2019).