EDGE CMT: Genetic basis of plant root growth traits and their response to environment

Predicting how complex phenotypes emerge from the interaction of genetic variation and developmental environment (i.e., genotype by environment interaction) has become increasingly feasible in model systems. The ability to make this kind of prediction is important in many fields, for example: understanding of disease risk, increase the potential for mitigating the effects of a changing environment, and improve the efficiency of breeding for advanced agricultural varieties.

However, accurate prediction does not mean we have achieved mechanistic understanding at the individual or population level. Addressing this challenge of a mechanistic understanding requires the ability to replicate many genotypes across environments, a difficulty that has hindered studies of complex traits in humans and vertebrate model systems. Crops are ideal for generating the data needed for dissecting mechanisms of genotype by environment interactions and are the original model systems for quantitative genetics.

This research provides an opportunity to develop and test methods for studying genotype by environment interactions using maize field trials, a system where such replication is highly feasible. To extract mechanistic understanding of pathways from these data, this project will develop statistical methods and software that will be useful for a broad range of species.

The results, methods, and concepts addressed in our research could also be extended by facilitating a shared learning experience that help traditionally unrepresented students challenge barriers and build collaborative relationships with researchers with experience in bridging fundamental science with pragmatic application in agriculture.

This main goal of this project is to provide mechanistic understanding how genes interact with variable environments to produce complex phenotypes, specifically root system architecture and gene expression in nodal root tissues. The phenomenon of a genotype producing different phenotypes in response to different environmental conditions is termed phenotypic plasticity and is a ubiquitous aspect of biology.

This project will use maize root system architecture traits as a model system for mechanistic understanding of the genetics of complex traits. The research will refine and validate field-based high-throughput phenotyping (HTP) of plant root system architecture under agriculturally and ecologically relevant well-watered and controlled-drought conditions.

These HTP root phenotypes will be collected across the lifecycle to understand the polymorphisms underlying differences in growth trajectories. Gene expression analysis of root tissue will allow eQTL mapping and test the relative role of cis and trans polymorphisms on expression of core genes. Phenotyping will be performed on bi-parental recombinant inbred populations of maize.

Linking phenotypes to causal polymorphisms in these populations will involve the development of new computational tools for extracting the maximum amount of information from empirical studies of Genotype x Environment (GxE) interactions and time-series data. Together, these novel data and analysis methods will be used to identify polymorphisms underlying genotype x environment interactions (GxE) and genotype x time interactions (GxT).

The functional roles of candidate genes and the utility of simple, multi-gene models of GxE will be tested using single and higher order mutants in maize and Arabidopsis thaliana. This project has four specific aims : 1)Perform empirical studies of QTL x E x Time in maize roots in field studies, 2) Perform whole genome transcript abundance analysis of root tissue to allow eQTL mapping and test predictions of the role of cis effects on core gene expression, 3) Use mutants, CRISPR edits and overexpression of large effect size GxE QTL to test models of plasticity to soil moisture in both dicot and monocot species, 4) Develop QTL mapping methods based on multivariate linear models to handle QTL x E x Time data.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.